# Earnings-call review

A named 61-company census across US producers, global operators, oilfield services, midstream and refining. Calendar reference periods are Q3 2024–Q2 2026. Semiannual, fiscal-year, private-company and listing exceptions are documented separately. This is not every global issuer.

Three parallel Luna researchers and an editor searched issuer archives and transcripts. Full review means the available transcript was keyword-screened and relevant passages inspected, including Q&A where supplied. It is not an audio replay or independent verification of management claims. Searches cover artificial intelligence, machine learning, generative AI, agents, automation, autonomy, analytics, software, data platforms, remote operations and business transformation. A separate researcher spot-checked ten US/global call records; the editor checked selected consequential claims and negatives. These checks do not constitute a second review of every transcript.

Pending and inaccessible rows do not mean no AI activity. Prepared remarks and highlights do not establish a full-call negative. Demand for AI-related power is separate from internal use. Findings supplement the main initiative register; they do not increase its deployment or outcome counts.

As of 2026-10-08: 453 full transcript screens; 5 partial reviews; 344 passages. Coverage remains incomplete.

[Editor source checks](earnings-calls-editor-audit.md) · [Cross-cohort spot audit](earnings-calls-cross-audit.md)

## Aker BP · Q3 2024

reviewed_relevant · Full issuer transcript reviewed: management presentation, analyst Q&A, final remarks and close.

[Aker BP ASA Q3 2024 Earnings Call Raw Transcript](https://akerbp.com/wp-content/uploads/2024/10/raw-transcript-aker-bp-asaakrbp-no-q3-2024-earnings-call-30-october-2024-3-30-am-et.pdf)

### Digital decision architecture for exploration and project selection

Management described a digital architecture for rapid reservoir-model updates and a concept-selection framework developed with Landmark, intended to shorten the path from discovery to a development decision.

Speaker: Unverified participant (issuer raw transcript label). Locator: Q&A, transcript lines 681–695; PDF page 17; printed page 17.. Measurement: Management said a prior example moved from exploration to production drilling in weeks.. Limit: No AI/ML disclosure or quantified digital-only schedule, cost or value outcome; speaker is unverified in the issuer raw transcript..

Issuer-hosted full raw transcript includes presentation, Q&A and close. Screened AI/artificial intelligence, machine learning, digital architecture, automation, analytics, software, agents, and data-center terms. No AI mention found; the digital architecture and concept-selection framework are adjacent digital strategy. First management speaker is labeled “Unverified Participant,” so attribution is not inferred. Call date Oct. 30, 2024.

## Aker BP · Q4 2024

reviewed_relevant · Issuer-hosted corrected full transcript reviewed across management presentation, analyst Q&A and call close. Screened AI/artificial intelligence, machine learning, digitalization, analytics, automation, software, remote operations and data-center terms.

[Aker BP Q4 2024 earnings call corrected transcript](https://akerbp.com/wp-content/uploads/2025/02/aker-bp-2024-q4-transcript.pdf)

### AI-assisted industrial fault finding

CEO Karl Johnny Hersvik said Aker BP and Cognite were testing AI algorithms on Cognite Data Fusion to help identify complex faults and root causes. He described the productivity effect as potential and the testing as ongoing.

Speaker: Karl Johnny Hersvik, CEO. Locator: PDF P7 (printed p8), lines 294–300.. Measurement: Management said a 70–90% productivity improvement appeared within reach.. Limit: This was an estimate during ongoing testing, not a realized or independently measured result..

### Digital asset planning and remote visualization

Hersvik described the Cognite data platform, Agile Asset Management with Halliburton Landmark, and Aize visualization for remote field planning. Parts of Agile Asset Management were already used in development projects; Aize was in use across several assets.

Speaker: Karl Johnny Hersvik, CEO. Locator: PDF P7 (printed p8), lines 266–293.. Measurement: Not quantified. Limit: The call described time savings and improved decisions qualitatively, without a quantified outcome; these platforms are not all identified as AI..

Locators distinguish PDF P7 from printed page 8 (PDF cover is page 1). Full corrected transcript includes Q&A; AI productivity figure is prospective.

## Aker BP · Q1 2025

reviewed_relevant · Full corrected issuer transcript reviewed: management presentation, analyst Q&A and close.

[Aker BP ASA Q1 2025 Earnings Call Corrected Transcript](https://akerbp.com/wp-content/uploads/2025/05/corrected-transcript-aker-bp-asaakrbp-no-q1-2025-earnings-call-7-may-2025-2-30-am-et.pdf)

### Company-wide AI tool rollout

CEO Karl Johnny Hersvik said Aker BP was seeing strong momentum in rolling out AI tools across the organization, alongside its digital leadership and supplier-alliance model.

Speaker: Karl Johnny Hersvik, CEO. Locator: Management discussion, transcript lines 66–75; PDF page 3; printed page 3.. Measurement: No deployment count or performance metric was stated.. Limit: No AI product, workflow, business unit, scale, or realized operational or financial outcome was identified..

### Power-from-shore as an enabler for automation

In Q&A, Hersvik said shore power on the Norwegian shelf improves operational efficiency and makes automation easier than local power generation.

Speaker: Karl Johnny Hersvik, CEO. Locator: Q&A, transcript lines 344–355; PDF page 8; printed page 9.. Measurement: No standalone automation metric.. Limit: This is a general electrification/automation comment, not an AI claim or measured automation outcome..

Official corrected 20-page issuer transcript includes prepared remarks, analyst Q&A and close. Screened AI/artificial intelligence, machine learning, digital, automation, analytics, software, agents/Copilot and data-center terms. AI disclosure is limited to a company-wide rollout statement; no use case or realized outcome is named. Webcast and corrected-transcript cover confirm date May 7, 2025.

## Aker BP · Q2 2025

reviewed_relevant · Full corrected issuer transcript reviewed: management presentation, analyst Q&A and close.

[Aker BP ASA Q2 2025 Earnings Call Corrected Transcript](https://akerbp.com/wp-content/uploads/2025/07/aker-bp-2025-q2-transcript.pdf)

### Digital engineering and project execution technologies

CEO Karl Johnny Hersvik described a digital ecosystem supporting project execution and cited digital technologies, machining and fiber lasers among early-phase investments that improved preassembly and prefabrication productivity and helped offset cost inflation and protect project schedules.

Speaker: Karl Johnny Hersvik, CEO. Locator: Management remarks and Q&A, transcript lines 78–96 and 882–893; PDF pages 3 and 20; printed pages 3 and 20.. Measurement: No separate digital-technology productivity or schedule metric.. Limit: No AI/ML or digital-only attribution; the described gains apply to a package of alliance and manufacturing investments..

Official corrected 24-page issuer transcript includes presentation, analyst Q&A and closing remarks. Searched AI/artificial intelligence, machine learning, digital, automation, analytics, software, agents and data-center terms. No explicit AI/ML disclosure; adjacent project-engineering technologies are described. Actual call date July 15, 2025.

## Aker BP · Q3 2025

reviewed_relevant · Full corrected issuer transcript reviewed: management presentation, analyst Q&A, final remarks and close.

[Aker BP ASA Q3 2025 Earnings Call Corrected Transcript](https://akerbp.com/wp-content/uploads/2025/10/aker-bp-2025-q3-transcript.pdf)

### AI, agents and machine learning for exploration, subsurface and drilling

CEO Karl Johnny Hersvik described Omega Alfa as a test bed for wired-pipe drilling and a data ecosystem using models and machine learning to optimize drilling, with potential application to exploration and production drilling. He also described AI, digital tools and agents searching public and internal structured and unstructured North Sea data to improve target selection and reservoir understanding and compress exploration-to-development timelines.

Speaker: Karl Johnny Hersvik, CEO. Locator: Q&A, transcript lines 563–590 and 887–910; PDF pages 13–14 and 20; printed pages 13–14 and 20.. Measurement: No AI-specific production, discovery, time-saved or financial metric was reported.. Limit: Management described test cases and capabilities; transcript does not quantify realized AI impact or isolate AI from wired-pipe, drilling and other digital investments..

Official corrected transcript includes management presentation, analyst Q&A, closing remarks and close. Screened explicit AI/ML/agents, digital tools, automation, analytics, software and data-center terms. Call date Oct. 22, 2025 (printed corrected-transcript date). PDF-page references are distinct from printed page numbers where they differ.

## Aker BP · Q4 2025

reviewed_relevant · Issuer-hosted 25-page transcript searched; PDF page indices are cited as P#, and footer/printed page numbers are identified separately. Prepared remarks and Q&A reviewed.

[Aker BP Q4 2025 Earnings Call Transcript](https://akerbp.com/wp-content/uploads/2026/02/aker-bp-2025-q4-transcript.pdf)

### AI agents and workflow compression

CEO described AI agents for troubleshooting and operations, and said offshore maintenance planning had shifted from about a week to minutes while root-cause analysis shifted from weeks to hours.

Speaker: Karl Johnny Hersvik, CEO. Locator: Transcript web PDF P5–P6 (printed pages 6–7) for AI-agent overview; Q&A web PDF P17 (printed page 18), lines 771–783.. Measurement: Maintenance planning: about one week to minutes; root-cause analysis: weeks to hours.. Limit: Company-reported workflow time comparisons; the CEO explicitly said he struggled to assign comparable dollar savings and recommended tracking time-to-first-oil, discovery, uptime and efficiency instead..

### AI-assisted subsurface and application work

CEO described in-house AI tools screening geological/seismic data and supporting APA licensing-round application drafting; he said this accelerated assessment and contributed to stronger applications.

Speaker: Karl Johnny Hersvik, CEO. Locator: Transcript web PDF P9 (printed page 10), lines 405–426; later Q&A confirmation on web PDF P23 (printed page 24), lines 1051–1086.. Measurement: No quantified model accuracy or standalone financial outcome.. Limit: The APA award is discussed alongside AI work but is not isolated as an AI-caused result; the CEO noted data sufficiency and human-AI balance..

Primary issuer transcript. The transcript also includes Microsoft CRO praise for Copilot adoption; that partner praise is not treated as independent measurement. Locator clarification after PDF cross-check: web extraction labels cover as P0; displayed/footer page numbers run one higher (e.g., the AI workflow comparison is PDF P17, printed page 18; subsurface passage is PDF P9, printed page 10).

## Aker BP · Q1 2026

reviewed_relevant · Full issuer transcript reviewed: management presentation, analyst Q&A and CEO/operator close.

[Aker BP Q1 2026 Earnings Call Transcript](https://akerbp.com/wp-content/uploads/2026/05/transcript-aker-bp-q1-2026.pdf)

### Digital execution model and integrated value chain

CEO Karl Johnny Hersvik described an alliance-based value chain and digital execution model that he said was becoming industry-leading, alongside the company’s subsea-tieback capability.

Speaker: Karl Johnny Hersvik, CEO. Locator: Q&A, transcript lines 496–500; PDF page 13; printed page 12.. Measurement: No standalone digital execution metric.. Limit: No AI/ML, software product or digital-only operating result was stated in this passage..

Official issuer transcript includes presentation, analyst Q&A and close. Screened AI/artificial intelligence, machine learning, digital, automation, analytics, software and agent terms; no AI/ML language found. A digital execution-model statement is included as adjacent strategy. Event date May 7, 2026, confirmed by the issuer results page. Web-transcript line numbers are retained alongside PDF/printed-page distinction.

## Aker BP · Q2 2026

reviewed_relevant · Full transcript reviewed, including CEO/CFO presentation, analyst Q&A, and closing remarks. Selected AI/artificial-intelligence, machine-learning, automation, digital-transformation, data-center/hyperscaler terms screened.

[Aker BP ASA Q2 2026 earnings-call full transcript](https://stockanalysis.com/quote/osl/AKRBP/transcripts/360655-q2-2026/)

### Artificial intelligence deployment across work processes

CEO said Aker BP is actively deploying AI across the company and emphasized work-process redesign over technology alone. He cited engineering and repetitive process work becoming concurrent/iterative, with cycle times compressed from months or weeks to days or hours. He also said AI/systemic processes could offset inflation pressure without changing breakeven criteria.

Speaker: Karl Johnny Hersvik, CEO. Locator: Lines 352–366; follow-up lines 393–401.. Measurement: Management described orders-of-magnitude cycle-time compression (months/weeks to days/hours); no quantified realized financial or production benefit provided.. Limit: High-level qualitative commentary; no named deployment, validation, budget, or measured realized savings disclosed..

Full Quartr transcript contains prepared remarks and Q&A; AI discussion arises in analyst Q&A. No separate data-center/AI-energy-demand disclosure located in the selected full-call screen.

## Antero Resources · Q3 2024

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Antero Resources (AR) Q3 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/ar/transcripts/217968-q3-2024/)

### AI, data-center and power-demand context

Management attributed higher power burn partly to future AI-data-center, crypto-mining and EV demand and said Antero had regional data-center/utility exposure.

Speaker: Justin Fowler, SVP of Natural Gas Marketing. Locator: Prepared remarks lines 130–132.. Measurement: No AI-specific financial or contracted-volume outcome disclosed.. Limit: Demand drivers were grouped; no AI-specific contracts, volumes or revenue disclosed..

Classified as demand context where AI was not explicitly linked; no supply commitments or value are inferred.

## Antero Resources · Q4 2024

reviewed_no_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Antero Resources (AR) Q4 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/ar/transcripts/242710-q4-2024/)

No qualifying AI/ML/automation or adjacent disclosure found after full-call screening.

## Antero Resources · Q1 2025

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Antero Resources (AR) Q1 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/ar/transcripts/315632-q1-2025/)

### Gas, power and data-center demand

Management described natural-gas power and data-center projects in Appalachia; two named power plants represented 6.5 GW and an estimated 1.2 Bcf/d incremental regional gas demand.

Speaker: Justin Fowler, SVP of Natural Gas Marketing. Locator: Prepared remarks lines 136–140.. Measurement: No AI-specific financial or contracted-volume outcome disclosed.. Limit: No explicit AI link; regional/project demand estimate is not company-specific contracted volume..

Classified as demand context where AI was not explicitly linked; no supply commitments or value are inferred.

## Antero Resources · Q2 2025

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Antero Resources (AR) Q2 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/ar/transcripts/340964-q2-2025/)

### AI, data-center and power-demand context

CEO cited West Virginia’s microgrid legislation as supporting data-center and AI-buildout opportunities and said Antero was favorably positioned.

Speaker: Paul Rady, CEO. Locator: Q&A lines 455–460.. Measurement: No AI-specific financial or contracted-volume outcome disclosed.. Limit: No AI-linked contract, volume, price or financial outcome disclosed..

Classified as demand context where AI was not explicitly linked; no supply commitments or value are inferred.

## Antero Resources · Q3 2025

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Antero Resources (AR) Q3 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/ar/transcripts/369297-q3-2025/)

### Gas, power and data-center demand

Management discussed data-center cooling and water-system opportunities and a dry-gas proof-of-concept to demonstrate deliverability to local facilities.

Speaker: Brendan Krueger, CFO; Michael Kennedy, CEO. Locator: Q&A lines 317–322 and 377–390.. Measurement: No AI-specific financial or contracted-volume outcome disclosed.. Limit: No AI link or contracted data-center supply volume disclosed..

Classified as demand context where AI was not explicitly linked; no supply commitments or value are inferred.

## Antero Resources · Q4 2025

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Antero Resources (AR) Q4 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/ar/transcripts/396577-q4-2025/)

### Gas, power and data-center demand

Management linked regional demand growth to natural-gas power generation and data-center projects and described Antero’s gas, midstream and water infrastructure position.

Speaker: Justin Fowler, SVP of Natural Gas Marketing. Locator: Prepared remarks lines 142–144.. Measurement: No AI-specific financial or contracted-volume outcome disclosed.. Limit: No explicit AI link or named supply contract disclosed..

Classified as demand context where AI was not explicitly linked; no supply commitments or value are inferred.

## Antero Resources · Q1 2026

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Antero Resources (AR) Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/ar/transcripts/556002-q1-2026/)

### AI, data-center and power-demand context

Management discussed AI power contracts and data-center demand, including announced projects and active supply discussions; it said terms would depend on transport and infrastructure.

Speaker: Brendan Krueger, CFO; Michael Kennedy, CEO. Locator: Prepared remarks lines 146–149; Q&A lines 620–627, 271–282 and 381–389.. Measurement: No AI-specific financial or contracted-volume outcome disclosed.. Limit: Regional forecasts and conversations are not executed contracts; no AI-specific volume or value disclosed..

Classified as demand context where AI was not explicitly linked; no supply commitments or value are inferred.

## Antero Resources · Q2 2026

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Antero Resources (AR) Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/ar/transcripts/658179-q2-2026/)

### Gas, power and data-center demand

Management forecast gas demand growth from announced data-center and power projects and described talks with regional projects; it said local projects must compete with other markets for supply.

Speaker: Michael Kennedy, CEO; Justin Fowler, SVP of Natural Gas Marketing. Locator: Prepared remarks lines 122–130 and 148–150.. Measurement: No AI-specific financial or contracted-volume outcome disclosed.. Limit: No explicit AI link or contracted data-center supply volume disclosed..

Classified as demand context where AI was not explicitly linked; no supply commitments or value are inferred.

## APA Corporation · Q3 2024

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and Q&A; searched AI, artificial intelligence, machine learning, automation, analytics, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[APA Corporation (APA) Q3 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/apa/transcripts/217990-q3-2024/)

Archive confirms period and call date. No qualifying mentions found for screened terms.

## APA Corporation · Q4 2024

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and Q&A; searched AI, artificial intelligence, machine learning, automation, analytics, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[APA Corporation (APA) Q4 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/apa/transcripts/240575-q4-2024/)

### Field automation and longer-term systems/process simplification

CEO described cost-structure changes including field-operating-practice improvements and, over the longer term, field automation, IT-system/infrastructure changes, and accounting-application/procedure changes.

Speaker: John Christmann, CEO. Locator: Prepared remarks, lines 161–166.. Measurement: Targeted $350 million annualized cost savings by end-2027; $100–$125 million run-rate target by end-2025 and approximately $60 million in-year savings expected.. Limit: Savings targets cover a broad cost program; no automation-specific portion was isolated..

Full call reviewed; interpret adjacent automation / potential AI scope narrowly as noted.

## APA Corporation · Q1 2025

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and Q&A; searched AI, artificial intelligence, machine learning, automation, analytics, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[APA Corporation (APA) Q1 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/apa/transcripts/320318-q1-2025/)

Archive confirms period and call date. No qualifying mentions found for screened terms.

## APA Corporation · Q2 2025

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and Q&A; searched AI, artificial intelligence, machine learning, automation, analytics, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[APA Corporation (APA) Q2 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/apa/transcripts/344280-q2-2025/)

### AI as a potential overhead simplification tool

CFO said APA was evaluating whether AI could support process simplification in corporate groups representing about one-third of overhead. The effort was one of roughly seven ongoing projects focused on streamlining processes and using technology efficiently.

Speaker: Ben Rodgers, EVP and CFO. Locator: Q&A, lines 510–513.. Measurement: Not quantified. Limit: Evaluation only; no deployment, realized savings, or AI-specific impact disclosed..

Full call reviewed; interpret adjacent automation / potential AI scope narrowly as noted.

## APA Corporation · Q3 2025

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and Q&A; searched AI, artificial intelligence, machine learning, automation, analytics, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[APA Corporation (APA) Q3 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/apa/transcripts/371629-q3-2025/)

Archive confirms period and call date. No qualifying mentions found for screened terms.

## APA Corporation · Q4 2025

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and Q&A; searched AI, artificial intelligence, machine learning, automation, analytics, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[APA Corporation (APA) Q4 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/apa/transcripts/402889-q4-2025/)

Archive confirms period and call date. No qualifying mentions found for screened terms.

## APA Corporation · Q1 2026

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and Q&A; searched AI, artificial intelligence, machine learning, automation, analytics, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[APA Corporation (APA) Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/apa/transcripts/571943-q1-2026/)

Archive confirms period and call date. No qualifying mentions found for screened terms.

## APA Corporation · Q2 2026

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and Q&A; searched AI, artificial intelligence, machine learning, automation, analytics, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[APA Corporation (APA) Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/apa/transcripts/662493-q2-2026/)

Archive confirms period and call date. No qualifying mentions found for screened terms.

## Aramco · Q3 2024

reviewed_no_relevant · Full transcript reviewed from operator opening through analyst Q&A close; screened AI/artificial intelligence, machine learning, generative AI, digitalization, automation, analytics, software, data centers and energy demand.

[Saudi Aramco Q3 2024 earnings call full transcript](https://www.alphaspread.com/security/sau/2222/investor-relations/earnings-call/q3-2024)

Call date confirmed by issuer archive and transcript. Full transcript includes prepared remarks and Q&A; broad targeted-term screen found no relevant disclosure.

## Aramco · Q4 2024

reviewed_relevant · Full 78-minute transcript reviewed from operator opening through Q&A/close; screened AI, ML, genAI, digitalization, analytics, automation, software, data centers and power demand.

[Saudi Aramco FY2024/Q4 earnings call full transcript](https://earningsapi.io/transcripts/saudi-arabian-oil-company_2222_earnings_call_transcript_2025-03-04)

### AI and digital technology across operations, subsurface and trading

CEO Amin Nasser said Aramco was using its long-held seismic and production data with AI, machine learning and digital tools to improve operating efficiency. Examples included an AI petrophysical prediction tool to reduce operating costs and rig standby, real-time geosteering updates, and integrated models for downstream assets and trading. The company reported more than 420 AI/digital use cases and $6bn of technology value over two years.

Speaker: Amin Nasser, President and CEO. Locator: Prepared remarks segments #3–#4; transcript lines 51–62.. Measurement: >420 AI/digital use cases; $6bn total technology realized value over the past two years; 10bn facility data points collected daily.. Limit: Technology-value figure includes non-AI solutions and is company-reported; transcript does not quantify AI-only contribution or isolate outcomes by project..

Issuer archive lists FY2024/Q4 results webcast and confirms March 4, 2025. Transcript identifies a recorded webcast with Q&A; transcript closes after the operator conclusion.

## Aramco · Q1 2025

reviewed_no_relevant · Full transcript reviewed from operator opening through analyst Q&A close; screened AI/artificial intelligence, machine learning, generative AI, digitalization, analytics, automation, software, data centers and energy demand.

[Saudi Aramco Q1 2025 results call full transcript](https://www.alphaspread.com/de/security/sau/2222/investor-relations/earnings-call/q1-2025)

Call took place May 12, 2025 (issuer archive dates Q1 results webcast May 12; May 11 is press-release date). Full transcript contains prepared remarks, analyst Q&A and closing operator language. No relevant term or adjacent disclosure found.

## Aramco · Q2 2025

reviewed_relevant · Full transcript reviewed through CEO presentation, analyst Q&A and operator close. Screen included AI/artificial intelligence, machine learning, genAI/LLM, digitalization, automation, analytics, software, data-center and energy-demand terms.

[Saudi Aramco H1/Q2 2025 results call full transcript](https://earningscalls.dev/transcripts/saudi-arabian-oil-company_2222_earnings_call_transcript_2025-08-05)

### Digital/AI-enabled field operations and productivity value

CEO Amin Nasser said Aramco had developed an internal large language model and was applying AI to geosteering, exploration, seismic interpretation, plant maintenance and efficiency, emissions monitoring and well productivity. He said some wells had reached two to three times productivity in certain areas, described close to 500 use cases and said over 6,000 subject-matter professionals had AI training. The company reported $4bn total technology value in 2024, almost half attributed to AI, and a $2–4bn total technology-value outlook for 2025.

Speaker: Amin Nasser, President and CEO. Locator: Prepared remarks and Q&A lines 208–221, 333–335.. Measurement: Close to 500 use cases; >6,000 AI-trained staff; 2024 technology value $4bn with almost half attributed to AI; 2025 total technology-value outlook $2–4bn; reported 2–3x well productivity in certain areas.. Limit: Technology-value totals combine AI and other technology; outlook is not realized value. The 2–3x productivity statement is limited to certain areas and lacks a baseline or sample size in the call..

The event is officially described as Q2 and half-year 2025 results. EarningsCalls.dev exposes the complete transcript and call close (operator says call is concluded at line 414); the separate GuruFocus page exposes only the opening excerpt, so the complete transcript URL is used.

## Aramco · Q3 2025

reviewed_relevant · Full 856-line transcript reviewed from prepared remarks through final Q&A close; screened AI, ML, digitalization, analytics, automation, software, data centers and energy demand.

[Earnings call transcript: Saudi Aramco Q3 2025 posts 14% income rise](https://www.investing.com/news/transcripts/earnings-call-transcript-saudi-aramco-q3-2025-posts-14CH-4330375)

### Industrial AI and digitalization portfolio with technology-value reporting

Nasser described industrial AI across wells and processing equipment, supported by supercomputers and decades of geological data. He said aggregate technology value was independently verified and cited a $2–4bn technology-value target for 2025 and beyond.

Speaker: Amin Nasser, President and CEO. Locator: Prepared remarks lines 99–104; Q&A lines 137–146.. Measurement: $6bn technology value across 2023–24; about half of 2024 $4bn attributed to AI; >400 use cases; 6,000 AI-trained staff; 10bn data points/day.. Limit: Portfolio-level issuer-reported metrics; disclosed value includes technology beyond AI and no project-level attribution. Third-party verification applies to aggregate technology value per management, with methodology not in transcript..

### Saudi data-center and AI demand as gas-growth driver

Nasser linked future domestic gas demand to AI data centers. Requirements remained under evaluation; supply could include gas, solar and wind.

Speaker: Amin Nasser, President and CEO. Locator: Prepared remarks lines 96–97, 103–104; Q&A lines 150–154, 189–196.. Measurement: No data-center gas allocation, power capacity, named buyer or contract; gas production-capacity growth target increased to about 80% by 2030 versus 2021.. Limit: Demand and supply outlook, not a committed data-center project; management said data-center requirements were still being evaluated..

Source is an edited third-party transcript. Prepared remarks and Q&A both present; moderator confirms end of Q&A.

## Aramco · Q4 2025

reviewed_relevant · Full transcript reviewed from operator opening through final operator close; screened AI/ML/genAI, digitalization, analytics, automation, software, data centers and power demand.

[Saudi Aramco FY2025/Q4 2025 earnings call full transcript](https://stockanalysis.com/quote/tadawul/2222/transcripts/385865-q4-2025/)

### AI-enabled operational value and upstream cost/capital efficiency

CEO Amin Nasser said Aramco generated $5.3bn of technology realized value in 2025, around half from AI solutions, and $11.3bn since 2023. He cited reduced drilling time and AI/digitalization in upstream operations as ways to lower project operating costs and capital expenditure. The company targeted a further $3–5bn of technology realized value in 2026.

Speaker: Amin Nasser, President and CEO. Locator: Prepared remarks lines 141–144.. Measurement: $5.3bn total technology realized value in 2025 (around half from AI); $11.3bn cumulative since 2023; $3–5bn 2026 technology-value target.. Limit: Technology realized value covers AI and other technologies; the call gives no AI-only dollar split or project-level drilling savings. The 2026 figure is a target..

### Planned investment in HUMAIN to build AI capability

Nasser said Aramco planned to invest in HUMAIN as a means to strengthen its AI capability.

Speaker: Amin Nasser, President and CEO. Locator: Prepared remarks line 144.. Measurement: No investment amount or ownership share stated in this call.. Limit: The statement describes a plan, not a completed investment or quantified return..

Issuer archive lists FY2025 results webcast on Mar 10, 2026; StockAnalysis/Quartr transcript includes presentation, Q&A and operator close. Annual technology-value measure is distinguished from AI-only attribution.

## Aramco · Q1 2026

reviewed_relevant · Full 65-minute transcript reviewed across presentation and analyst Q&A through operator close; screened AI, ML, digitalization, analytics, automation, software, data centers and power-demand terms.

[Saudi Aramco Q1 2026 results call full transcript](https://earningsapi.io/transcripts/saudi-arabian-oil-company_2222_earnings_call_transcript_2026-05-11)

### AI-supported operational monitoring and disruption response

In response to a question about AI progress, an executive whose identity is not supplied in the transcript described AI-based monitoring and surveillance across pipeline networks and operations, using one million data points per day. The executive said existing cameras and sensors supported assessments after attacks and helped restore facilities and maintain delivery commitments.

Speaker: Unknown executive (transcript label; identity unconfirmed). Locator: Q&A lines 195–210, especially 200–210.. Measurement: 1 million data points per day referenced for AI-based monitoring/surveillance.. Limit: The speaker is not identified in the third-party transcript, and no quantified uptime, recovery-time or financial impact is attributed specifically to AI..

Issuer archive confirms Q1 webcast dated May 11, 2026. The full transcript contains Q&A; transcript renders the executive answering the AI question as “Unknown Executive,” so no personal attribution is inferred. Prepared remarks also cite $11.3bn total technology realized value since 2023, not disaggregated by AI in this call.

## Aramco · Q2 2026

reviewed_no_relevant · Full transcript reviewed through prepared remarks, analyst Q&A and operator close. Screened AI/artificial intelligence, machine learning, automation/digital transformation, data-center/hyperscaler terms. Official issuer investor-results page confirms the H1 2026 event on Aug 4.

[Saudi Aramco Q2/H1 2026 earnings-call full transcript](https://www.alphaspread.com/de/security/sau/2222/investor-relations/earnings-call/q2-2026)

Issuer holds a combined Q2/H1 call; no separate quarterly call inferred. Full third-party transcript contains Q&A and close. Selected-term screen found no relevant AI or AI-energy-demand disclosure.

## Baker Hughes · Q3 2024

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital terms.

[Baker Hughes Q3 2024 earnings call full transcript](https://stockanalysis.com/stocks/bkr/transcripts/216348-q3-2024/)

### AI capabilities in Leucipa and equipment monitoring

Baker Hughes said it was collaborating with Repsol to develop AI capabilities for Leucipa across Repsol’s upstream portfolio and described use of AI capabilities with equipment monitoring data through iCenter.

Speaker: Lorenzo Simonelli, Chairman and CEO. Locator: Transcript lines 123 and 146. Measurement: Not disclosed.. Limit: Customer-facing product and service capabilities; no realized operating or financial outcome quantified..

### Internal industrial automation

CEO said IET was applying industrial automation and lean initiatives in its own operations.

Speaker: Lorenzo Simonelli, Chairman and CEO. Locator: Transcript line 176. Measurement: Over 100 Kaizen projects initiated that year.. Limit: Adjacent automation; the call does not characterize these projects as AI..

StockAnalysis-hosted Quartr transcript. Distinguishes AI product claims and energy-demand exposure from internal use; AI energy demand is not treated as Baker Hughes internal AI adoption.

## Baker Hughes · Q4 2024

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital terms.

[Baker Hughes Q4 2024 earnings call full transcript](https://stockanalysis.com/stocks/bkr/transcripts/248496-q4-2024/)

### AI drilling optimization product

Baker Hughes described an AI rate-of-penetration optimization project with AIQ, ADNOC and Corva that provides real-time drilling-efficiency insights and recommendations.

Speaker: Lorenzo Simonelli, Chairman and CEO. Locator: Transcript line 131. Measurement: Not disclosed.. Limit: Announced customer-facing project; no deployment scale, revenue or outcome metric given..

### Generative AI in equipment digital services

Gas Technology Services was combining its monitoring data and iCenter capabilities with generative AI and the Cordant platform to develop customer equipment-optimization solutions.

Speaker: Lorenzo Simonelli, Chairman and CEO. Locator: Transcript lines 151–153. Measurement: Digital orders grew about 60% in 2024 with over 1,800 units connected at year-end; management expected digital orders to double by 2026.. Limit: Digital order/connectivity measures cover the broader GTS digital portfolio and are not attributed solely to AI..

StockAnalysis-hosted Quartr transcript. Distinguishes AI product claims and energy-demand exposure from internal use; AI energy demand is not treated as Baker Hughes internal AI adoption.

## Baker Hughes · Q1 2025

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital terms.

[Baker Hughes Q1 2025 earnings call full transcript](https://stockanalysis.com/stocks/bkr/transcripts/311273-q1-2025/)

### AI-related data-center energy demand

Management cited AI-led demand for gas-powered data centers as a driver of North American gas-infrastructure opportunity and described Baker Hughes turbine/compression products serving that market.

Speaker: Lorenzo Simonelli, Chairman and CEO. Locator: Transcript line 137. Measurement: Not disclosed in the AI reference.. Limit: Energy-demand exposure only; no Baker Hughes internal AI adoption or AI product disclosure in the searched terms..

StockAnalysis-hosted Quartr transcript. Distinguishes AI product claims and energy-demand exposure from internal use; AI energy demand is not treated as Baker Hughes internal AI adoption.

## Baker Hughes · Q2 2025

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital terms.

[Baker Hughes Q2 2025 earnings call full transcript](https://stockanalysis.com/stocks/bkr/transcripts/337943-q2-2025/)

### Leucipa AI-enabled production optimization

Baker Hughes described Leucipa digital automation as receiving awards for next-generation AI capabilities and deployment for ESP optimization and AI-driven predictive analytics.

Speaker: Lorenzo Simonelli, Chairman and CEO. Locator: Transcript lines 139–141. Measurement: Not disclosed.. Limit: Customer-facing product awards/deployment; no measured production or financial result attributable to AI disclosed..

StockAnalysis-hosted Quartr transcript. Distinguishes AI product claims and energy-demand exposure from internal use; AI energy demand is not treated as Baker Hughes internal AI adoption.

## Baker Hughes · Q3 2025

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital terms.

[Baker Hughes Q3 2025 earnings call full transcript](https://stockanalysis.com/stocks/bkr/transcripts/366209-q3-2025/)

### AI-driven power and data-center demand

CEO described generative-AI investment as a macro growth stimulus and said Baker Hughes’ power solutions were positioned to serve accelerating AI-company infrastructure commitments.

Speaker: Lorenzo Simonelli, Chairman and CEO. Locator: Transcript lines 137–149. Measurement: The call cited over $1.5 trillion of projected global data-center infrastructure investment over three years; no Baker Hughes AI-specific revenue was reported.. Limit: Industry growth forecasts and company positioning; no operator internal-AI use disclosed..

StockAnalysis-hosted Quartr transcript. Distinguishes AI product claims and energy-demand exposure from internal use; AI energy demand is not treated as Baker Hughes internal AI adoption.

## Baker Hughes · Q4 2025

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital terms.

[Baker Hughes Q4 2025 earnings call full transcript](https://stockanalysis.com/stocks/bkr/transcripts/396521-q4-2025/)

### AI data-center demand as power-market driver

Management said AI and digital infrastructure were adding a durable layer of electricity demand and that this supports demand for scalable, dispatchable and behind-the-meter power solutions.

Speaker: Lorenzo Simonelli, Chairman and CEO. Locator: Transcript lines 140–142 and 153–154. Measurement: Baker Hughes expected about $3 billion of data-center-related orders over 2025–2027; projections cited 12% CAGR in data-center power demand through 2040.. Limit: AI is discussed as a customer/end-market demand driver; infrastructure spending estimates are third-party industry estimates, not company results..

StockAnalysis-hosted Quartr transcript. Distinguishes AI product claims and energy-demand exposure from internal use; AI energy demand is not treated as Baker Hughes internal AI adoption.

## Baker Hughes · Q1 2026

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital terms.

[Baker Hughes Q1 2026 earnings call full transcript](https://stockanalysis.com/stocks/bkr/transcripts/546601-q1-2026/)

### AI-enabled data-center power optimization collaboration

Baker Hughes announced a Google Cloud collaboration to develop AI-enabled power-optimization and sustainability solutions for data-center applications.

Speaker: Lorenzo Simonelli, Chairman and CEO. Locator: Transcript line 133. Measurement: Not disclosed.. Limit: Development collaboration; no launch, customer adoption, revenue or measured operating outcome reported..

StockAnalysis-hosted Quartr transcript. Distinguishes AI product claims and energy-demand exposure from internal use; AI energy demand is not treated as Baker Hughes internal AI adoption.

## Baker Hughes · Q2 2026

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital terms.

[Baker Hughes Q2 2026 earnings call full transcript](https://stockanalysis.com/stocks/bkr/transcripts/654462-q2-2026/)

### AI-driven power demand and autonomous well construction

CEO linked projected power-system growth to AI and other compute-intensive workloads, and cited an award for Kantori, Baker Hughes’ autonomous well-construction offering.

Speaker: Lorenzo Simonelli, Chairman and CEO. Locator: Transcript lines 139 and 146. Measurement: Management expected Horizon Two IET orders above $45 billion; company noted a 1.3 GW data-center/oil-and-gas power award and a framework up to 1.8 GW.. Limit: AI references concern end-market power demand; Kantori is described as autonomous, not explicitly AI-enabled..

StockAnalysis-hosted Quartr transcript. Distinguishes AI product claims and energy-demand exposure from internal use; AI energy demand is not treated as Baker Hughes internal AI adoption.

## bp · Q3 2024

reviewed_relevant · Transcript text reviewed for AI/artifical-intelligence/machine-learning terms and relevant passage; lines 822–836.

[BP Q3 2024 Earnings Call Transcript](https://www.roic.ai/quote/BP-B.L/transcripts/2024-year/3-quarter)

### Hyperscaler energy demand

CEO said BP was in discussions with hyperscalers seeking energy with lower-carbon attributes and discussed integrated gas/renewables and hedging capabilities as a commercial fit.

Speaker: Murray Auchincloss, CEO. Locator: Q&A, lines 822–836.. Measurement: No contracted AI-specific supply or sales figure.. Limit: Discussions and strategic positioning only; no named customer commitment in this answer..

Third-party transcript only; inherited source was a lead. The reviewed passage is AI-demand strategy, not an internal AI deployment.

## bp · Q4 2024

reviewed_no_relevant · Full transcript screened, including prepared remarks, Q&A, close, and operator close. AI, artificial intelligence, machine learning, automation, analytics, software, digital, and data-center/hyperscaler terms checked.

[BP p.l.c. (NYSE:BP) Q4 2024 Earnings Call Transcript](https://www.insidermonkey.com/blog/bp-p-l-c-nysebp-q4-2024-earnings-call-transcript-1450494/)

Insider Monkey page provides the full call transcript through closing remarks and operator close. The bp issuer archive confirms the date and hosts the separate Q&A transcript; its page was reachable in search results but returned 403 when opened.

## bp · Q1 2025

reviewed_relevant · Issuer Q&A transcript reviewed; presentation/prepared remarks are published separately and were not screened here. Searched Q&A for AI/artificial intelligence, machine learning, digital, analytics, automation, data-center and adjacent terms.

[bp 1Q 2025 results webcast Q&A transcript](https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/investors/bp-first-quarter-2025-results-qa-transcript.pdf)

### Drilling automation and well-design technology

In Q&A, Murray Auchincloss said BPX uses matter-pressure drilling, insulated drill pipe and drilling automation as part of its approach to resource recovery and well economics.

Speaker: Murray Auchincloss, CEO. Locator: EarningsCall Q1 transcript, lines 127–135; companion official Q&A PDF.. Measurement: Not quantified. Limit: No automation-specific cost, productivity or production result was quantified; the prepared presentation was not reviewed in this partial source screen..

Q&A-only source; not a complete call screen. The issuer archive provides presentation/script and Q&A separately. No full-call negative inference.

## bp · Q2 2025

reviewed_relevant · Q&A transcript reviewed for AI, artificial intelligence, machine learning, automation, data center, and digital transformation. The page has brief opening remarks but not the full prepared-results presentation; that companion has not been screened.

[BP Q2 2025 Earnings Call Transcript — Q&A](https://stockanalysis.com/quote/lon/BP/transcripts/313801-q2-2025-q-a/)

### AI and data platform for supplier-cost controls

EVP of Technology Emeka Emembolu said bp was using Palantir and internal AI systems to inspect invoices and contract-value leakage in supplier payments. He also said bp managed about 100,000 data pipelines and was implementing a unified company-wide platform with Palantir and Databricks, planned to consolidate them into one; he said this data environment was needed for consistent AI use across the company.

Speaker: Emeka Emembolu, EVP of Technology. Locator: Q&A, lines 205–226; AI supply-chain use at 210–214, pipeline count and schedule at 213–226.. Measurement: About 100,000 existing data pipelines; target to consolidate to one by mid-2026; no AI-caused savings quantified.. Limit: Management says AI is applied in invoice/contract review; no accuracy, prevented overpayment, realized savings, or control details disclosed. Unified platform is a plan, not complete..

### AI-supported seismic interpretation and exploration

Management described AI algorithms alongside seismic tools as helping illuminate subsurface geology and support exploration interpretation. The passage discusses exploration success and technology, but does not isolate AI’s contribution to discoveries or quantify an uplift.

Speaker: Gordon Birrell, EVP of Production and Operations; Emeka Emembolu, EVP of Technology. Locator: Q&A exploration answer, lines 370–390; AI mention lines 378–390.. Measurement: No AI-attributed discovery, success-rate, or value metric.. Limit: AI is one factor alongside experienced geoscientists, seismic quality, compute, and service-provider technology; no causal contribution is isolated..

This source is the Q&A portion plus brief call-opening remarks, not the full prepared-results presentation. Prepared-results companion remains unscreened; no full-call conclusion.

## bp · Q3 2025

reviewed_relevant · Q&A transcript reviewed across AI, artificial intelligence, machine learning, agentic AI, automation, data center, and digital transformation. The companion page includes brief opening remarks but not the full prepared-results presentation; that companion has not been screened.

[BP Q3 2025 Earnings Call Transcript — Q&A](https://stockanalysis.com/quote/lon/BP/transcripts/344717-q3-2025-q-a/)

### AI-assisted drilling, equipment reliability and well planning

Auchincloss described LLM kick detection, AI fault prediction with equipment investment, and AI-assisted well planning.

Speaker: Murray Auchincloss, CEO. Locator: Q&A, response to Lydia Rainforth, lines 193–210; AI operating examples and metrics lines 202–207.. Measurement: About 98% kick detection; nearly 97% upstream availability; 90% reduction in well-planning effort/time as described by management.. Limit: Company-reported; no kick denominator, false-positive rate, validation, AI-versus-equipment attribution, or baseline for the 90% well-planning claim..

### AI agent for retail inventory and food-waste management

An Aral AI inventory agent responded to customer preferences in a German service-station trial.

Speaker: Murray Auchincloss, CEO. Locator: Q&A, response to Lydia Rainforth, lines 202–210; pilot and metric lines 208–209.. Measurement: 20 pilot service stations; 45% lower waste after three months.. Limit: Company-reported trial; baseline, comparison group, measurement method, and whether the waste metric is food-only are not specified..

### AI-assisted seismic exploration and interpretation

AI, seismic data and expanded HPC supported exploration interpretation and theory testing.

Speaker: Gordon Birrell, EVP of Production and Operations; Emeka Emembolu, EVP of Technology. Locator: Q&A exploration discussion, lines 324–347; compute and processing metrics at 343–345.. Measurement: HPC capacity quadrupled; algorithm-processing speed up about 5–10x, with certain steps up to 50x.. Limit: Compute and algorithm processing metrics are not an AI-attributed discovery or commercial-return measure; results also depend on experts and seismic/service-provider inputs..

This source is the Q&A portion plus brief call-opening remarks, not the full prepared-results presentation. Q&A findings are partial evidence; do not claim full-call coverage.

## bp · Q4 2025

reviewed_relevant · Full StockAnalysis/Quartr transcript screened from prepared results remarks through analyst Q&A and closing remarks. Despite the Q&A suffix in the URL, the page contains both the prepared presentation and Q&A.

[BP Q4 2025 Earnings Call Transcript](https://stockanalysis.com/quote/lon/BP/transcripts/398749-q4-2025-q-a/)

### AI-enabled well-site knowledge and kick detection

Birrell described Wells Advisor’s access to historical well knowledge and AI detection of small kicks.

Speaker: Gordon Birrell, EVP Production and Operations. Locator: Q&A, Lydia Rainforth question and Gordon Birrell answer, lines 459–473.. Measurement: Company-reported 90% success rate for detecting small kicks within roughly one minute.. Limit: The passage does not supply denominator, false-positive rate, validation method, population, or quantified financial/safety outcomes..

### Digital twins, AI and automation in operational monitoring

Dynamic digital twins, AI and automation jointly supported reservoir, well and facility monitoring.

Speaker: Gordon Birrell, EVP Production and Operations. Locator: Prepared remarks, lines 163–168.. Measurement: Management associated the combined package with c.2% average annual production growth and c.4% more production protected from going offline over five years.. Limit: Combined attribution, not AI-specific; no methodology or counterfactual. Separate seismic/HPC discussion had no AI-attributed discovery or return metric..

### AI opportunity for cost and productivity

Thomson described early-stage exploration of AI for company-wide cost and productivity improvements.

Speaker: Kate Thomson, CFO. Locator: Q&A, analyst exchange and Thomson answer, lines 615–621.. Measurement: No quantified AI outcome.. Limit: Opportunity statement, not a deployed capability/result; structural cost target was not attributed to AI..

### Technology and AI in future platforms

Birrell linked AI to future platform design, then described Azeri Central East’s onshore control and lower offshore staffing.

Speaker: Gordon Birrell, EVP Production and Operations. Locator: Q&A, lines 684–688.. Measurement: No AI-specific metric; management said the described onshore-control configuration uses much less offshore staff.. Limit: No specific AI system was attributed to the example or quantified as the cause of cost/safety effects..

The StockAnalysis/Quartr source body contains full prepared remarks and Q&A through close; the Q&A suffix in the URL does not indicate a Q&A-only source.

## bp · Q1 2026

reviewed_no_relevant · Full transcript screened from CEO opening through Q&A and closing remarks. StockAnalysis/Quartr transcript was also opened but contains management video/prepared remarks only; ROIC transcript supplied the Q&A. Screened explicit AI/ML/genAI/agentic and adjacent digital/analytics/software/automation/autonomy/remote/data-center/hyperscaler terms.

[BP p.l.c. Q1 FY2026 Earnings Call Transcript](https://www.roic.ai/quote/BP.SW/transcripts/2026-year/1-quarter)

Call held 28 April 2026. The StockAnalysis page presents the management video transcript, ending at the hand-off to Q&A, so it was not used alone to claim full-call review. ROIC provides the full Q&A and closing remarks; selected keyword checks returned no relevant content. Search result metadata date is preserved from transcript.

## bp · Q2 2026

reviewed_no_relevant · Q&A transcript and companion prepared-remarks video transcript reviewed as a combined full-call screen. Searched artificial intelligence, AI, ML, automation/digital transformation, data-center/hyperscaler terms.

[BP Q2 2026 earnings-call transcript with Q&A](https://stockanalysis.com/quote/lon/BP/transcripts/556848-q2-2026-q-a/)

Archive lists two Q2 2026 event variants on the same Aug 4 date: the Q&A transcript and companion prepared-remarks video text. They are consolidated into this single period row; selected-term combined full-call screen found no relevant passage.

## Canadian Natural Resources · Q3 2024

reviewed_no_relevant · Full transcript reviewed from operator opening through final close; Screened AI/artificial intelligence, machine learning, genAI, digitalization, analytics, automation, software, workflow, remote operations, data centers and power demand.

[Canadian Natural Q3 2024 full earnings-call transcript](https://www.roic.ai/quote/CNQ/transcripts/2024-year/3-quarter)

Transcript states the call was recorded Oct 31, 2024 at 9 a.m. Mountain Time. Prepared remarks and Q&A were present; full targeted screen found no relevant AI, digital, automation or data-center disclosure.

## Canadian Natural Resources · Q4 2024

inaccessible · Attempted transcript acquisition from two third-party full-transcript listings and issuer archive; providers returned cache-miss/access error before the transcript text could be reviewed.

[Canadian Natural Q4 2024 results call transcript listing (full text fetch unavailable)](https://earningslabs.com/stock/CNQ/2024/Q4)

The public transcript is located, but both Earnings Labs and EarningsCall.ai transcript URLs failed to render (cache miss). No keyword screen or no-mention conclusion is claimed. Issuer IR archive confirms the annual results event, not a transcript text.

## Canadian Natural Resources · Q1 2025

reviewed_no_relevant · Full transcript reviewed across prepared remarks, Q&A and closing/operator disconnect; Screened AI/artificial intelligence, machine learning, genAI, digitalization, analytics, automation, software, workflow, remote operations, data centers and power demand.

[Canadian Natural Q1 2025 earnings-call full transcript](https://earningslabs.com/stock/CNQ/2025/Q1)

Call date and opening establish Q1 2025; separate EarningsCall.biz transcript also identifies May 8 event. Full transcript targeted screen found no relevant AI/digital/analytics/automation/data-center disclosure. Autonomy is mentioned in a question, but no automation or autonomous deployment disclosure was made by CNQ.

## Canadian Natural Resources · Q2 2025

reviewed_no_relevant · Full transcript reviewed across prepared remarks, Q&A and closing; Screened AI/artificial intelligence, machine learning, genAI, digitalization, analytics, automation, software, workflow, remote operations, data centers and power demand.

[Canadian Natural Q2 2025 earnings-call full transcript](https://earningslabs.com/stock/CNQ/2025/Q2)

Earnings Labs transcript page gives Q2 call text, Q&A and closing sections; Seeking Alpha separately confirms Aug 7, 2025 call time/participants though its accessible view ends after opening. Full targeted-term search on the Earnings Labs transcript found no relevant AI, analytics, software, digitalization, automation or data-center discussion.

## Canadian Natural Resources · Q3 2025

reviewed_no_relevant · Full transcript reviewed across management presentation, Q&A and close. Screened AI/artificial intelligence, machine learning, digital/digitalization, analytics, automation, software, data-center/hyperscaler, workflow and remote-operation terms.

[Canadian Natural Resources Q3 2025 earnings-call full transcript](https://earningslabs.com/stock/CNQ/2025/Q3)

No relevant internal AI, adjacent digital/automation, or AI/data-center demand disclosure found in this full-call screen.

## Canadian Natural Resources · Q4 2025

reviewed_relevant · Full Q4/FY 2025 call transcript reviewed across management discussion, analyst Q&A and close. Screened AI/artificial intelligence, machine learning, digital, analytics, automation, software, data-center/hyperscaler and remote-operation terms.

[Canadian Natural Resources Q4 2025 earnings-call full transcript](https://stocksguide.com/en/stock/Canadian-Natural-Resources-Limited-CA1363851017?earnings_event_id=5473581)

### AI-supported thermal operations and oil-sands mining

Jay Froc said AI is used to optimize steam throughput, fuel use and steam quality, and described AI virtual operators adjusting flotation cells in real time. The call also described AI-based monitoring for reliability and pipe-integrity checks.

Speaker: Jay Froc, COO, Oil Sands. Locator: Q4 2025 transcript lines 2937–2943 and 2951–2979.. Measurement: Management cited 400,000 additional barrels per year from flotation-cell virtual operators; AI monitoring was said to reduce unplanned downtime and extend pump-maintenance intervals, without amounts.. Limit: The speaker corrected the initial “per day” formulation to “per year”; no baseline or attribution method for the 400,000-barrel estimate was given. Reliability effects were not quantified..

### Real-time analytics in conventional drilling and completions

Management described data analytics and customized dashboards for real-time Montney drilling decisions, plus monitoring and analytics to improve Duvernay completions.

Speaker: Robin Zabek, COO, E&P. Locator: Q4 2025 transcript lines 2914–2923.. Measurement: Montney unit costs were reported 9% lower since 2023; Duvernay completions costs 17% lower year over year.. Limit: The transcript attributes improvements to integrated teams, analytics and other execution practices together, without isolating analytics’ contribution..

Corrected period attribution: these AI and analytics passages are in the Q4/FY 2025 transcript, not Q1 2026. Metrics are company-reported.

## Canadian Natural Resources · Q1 2026

reviewed_no_relevant · Full Q1 2026 transcript reviewed across management discussion, analyst Q&A and close. Screened AI/artificial intelligence, machine learning, digital/digitalization, analytics, automation, software/workflow, remote operations and data-center/hyperscaler terms.

[Canadian Natural Resources Q1 2026 earnings-call full transcript](https://stocksguide.com/en/stock/Canadian-Natural-Resources-Limited-CA1363851017?earnings_event_id=5473581)

No relevant AI, adjacent digital/analytics/automation, or data-center-demand disclosure found in the Q1 2026 call. AI use disclosures in the shared transcript page belong to Q4 2025, not Q1 2026.

## Canadian Natural Resources · Q2 2026

reviewed_no_relevant · Full third-party transcript reviewed from prepared remarks through Q&A close; screened for AI/artificial intelligence, machine learning, generative/agentic AI, automation, digital transformation, data centers and hyperscalers.

[Canadian Natural Resources Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/tsx/CNQ/transcripts/662249-q2-2026/)

Full prepared presentation and Q&A through close reviewed; no relevant term or AI-energy demand passage found.

## Cenovus · Q3 2024

reviewed_no_relevant · Full transcript, including presentation, analyst Q&A and operator close, reviewed. Selected-term screen covered AI/artificial intelligence, machine learning, generative AI, agentic, automation, digital transformation/digitalization, predictive, autonomous, analytics, software, workflow, remote operations, data-center/centre and hyperscaler terms.

[Cenovus Energy Q3 2024 earnings-call transcript](https://www.marketbeat.com/earnings/reports/2024-10-31-cenovus-energy-inc-stock/)

Complete third-party transcript was accessible and reviewed through operator close, including Q&A. Selected-term screen found no relevant AI, digital/analytics/software/automation or AI/data-center demand disclosure. The “AI Generated” label at line 309 belongs to MarketBeat’s page summary, not the call transcript.

## Cenovus · Q4 2024

reviewed_no_relevant · Full transcript, including presentation, analyst Q&A and operator close, reviewed. Selected-term screen covered AI/artificial intelligence, machine learning, generative AI, agentic, automation, digital transformation/digitalization, predictive, autonomous, analytics, software, workflow, remote operations, data-center/centre and hyperscaler terms.

[Cenovus Energy Inc. Q4 2024 earnings-call transcript](https://www.insidermonkey.com/blog/cenovus-energy-inc-nysecve-q4-2024-earnings-call-transcript-1459783/)

Insider Monkey’s accessible transcript reports the call date as February 20, 2025; an alternate MarketScreener transcript title also gives Feb. 20. The prior archive row’s Feb. 13 date was incorrect and is corrected. Complete remarks and Q&A through the operator close were screened; no relevant AI, digital/analytics/software/automation or AI/data-center demand disclosure was found.

## Cenovus · Q1 2025

reviewed_no_relevant · Full transcript, including presentation, analyst Q&A and operator close, reviewed. Selected-term screen covered AI/artificial intelligence, machine learning, generative AI, agentic, automation, digital transformation/digitalization, predictive, autonomous, analytics, software, workflow, remote operations, data-center/centre and hyperscaler terms.

[Q1 2025 Cenovus Energy Inc Earnings Call Transcript](https://www.gurufocus.com/stock/CVE/transcripts/2847482)

Refinitiv StreetEvents transcript gives the event date as May 8, 2025; prior archive row’s May 1 date was incorrect and is corrected. Full presentation, Q&A and operator close screened; no relevant AI, digital/analytics/software/automation or AI/data-center demand disclosure was found.

## Cenovus · Q2 2025

reviewed_no_relevant · Full transcript, including presentation, analyst Q&A and operator close, reviewed. Selected-term screen covered AI/artificial intelligence, machine learning, generative AI, agentic, automation, digital transformation/digitalization, predictive, autonomous, analytics, software, workflow, remote operations, data-center/centre and hyperscaler terms.

[Q2 2025 Earnings Call Transcript](https://www.cenovus.com/-/media/Project/WWW/docs/investors/2025/2025-q2-transcript.pdf?hash=868ED561108CBC93B2835AE3C93386FE&sc_lang=en)

Official issuer transcript confirms the July 31, 2025 event date; Q&A and operator close are present. Full 19-page transcript screened across remarks and Q&A; no relevant AI, digital/analytics/software/automation or AI/data-center demand disclosure was found.

## Cenovus · Q3 2025

reviewed_no_relevant · Full transcript, including presentation, analyst Q&A and operator close, reviewed. Selected-term screen covered AI/artificial intelligence, machine learning, generative/agentic AI, automation, digital transformation/digitalization, predictive, autonomous, analytics, software, workflow, remote operations, data-center/centre and hyperscaler terms.

[Cenovus Energy Inc. Earnings Call Transcript Q3 2025](https://www.roic.ai/quote/CVE/transcripts/2025-year/3-quarter)

ROIC.ai archive confirms Oct. 31, 2025. The accessible transcript includes presentation, Q&A and operator close. Broad selected-term screening found no relevant AI/digital/analytics/software/automation or AI/data-center demand disclosure.

## Cenovus · Q4 2025

reviewed_no_relevant · Full transcript, remarks and Q&A reviewed through operator close; selected-term search for AI/artificial intelligence, machine learning, generative AI, automation, digital transformation, predictive, autonomous, and data-center/centre/hyperscaler terms returned no relevant passage.

[Cenovus Energy Q4 2025 earnings call transcript](https://stockanalysis.com/quote/tsx/CVE/transcripts/394580-q4-2025/)

Complete transcript text was accessible on the cited third-party page; review includes the Q&A and recorded call close.

## Cenovus · Q1 2026

reviewed_no_relevant · Full transcript, including presentation, analyst Q&A and operator close, reviewed. Selected-term screen covered AI/artificial intelligence, machine learning, generative/agentic AI, automation, digital transformation/digitalization, predictive, autonomous, analytics, software, workflow, remote operations, data-center/centre and hyperscaler terms.

[Earnings call transcript: Cenovus Energy Q1 2026 sees strong EPS, stock dips](https://ca.investing.com/news/stock-market-news/earnings-call-transcript-cenovus-energy-q1-2026-sees-strong-eps-stock-dips-93CH-4629488)

The issuer confirms the May 6, 2026 event date; this full transcript was published May 12. Prepared remarks, Q&A and operator close are present. No relevant AI/digital/analytics/software/automation or AI/data-center demand disclosure was found. Investing.com’s auto-generated transcript mislabels some speaker turns as “Andrew, Executive (likely VP Operations or Development)”; no speaker attribution is used for this no-mention result.

## Cenovus · Q2 2026

reviewed_no_relevant · Full transcript reviewed, including prepared remarks, analyst Q&A, and operator close. Selected AI/artificial-intelligence, machine-learning, predictive/autonomy, digital-transformation, automation and data-center terms screened.

[Cenovus Energy Q2 2026 earnings-call full transcript](https://stockanalysis.com/quote/tsx/CVE/transcripts/661539-q2-2026/)

Selected-term full-call screen found no relevant AI, digital transformation/automation or data-center/AI-energy-demand passage.

## Chevron · Q3 2024

reviewed_relevant · Full transcript reviewed, including Q&A; searched AI, artificial intelligence, machine learning, automation, data center, hyperscaler, digital transformation, agentic, and predictive analytics.

[Chevron Q3 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/cvx/transcripts/218450-q3-2024/)

### AI-guided exploration and geologic-risk prediction

CEO said Chevron was working on AI tools to guide exploration focus areas and improve prediction of geologic risk factors in the Gulf of Mexico. The passage frames this as work in progress; it does not report a model name, operating deployment date, accuracy, resource uplift, or economics.

Speaker: Mike Wirth, Chairman and CEO. Locator: Q&A, Betty Jiang question and Wirth answer, lines 301–313 (AI statement at 311).. Measurement: No deployment, accuracy, uplift, or financial metric disclosed.. Limit: Work-in-progress statement only; no evidence of measured outcome or production deployment..

StockAnalysis/Quartr full transcript includes prepared remarks and Q&A; Q&A is explicitly opened at line 141.

## Chevron · Q4 2024

reviewed_relevant · Full transcript reviewed, including Q&A; searched AI, artificial intelligence, machine learning, automation, robotics, data center, hyperscaler, digital transformation, agentic, and predictive analytics.

[Chevron Q4 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/cvx/transcripts/251754-q4-2024/)

### AI-linked power strategy and hyperscaler demand

CEO said Chevron had discussed customer needs with hyperscalers for a year or more, had seven large GE Vernova turbine delivery reservations beginning in late 2026 and 2027, and considered power for AI within its investment portfolio. He described behind-the-meter supply as a way to meet customer demand without further burdening the grid.

Speaker: Mike Wirth, Chairman and CEO. Locator: Q&A, Neil Mehta question and Wirth answer, lines 207–217; investment-screen answer, lines 481–487.. Measurement: Seven large turbine delivery slots reserved for delivery from late 2026 into 2027; no named hyperscaler contract or capacity stated in this answer.. Limit: Customer discussions and reserved equipment slots; not evidence of a signed named AI-specific offtake or completed power project..

### AI for exploration and subsurface cycle-time reduction

CFO Eimear Bonner cited AI for reducing exploration and subsurface-work cycle times among technology changes Chevron was deploying. This was presented under a broader structural-cost program and no AI-attributed savings were separated out.

Speaker: Eimear Bonner, CFO. Locator: Q&A, response to Nitin Kumar on structural cost reductions, lines 496–505 (AI example at 503–505).. Measurement: The overall company structural-cost target is $2–3 billion off a 2024 base; transcript did not assign a portion to AI.. Limit: Broad cost target is not AI-attributed; specific AI workflow, stage, baseline, and realized savings are not stated..

Full call contains both demand-side power-for-AI discussion and an internal exploration/subsurface AI example. These are separated so power-market demand is not conflated with operational deployment.

## Chevron · Q1 2025

reviewed_relevant · Full transcript reviewed, including Q&A; target-term scan covered AI, artificial intelligence, machine learning, agentic, automation, data center, and digital transformation.

[Chevron Q1 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/cvx/transcripts/311297-q1-2025/)

### Power solutions for AI data-center demand

CEO Mike Wirth described a venture advancing gigawatt-scale power solutions for the US AI data-center market. In Q&A he said prospective customer demand was strong, sites were being narrowed, and the project was moving into engineering/EPC with an aim to reach FID before year-end. This is demand-side energy supply, not an internal AI deployment.

Speaker: Mike Wirth, Chairman and CEO. Locator: Prepared remarks lines 113–119; CEO Q&A answer lines 399–412.. Measurement: Chevron reported $400 million of Q1 inorganic capital for the venture and an objective to reach FID before year-end; no contracted customer or completed project was identified in the passage.. Limit: Prospective demand and project development only; amount is venture capital, not an AI productivity result, and the passage does not identify a signed AI-specific supply contract..

Full transcript text includes prepared remarks and Q&A. The call has an AI-linked data-center power-demand finding; no internal AI deployment was found in the full-call keyword screen.

## Chevron · Q2 2025

reviewed_relevant · Full transcript reviewed, including Q&A; searched AI, artificial intelligence, machine learning, automation, data center, hyperscaler, digital transformation, agentic, and predictive analytics.

[Chevron Q2 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/cvx/transcripts/341388-q2-2025/)

### AI for real-time fract optimization and exploration data analysis

Vice Chairman Mark Nelson said Chevron expected engineering hubs to accelerate innovation and scale solutions including artificial intelligence to optimize fractures in real time and speed exploration-data analysis. He connected this work to a $2–3 billion structural-cost-reduction target but did not attribute savings to AI.

Speaker: Mark Nelson, Vice Chairman. Locator: Prepared remarks, lines 120–129 (AI passage at 127–129); Q&A is present and ends at line 510.. Measurement: Company target of $2–3 billion structural cost reductions by end-2026 across operational efficiencies; no AI-specific contribution stated.. Limit: Expected use and scale-up, without implementation status, model detail, measured effect, or AI-attributed share of the cost target..

Full-call transcript reviewed; prepared remarks contain an internal AI use case. Targeted term search found no separate AI data-center demand item.

## Chevron · Q3 2025

reviewed_relevant · Full transcript reviewed, including Q&A; searched AI, artificial intelligence, machine learning, automation, data center, hyperscaler, digital transformation, agentic, and predictive analytics.

[Chevron Q3 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/cvx/transcripts/369738-q3-2025/)

### AI-associated electricity-system pressure as a long-term investment context

CEO said the growth of AI and resulting stress on the U.S. power system reinforced the value of affordable, reliable energy and the case for long-term investment. The passage did not describe an AI-specific customer project, contract, internal deployment, or quantified demand.

Speaker: Mike Wirth, Chairman and CEO. Locator: Q&A, Paul Sankey question and Wirth response, lines 393–408, especially 400–405.. Measurement: No customer, capacity, investment amount, or demand forecast quantified in this passage.. Limit: Strategic context only; no company-specific AI power contract or AI implementation evidenced..

AI appears in a general strategy discussion. This record does not claim a specific AI-linked power project or internal AI deployment.

## Chevron · Q4 2025

reviewed_relevant · Full transcript reviewed, including Q&A; searched AI, artificial intelligence, machine learning, data center, hyperscaler, automation, and digital transformation.

[Chevron Q4 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/cvx/transcripts/406295-q4-2025/)

### AI-assisted supply-chain negotiation analysis

CFO Eimear Bonner said the supply-chain team was using AI to gather intelligence for approaching certain negotiations. She characterized AI as increasingly used across the business, but gave no tool, specific negotiation, procurement savings, or attribution of cost reductions to AI.

Speaker: Eimear Bonner, CFO. Locator: Q&A, response to Devin McDermott, lines 266–277 (AI statement at 275–276).. Measurement: Company reported $1.5 billion of overall cost savings delivered to date and a run rate above $2 billion; savings sources included divestments, efficiencies, and technology. No AI-specific share disclosed.. Limit: Specific internal use case, but savings/benefit attribution, system identity, adoption scope, and controls were not provided. Broad cost program is not an AI result..

StockAnalysis/Quartr full transcript includes prepared remarks and Q&A; full-call synonym screen completed. The AI disclosure is internal workflow use, not data-center electricity demand.

## Chevron · Q1 2026

reviewed_no_relevant · Full transcript reviewed, including Q&A; searched artificial intelligence, AI, machine learning, automation, autonomous, data center, agentic, digital transformation, genAI, predictive analytics, hyperscaler, and digital.

[Chevron Q1 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/cvx/transcripts/549372-q1-2026/)

Full-call screen completed; no relevant AI or AI-linked data-center demand evidence located.

## Chevron · Q2 2026

reviewed_relevant · Full transcript keyword-search and relevant passage review; transcript page is 539 lines.

[Chevron Q2 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/cvx/transcripts/659246-q2-2026/)

### AI-driven power demand

Chevron framed AI growth as accelerating U.S. electricity demand and described its natural-gas, behind-the-meter power and customer/partner capabilities as a response opportunity.

Speaker: Jeff Gustavson, President, New Energies. Locator: Prepared remarks lines 121–124; later discussion of power opportunity in transcript.. Measurement: No specific AI-linked capacity, contract or revenue quantity in reviewed passage.. Limit: Demand/opportunity framing, not evidence of a completed AI-campus power contract..

### Permian and exploration tools

CEO said AI is among technologies under work to improve Permian productivity and that it may change exploration cycle time and outcomes.

Speaker: Mike Wirth, Chairman and CEO. Locator: Q&A lines 227–248; exploration response lines 324–332.. Measurement: No quantified AI-specific result.. Limit: Forward-looking/qualitative; no named model, workflow deployment scope or isolated performance metric in reviewed passage..

## Chord Energy · Q3 2024

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Chord Energy (CHRD) Q3 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/chrd/transcripts/220115-q3-2024/)

not relevant: keyword scan found no qualifying AI, ML, automation, digital, predictive, data-center or AI/power-demand disclosure. Full transcript page lists opening remarks and Q&A; call date and quarter verified against archive.

## Chord Energy · Q4 2024

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Chord Energy (CHRD) Q4 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/chrd/transcripts/237895-q4-2024/)

not relevant: keyword scan found no qualifying AI, ML, automation, digital, predictive, data-center or AI/power-demand disclosure. Full transcript page lists opening remarks and Q&A; call date and quarter verified against archive.

## Chord Energy · Q1 2025

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Chord Energy (CHRD) Q1 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/chrd/transcripts/319683-q1-2025/)

### Predictive maintenance and remote well-site monitoring as potential LOE improvement tools

Management listed artificial-lift optimization, faster cycle times and logistics improvements, and potentially newer technologies such as predictive maintenance and remote well-site monitoring among multiple initiatives to improve LOE.

Speaker: Danny Brown, CEO. Locator: Prepared remarks, line 138.. Measurement: Not quantified. Limit: Predictive maintenance and remote monitoring were potential/future initiatives, not a disclosed deployed AI system or quantified result..

adjacent predictive maintenance / remote monitoring mentioned as potential future LOE initiatives; no AI/ML claim. Full transcript page lists opening remarks and Q&A; call date and quarter verified against archive.

## Chord Energy · Q2 2025

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Chord Energy (CHRD) Q2 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/chrd/transcripts/343494-q2-2025/)

### AI/ML models and analytics for production, reservoir, and operational planning

Management described AI/ML to optimize ESP-to-rod-lift conversion decisions by generating production profiles, forecasting ESP run life and calibrating well economics; an algorithm models gas-lift optimization curves; an ML model estimates geologic production/EUR contributions. Optimization tools plan workovers and frac protection, and interactive dashboards were being rolled out. Executives said a $7 million reduction in full-year cash G&A included efficiencies from these tools but did not isolate their share.

Speaker: Danny Brown, CEO; Michael Lou, Chief Strategy Officer/Chief Commercial Officer. Locator: Prepared remarks, lines 131–135 and 160; Q&A, lines 274–315.. Measurement: Full-year cash G&A guidance reduced by $7 million, said to include efficiencies from analytics/ML/AI; not an AI-only attribution.. Limit: CEO said it was early and too difficult to quantify cost gains; implementation cost described as small, with clean data and internal employee training. No individual model-level savings disclosed..

AI/ML and analytics programs described across production, reservoir, planning and workflows; prospective cost benefit unquantified. Full transcript page lists opening remarks and Q&A; call date and quarter verified against archive.

## Chord Energy · Q3 2025

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Chord Energy (CHRD) Q3 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/chrd/transcripts/372170-q3-2025/)

### AI rod-pump control and production-base optimization

COO said AI was controlling rod-pump parameters, with improved run times, less downtime and fewer workovers; company was evaluating AI control for ESPs. CEO characterized deployment as early innings and said rod-pump automation was already broadly deployed.

Speaker: Darrin Henke, COO; Danny Brown, CEO. Locator: Q&A, lines 235–239 and 541–544.. Measurement: Thousands of wells identified as having automation-improvement opportunity; no quantified AI-specific production or savings result.. Limit: Management said results might be quantifiable the following year; no AI-attributed financial/production metric..

### Adjacent field automation and connectivity technologies

Management discussed automation opportunities across thousands of wells, workover automation software and emerging connectivity (including Starlink) and drone technology as ways to improve remote communications and reduce field visits/truck trips.

Speaker: Danny Brown, CEO; Darrin Henke, COO. Locator: Q&A, lines 234, 536–544.. Measurement: Thousands of wells cited as possible automation-improvement scope; no savings reported.. Limit: These are broader automation/connectivity opportunities; no measured impact or deployment rate disclosed beyond rod-pump controls..

AI controls rod-pump parameters, improves run times/reduces downtime and workovers; broader automation and future ML opportunities. Full transcript page lists opening remarks and Q&A; call date and quarter verified against archive.

## Chord Energy · Q4 2025

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Chord Energy (CHRD) Q4 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/chrd/transcripts/395370-q4-2025/)

not relevant: keyword scan found no qualifying AI, ML, automation, digital, predictive, data-center or AI/power-demand disclosure. Full transcript page lists opening remarks and Q&A; call date and quarter verified against archive.

## Chord Energy · Q1 2026

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Chord Energy (CHRD) Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/chrd/transcripts/571953-q1-2026/)

### AI-enhanced artificial-lift optimization on older rod-pump wells

Management said it adjusted AI to maximize production from older rod-pump wells after lowering some pumps, reporting a positive effect on base production and arrested decline in the targeted group.

Speaker: Darrin Henke, COO. Locator: Q&A, lines 184–190.. Measurement: Management described a “dramatic” positive impact on production/decline, without a numerical measure.. Limit: Executives said sustainability of the well-level changes remained to be tested over time..

AI/artificial-lift optimization discussed, with early production uplift; durability not yet established. Full transcript page lists opening remarks and Q&A; call date and quarter verified against archive.

## Chord Energy · Q2 2026

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Chord Energy (CHRD) Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/chrd/transcripts/661522-q2-2026/)

### AI optimization of rod-pump artificial lift

CEO said the company had broadly deployed computer-based AI optimization across rod-pump wells to improve pump loading, reduce pump wear and improve production; management was considering more near-instantaneous optimization in other operating areas.

Speaker: Danny Brown, President and CEO. Locator: Prepared remarks, lines 126–129; Q&A, lines 332–339.. Measurement: Nearly every field well ultimately uses rod pump; no quantified incremental barrels, cost or savings attributed to AI.. Limit: Management characterized the initiative as broadly implemented but said further optimization opportunities remained; no quantified AI-only outcome..

AI optimizes rod-pump artificial lift broadly across field; pump wear reduction and production improvement claimed without quantified magnitude. Full transcript page lists opening remarks and Q&A; call date and quarter verified against archive.

## ConocoPhillips · Q3 2024

reviewed_no_relevant · Full speaker-labelled transcript including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center/hyperscaler and power-demand references.

[ConocoPhillips (COP) Q3 2024 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2024/10/31/conocophillips-cop-q3-2024-earnings-call-transcrip/)

Official issuer archive enumerates quarter transcript; full transcript located and reviewed. Keyword scan returned no matching in-scope terms beyond the explicitly recorded finding.

## ConocoPhillips · Q4 2024

reviewed_relevant · Full speaker-labelled transcript including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center/hyperscaler and power-demand references.

[ConocoPhillips (COP) Q4 2024 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2025/02/06/conocophillips-cop-q4-2024-earnings-call-transcrip/)

### Domestic power and data-center opportunity

CEO Ryan Lance said ConocoPhillips was evaluating inbound power opportunities as another route to monetize produced natural gas. In response to an analyst question about U.S. data-center power demand, Lance said hyperscalers and data centers would need fast, inexpensive power, the Permian position could fit some attributes, and the company was assessing opportunities against its capital framework. The transcript does not explicitly attribute data-center demand to AI.

Speaker: Ryan Lance, Chairman and CEO. Locator: Q&A, lines 354–365. Measurement: Not quantified. Limit: No executed contract, project, load or gas-volume estimate was disclosed; the transcript discusses hyperscalers/data centers, not AI explicitly..

Official issuer earnings archive lists quarterly transcript. Full third-party transcript reviewed, including prepared remarks and Q&A; finding is adjacent strategic context, not evidence of operational AI deployment.

## ConocoPhillips · Q1 2025

reviewed_no_relevant · Full speaker-labelled transcript including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center/hyperscaler and power-demand references.

[Q1 2025 ConocoPhillips Earnings Call](https://static.conocophillips.com/files/resources/cop-usq_transcript_2025-05-08.pdf)

Official issuer archive enumerates quarter transcript; full transcript located and reviewed. Keyword scan returned no matching in-scope terms beyond the explicitly recorded finding.

## ConocoPhillips · Q2 2025

reviewed_no_relevant · Full speaker-labelled transcript including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center/hyperscaler and power-demand references.

[Q2 2025 ConocoPhillips Earnings Call](https://static.conocophillips.com/files/resources/cop-usq_transcript_2025-08-07.pdf)

Official issuer archive enumerates quarter transcript; full transcript located and reviewed. Keyword scan returned no matching in-scope terms beyond the explicitly recorded finding.

## ConocoPhillips · Q3 2025

reviewed_no_relevant · Full speaker-labelled transcript including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center/hyperscaler and power-demand references.

[Q3 2025 ConocoPhillips Earnings Call](https://static.conocophillips.com/files/resources/cop-usq_transcript_2025-11-06.pdf)

Official issuer archive enumerates quarter transcript; full transcript located and reviewed. Keyword scan returned no matching in-scope terms beyond the explicitly recorded finding.

## ConocoPhillips · Q4 2025

reviewed_no_relevant · Full speaker-labelled transcript including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center/hyperscaler and power-demand references.

[Q4 2025 ConocoPhillips Earnings Call](https://static.conocophillips.com/files/resources/cop-usq_transcript_2026-02-05.pdf)

Official issuer archive enumerates quarter transcript; full transcript located and reviewed. Keyword scan returned no matching in-scope terms beyond the explicitly recorded finding.

## ConocoPhillips · Q1 2026

reviewed_no_relevant · Full speaker-labelled transcript including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center/hyperscaler and power-demand references.

[Q1 2026 ConocoPhillips Earnings Call](https://static.conocophillips.com/files/resources/cop_transcript_2026-04-30.pdf)

Official issuer archive enumerates quarter transcript; full transcript located and reviewed. Keyword scan returned no matching in-scope terms beyond the explicitly recorded finding.

## ConocoPhillips · Q2 2026

reviewed_relevant · Full speaker-labelled transcript including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center/hyperscaler and power-demand references.

[ConocoPhillips (COP) Q2 2026 Earnings Call Transcript](https://equibles.com/stocks/cop/calls/2026-q2)

### AI-driven energy-security context

In Q&A on his retirement and advice to investors, CEO Ryan Lance said energy security had become important worldwide and connected that importance to the coming AI revolution, adding that ConocoPhillips would benefit. He did not identify a specific demand forecast, project, customer or internal AI deployment.

Speaker: Ryan Lance, Chairman and CEO. Locator: Q&A, lines 351–360; especially line 359. Measurement: Not quantified. Limit: Broad strategic observation only; no AI-attributed energy volumes or company-specific benefit were quantified..

Official issuer earnings archive lists quarterly transcript. Full third-party transcript reviewed, including prepared remarks and Q&A; finding is adjacent strategic context, not evidence of operational AI deployment.

## Continental Resources · Q3 2024

reporting_exception · Public issuer reporting-status exception; not a review of private debt-holder communications.

[Continental Resources 2023 Form 10-K (take-private disclosure)](https://www.sec.gov/Archives/edgar/data/732834/000095017024018868/ck0000732834-20231231.htm)

This is a public-equity reporting exception and does not establish whether private debt-holder communications occurred.

## Continental Resources · Q4 2024

reporting_exception · Public issuer reporting-status exception; not a review of private debt-holder communications.

[Continental Resources 2023 Form 10-K (take-private disclosure)](https://www.sec.gov/Archives/edgar/data/732834/000095017024018868/ck0000732834-20231231.htm)

This is a public-equity reporting exception and does not establish whether private debt-holder communications occurred.

## Continental Resources · Q1 2025

reporting_exception · Public issuer reporting-status exception; not a review of private debt-holder communications.

[Continental Resources 2023 Form 10-K (take-private disclosure)](https://www.sec.gov/Archives/edgar/data/732834/000095017024018868/ck0000732834-20231231.htm)

This is a public-equity reporting exception and does not establish whether private debt-holder communications occurred.

## Continental Resources · Q2 2025

reporting_exception · Public issuer reporting-status exception; not a review of private debt-holder communications.

[Continental Resources 2023 Form 10-K (take-private disclosure)](https://www.sec.gov/Archives/edgar/data/732834/000095017024018868/ck0000732834-20231231.htm)

This is a public-equity reporting exception and does not establish whether private debt-holder communications occurred.

## Continental Resources · Q3 2025

reporting_exception · Public issuer reporting-status exception; not a review of private debt-holder communications.

[Continental Resources 2023 Form 10-K (take-private disclosure)](https://www.sec.gov/Archives/edgar/data/732834/000095017024018868/ck0000732834-20231231.htm)

This is a public-equity reporting exception and does not establish whether private debt-holder communications occurred.

## Continental Resources · Q4 2025

reporting_exception · Public issuer reporting-status exception; not a review of private debt-holder communications.

[Continental Resources 2023 Form 10-K (take-private disclosure)](https://www.sec.gov/Archives/edgar/data/732834/000095017024018868/ck0000732834-20231231.htm)

This is a public-equity reporting exception and does not establish whether private debt-holder communications occurred.

## Continental Resources · Q1 2026

reporting_exception · Public issuer reporting-status exception; not a review of private debt-holder communications.

[Continental Resources 2023 Form 10-K (take-private disclosure)](https://www.sec.gov/Archives/edgar/data/732834/000095017024018868/ck0000732834-20231231.htm)

This is a public-equity reporting exception and does not establish whether private debt-holder communications occurred.

## Continental Resources · Q2 2026

reporting_exception · Public issuer reporting-status exception; not a review of private debt-holder communications.

[Continental Resources 2023 Form 10-K (take-private disclosure)](https://www.sec.gov/Archives/edgar/data/732834/000095017024018868/ck0000732834-20231231.htm)

This is a public-equity reporting exception and does not establish whether private debt-holder communications occurred.

## Coterra Energy · Q3 2024

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Coterra Energy Q3 2024 results/call source](https://stockanalysis.com/stocks/ctra/transcripts/218336-q3-2024/)

### Machine-learning-assisted reservoir and completion design

COO said Coterra reservoir engineers worked with its machine-learning team to iterate frac design and well spacing to improve capital efficiency and NPV across developments.

Speaker: Blake Sirgo, SVP of Operations. Locator: Prepared remarks, line 151.. Measurement: Not quantified. Limit: No model details or ML-specific financial/production outcomes disclosed..

Archive-index lead only; this pending row is not evidence of transcript review or no relevant mention.

## Coterra Energy · Q4 2024

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Coterra Energy Q4 2024 results/call source](https://stockanalysis.com/stocks/ctra/transcripts/238693-q4-2024/)

### Automation in hydraulic-fracturing operations

COO said Coterra and frac providers developed pumping strategies and automation that set fleet records for pumping hours.

Speaker: Blake Sirgo, SVP of Operations. Locator: Prepared remarks, lines 149–150.. Measurement: Record pumping hours across fleets; no numerical rate given.. Limit: Automation’s isolated contribution was not quantified..

### Exploratory natural-gas power and data-center opportunities

In response to an analyst, COO said Coterra was discussing Permian gas-to-power opportunities with parties interested in base-load generation and data-center loads, including combined-cycle and behind-the-meter solutions.

Speaker: Blake Sirgo, SVP of Operations; response to Nitin Kumar, Mizuho. Locator: Q&A, lines 259–264.. Measurement: Not quantified. Limit: Exploratory discussions; no executed contract, capacity, price or revenue disclosed. The call did not explicitly link data-center demand to AI..

Archive-index lead only; this pending row is not evidence of transcript review or no relevant mention.

## Coterra Energy · Q1 2025

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Coterra Energy Q1 2025 results/call source](https://stockanalysis.com/stocks/ctra/transcripts/319258-q1-2025/)

Archive-index lead only; this pending row is not evidence of transcript review or no relevant mention.

## Coterra Energy · Q2 2025

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Coterra Energy Q2 2025 results/call source](https://stockanalysis.com/stocks/ctra/transcripts/341344-q2-2025/)

Archive-index lead only; this pending row is not evidence of transcript review or no relevant mention.

## Coterra Energy · Q3 2025

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Coterra Energy Q3 2025 results/call source](https://stockanalysis.com/stocks/ctra/transcripts/370614-q3-2025/)

### Natural-gas demand context from LNG and electricity use

CEO said LNG exports and growing electricity demand were constructive for the medium/long-term gas outlook; Coterra was discussing additional supply arrangements and cited existing LNG and power-plant sales.

Speaker: Tom Jorden, Chairman, CEO and President. Locator: Prepared remarks, lines 119–121.. Measurement: Management cited contracted volumes comprising about 30% of company gas output.. Limit: Broad gas/electricity outlook and existing supply deals; no data-center or AI attribution and no AI-specific contract..

Archive-index lead only; this pending row is not evidence of transcript review or no relevant mention.

## Coterra Energy · Q4 2025

reporting_exception · No public call: issuer release explicitly states Coterra would not host a call or webcast due to pending Devon merger.

[Coterra Energy Q4 2025 results/call source](https://s28.q4cdn.com/696626308/files/doc_financials/2025/q4/CTRA-2-26-26-Earnings-Release.pdf)

Reporting exception, not a transcript no-hit. Primary earnings release says no call/webcast was held because merger was pending.

## Coterra Energy · Q1 2026

inaccessible · Transcript not located. Quartr summary/event record exists, but full text was not retrievable in this search pass; not screened.

[Coterra Energy Q1 2026 results/call source](https://quartr.com/events/coterra-energy-ctra-q1-2026_of8Ya7Qr)

A Q1 results call/event is indexed, but full transcript source was not located; do not infer no mention.

## Coterra Energy · Q2 2026

reporting_exception · No standalone Coterra Q2 2026 call because Coterra merged into Devon May 7, 2026.

[Devon Energy and Coterra Energy Complete Merger](https://www.devonenergy.com/news/2026/Devon-Energy-and-Coterra-Energy-Complete-Merger)

Coterra ceased to exist as a standalone issuer before the Q2 2026 reporting period; reporting exception.

## Devon Energy · Q3 2024

reviewed_no_relevant · Full speaker-labelled transcript including prepared remarks and analyst Q&A reviewed and keyword-scanned for AI, artificial intelligence, machine learning, generative AI, agents, copilots, predictive analytics, autonomous systems, automation, digital transformation, and AI/data-center demand.

[Devon Energy (DVN) Q3 2024 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2024/11/06/devon-energy-dvn-q3-2024-earnings-call-transcript/)

Full speaker-labelled third-party transcript reviewed. No relevant AI, AI-energy-demand, or in-scope AI-adjacent automation disclosure identified. General operational efficiencies and electric-frac discussion were not treated as AI disclosures.

## Devon Energy · Q4 2024

reviewed_relevant · Full speaker-labelled transcript including prepared remarks and analyst Q&A reviewed and keyword-scanned for AI, artificial intelligence, machine learning, generative AI, agents, copilots, predictive analytics, autonomous systems, automation, digital transformation, and AI/data-center demand.

[Devon Energy (DVN) Q4 2024 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2025/02/19/devon-energy-dvn-q4-2024-earnings-call-transcript/)

### Real-time diagnostics and artificial-lift technology

Management described artificial-lift and real-time diagnostics as technology opportunities to improve base-production decline, and identified operational technology from other basins as part of Eagle Ford cost savings. No AI or machine-learning system was identified in the transcript.

Speaker: Clay Gaspar, COO. Locator: Prepared remarks, lines 175–177; Q&A, lines 282 and 381. Measurement: Management expected >$2 million D&C savings per Eagle Ford well from design, supply-chain and operational-technology improvements; no amount attributed to diagnostics/AI.. Limit: This is operational technology/diagnostics, not a stated AI deployment; no implementation scale or measured AI effect..

Full transcript reviewed. Relevant adjacent operational-technology passages are retained separately from AI claims; transcript contains no explicit AI or machine-learning use.

## Devon Energy · Q1 2025

reviewed_relevant · Full 21-page issuer-hosted corrected transcript includes prepared remarks and Q&A; full text screened for AI and adjacent terms.

[Devon Q1 2025 Earnings Call — Corrected Transcript](https://s2.q4cdn.com/462548525/files/doc_financials/quarterly/2025/q1/Q1-2025-DVN-Q-A-Webcast-Commentary-Transcript.pdf)

### AI models for real-time centralized gas-lift calibration

CTO Trey Lowe said Devon was developing real-time analytics and AI models to allocate gas-lift injection among constrained centralized-lift wells; recommended rates would be pushed to end devices. Management expected less downtime, flatter base decline and improved production.

Speaker: Trey Lowe, SVP Technology & CTO. Locator: Q&A, p. 7, lines 300–313. Measurement: No deployment count or realized uplift quantified in this call.. Limit: Described as a project being advanced; expected outcomes were forward-looking..

### Employee AI platform and engineering/geoscience applications

Lowe said Devon had rolled out a new employee platform and cited examples using AI to optimize final frac design and interpret mud logs/cutting descriptions for improved geological answers. He reported project productivity gains in some domains.

Speaker: Trey Lowe, SVP Technology & CTO. Locator: Q&A, p. 10, lines 439–446. Measurement: Management cited productivity gains of 15%–30% on projects in various domains.. Limit: The metric is management-reported without measurement method, sample size or attribution to AI alone..

### Condition-based maintenance using advanced analytics

Management described using advanced analytics and KPIs to shift from calendar-based maintenance to condition-based maintenance. This passage did not itself identify AI as the method.

Speaker: Trey Lowe, SVP Technology & CTO. Locator: Q&A, p. 7, lines 293–299. Measurement: No maintenance savings or avoided events quantified in this passage.. Limit: Adjacent analytics disclosure; do not infer an AI model..

The corrected issuer transcript is dated 07-May-2025 and includes complete management discussion and Q&A. The call was held May 7, 2025, notwithstanding some third-party transcript pages that show May 6.

## Devon Energy · Q2 2025

reviewed_relevant · Full speaker-labelled transcript including prepared remarks and analyst Q&A reviewed and keyword-scanned for AI, artificial intelligence, machine learning, generative AI, agents, copilots, predictive analytics, autonomous systems, automation, digital transformation, and AI/data-center demand. Transcript index identifies 9,645 words; opening remarks, Q&A and closing are present.

[Devon Energy Corporation Q2 2025 Earnings Call Transcript](https://tickertrends.io/transcripts/DVN/Q2-earnings-transcript-2025)

### AI agents in drilling and completion operations

CEO said Devon used extensive data streams and proprietary in-frac and in-drill AI agents to provide real-time operational enhancements. He also described company AI tools and agents spanning drilling, completions and real-time production optimization.

Speaker: Clay Gaspar, CEO. Locator: Prepared remarks, lines 39–45. Measurement: Management reported year-over-year drilling-cost improvement of 12% and completion-cost improvement of 15%; those figures were attributed to the combined operational program, not AI alone.. Limit: Management did not isolate savings or production effects caused solely by AI or disclose the number of wells using these agents..

### Enterprise AI adoption and production-fault analytics

CTO said leaders expected employees to use new tools and described a new analytics workflow tying real-time field data streams to AI systems and agents to analyze production faults. He said the work was expected to result in millions of dollars of savings.

Speaker: Trey Lowe, SVP Technology & CTO; Clay Gaspar, CEO. Locator: Q&A, lines 77–84. Measurement: Management said expected savings were in the millions of dollars; no realized amount or time horizon was given.. Limit: Savings are forward-looking and management-reported; the transcript does not separate AI impact from the broader optimization program..

Full transcript page states 9,645 words and includes prepared remarks, analyst Q&A, operator close and 16 speakers. Transcript date/call date 2025-08-06. It also discusses a power-generation opportunity, but no explicit AI/data-center link was found, so it is not coded as AI energy demand.

## Devon Energy · Q3 2025

reviewed_relevant · Full call reviewed using the complete third-party transcript (14 speakers, 6,998 words, 55 segments), including prepared remarks, Q&A, opening and operator close; issuer-hosted FactSet correction independently used to corroborate Q&A passages.

[Devon Energy Corp. Q3 2025 Earnings Call Transcript — Original Transcript](https://goodmoat.com/stocks/dvn/transcripts/2025-year/3-quarter)

### AI fault diagnosis and office productivity

CTO described using AI to investigate operational faults and causes as part of a downtime-reduction workstream, and said essentially all office-based employees used AI for productivity. He described deeper integration of AI into work processes as a later implementation wave.

Speaker: Trey Lowe, CTO & SVP; Clay Gaspar, CEO. Locator: Prepared remarks and Q&A; full transcript lines 136 and 254–260; issuer Q&A correction pp. 5–6, lines 247–257. Measurement: Management associated the broader workstream with more than $10 million estimated for 2026; not all of this amount was attributed to AI.. Limit: Workstream value is not AI-only; later process integration was described as beginning/forward-looking..

### AI models optimize gas-lift injection

SVP Asset Management said AI models continuously optimized gas-injection rates in centralized gas-lift wells. A Q2 pilot reportedly produced uplift that led to Delaware deployment, with potential applications in Williston and Eagle Ford.

Speaker: John Raines, SVP E&P Asset Management. Locator: Q&A, full transcript lines 327–336; issuer Q&A correction pp. 8–9. Measurement: Management-reported 3%–5% pilot uplift; Delaware deployment was targeted for year-end 2025.. Limit: Management-reported pilot without stated baseline; portfolio production outcomes not attributed solely to AI..

### AI-assisted drilling-process optimization

SVP E&P Operations said AI tools benchmark and analyze drilling parameters to support faster drilling, tripping, curve drilling and casing operations.

Speaker: Thomas J. Hellman, SVP E&P Operations. Locator: Q&A, full transcript lines 461–462; issuer Q&A correction pp. 15–16, lines 737–750. Measurement: Management said these activities were about 30% faster and saved millions; Delaware reached a record near 1,800 feet/day.. Limit: Aggregate savings caused solely by AI and deployment counts were not disclosed..

This upgrades the prior Q&A-only review to a full-call review: the official Devon/FactSet corrected transcript is an 18-page Q&A commentary transcript, but GoodMoat exposes an original full transcript with opening/management remarks, analyst Q&A and call close. Full text contains 14 speakers, 6,998 words and 55 segments. GoodMoat labels its page Apr 5, 2026; the call date is Nov 6, 2025. Q3 AI passages were corroborated to the corrected issuer Q&A PDF. Earlier review was not full-call; it is superseded by this entry.

## Devon Energy · Q4 2025

reviewed_relevant · Full speaker-labelled transcript including prepared remarks and analyst Q&A reviewed and keyword-scanned for AI, artificial intelligence, machine learning, generative AI, agents, copilots, predictive analytics, autonomous systems, automation, digital transformation, and AI/data-center demand.

[Devon (DVN) Q4 2025 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2026/02/18/devon-dvn-q4-2025-earnings-call-transcript/)

### AI-enabled artificial-lift optimization and advanced analytics at scale-up stage

Management said AI-enabled artificial-lift optimization and advanced analytics were moving beyond pilot programs, alongside condition-based maintenance and faster drilling/completion cycles. CTO said AI and platforms built over the prior year were beginning to scale production benefits.

Speaker: Clay Gaspar, CEO; Trey Lowe, EVP & CTO. Locator: Prepared remarks, lines 205–209; Q&A, lines 225–228. Measurement: Devon said it had achieved 85% of a $1 billion business-optimization target; the call does not attribute the 85% specifically to AI.. Limit: Lift/analytics implementation was accelerating beyond pilots; management did not quantify the portion of savings or production outcomes due to AI..

Full third-party transcript lists both prepared remarks and Q&A. Search of the transcript located no AI-energy-demand or data-center disclosure. AI findings are internal operational use, distinct from the broader optimization savings target.

## Devon Energy · Q1 2026

reviewed_relevant · Full speaker-labelled transcript including prepared remarks and analyst Q&A reviewed and keyword-scanned for AI, artificial intelligence, machine learning, generative AI, agents, copilots, predictive analytics, autonomous systems, automation, digital transformation, and AI/data-center demand.

[Devon Energy (DVN) Q1 2026 Earnings Transcript](https://www.fool.com/earnings/call-transcripts/2026/05/06/devon-energy-dvn-q1-2026-earnings-transcript/)

### Enterprise AI, ChatDVN and workflow redesign

Gaspar described internal ChatDVN, AI-assisted calculations/code and engineering analysis, and redesigning workflows around AI.

Speaker: Clay Gaspar, CEO. Locator: Prepared remarks, lines 196–202. Measurement: ChatDVN described as operating for three years and now a standard daily workflow; no employee adoption rate or standalone financial return quantified.. Limit: Management description; no independent usage audit, model/vendor details, or isolated financial attribution..

### Autonomous AI artificial-lift optimization

Smart Gas Lift models control injection rates. Raines said scaled uplift exceeded the 2025 pilot, without quantifying it.

Speaker: Clay Gaspar, CEO; John Raines, SVP E&P Asset Management. Locator: Prepared remarks lines 200–201; Q&A lines 291–299, especially 295–297.. Measurement: 2025 pilot: about 2%–3% uplift (management-reported); more than 850 wells then in implementation and rollout planned to 1,500 across the portfolio.. Limit: The pilot percentage lacks a baseline/denominator. The >850-well implementation count is deployment scale, not its outcome denominator; scaled uplift is unquantified..

### AI for drilling/completions and engineering decisions

AI helped turn drilling, completion and production data into engineering information.

Speaker: Clay Gaspar, CEO. Locator: Prepared remarks, lines 199–202. Measurement: No AI-specific time or cost metric in this call.. Limit: Capital-cost benefit was attributed to the broader wave-two technology program, not solely AI..

Full Motley Fool transcript includes prepared remarks and Q&A. Call/publication date May 6, 2026. Keep the 2%–3% figure attached only to the 2025 pilot; no denominator/baseline is specified. The >850 count is deployment scale, and management says current scaled uplift exceeds the pilot without quantifying it. This is earlier than Devon Q2 2026’s 1,000-well count (AI-006).

## Devon Energy · Q2 2026

reviewed_relevant · Full speaker-labelled transcript reviewed and searched AI, artificial intelligence, machine learning and automation; prepared remarks and Q&A read.

[Devon Energy Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/dvn/transcripts/661554-q2-2026/)

### Closed-loop AI gas-lift optimization at scale

Real-time closed-loop AI gas-lift control expanded from a few wells to hundreds in March and 1,000 by the call. ESP and rod-pump models are separate applications.

Speaker: Clay Gaspar, CEO; Trey Lowe, EVP & CTO. Locator: Prepared remarks, lines 142–145; Q&A, lines 434–436.. Measurement: 1,000 active gas-lift wells; >2,000 additional Permian candidates.. Limit: Candidates are prospective; no uplift quantified..

### Enterprise AI strategy, subsurface planning and drilling/completions analytics

Enterprise AI spans legacy Devon production, drilling, completions and subsurface; Coterra ML supports well prediction and capital planning. Tools scan operating data for benchmark gaps.

Speaker: Clay Gaspar, CEO; Trey Lowe, EVP & CTO. Locator: Prepared remarks, lines 142–146; Q&A, lines 245–249 and 421–436. Measurement: No standalone operating or financial outcome.. Limit: Model/vendor details and discrete attribution absent..

### AI-enabled integration workflow accelerates data-table creation

During integration, new data-table creation fell from months to two days while systems and AI tools were integrated.

Speaker: Trey Lowe, EVP & CTO. Locator: Q&A, lines 430–434.. Measurement: Months to two days.. Limit: Table type and business value undisclosed..

Issuer event page lists the call and Q&A commentary transcript. Full Quartr transcript was independently reviewed.

## Diamondback Energy · Q3 2024

reviewed_relevant · Full speaker-labelled conference-call transcript reviewed, including opening remarks and analyst Q&A, and screened for AI, artificial intelligence, machine learning, generative AI, automation, digital, predictive, autonomous, hyperscaler, data center and power-demand terms.

[Diamondback Energy (FANG) Q3 2024 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2024/11/05/diamondback-energy-fang-q3-2024-earnings-call-tran/)

### Permian gas and acreage positioned for data-center power opportunity

After an analyst deferred AI/data-center questions to a later call, CEO Travis Stice discussed the broader data-center power opportunity: Diamondback had abundant gas and surface acreage while electricity was scarce, and was exploring ways to create more value from its gas stream. He said data-center operators had not yet focused on the Permian but conversations were occurring.

Speaker: Travis Stice, Chairman and CEO; Arun Jayaram, analyst. Locator: Q&A, lines 168, 205–210 and 346.. Measurement: CEO cited >65,000 pro forma surface acres; no AI load, data-center customer, signed contract, power capacity, or revenue disclosed.. Limit: The AI/data-center framing was introduced by the analyst; management discussed data centers and electricity generally and did not explicitly attribute load to AI. Conversations were exploratory..

Call date verified in issuer archive. Full Motley Fool transcript reviewed. No internal AI or AI-specific power-demand volume disclosed; retain as early data-center/power discussion with explicit caveat that management did not itself link the opportunity to AI.

## Diamondback Energy · Q4 2024

reviewed_relevant · Full speaker-labelled conference-call transcript reviewed, including opening remarks and analyst Q&A, and screened for AI, artificial intelligence, machine learning, generative AI, automation, digital, predictive, autonomous, hyperscaler, data center and power-demand terms.

[Diamondback Energy (FANG) Q4 2024 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2025/02/25/diamondback-energy-fang-q4-2024-earnings-call-tran/)

### Behind-the-meter gas plant and hyperscaler data-center discussions

CFO Kaes Van’t Hof said Diamondback was working with a large independent power producer on a potential behind-the-meter gas plant using Diamondback gas and supplying power to a hyperscaler data-center operator. He said confidential discussions with hyperscalers were underway and the structure could include power returned to Diamondback.

Speaker: Kaes Van’t Hof, President and CFO. Locator: Q&A, lines 155–156 and 327–331.. Measurement: No plant size, load, term, gas volume or financial return disclosed.. Limit: The project remained in development and under confidential discussions; no binding agreement or AI-specific demand attribution was reported..

Full transcript and Q&A reviewed. Management described a hyperscaler/data-center customer but did not explicitly say AI; coded as an adjacent AI-energy-demand lead with this limit.

## Diamondback Energy · Q1 2025

reviewed_no_relevant · Full speaker-labelled conference-call transcript reviewed, including opening remarks and analyst Q&A, and screened for AI, artificial intelligence, machine learning, generative AI, automation, digital, predictive, autonomous, hyperscaler, data center and power-demand terms.

[Diamondback (FANG) Q1 2025 Earnings Transcript](https://www.fool.com/earnings/call-transcripts/2026/05/04/diamondback-fang-q1-2025-earnings-transcript/)

The call was held May 6, 2025 (confirmed by Diamondback event archive and scheduling release); Motley Fool currently displays this transcript under a 2026 page/update date. AI/ML/automation/data-center terms did not occur in the transcript body. The Motley Fool AI-quality-control boilerplate is excluded from call findings.

## Diamondback Energy · Q2 2025

reviewed_no_relevant · Full speaker-labelled conference-call transcript reviewed, including opening remarks and analyst Q&A, and screened for AI, artificial intelligence, machine learning, generative AI, automation, digital, predictive, autonomous, hyperscaler, data center and power-demand terms.

[Diamondback (FANG) Q2 2025 Earnings Transcript](https://www.fool.com/earnings/call-transcripts/2026/05/04/diamondback-fang-q2-2025-earnings-transcript/)

The call date is August 5, 2025, corroborated by issuer IR archive, press release and Equibles transcript. No AI, machine-learning, automation or data-center disclosure identified in the call body. General workover and production-optimization remarks do not establish AI use. Fool page is updated May 4, 2026; date is recorded as the current publisher update.

## Diamondback Energy · Q3 2025

reviewed_no_relevant · Full speaker-labelled conference-call transcript reviewed, including opening remarks and analyst Q&A, and screened for AI, artificial intelligence, machine learning, generative AI, automation, digital, predictive, autonomous, hyperscaler, data center and power-demand terms.

[Diamondback (FANG) Q3 2025 Earnings Transcript](https://www.fool.com/earnings/call-transcripts/2026/05/04/diamondback-fang-q3-2025-earnings-transcript/)

The November 4, 2025 call date is corroborated by issuer event archive and scheduling release. Transcript begins with Q&A. No AI, machine-learning, automation, digital, or data-center disclosure identified in the call body. Broad operational efficiencies and private well-level data discussion were not coded as AI. Publisher page currently shows May 4, 2026 update date.

## Diamondback Energy · Q4 2025

reviewed_relevant · Full speaker-labelled conference-call transcript reviewed, including opening remarks and analyst Q&A, and screened for AI, artificial intelligence, machine learning, generative AI, automation, digital, predictive, autonomous, hyperscaler, data center and power-demand terms.

[Diamondback (FANG) Q4 2025 Earnings Transcript](https://www.fool.com/earnings/call-transcripts/2026/05/04/diamondback-fang-q4-2025-earnings-transcript/)

### Data-center acreage and natural-gas power purchase structuring

Analyst asked about the hyperscaler/data-center opportunity. Management said Diamondback was making progress with counterparties and considering power-purchase agreements to improve gas realizations on company acreage; CEO said nothing would be announced until binding.

Speaker: Kaes Van’t Hof, President and CFO; analyst Neil Mehta. Locator: Q&A, lines 228–230 and following discussion.. Measurement: No contracted load, gas volume, timing or financial value disclosed.. Limit: Talks remained nonbinding; management discussed data centers/hyperscalers but did not explicitly attribute demand to AI..

Complete Fool transcript reviewed; the “AI” body match occurs only in publisher boilerplate, while a distinct transcript-body data-center/hyperscaler discussion is relevant as a prospective power-market lead. Publisher page currently shows May 4, 2026 update date; call was Feb 24, 2026.

## Diamondback Energy · Q1 2026

reviewed_relevant · Full speaker-labelled conference-call transcript reviewed, including opening remarks and analyst Q&A, and screened for AI, artificial intelligence, machine learning, generative AI, automation, digital, predictive, autonomous, hyperscaler, data center and power-demand terms.

[Diamondback (FANG) Q1 2026 Earnings Transcript](https://www.fool.com/earnings/call-transcripts/2026/05/05/diamondback-fang-q1-2026-earnings-transcript/)

### Machine learning and AI implementation in field operations

COO said machine learning was being applied to operating data streams and processes and that AI was being implemented in field operations to reduce downtime. He cited these changes as a contributor to production performance, alongside completion design, workovers and other interventions. CEO also referred generally to AI or automation as part of improving base production.

Speaker: Daniel N. Wesson, EVP & COO; Kaes Van’t Hof, CEO. Locator: Q&A, lines 211–215.. Measurement: No AI deployment count or standalone production/savings contribution disclosed.. Limit: Management attributed Q1 performance to multiple field interventions; the transcript does not isolate AI/ML’s causal contribution..

### Natural-gas power and data-center project

CFO said progress continued with partners on a power/data-center project intended to use in-basin natural gas at advantageous pricing; details would follow after project finalization.

Speaker: Jere W. Thompson, CFO. Locator: Q&A, lines 292–295.. Measurement: No load, power capacity, gas volume, signed contract or financial estimate disclosed.. Limit: Project remained under development; data-center demand was not explicitly attributed to AI..

Full conference call reviewed. Issuer archive confirms May 5, 2026 call date. The Motley Fool transcript page is dated May 5, 2026. Its AI/ML disclosure is internal field operations; the separate data-center project remains prospective.

## Diamondback Energy · Q2 2026

reviewed_relevant · Full speaker-labelled transcript reviewed; keyword scan included AI, machine learning, automation and digital.

[Diamondback Energy Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/fang/transcripts/660684-q2-2026/)

### Early AI/automation deployment for artificial lift and production-base maintenance

CEO said AI was already helping office work and that AI plus automation could help the production base become shallower-decline or less costly to maintain. In response to a question about predictive maintenance and remote sensing, he said the work remained at an early stage. Operations EVP said the first focus was artificial lift: AI and automation were being used for day-to-day optimization and to manage downtime.

Speaker: Kaes Van’t Hof, CEO; Chad McAllaster, EVP Operations. Locator: Transcript Q&A, lines 451–455 and 501–516. Measurement: No deployment count or quantified savings/uplift disclosed. CEO described a 1 percentage point decline-rate change as potentially material, not as a measured AI result.. Limit: Management characterized the work as early (“first inning”); no causal savings, uptime or production data were supplied..

### Pump-by-exception operating model and field automation

CEO said field integration was beginning to enable a pump-by-exception operating model and more automation.

Speaker: Kaes Van’t Hof, CEO. Locator: Transcript Q&A, lines 451–455. Measurement: No quantitative automation or labor reduction metric.. Limit: The transcript does not detail the system or implementation scope..

Inherited R047 references the Q1 2026 call, not this Q2 call. This Q2 transcript is a separate verified observation. Existing AI-045 covers earlier Diamondback AI profile material; it may be related but the Q2 passage and its “early stage” caveat should remain distinct.

## Diversified Energy · Q3 2024

reviewed_relevant · Full speaker-labelled transcript including Q&A reviewed; searched AI/artificial intelligence, generative AI/GenAI, machine learning, analytics, automation/digital transformation, predictive/autonomous/agentic/copilot, and data-center/power demand terms.

[Diversified Energy Company PLC Earnings Call Transcript Q3 2024](https://www.roic.ai/quote/DEC/transcripts/2024-year/3-quarter)

### Technology-driven field optimization and data analytics

Management described a field-management process using technology and data-driven analytics to optimize mature assets, plus scalable systems supporting process efficiency and reliability.

Speaker: Robert Russell Hutson, CEO. Locator: Prepared remarks, lines 121–125 and 138–143.. Measurement: No technology- or analytics-specific outcome quantified.. Limit: No AI/ML method or automated decision capability specified..

### Data-center and LNG energy-demand context

Management said its diversified production base positioned it to participate in LNG exports and data-center energy needs.

Speaker: Brad Gray, President and CFO. Locator: Prepared remarks, lines 168–172.. Measurement: No data-center volume or contract reported.. Limit: No explicit AI linkage..

Issuer past-events archive used to establish public call cadence and call dates. Issuer labels H1 interim and annual final-results events; transcript archive may label calendar mapping differently. Q3 2024 transcript page labels Nov 13 while issuer archive and the transcript remarks date the call Nov 12.

## Diversified Energy · Q4 2024

reviewed_relevant · Full speaker-labelled transcript including Q&A reviewed; searched AI/artificial intelligence, generative AI/GenAI, machine learning, analytics, automation/digital transformation, predictive/autonomous/agentic/copilot, and data-center/power demand terms.

[Diversified Energy Company PLC Earnings Call Transcript Q4 2024](https://www.roic.ai/quote/DEC/transcripts/2024/4)

### Data-center power opportunity

Company linked its gas production and midstream footprint to possible data-center supply, describing FuelCell microgrid collaboration and projects under development in three Appalachian states.

Speaker: Robert Russell Hutson, CEO. Locator: Prepared remarks, lines 137–151 and 183–186; Q&A line 221.. Measurement: Several projects identified; no capacity, signed contracts, or revenue disclosed.. Limit: No explicit AI linkage; projects were prospective..

Issuer past-events archive used to establish public call cadence and call dates. Issuer labels H1 interim and annual final-results events; transcript archive may label calendar mapping differently. Q3 2024 transcript page labels Nov 13 while issuer archive and the transcript remarks date the call Nov 12.

## Diversified Energy · Q1 2025

reviewed_relevant · Full speaker-labelled transcript including Q&A reviewed; searched AI/artificial intelligence, generative AI/GenAI, machine learning, analytics, automation/digital transformation, predictive/autonomous/agentic/copilot, and data-center/power demand terms.

[Diversified Energy Company PLC Q1 FY2025 Earnings Call Transcript](https://www.roic.ai/quote/DEC/transcripts/2025-year/1-quarter)

### Data-center and LNG energy-demand context

CFO said the enlarged production base positioned Diversified to participate in LNG exports and data-center needs.

Speaker: Brad Gray, President and CFO. Locator: Prepared remarks, lines 145–148.. Measurement: No demand volume, customer contract, or revenue disclosed.. Limit: No explicit AI linkage..

### Technology platform supports acquisition integration

CEO said the company’s technology platform supports an integration process intended to streamline operations and capture acquisition synergies.

Speaker: Robert Russell Hutson, CEO. Locator: Prepared remarks, lines 175–181.. Measurement: Management cited 27 integrations over seven years, not a technology-attributed outcome.. Limit: No AI/ML, automation detail, or standalone technology benefit specified..

Issuer past-events archive used to establish public call cadence and call dates. Issuer labels H1 interim and annual final-results events; transcript archive may label calendar mapping differently. Q3 2024 transcript page labels Nov 13 while issuer archive and the transcript remarks date the call Nov 12.

## Diversified Energy · Q2 2025

reviewed_relevant · Full speaker-labelled transcript including Q&A reviewed; searched AI/artificial intelligence, generative AI/GenAI, machine learning, analytics, automation/digital transformation, predictive/autonomous/agentic/copilot, and data-center/power demand terms.

[Diversified Energy Company PLC Q2 FY2025 Earnings Call Transcript](https://www.roic.ai/quote/DECPF/transcripts/2025-year/2-quarter)

### AI-data-center gas demand and power opportunities

CEO discussed regional AI/data-center demand as a gas opportunity; CFO said the company was evaluating small-scale off-grid power-generation opportunities.

Speaker: Robert Russell Hutson, CEO; Bradley Grafton Gray, President and CFO. Locator: Prepared remarks, lines 114–127; Q&A, lines 276–281.. Measurement: CFO cited $90 billion in announced regional power-generation and data-center investments.. Limit: No Diversified contract, capacity, revenue, or committed project disclosed..

Issuer past-events archive used to establish public call cadence and call dates. Issuer labels H1 interim and annual final-results events; transcript archive may label calendar mapping differently. Q3 2024 transcript page labels Nov 13 while issuer archive and the transcript remarks date the call Nov 12.

## Diversified Energy · Q3 2025

reviewed_relevant · Full speaker-labelled transcript including Q&A reviewed; searched AI/artificial intelligence, generative AI/GenAI, machine learning, analytics, automation/digital transformation, predictive/autonomous/agentic/copilot, and data-center/power demand terms.

[Diversified Energy Company Q3 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/dec/transcripts/402118-q3-2025-tu/)

### Data-center and LNG energy-demand context

Management said production growth positioned the company to participate in LNG exports and data-center energy demand.

Speaker: Bradley Grafton Gray, President and CFO. Locator: Prepared remarks, line 157.. Measurement: No data-center volume, contract, or revenue disclosed.. Limit: No explicit AI linkage..

Issuer past-events archive used to establish public call cadence and call dates. Issuer labels H1 interim and annual final-results events; transcript archive may label calendar mapping differently. Q3 2024 transcript page labels Nov 13 while issuer archive and the transcript remarks date the call Nov 12.

## Diversified Energy · Q4 2025

reviewed_relevant · Full speaker-labelled transcript including Q&A reviewed; searched AI/artificial intelligence, generative AI/GenAI, machine learning, analytics, automation/digital transformation, predictive/autonomous/agentic/copilot, and data-center/power demand terms.

[Diversified Energy Company H2 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/dec/transcripts/385203-h2-2025/)

### Data-center and LNG energy-demand context

CFO linked production growth to LNG exports and data-center energy demand; an analyst asked how that demand affected gas-weighted acquisition opportunities.

Speaker: Bradley Grafton Gray, President and CFO; Robert Hutson, CEO. Locator: Prepared remarks, lines 143–146; Q&A, lines 399–407.. Measurement: No data-center volume, contract, or revenue disclosed.. Limit: No explicit AI linkage..

Issuer past-events archive used to establish public call cadence and call dates. Issuer labels H1 interim and annual final-results events; transcript archive may label calendar mapping differently. Q3 2024 transcript page labels Nov 13 while issuer archive and the transcript remarks date the call Nov 12.

## Diversified Energy · Q1 2026

reviewed_no_relevant · Full speaker-labelled transcript including Q&A reviewed; searched AI/artificial intelligence, generative AI/GenAI, machine learning, analytics, automation/digital transformation, predictive/autonomous/agentic/copilot, and data-center/power demand terms.

[Diversified Energy Company Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/dec/transcripts/577147-q1-2026/)

Issuer past-events archive used to establish public call cadence and call dates. Issuer labels H1 interim and annual final-results events; transcript archive may label calendar mapping differently. Q3 2024 transcript page labels Nov 13 while issuer archive and the transcript remarks date the call Nov 12.

## Diversified Energy · Q2 2026

reviewed_no_relevant · Full speaker-labelled transcript including Q&A reviewed; searched AI/artificial intelligence, generative AI/GenAI, machine learning, analytics, automation/digital transformation, predictive/autonomous/agentic/copilot, and data-center/power demand terms.

[Diversified Energy Company Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/dec/transcripts/662914-q2-2026/)

Issuer past-events archive used to establish public call cadence and call dates. Issuer labels H1 interim and annual final-results events; transcript archive may label calendar mapping differently. Q3 2024 transcript page labels Nov 13 while issuer archive and the transcript remarks date the call Nov 12.

## Ecopetrol · Q3 2024

reviewed_relevant · Full English transcript screened from operator opening through management close, Q&A and operator end-of-call (lines 91–316); keyword searches included artificial intelligence, machine learning, automation, digital, analytics, data and software. Transcript is an English rendition of the Spanish/English conference.

[Earnings call: Ecopetrol reports highest production in nine years](https://www.investing.com/news/transcripts/earnings-call-ecopetrol-reports-highest-production-in-nine-years-93CH-3725719)

### Digital solutions for efficiency and decarbonization

CEO Ricardo Roa said Ecopetrol had implemented more than 100 digital solutions to promote energy efficiency and decarbonization. This broad digital portfolio is an adjacent operational technology disclosure; the call did not identify AI or machine learning as the method.

Speaker: Ricardo Roa, CEO. Locator: Transcript lines 103–106. Measurement: More than 100 digital solutions implemented.. Limit: No individual solution, AI use, operating result, or attributable financial return was specified..

Full-call screen includes Q&A through operator close. Investing.com states its article was AI-supported/editor-reviewed, but the cited transcript is used only as a transcript source; its editorial AI statement is not company disclosure.

## Ecopetrol · Q4 2024

reviewed_no_relevant · Full third-party English transcript screened from operator opening through prepared remarks, Q&A, CEO closing remarks and operator close (lines 178–841); searches covered artificial intelligence, machine learning, automation, digital, analytics, data, software and related adjacent terms.

[Ecopetrol S.A. Q4 FY2024 Earnings Call Transcript](https://www.roic.ai/quote/EC/transcripts/2024-year/4-quarter)

No relevant AI, digital, automation or adjacent technology disclosures located in the transcript. The clearly rendered token “AI platform” at line 678 occurs in garbled remarks about ISA power-transmission operations and is not a reliable AI reference; treated as a transcription artifact, not company AI disclosure.

## Ecopetrol · Q1 2025

reviewed_no_relevant · Full third-party English transcript screened from operator opening through management, analyst Q&A and operator close (lines 174–1033); searches included artificial intelligence, machine learning, automation, digital, analytics, data, software and related adjacent terms.

[Ecopetrol S.A. Q1 2025 Earnings Call Transcript](https://www.roic.ai/quote/EC/transcripts/2025-year/1-quarter)

No relevant AI, digital, automation or adjacent technology disclosure located. A phrase transcribed as “digital process” at line 473 concerns the duration of a legal process and is not a digital technology reference.

## Ecopetrol · Q2 2025

reviewed_relevant · Full English transcript screened from operator opening through close (lines 86–325), including Q&A; searched artificial intelligence, machine learning, automation, digitalization, analytics, data and software. It is an English rendition of the Spanish/English call.

[Earnings call transcript: Ecopetrol Q2 2025 reports mixed results, stock rises](https://www.investing.com/news/transcripts/earnings-call-transcript-ecopetrol-q2-2025-reports-mixed-results-stock-rises-93CH-4202942)

### Digitalization-related corporate efficiency

CFO Camilo Barco said the group generated COP 400 billion of corporate efficiencies through digitalization initiatives and demand-control operations. The call attributed no specific AI, ML or software tool to these efficiencies.

Speaker: Camilo Barco, CFO. Locator: Transcript lines 169–178. Measurement: COP 400 billion corporate efficiencies, as transcribed.. Limit: The transcript does not isolate digitalization from demand control, identify specific initiatives, or attribute the stated amount exclusively to digitalization..

Investing.com reports publication on 2025-08-20; issuer archive identifies the earnings call on 2025-08-14. Its editorial note says the article was AI-supported/editor-reviewed; that is not a company AI disclosure.

## Ecopetrol · Q3 2025

reviewed_relevant · Full English transcript screened from operator introduction through management close, Q&A and hang-up (lines 86–226); searched artificial intelligence, machine learning, automation, digitalization, analytics, data and software. English rendition of Spanish/English call.

[Earnings call transcript: Ecopetrol Q3 2025 misses forecasts, stock up 3.75%](https://www.investing.com/news/transcripts/earnings-call-transcript-ecopetrol-q3-2025-misses-forecasts-stock-up-375-93CH-4359626)

### Digitalization in cross-cutting efficiency program

Management described procurement controls that advance digitalization as one of four efficiency levers. Ecopetrol reported COP 4.1 trillion in total efficiencies year to date, 40% above target; that total is for the full program, not digitalization alone.

Speaker: Unconfirmed; transcript around lines 143–149 does not preserve a clear speaker label for this passage.. Locator: Transcript lines 143–149. Measurement: COP 4.1 trillion total efficiencies year to date; 40% above target.. Limit: No specific digital system, AI/ML, or digitalization-attributable amount/result was identified..

The secondary transcript has a period-label error in its operator introduction (it says Q2 2025), but management speaks of the third quarter and the issuer calendar identifies the call date as 2025-11-14. Investing.com editorial AI-generation note is not a company disclosure.

## Ecopetrol · Q4 2025

reviewed_relevant · Full English transcript screened from management opening through complete prepared remarks, analyst Q&A, executive close and operator hang-up (lines 189–441); searched AI, machine learning, automation, digitalization, analytics, data, software and related terms.

[Ecopetrol (EC) Q4 2025 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2026/03/18/ecopetrol-ec-q4-2025-earnings-call-transcript/)

### Digitalization and energy-management efficiency

Energy Transition EVP Bayron Triana described 80 energy-efficiency initiatives, including operational process control, equipment upgrades and transport energy-management systems. CFO Alfonso Camilo Muñoz separately cited digitalization among OpEx efficiency levers.

Speaker: Bayron Triana Arias, EVP Energy Transition; Alfonso Camilo Muñoz, CFO. Locator: Transcript lines 240–250. Measurement: 80 energy-efficiency initiatives; 2025 OpEx efficiencies of COP 1.8 trillion, across energy, maintenance and digitalization.. Limit: No AI/ML or specific digital system was identified, and COP 1.8 trillion was not attributed to digitalization alone..

Full call, including Q&A and operator hang-up, was available and reviewed. The Motley Fool notes that portions of its article used LLMs; that editorial process is not company disclosure.

## Ecopetrol · Q1 2026

reviewed_relevant · Full English transcript screened from management opening through prepared remarks and Q&A (lines 190–368); AI/ML, automation and broad digital/analytics/software terms checked. Q&A is present and runs through the operator’s final thanks; the source does not provide a separate hang-up phrase.

[Ecopetrol (EC) Q1 2026 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2026/05/20/ecopetrol-ec-q1-2026-earnings-call-transcript/)

### Digital solutions in cost and operational-efficiency work

Hydrocarbons EVP Carlos Ávila linked cost management to contract optimization, maintenance efficiencies, digital solutions and infrastructure, and energy efficiency/flexibility. The transcript does not identify AI or a particular digital tool.

Speaker: Carlos Ávila Saldarriaga, EVP of Hydrocarbons. Locator: Transcript lines 230–234. Measurement: No digital-solution-specific metric disclosed.. Limit: The cited sentence groups digital solutions with other cost levers; it quantifies no standalone digital or AI contribution..

The secondary transcript has an editorial disclosure that some article material used LLMs; it is a secondary transcript rendition, not a company AI disclosure.

## Ecopetrol · Q2 2026

reviewed_no_relevant · Full English transcript reviewed through prepared remarks, analyst Q&A and final operator close. Screened AI/artificial intelligence, Spanish IA/inteligencia artificial/aprendizaje automático/digitalización/automatización, and data-center terms.

[Ecopetrol Q2 2026 earnings-call full transcript (English transcript)](https://stockanalysis.com/quote/bvc/ECOPETROL/transcripts/668074-q2-2026/)

Selected-term full-call screen found no relevant AI, digitalization/automation, or data-center/AI-energy-demand passage. Accessible text is English, with some exchanges labeled non-English; Spanish-specific spellings were also searched.

## Enbridge · Q3 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Enbridge Q3 2024 earnings call transcript](https://www.enbridge.com/~/media/Enb/Documents/Investor-Relations/2024/2024_Q3_Financials_Transcript.pdf?hash=8A8C5EB8B3EBA7B6AA016A40ACDB7708&rev=4594a3d241fd485995ea79484a86c45c)

Actual call date verified and within the requested disclosure window; Enbridge reports calendar quarters. AI-specific terms absent; data-center references were not assumed to mean AI.

## Enbridge · Q4 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Enbridge Q4 2024 earnings call transcript](https://www.enbridge.com/~/media/Enb/Documents/Investor-Relations/2024/2024_Q4_Financials_Transcript.pdf?hash=D5F653C9FACE2746B9318A839663FACB&rev=adb0339f033c4fedb502fd36e3981288)

Actual call date verified and within the requested disclosure window; Enbridge reports calendar quarters. AI-specific terms absent; data-center references were not assumed to mean AI.

## Enbridge · Q1 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Enbridge Q1 2025 earnings call transcript](https://stockanalysis.com/quote/tsx/ENB/transcripts/315992-q1-2025/)

Actual call date verified and within the requested disclosure window; Enbridge reports calendar quarters. AI-specific terms absent; data-center references were not assumed to mean AI.

## Enbridge · Q2 2025

reviewed_relevant · Full transcript reviewed (StockAnalysis/Quartr; Q&A present), including AI references in prepared remarks and Q&A.

[Enbridge Q2 2025 earnings call transcript](https://stockanalysis.com/quote/tsx/ENB/transcripts/344858-q2-2025/)

### Renewable and gas infrastructure for AI/data-center customers

CEO Greg Ebel said the Clear Fork project was contracted with Meta to support its data-center operations and identified Meta among Enbridge’s AI-related and data-center customers. He later said the renewable business provides power to major AI/data-center players.

Speaker: Greg Ebel (President and CEO). Locator: Prepared remarks, lines 126–132; related customer-contract question in Q&A, lines 577 onward. Measurement: Clear Fork project value was stated as $900 million; this is project capital, not AI-specific revenue.. Limit: Infrastructure supply/customer segment; no AI-specific offtake volumes, project share, or internal AI deployment quantified..

Actual call date verified and within the requested disclosure window; Enbridge reports calendar quarters. 

## Enbridge · Q3 2025

reviewed_relevant · Full transcript reviewed (StockAnalysis/Quartr; Q&A present), including AI demand references in Q&A.

[Enbridge Q3 2025 earnings call transcript](https://stockanalysis.com/quote/tsx/ENB/transcripts/369713-q3-2025/)

### AI/data-center power demand as broad commodity driver

CEO Greg Ebel characterized data-center and AI growth as a tertiary demand driver likely to support broader commodities, including oil, through higher GDP and industrial activity. He also discussed data-center-related utility opportunities in Ohio and Utah.

Speaker: Greg Ebel (President and CEO); Michelle Harradence (EVP and President, Gas Distribution and Storage). Locator: Q&A, lines 193–205. Measurement: Management cited 8 GW of early-stage developments and over 6 GW in mid-stage service estimates in Ohio and Utah; these are data-center/power figures, not AI-only.. Limit: Market-demand thesis; no operator-internal AI deployment or AI-specific revenue was described..

Actual call date verified and within the requested disclosure window; Enbridge reports calendar quarters. 

## Enbridge · Q4 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Enbridge Q4 2025 earnings call transcript](https://stockanalysis.com/quote/tsx/ENB/transcripts/413799-q4-2025/)

Actual call date verified and within the requested disclosure window; Enbridge reports calendar quarters. AI-specific terms absent; data-center references were not assumed to mean AI.

## Enbridge · Q1 2026

reviewed_relevant · Full transcript reviewed (StockAnalysis/Quartr; Q&A present), including CEO’s AI-energy constraint comment.

[Enbridge Q1 2026 earnings call transcript](https://stockanalysis.com/quote/tsx/ENB/transcripts/556799-q1-2026/)

### AI growth constrained by energy access

CEO Greg Ebel said access to energy, rather than GPU architecture, was the primary constraint preventing AI from progressing further, positioning Enbridge gas and renewable infrastructure as ways to serve large technology customers.

Speaker: Greg Ebel (President and CEO). Locator: Q&A, lines 214–216. Measurement: Not quantified. Limit: Executive market observation; no Enbridge-specific AI revenue, customer deployment count, or AI-attributable project metric..

Actual call date verified and within the requested disclosure window; Enbridge reports calendar quarters. 

## Enbridge · Q2 2026

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Enbridge Q2 2026 earnings call transcript](https://stockanalysis.com/quote/tsx/ENB/transcripts/663990-q2-2026/)

Actual call date verified and within the requested disclosure window; Enbridge reports calendar quarters. AI-specific terms absent; data-center references were not assumed to mean AI.

## Energy Transfer · Q3 2024

reviewed_relevant · AI energy-demand discussion appears in prepared remarks; transcript contains full Q&A through conclusion.

[Energy Transfer LP Q3 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/et/transcripts/219750-q3-2024/)

### AI-related energy demand

Management tied forecast AI and data-center growth to rising electricity needs and said its gas network was positioned to serve associated data centers and power plants.

Speaker: Thomas Long, Co-CEO. Locator: Prepared remarks, lines 127-128 and 134. Measurement: Not quantified. Limit: Market-demand opportunity only; no quantified AI-related contracted revenue in the cited passage..

Call date and quarter confirmed on the issuer/Stock Analysis earnings-call archive. Transcript includes Q&A; term screen included AI, artificial intelligence, machine learning, automation, data center, and predictive analytics.

## Energy Transfer · Q4 2024

reviewed_relevant · Full transcript reviewed, including Q&A; Q&A includes discussion of AI/data-center demand and competitive landscape.

[Energy Transfer LP Q4 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/et/transcripts/238704-q4-2024/)

### AI data-center gas supply agreement

Energy Transfer said it had entered a long-term agreement to supply gas to Cloudburst’s AI-focused Central Texas data-center development; management described a potential firm supply volume and generation capacity.

Speaker: Thomas Long, Co-CEO; Marshall McCrea, Co-CEO. Locator: Prepared remarks, lines 135-143; Q&A lines 302-308. Measurement: Up to 450,000 MMBtu/day; enough for up to approximately 1.2 GW of direct or behind-the-meter power.. Limit: Subject to Cloudburst reaching FID with its customers; management called this its first direct gas supply arrangement to a data-center site..

Call date and quarter confirmed on the issuer/Stock Analysis earnings-call archive. Transcript includes Q&A; term screen included AI, artificial intelligence, machine learning, automation, data center, and predictive analytics.

## Energy Transfer · Q1 2025

reviewed_relevant · Full transcript reviewed, including Q&A; transcript contains operator Q&A and call close.

[Energy Transfer LP Q1 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/et/transcripts/314892-q1-2025/)

### AI-focused data-center gas supply

Management reiterated the Cloudburst long-term agreement to supply natural gas to its AI-focused Central Texas data-center development and said such projects were expected to require relatively low capital and generate revenue relatively quickly.

Speaker: Thomas Long, Co-CEO. Locator: Prepared remarks, lines 128 and 133; Q&A lines 276-282. Measurement: Not quantified. Limit: No AI-attributed financial contribution quantified; prior agreement was conditional on customer FID..

Call date and quarter confirmed on the issuer/Stock Analysis earnings-call archive. Transcript includes Q&A; term screen included AI, artificial intelligence, machine learning, automation, data center, and predictive analytics.

## Energy Transfer · Q2 2025

reviewed_relevant · Full transcript reviewed, including Q&A; analyst explicitly asks about AI power projects and CEO responds.

[Energy Transfer LP Q2 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/et/transcripts/343083-q2-2025/)

### AI/data-center power project economics

An analyst asked about the EBITDA contribution from hyperscale/AI power projects located near Energy Transfer assets. Co-CEO said the opportunity could become impactful but declined to quantify project EBITDA at that stage.

Speaker: Zackery Van Everen, analyst; Marshall McCrea, Co-CEO. Locator: Q&A, lines 421-426. Measurement: Not quantified. Limit: Management said it was too early to quantify; no projected EBITDA range was provided..

Call date and quarter confirmed on the issuer/Stock Analysis earnings-call archive. Transcript includes Q&A; term screen included AI, artificial intelligence, machine learning, automation, data center, and predictive analytics.

## Energy Transfer · Q3 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Energy Transfer LP Q3 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/et/transcripts/371520-q3-2025/)

Call date and quarter confirmed on the issuer/Stock Analysis earnings-call archive. Transcript includes Q&A; term screen included AI, artificial intelligence, machine learning, automation, data center, and predictive analytics.

## Energy Transfer · Q4 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Energy Transfer LP Q4 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/et/transcripts/396578-q4-2025/)

Call date and quarter confirmed on the issuer/Stock Analysis earnings-call archive. Transcript includes Q&A; term screen included AI, artificial intelligence, machine learning, automation, data center, and predictive analytics.

## Energy Transfer · Q1 2026

reviewed_relevant · Full transcript reviewed, including Q&A; explicit AI hyperscale campus disclosure in prepared remarks.

[Energy Transfer LP Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/et/transcripts/558487-q1-2026/)

### AI hyperscale campus gas service

Energy Transfer reported a long-term firm gas transportation agreement supporting the Nexus Hubbard behind-the-meter AI hyperscale campus, which will use on-site natural-gas generation.

Speaker: Thomas Long, Co-CEO. Locator: Prepared remarks, lines 148-152. Measurement: Initial volume expected at approximately 150 MMcf/day; transporter has rights to elect increased capacity.. Limit: Transportation costs are expected to be fully reimbursed; service expected by year-end 2026..

Call date and quarter confirmed on the issuer/Stock Analysis earnings-call archive. Transcript includes Q&A; term screen included AI, artificial intelligence, machine learning, automation, data center, and predictive analytics.

## Energy Transfer · Q2 2026

reviewed_relevant · Full transcript reviewed, including Q&A; AI Factory project is explicit in prepared remarks, and Q&A continues beyond prepared remarks.

[Energy Transfer LP Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/et/transcripts/660740-q2-2026/)

### AI Factory gas infrastructure

Energy Transfer entered an agreement to build facilities for natural-gas service supporting the previously announced 900-MW expansion at the AI Factory campus in Abilene.

Speaker: Thomas Long, Co-CEO. Locator: Prepared remarks, lines 126-130. Measurement: Campus expansion: 900 MW.. Limit: Gas volume, contract economics and attributable EBITDA were not stated in the cited disclosure..

Call date and quarter confirmed on the issuer/Stock Analysis earnings-call archive. Transcript includes Q&A; term screen included AI, artificial intelligence, machine learning, automation, data center, and predictive analytics.

## Eni · Q3 2024

reviewed_no_relevant · Full 19-page issuer transcript reviewed, presentation and Q&A through conference close; searched AI/artificial intelligence, machine learning, data-center/hyperscaler, automation and digital-transformation terms.

[Eni 3Q 2024 Results conference-call transcript](https://www.eni.com/content/dam/enicom/documents/eng/investor/presentations/2024/2024-third-quarter-results/2024-Third-quarter-results-Transcript.pdf)

Official full transcript is dated 25 October 2024 and includes the presentation and Q&A through operator close. Selected AI and AI-energy-demand terms returned no matches.

## Eni · Q4 2024

reviewed_relevant · Issuer full transcript reviewed, including presentation and Q&A (25-page PDF). AI, computing, data-center, digital, analytics, ML and automation terms screened.

[Eni 2025 Capital Markets Update & 2024 Full Year Results transcript](https://report.eni.com/annual-report-2024/it/_assets/downloads/2025-cmu-transcript-it.pdf)

### AI-enabled high-performance computing for subsurface work

CEO cited the startup of HPC6, a high-performance-computing system, as a way to analyze complex reservoirs and improve decision-making. The transcript frames it as an in-house technology capability, not a measured AI deployment result.

Speaker: Claudio Descalzi, CEO. Locator: PDF P2, lines 78–100 (HPC6 at 87–89).. Measurement: No AI-attributed production or economic metric. The separate exploration statistics at lines 93–100 are company-wide historical results, not attributed to HPC6.. Limit: No model details, AI-specific results, or causal link to exploration outcomes disclosed..

### AI/data-center demand and planned compute infrastructure

CEO said AI and digital services were expected to grow computational-power demand and clean-energy demand; he estimated European/Italian data-center power demand could triple by 2030. Eni cited an MoU with G42/MGX for a phased hyperscale data center at Ferrera Erbognone, targeting up to 500 MW IT capacity.

Speaker: Claudio Descalzi, CEO. Locator: Presentation PDF P8, lines 297–318.. Measurement: Forecast: European/Italian data-center power demand could triple by 2030; MoU target up to 500 MW total IT capacity at Ferrera Erbognone.. Limit: Demand statement is forecast, not realized load. 500 MW is target IT capacity, not an announced 500 MW power contract or delivered consumption; MoU/project remains planned..

Reporting exception: Eni’s Q4/FY 2024 conference event was held Feb 27, 2025 and combined the 2024 full-year results with a Capital Markets Update, not a separate standard Q4 results-call transcript. The issuer-hosted transcript includes both presentation and Q&A; do not treat it as an additional call.

## Eni · Q1 2025

reviewed_no_relevant · Full 16-page issuer transcript reviewed, presentation and Q&A through close; searched AI/artificial intelligence, machine learning, agentic/generative, data center/hyperscaler, automation and digital-transformation terms.

[Eni Q1 2025 Results conference-call transcript](https://www.eni.com/content/dam/enicom/documents/eng/investor/presentations/2025/2025-first-quarter-results/2025-first-quarter-results-Transcript.pdf)

Official transcript includes presentation and Q&A; no relevant AI or AI-energy-demand passage matched the selected terms.

## Eni · Q2 2025

reviewed_no_relevant · Full 15-page issuer transcript for Q2/H1 results reviewed, presentation and Q&A through close; searched AI/artificial intelligence, machine learning, agentic/generative, data center/hyperscaler, automation and digital-transformation terms.

[Eni 1H 2025 Results conference-call transcript](https://www.eni.com/content/dam/enicom/documents/eng/investor/presentations/2025/2025-second-quarter-results/2025-second-quarter-results-Transcript.pdf)

Official transcript labels the Q2/H1 2025 results call and includes full Q&A; no relevant AI or AI-energy-demand passage matched the selected terms.

## Eni · Q3 2025

reviewed_relevant · Full 16-page issuer transcript reviewed, presentation and Q&A through call close; searched AI/artificial intelligence, machine learning, agentic/generative, data centers/hyperscalers, automation and digital-transformation terms.

[Eni Q3 2025 Results conference-call transcript](https://www.eni.com/content/dam/enicom/documents/eng/investor/presentations/2025/2025-third-quarter-results/2025-third-quarter-results-Transcript.pdf)

### Internal AI portfolio and agentic direction

Director of Technology, R&D & Digital Lorenzo Fiorillo said Eni had more than 200 internal AI use cases in development, including optimization and decision-support applications, and described progressing toward agentic AI in following years.

Speaker: Lorenzo Fiorillo, Director Technology, R&D & Digital. Locator: Q&A, Naisheng Cui question and Fiorillo answer, PDF p. 12, lines 459–474.. Measurement: More than 200 use cases in development.. Limit: Use cases are being developed; no deployment count, operating outcome, savings, or measured performance disclosed..

Official full transcript includes presentation and Q&A. Director of Technology, R&D & Digital Lorenzo Fiorillo said Eni had more than 200 internal AI use cases under development, citing optimization/decision support and a future agentic-AI direction. These are development counts, not a count of deployed production systems or realized value. No relevant AI-energy-demand passage was found.

## Eni · Q4 2025

reviewed_relevant · Full 14-page issuer transcript reviewed, presentation and Q&A through close; searched AI, artificial intelligence, machine learning, data centers, hyperscalers and automation.

[Eni 4Q 2025 Results conference-call transcript](https://www.eni.com/content/dam/enicom/documents/eng/investor/presentations/2025/2025-full-year-results/2025-full-year-results-Transcript.pdf)

### Planned AI data center and gas-fired power pairing

Guido Brusco said Eni had opened a data-center business line and planned with international partners to develop an AI data center in northern Italy near Milan, paired with gas-fired generation.

Speaker: Guido Brusco, Director General Global Natural Resources. Locator: Q&A, Lydia Rainforth question and Brusco answer, PDF p. 6, lines 203–223.. Measurement: Plan described up to 500 MW in phases, with an initial 80–100 MW phase; Brusco cited an Italian AI-center demand forecast up to 1 GW by 2030.. Limit: Development plan, not evidence of a contracted customer or sanctioned/operating capacity; partner/customer identities and execution timing were not supplied. The 1 GW is a market-demand forecast, not Eni project backlog..

### Potential internal AI applications

Brusco said AI would also be applied in upstream production improvement, drilling, project improvement and rotating machinery; he cited Eni’s exploration technology/computing capability as an example.

Speaker: Guido Brusco, Director General Global Natural Resources. Locator: Q&A, PDF p. 6, lines 207–223.. Measurement: No AI-attributed operating metric. Brusco separately cited less than 1% production-facility downtime versus an industry average of about 3.5%.. Limit: Future application and capability discussion; the downtime comparison is not attributed to AI, and exploration success rate was not attributed to AI specifically..

### Data-center power strategy

CEO Claudio Descalzi included hyperscaler data centers coupled with blue power among Eni’s energy-transition development areas.

Speaker: Claudio Descalzi, CEO. Locator: Presentation, PDF p. 3, lines 99–111.. Measurement: No additional metric beyond the project/demand figures discussed in Q&A.. Limit: Strategic area, not a committed customer-specific contract..

Official transcript dated 26 Feb 2026 and explicitly includes a presentation and Q&A. AI answer at Q&A lines 207–223 is by Guido Brusco, DG Global Natural Resources. The 500 MW Italian AI-data-center project was described as a plan with international partners, with phased capacity; no customer contract, FID, or committed schedule is specified. The 1 GW figure is Eni’s stated estimate of Italian AI-center demand by 2030, not the project’s secured demand. Planned internal AI uses and current downtime are not tied to quantified AI-attributed outcomes.

## Eni · Q1 2026

reviewed_no_relevant · Full 17-page issuer transcript reviewed, presentation and Q&A through close; searched AI/artificial intelligence, machine learning, agentic/generative, data center/hyperscaler, automation and digital-transformation terms.

[Eni 2026 First Quarter Results conference-call transcript](https://www.eni.com/content/dam/enicom/documents/eng/investor/presentations/2026/2026-first-quarter-results/2026-first-quarter-results-Transcript.pdf)

An initial third-party archive path was inaccessible, but Eni’s issuer IR page links a full English transcript PDF. This official source includes presentation and Q&A; no relevant AI or AI-energy-demand passage matched the selected terms.

## Eni · Q2 2026

reviewed_relevant · Full transcript keyword search and relevant passage review; page is 656 lines.

[Eni Q2/H1 2026 Results Conference Call](https://stockanalysis.com/quote/bit/ENI/transcripts/549696-q2-2026/)

### AI and energy demand

CEO cited hyperscale data centres and AI growth among drivers of rising energy needs and positioned Eni’s oil-and-gas project pipeline as able to respond.

Speaker: Claudio Descalzi, CEO. Locator: Q&A lines 274–283.. Measurement: No AI-specific energy-demand estimate or sales commitment.. Limit: Demand framing only; not an internal AI deployment disclosure..

## EOG Resources · Q3 2024

reviewed_relevant · Full speaker-labelled transcript opened and scanned for AI, artificial intelligence, machine learning, automation, digital, predictive and related terms.

[EOG Resources Q3 2024 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2024/11/08/eog-resources-eog-q3-2024-earnings-call-transcript/)

### AI and electrification in natural-gas power-demand outlook

CEO included new power demand from AI and electrification among drivers of expected gas-demand growth, alongside coal retirements, industrial demand and Mexico exports.

Speaker: Ezra Y. Yacob, Chairman and CEO. Locator: Prepared remarks, transcript line 126. Measurement: Management estimated 10–12 Bcf/d aggregate natural-gas demand growth through the end of the decade; not an AI-only estimate.. Limit: No AI-specific load share, contracted EOG volume or realized data-center load disclosed. No internal AI use identified in this call..

Motley Fool transcript published 2024-11-08; the call was on that date. AI appears in the transcript body.

## EOG Resources · Q4 2024

reviewed_relevant · Full speaker-labelled transcript opened and searched for AI, artificial intelligence, machine learning, generative AI, automation, digital, predictive and data-center terms.

[EOG Resources Q4 2024 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2025/02/28/eog-resources-eog-q4-2024-earnings-call-transcript/)

### Data-center and hyperscaler-related gas-market opportunity

CEO said EOG studied how data-center development might progress and how the company could participate, including regional pricing uplift from increased electricity demand and possible siting of data centers near generation and natural-gas fields. South Texas/Dorado was identified as a potential beneficiary.

Speaker: Ezra Y. Yacob, Chairman and CEO. Locator: Q&A, transcript lines 434–444. Measurement: No data-center capacity, AI-specific load, contract or EOG-specific volume quantified.. Limit: Management did not explicitly attribute data-center demand to AI; code as data-center energy-market adjacency, not direct AI demand..

Call date 2025-02-27; third-party transcript published 2025-02-28. AI/artificial intelligence not used in body, but Q&A addresses hyperscalers, data centers and electricity-related gas opportunities.

## EOG Resources · Q1 2025

reviewed_no_relevant · Full speaker-labelled transcript opened; searched AI, artificial intelligence, machine learning, generative AI, automation, digital, predictive, autonomous, agentic, copilot, data-center and power-demand terms.

[EOG Resources Q1 2025 Earnings Call Transcript](https://equibles.com/stocks/eog/calls/2025-q1)

No in-scope internal AI, AI-linked demand or relevant automation/digital-transformation disclosure found. CEO discussed 4%–6% gas-demand growth led by LNG and power but did not connect it to AI or data centers. Issuer archive lists call on May 2; Equibles metadata says “concluded May 1,” so issuer date is used.

## EOG Resources · Q2 2025

reviewed_relevant · Full transcript reviewed; prepared remarks and Q&A searched, including AI/ML, generative AI, analytics, automation, and data-center/power-demand terms.

[EOG Resources, Inc. Q2 FY2025 Earnings Call Transcript](https://www.roic.ai/quote/EOG:US/transcripts/2025-year/2-quarter)

### Proprietary generative AI and machine-learning operations platform

COO said EOG had long used machine learning for production optimization and cost savings, and deployed proprietary generative AI to improve staff collaboration, automate data capture and provide operational insights across operations.

Speaker: Jeff Leitzell, EVP & COO. Locator: Prepared remarks, lines 314–320.. Measurement: No AI-specific savings or production uplift quantified; separate sensor platform had benefited 50+ wells.. Limit: The 50+ well figure describes the separate high-frequency sensor platform, not generative AI..

### AI-enabled natural-gas demand from hyperscalers

CEO said EOG had discussed long-term supply with LNG counterparties and hyperscalers and viewed its Dorado and Utica dry-gas assets as positioned to serve that demand.

Speaker: Ezra Yacob, CEO. Locator: Q&A lines 388–408.. Measurement: No AI-specific contract, volume, price, or data-center supply commitment disclosed.. Limit: Hyperscalers are mentioned with LNG; transcript does not quantify or separate AI-driven demand..

### Proprietary subsurface sensor and drilling-data platform

COO described high-frequency downhole sensors integrating subsurface and equipment data to identify rock properties, faults and equipment performance; management said it improved completion design, well performance and cost efficiency.

Speaker: Jeff Leitzell, EVP & COO. Locator: Prepared remarks, lines 314–318; Q&A line 574.. Measurement: More than 50 wells had used the sensor platform in 2025.. Limit: Not identified as AI; outcomes were not quantified..

StockAnalysis archive offered call discovery; full ROIC transcript used for text review. Prepared remarks and Q&A were present.

## EOG Resources · Q3 2025

reviewed_relevant · Full speaker-labelled transcript reviewed; keyword scan included AI, artificial intelligence, generative AI, deep learning, machine learning, agentic, predictive, automation.

[EOG Resources Q3 2025 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2025/11/27/eog-resources-eog-q3-2025-earnings-call-transcript/)

### Internal operational AI across subsurface, production, drilling, maintenance, process and safety

CEO said EOG develops proprietary apps and software in-house tied directly to field operations, including high-fidelity sensors and real-time downhole measurements. The company uses machine-learning algorithms for production optimization and other operations, is developing deep-learning tools that incorporate more data including human observations, and uses generative AI to organize geologic data and find patterns. He described real-time drilling optimization, equipment reliability, predictive maintenance, process optimization, some autonomous field operations, and anomaly detection for emissions, spills and safety.

Speaker: Ezra Yacob, Chairman and CEO. Locator: Transcript Q&A, lines 348–356. Measurement: No quantified AI-specific production, downtime, cost or safety result was disclosed. Management said EOG had used smart technology for centralized gas-lift systems for almost 10 years.. Limit: Descriptions are management claims about capabilities and use cases; the call did not quantify attributable financial or production outcomes, and CEO said EOG was not yet at true agentic intelligence..

### AI maturity and adoption strategy

Management characterized the industry transition as broad and said EOG has a longer-running, internally built technology program linked to field use. Yacob distinguished existing machine-learning use and emerging deep learning and generative AI from a future state of agentic intelligence.

Speaker: Ezra Yacob, Chairman and CEO. Locator: Transcript Q&A, lines 348–356. Measurement: Nearly 10 years of centralized gas-lift smart technology; no AI-specific financial metric.. Limit: No dated roadmap, deployment count or spending level was provided..

Verified against the complete Motley Fool transcript: the call occurred Nov. 7, 2025, while the article URL slug and publication date are Nov. 27, 2025. Use the transcript-stated call date for window eligibility; the inherited reference conflated publication/source date with call date. Q&A includes the stated AI findings.

## EOG Resources · Q4 2025

reviewed_relevant · Full speaker-labelled transcript reviewed; searched AI, machine learning, automation, digital, predictive, artificial intelligence.

[EOG Resources Q4 2025 Earnings Transcript](https://www.fool.com/earnings/call-transcripts/2026/02/25/eog-resources-eog-q4-2025-earnings-transcript/)

### Machine-learning production optimization

COO Jeff Leitzell attributed some lower lease operating expense to EOG’s proprietary production-optimizers program, which uses machine learning to optimize base production, improve run time and lower portfolio operating costs.

Speaker: Jeffrey R. Leitzell, COO. Locator: Transcript prepared remarks, lines 213–218. Measurement: No quantified amount attributable specifically to the machine-learning program.. Limit: Management described a contribution but did not separate its effect from other efficiency changes..

### Remote monitoring and acquired-asset automation

In Q&A, management said it was implementing additional automation across acquired Utica operations to remotely manage and monitor wells through a 24-hour control room, with the aim of improving efficiency and reducing man-hours.

Speaker: Jeffrey R. Leitzell, COO. Locator: Transcript Q&A, lines 352–355. Measurement: No quantified savings or uptime improvement disclosed.. Limit: Implementation was described as in process; no rollout completion or realized benefit was stated..

Transcript is full and publicly accessible from The Motley Fool. The source page contains generated summary text as well; findings above are grounded in speaker-labelled transcript lines only.

## EOG Resources · Q1 2026

reviewed_relevant · Full speaker-labelled transcript reviewed; scanned AI, artificial intelligence, machine learning, predictive, automation, digital, optimization and technology terms.

[EOG Q1 2026 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2026/05/06/eog-eog-q1-2026-earnings-call-transcript/)

### Operational optimization technology, without an AI attribution

COO said in-house production optimizers and area-specific control rooms helped teams manage remote operations and limit downtime during a winter storm. He also described applying data and technology to reduce time on location and using real-time geology, drilling and completions data to improve well performance. The reviewed transcript did not label these capabilities AI or machine learning.

Speaker: Jeffrey R. Leitzell, COO; Keith P. Trasko, SVP Exploration & Production. Locator: Transcript prepared remarks, lines 219–233; Q&A, line 291. Measurement: No technology-specific outcome separated from overall weather resilience or well-efficiency changes.. Limit: Adjacent operational technology only; do not infer AI deployment from generic data/technology language..

The inherited EOG AI profile concerns other sources and was not used to upgrade this transcript. Search found no AI or machine-learning terms in the transcript body.

## EOG Resources · Q2 2026

reviewed_relevant · Full speaker-labelled transcript reviewed; scanned AI, artificial intelligence, machine learning, generative AI, automation, digital, predictive and autonomous.

[EOG Resources Q2 2026 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2026/08/11/eog-resources-eog-q2-2026-earnings-call-transcript/)

### AI-linked U.S. natural-gas demand

CEO said EOG’s medium-term gas-demand view reflects LNG feed gas and electricity consumption; he specifically named AI-powered electricity demand alongside LNG exports, industrial reshoring and 24/7 baseload power as drivers of higher U.S. gas demand.

Speaker: Ezra Yacob, Chairman and CEO. Locator: Transcript Q&A, lines 334–339. Measurement: EOG forecast U.S. natural-gas demand growth of 3%–5% CAGR through the end of the decade; that forecast covered multiple drivers, not AI alone.. Limit: Demand forecast is management guidance and does not isolate AI load or specify realized data-center load attributable to EOG. No internal EOG AI use was found in the transcript..

The transcript itself supports the AI-energy-demand reference; this was not taken solely from the Motley Fool generated takeaway. The AI occurrence is in the Q&A, not a disclosure of EOG internal AI.

## EQT · Q3 2024

reviewed_relevant · Full issuer-hosted corrected transcript opened and keyword-scanned in this pass; inherited EQT ledger used as a lead and independently rechecked.

[EQT Q3 2024 Earnings Call — Corrected Transcript](https://s205.q4cdn.com/630272887/files/doc_financials/2024/q3/EQT-Q3-2024-Earnings-Call-Transcript.pdf)

### AI and data-center electricity demand as natural-gas growth drivers

CEO described natural-gas power demand, including power generation associated with AI, as part of late-decade demand growth and discussed data-center load in the Marcellus and regional gas demand.

Speaker: Toby Z. Rice, President and CEO. Locator: Q&A, PDF pp. 11–12, lines 435–449; follow-up 609–619. Measurement: 10–18 Bcf/d natural-gas demand-growth range for power in management’s plan; none isolated as AI-specific.. Limit: Forecast/market commentary, not contracted AI load; range covers power broadly, not AI alone..

Official corrected transcript opened and full text scanned; citations refer to PDF page and transcript line ranges.

## EQT · Q4 2024

reviewed_relevant · Full issuer-hosted corrected transcript opened and keyword-scanned in this pass; inherited EQT ledger used as a lead and independently rechecked.

[EQT Q4 2024 Earnings Call — Corrected Transcript](https://s205.q4cdn.com/630272887/files/doc_financials/2024/q4/EQT-Q4-2024-Earnings-Call-Transcript.pdf)

### Hyperscaler and data-center gas-supply opportunity

CFO said EQT was in discussions with hyperscalers and other power-sector counterparties about gas supply and power opportunities; CEO referenced AI-related infrastructure in discussing long-run gas demand and pricing.

Speaker: Jeremy T. Knop, CFO; Toby Z. Rice, President and CEO. Locator: Q&A, PDF pp. 10–11; p. 20. Measurement: No contracted AI load or attributable volume disclosed.. Limit: Opportunity discussion and market commentary; no specific AI customer or contract disclosed..

Official corrected transcript opened and searched. Incidental DeepSeek market discussion is not coded as operational AI.

## EQT · Q1 2025

reviewed_relevant · Full issuer-hosted corrected transcript opened and keyword-scanned in this pass; inherited EQT ledger used as a lead and independently rechecked.

[EQT Q1 2025 Earnings Call — Corrected Transcript](https://s205.q4cdn.com/630272887/files/doc_financials/2025/q1/EQT-Q1-2025-Earnings-Call-Transcript.pdf)

### AI data-center demand and in-basin gas-supply strategy

CEO described AI-related data-center development and local power demand as an opportunity for Appalachian gas and infrastructure; management discussed supply opportunities and expected data-center and industrial projects to strengthen in-basin demand.

Speaker: Toby Z. Rice, CEO; Jeremy T. Knop, CFO. Locator: Prepared remarks, PDF pp. 4–5; Q&A pp. 10–11, 18–20. Measurement: 6–7 Bcf/d local demand-growth opportunity by 2030 including gas-fired power and data-center projects; no AI-only share.. Limit: Projects were proposed or under discussion, not all sanctioned or contracted; forecast includes broader regional load growth..

Official corrected transcript opened and full text scanned; citations refer to PDF page and transcript line ranges.

## EQT · Q2 2025

reviewed_relevant · Full issuer-hosted corrected transcript opened and keyword-scanned in this pass; inherited EQT ledger used as a lead and independently rechecked.

[EQT Q2 2025 Earnings Call — Corrected Transcript](https://s205.q4cdn.com/630272887/files/doc_financials/2025/q2/EQT-Q2-2025-Earnings-Call-Transcript.pdf)

### AI-linked demand and new Appalachian power loads

Management discussed AI infrastructure and data-center loads around new Appalachian power projects, including Shippingport and Homer City, as prospective demand drivers for EQT gas.

Speaker: Toby Z. Rice, CEO; Jeremy T. Knop, CFO. Locator: Prepared remarks, PDF pp. 4–6; Q&A p. 10. Measurement: Combined new Appalachian load was estimated at about 3 Bcf/d, encompassing AI/data-center and other projects.. Limit: The total includes non-AI demand and prospective facilities; no AI-only contracted gas volumes stated..

Official corrected transcript opened and full text scanned; citations refer to PDF page and transcript line ranges.

## EQT · Q3 2025

reviewed_relevant · Full issuer-hosted corrected transcript opened and keyword-scanned in this pass; inherited EQT ledger used as a lead and independently rechecked.

[EQT Q3 2025 Earnings Call — Corrected Transcript](https://s205.q4cdn.com/630272887/files/doc_financials/2025/q3/EQT-Q3-2025-Earnings-Call-Transcript.pdf)

### Data-center and power-project demand, without explicit AI attribution

CEO discussed data-center projects and power demand in EQT’s region, but the transcript did not explicitly connect those loads to AI. This is adjacency only and is not coded as direct AI-energy demand.

Speaker: Toby Z. Rice, President and CEO. Locator: Prepared remarks, PDF pp. 4–5; Q&A pp. 8, 19–20. Measurement: Approximately 1.5 Bcf/d data-center demand cited, with more than 1 Bcf/d additional MVP takeaway as a separate opportunity.. Limit: AI was not named; project timing and demand are prospective..

Official corrected transcript opened and full text scanned; citations refer to PDF page and transcript line ranges.

## EQT · Q4 2025

reviewed_relevant · Full issuer-hosted corrected transcript opened and keyword-scanned in this pass; inherited EQT ledger used as a lead and independently rechecked.

[EQT Q4 2025 Earnings Call — Corrected Transcript](https://s205.q4cdn.com/630272887/files/doc_financials/2025/q4/EQT-Q4-2025-Earnings-Call-Transcript.pdf)

### AI/cloud-linked gas-fired generation and data-center demand

CEO linked some turbine orders to AI/cloud infrastructure and cited data-center construction as structural demand for natural gas.

Speaker: Toby Z. Rice, President and CEO. Locator: Prepared remarks, PDF p. 6, lines 224–233. Measurement: About 13 Bcf/d potential U.S. demand from turbine orders once commissioned, some but not all AI/cloud; about 45 GW of data-center capacity under construction.. Limit: No AI-only volume or realized load quantified; estimates span multiple end uses..

### AI-assisted supplier rebid targeting

CEO said AI tools could help identify which service categories to rebid to target potential savings. This is a targeting aid, not autonomous procurement or realized results.

Speaker: Toby Z. Rice, President and CEO. Locator: Q&A, PDF pp. 20–21, lines 902–911.. Measurement: CEO described prospective low-single-digit procurement cost improvement; denominator, realized savings, and AI-specific contribution were not stated.. Limit: Prospective management estimate with no denominator; not a realized result and not attributed specifically to AI..

Official corrected transcript opened and full text scanned; citations refer to PDF page and transcript line ranges.

## EQT · Q1 2026

reviewed_relevant · Full issuer-hosted corrected transcript opened and keyword-scanned in this pass; inherited EQT ledger used as a lead and independently rechecked.

[EQT Q1 2026 Earnings Call — Corrected Transcript](https://s205.q4cdn.com/630272887/files/doc_financials/2026/q1/EQT-Q1-2026-Earnings-Call-Transcript.pdf)

### Data-center-driven U.S. power and gas-demand opportunity; no AI term

CEO cited data-center-driven power demand and potential regional power, midstream and data-center projects; management described demand partnered with counterparties and a broader prospective opportunity set. Transcript does not use AI terminology.

Speaker: Toby Z. Rice, CEO; Jeremy T. Knop, CFO. Locator: Prepared remarks, PDF pp. 4–6; Q&A pp. 7–8, 14–15. Measurement: 6 Bcf/d base-case and 10 Bcf/d bull-case power-demand growth; 2–3 Bcf/d partnered depending on utilization.. Limit: Do not infer AI demand; projects prospective and estimates depend on utilization..

Official corrected transcript opened and full text scanned; citations refer to PDF page and transcript line ranges.

## EQT · Q2 2026

reviewed_no_relevant · Full speaker-labelled transcript reviewed; searched AI, artificial intelligence, machine learning, automation, digital, predictive and autonomous.

[EQT Corporation Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/eqt/transcripts/640541-q2-2026/)

Official IR quarterly-results archive lists transcript PDFs, but this pass used the accessible full StockAnalysis/Quartr transcript. Q&A includes large data-center/power-project demand discussion; no AI linkage was stated, so it is not coded as AI energy demand.

## Equinor · Q3 2024

reviewed_no_relevant · 15-page edited transcript screened from operator opening and CFO presentation through analyst Q&A and operator close. Searches covered AI/artificial intelligence, machine learning, agentic/predictive, digital, analytics, software, data platform, workflow, automation, autonomy, remote operation, and data-center/hyperscaler demand.

[Q3 2024 Equinor ASA Analyst Conference (Q&A) – edited transcript](https://cdn.equinor.com/files/h61q9gi9/global/d679fb1fb06c7a1681621d4f7e2475a689d62337.pdf?q3-2024-transcript-2024-10-24-equinor.pdf=)

Equinor’s official quarterly-results archive lists a Q3 2024 transcript with Q&A; the opened Refinitiv StreetEvents edited transcript is dated 24 October 2024, matches the analyst-conference event, and runs from presentation through close. Its disclaimer cautions that transcription may contain errors. Screened terms returned no relevant passage. This is not a claim that Equinor has no AI activity.

## Equinor · Q4 2024

reviewed_relevant · Full 20-page transcript screened from safety opening and CEO/CFO presentations through Q&A close. Search covered AI/artificial intelligence, ML/genAI/agentic, digital/automation, analytics/predictive, data-center/hyperscaler, software, autonomy, remote and workflow terms.

[Q4 2024 Equinor ASA Earnings Call – edited transcript](https://cdn.equinor.com/files/h61q9gi9/global/06ceb6d7e8d98e7ed524ec90e44d4a0fa071134e.pdf?eqnr-osl-transcript-2025-02-05.pdf=)

### AI-assisted field layout and well-path planning

CEO said Equinor uses AI across exploration, concept selection, operations and maintenance. In Johan Sverdrup Phase III planning, it generated more than one million alternative field layouts and well trajectories, which management said added $12 million in value; he said broader value depends on scaling to 50+ NCS projects.

Speaker: Anders Opedal, President & CEO. Locator: PDF P3–P4, lines 109–115.. Measurement: >1 million alternative layouts/trajectories; company-reported $12 million value in Johan Sverdrup III planning.. Limit: Company-reported, single project; no independent validation or method/baseline for the $12 million value. Further value from scaling was prospective, not achieved..

Reporting exception: Q4/FY2024 results were presented on 5 February 2025 together with the 2025 Capital Markets Update; this single call covers both and includes Q&A. Equinor official page confirms event and Q&A length; transcript is Refinitiv edited transcript hosted on Equinor CDN and carries Refinitiv accuracy disclaimer. AI-035 belongs exactly to Equinor (existing-register company field checked); linkage is to well/field planning, while this call documents the specific quantified Phase III use case.

## Equinor · Q1 2025

reviewed_no_relevant · Full 14-page transcript reviewed from CFO presentation through analyst Q&A and operator close. Screened explicit AI/ML/genAI/agentic terms and adjacent digital/analytics/software/automation/autonomy/predictive/remote/workflow and data-center/hyperscaler terms.

[Q1 2025 Equinor ASA Analyst Call – Q&A edited transcript](https://cdn.equinor.com/files/h61q9gi9/global/50ccb81afbabdbb06dace08bb53f5682b811d23b.pdf?eqnr-osl-transcript-2025-04-30.pdf=)

Call held 30 Apr 2025; Equinor’s results archive lists transcript incl. Q&A. Refinitiv edited transcript on Equinor CDN reviewed through call close. Selected explicit and adjacent terms returned no relevant content; this is not a claim that Equinor has no AI activity.

## Equinor · Q2 2025

reviewed_relevant · Full 14-page transcript reviewed from CFO results remarks through Q&A and operator close. Screened explicit AI/ML and adjacent data-center/hyperscaler, digital, analytics, software, automation, predictive, workflow, autonomy and remote-operation terms.

[Q2 2025 Equinor ASA Analyst Conference – Q&A edited transcript](https://cdn.equinor.com/files/h61q9gi9/global/4b850b0ef0024c18d08f28291c12e181a1bb7f96.pdf?eqnr-osl-transcript-2025-07-23.pdf=)

### Marcellus gas positioning alongside data-center and AI market growth

CFO said Marcellus gas is well located for U.S. data-center growth and competitiveness related to AI, with energy seen as an enabler. Asked whether Equinor would pursue gas-fired power, he said there were no concrete plans, while noting an increasing gas-power link.

Speaker: Torgrim Reitan, CFO. Locator: PDF P5–P6, lines 169–198; analyst question at 169–171 and CFO response at 192–198.. Measurement: No data-center load, customer agreement, plant capacity or dedicated AI-related sales volume stated.. Limit: Strategic market context only. Existing Marcellus position is not identified as contracted specifically to data centers, and Equinor disclosed no concrete gas-fired generation plans in this response..

Call held 23 July 2025; full Refinitiv edited transcript linked from issuer quarterly-results archive and includes presentation and analyst Q&A through close. CFO answer separates existing gas-position context from no concrete power-generation plans.

## Equinor · Q3 2025

reviewed_relevant · Full 15-page transcript screened from results remarks through analyst Q&A and operator close. Explicit AI and adjacent digital, automation, analytics, predictive, software, autonomy, workflow and data-center/hyperscaler terms checked.

[Q3 2025 Equinor ASA Analyst Conference – Q&A edited transcript](https://cdn.equinor.com/files/h61q9gi9/global/23f6738b1efcb9473111642bed7adf4f61419ea3.pdf?eqnr-osl-transcript-2025-10-29.pdf=)

### AI/data-center demand as a potential driver of U.S. gas and power markets

In discussing global gas markets, CFO said U.S. gas prices had become a domestic political topic because data centers and AI could significantly affect power prices and demand for power, with natural gas an important source. He also noted possible implications for LNG exports.

Speaker: Torgrim Reitan, CFO. Locator: PDF P6–P7, lines 197–235; data-center/AI discussion lines 225–233.. Measurement: No AI-specific load forecast, capacity, customer, contract, generation project or dedicated volume disclosed.. Limit: General market commentary and price-policy concern. It is not an Equinor internal AI-use disclosure or a company-specific demand forecast..

Call held 29 October 2025; transcript linked from Equinor results archive and includes CFO remarks and Q&A through operator close. This call’s AI/data-center mention is market context, not proof of a new Equinor power project.

## Equinor · Q4 2025

reviewed_relevant · Full accessible transcript page reviewed from CEO/CFO presentations through Q&A and moderator close. Screened AI/artificial intelligence, ML, data-center, digital, analytics, automation, predictive, software, autonomy and agentic terms; transcript page contains complete Q&A through close.

[EQNR Q4 2025 Earnings Call Transcript](https://tickertrends.io/transcripts/EQNR/Q4-earnings-transcript-2025)

### AI cost savings and exploration capability

CFO said AI had already contributed about NOK 1 billion to results but remained early-stage; he did not attribute the broader 10% reported OpEx/SG&A reduction to AI. CEO said AI and new seismic enabled additional discoveries, with 14 discoveries adding 125 million barrels of resources in 2025, and said AI use in exploration and operations saved $130 million last year.

Speaker: Torgrim Reitan, CFO; Anders Opedal, President & CEO. Locator: TickerTrends transcript lines 142–155; CFO disclosure lines 144–145; CEO exploration and savings answer lines 151–155.. Measurement: CFO: approx. NOK 1 billion AI contribution already reflected in results. CEO: 14 discoveries/125 million barrels of new resources in 2025 (not solely AI-attributed) and $130 million saved through AI in the prior year.. Limit: Company-reported; no methodology, project allocation or validation. Do not add NOK1bn and $130m: their scopes/bases may overlap and are unreconciled. The wider reported cost decline also reflected divestments and other structural changes; discovery/resource totals are not wholly attributable to AI..

Event was 4 February 2026; Equinor’s official results archive lists Q4 transcript including Q&A and official event page confirms call/Q&A. Primary transcript link was not exposed by the retrieved archive text; TickerTrends full transcript was reviewed end-to-end through moderator close. AI-033 and AI-036 exact Company values in research-data.json are Equinor; AI-036 is relevant to the seismic/exploration passage.

## Equinor · Q1 2026

reviewed_no_relevant · Full 512-line transcript reviewed from operator opening through Q&A close; screened AI, artificial intelligence, digitalization, analytics, data-center, automation, software and technology terms.

[Equinor ASA Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/osl/EQNR/transcripts/380489-q1-2026/)

Full transcript includes prepared remarks and Q&A; no relevant AI or adjacent technology disclosure found on full-call screen.

## Equinor · Q2 2026

reviewed_no_relevant · Full third-party transcript reviewed from prepared remarks through Q&A close; screened for AI/artificial intelligence, machine learning, generative/agentic AI, automation, digital transformation, data centers and hyperscalers.

[Equinor Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/osl/EQNR/transcripts/380491-q2-2026/)

Full prepared presentation and Q&A through close reviewed; no relevant term or AI-energy demand passage found.

## Expand Energy · Q3 2024

reviewed_relevant · Full speaker-labelled transcript opened and keyword-scanned; prepared remarks and Q&A reviewed.

[Expand Energy Q3 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/exe/transcripts/216978-q3-2024/)

### AI data-center power opportunity and gas-supply strategy

CFO said the combined company was consolidating Legacy Southwestern and Legacy Chesapeake efforts and speaking with developers, power generators, end users, midstream and upstream suppliers about AI data-center power demand. CEO cited the combined Appalachian and Gulf Coast gas portfolio as positioned for LNG and domestic power-generation demand. This is stakeholder engagement, not an executed AI-specific supply contract.

Speaker: Mohit Singh, CFO; Nick Dell’Osso, CEO. Locator: Q&A, lines 350–357 and 361–363. Measurement: No AI-specific load volume, contract value, or delivered gas volume disclosed.. Limit: Management described early-stage conversations and consolidation of legacy efforts; no signed AI data-center deal or realized AI-specific revenue was reported..

Correction: Expand closed the combination on 2024-10-01 and held its first combined-company Q3 2024 call on 2024-10-30. This is not a no-public-call exception.

## Expand Energy · Q4 2024

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Expand Energy (EXE) Q4 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/exe/transcripts/251105-q4-2024/)

### AI, data-center and gas-market disclosure

Energy demand context in prepared remarks: U.S. LNG capacity under construction and domestic power-market growth tied to data centers and consumer demand. No AI link.

Speaker: Expand Energy management. Locator: Prepared remarks line 130.. Measurement: No AI-specific financial, production, customer or contracted-volume outcome disclosed.. Limit: Commercial/customer discussion or operating gains were not isolated as AI-specific unless explicitly stated..

Finding kept at the stated specificity; no AI attribution or commercial metrics are inferred beyond the transcript.

## Expand Energy · Q1 2025

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Expand Energy (EXE) Q1 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/exe/transcripts/320193-q1-2025/)

### AI, data-center and gas-market disclosure

Q&A described active discussions with power producers and data centers across Appalachia, without naming customers or specifying commercial outcomes. No AI link.

Speaker: Expand Energy management. Locator: Q&A lines 294–296.. Measurement: No AI-specific financial, production, customer or contracted-volume outcome disclosed.. Limit: Commercial/customer discussion or operating gains were not isolated as AI-specific unless explicitly stated..

Finding kept at the stated specificity; no AI attribution or commercial metrics are inferred beyond the transcript.

## Expand Energy · Q2 2025

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Expand Energy (EXE) Q2 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/exe/transcripts/340112-q2-2025/)

### AI, data-center and gas-market disclosure

Management said AI and machine learning supported record drilling performance: 62% more footage per day in Northeast Appalachia and 25% in Haynesville. A speaker described AI agents analyzing historical drilling data for real-time optimization; gains were not isolated to AI.

Speaker: Expand Energy management. Locator: Prepared remarks line 125; Q&A line 220.. Measurement: No AI-specific financial, production, customer or contracted-volume outcome disclosed.. Limit: Commercial/customer discussion or operating gains were not isolated as AI-specific unless explicitly stated..

Finding kept at the stated specificity; no AI attribution or commercial metrics are inferred beyond the transcript.

## Expand Energy · Q3 2025

reviewed_no_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Expand Energy (EXE) Q3 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/exe/transcripts/368709-q3-2025/)

No qualifying AI/automation disclosure found after full-call screening.

## Expand Energy · Q4 2025

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Expand Energy (EXE) Q4 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/exe/transcripts/405195-q4-2025/)

### AI, data-center and gas-market disclosure

Management described AI advances refining well designs and enabling faster real-time drilling-parameter optimization to capture further drilling and completion savings. In a separate answer, it discussed data-center growth, but did not link AI to a specific supply commitment.

Speaker: Expand Energy management. Locator: Q&A lines 350–354; data-center discussion around lines 196–200.. Measurement: No AI-specific financial, production, customer or contracted-volume outcome disclosed.. Limit: Commercial/customer discussion or operating gains were not isolated as AI-specific unless explicitly stated..

Finding kept at the stated specificity; no AI attribution or commercial metrics are inferred beyond the transcript.

## Expand Energy · Q1 2026

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Expand Energy (EXE) Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/exe/transcripts/559245-q1-2026/)

### AI, data-center and gas-market disclosure

CEO linked AI power demand to Gulf Coast and Appalachia gas markets, cited growing hyperscaler/data-center exposure, and said machine learning and AI were beginning to lower costs and improve well productivity. No AI-attributed amount was disclosed.

Speaker: Expand Energy management. Locator: Prepared remarks lines 124–130, 136; Q&A lines 311–312.. Measurement: No AI-specific financial, production, customer or contracted-volume outcome disclosed.. Limit: Commercial/customer discussion or operating gains were not isolated as AI-specific unless explicitly stated..

Finding kept at the stated specificity; no AI attribution or commercial metrics are inferred beyond the transcript.

## Expand Energy · Q2 2026

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Expand Energy (EXE) Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/exe/transcripts/656934-q2-2026/)

### AI, data-center and gas-market disclosure

Management discussed nationwide utility relationships and data-center construction as a rationale for Twin Eagle’s marketing reach and gas-supply opportunities. No explicit AI link or supply deal was disclosed.

Speaker: Expand Energy management. Locator: Q&A lines 154–159; line 242.. Measurement: No AI-specific financial, production, customer or contracted-volume outcome disclosed.. Limit: Commercial/customer discussion or operating gains were not isolated as AI-specific unless explicitly stated..

Finding kept at the stated specificity; no AI attribution or commercial metrics are inferred beyond the transcript.

## ExxonMobil · Q3 2024

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, automation, data center, hyperscaler, digital transformation, and autonomy.

[ExxonMobil Q3 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/xom/transcripts/218294-q3-2024/)

### AI as an emerging enterprise technology and efficiency opportunity

In response to whether AI contributed to the company’s stated cost savings, CEO Darren Woods said AI was part of a broader technology effort, with teams assessing data-rich business areas to improve effectiveness and efficiency. CFO Kathy Mikells separately described planned process automation and IT simplification. The executives characterized the work as early and did not quantify AI-specific savings.

Speaker: Darren Woods, Chairman and CEO; Kathy Mikells, SVP and CFO. Locator: Q&A, lines 568–586; Woods lines 573–579, Mikells lines 581–586.. Measurement: No AI-attributed cost or revenue metric. The $15 billion referenced by the analyst was a broad company cost-savings program, not an AI claim.. Limit: Exploratory enterprise strategy and generic automation; no named production system, deployment, or measurable result..

Full-call transcript includes Q&A; finding is a broad AI/enterprise transformation disclosure, not an implementation case.

## ExxonMobil · Q4 2024

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, data centers, hyperscalers, automation, and digital transformation.

[ExxonMobil Q4 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/xom/transcripts/248786-q4-2024/)

### Decarbonized power strategy for AI data centers

CEO said Exxon was developing an integrated offering of low-carbon gas and carbon capture/storage for hyperscalers seeking decarbonized data-center power. He reported selected sites, early engineering and ongoing customer conversations, while indicating a rough possible sequence of site operation by 2028 and decarbonization by 2029, dependent on customer discussions and other variables.

Speaker: Darren Woods, Chairman and CEO. Locator: Q&A, response to Betty Jiang, lines 219–233.. Measurement: No site count, capacity, signed customer, or contracted value disclosed. Indicative schedule: possible first site operating by 2028, decarbonized in 2029.. Limit: Development strategy and continuing conversations, not a signed hyperscaler contract or delivered supply. Schedule explicitly described as rough and dependent on customers and other variables..

Full transcript includes Q&A. This is AI-linked energy demand and decarbonized power strategy, not an internal AI deployment.

## ExxonMobil · Q1 2025

reviewed_relevant · Complete-call review used the prepared-remarks transcript and separate Q&A transcript; screened AI, artificial intelligence, machine learning, data center, hyperscaler, automation, software, analytics, and digital transformation across both.

[ExxonMobil Q1 2025 Earnings Call Transcript (prepared remarks and Q&A source)](https://stockanalysis.com/stocks/xom/transcripts/311265-q1-2025-q-a/)

### BlackLine accounting software automation

CFO Kathryn Mikells said ExxonMobil recently made a larger implementation of BlackLine accounting software. She said it automates previously manual accounting work, saving tens of thousands of hours; she also described broader centralized-data and automation opportunities.

Speaker: Kathryn Mikells, VP and CFO. Locator: Q&A transcript, lines 456–466; BlackLine and hour-savings statement at lines 462–464.. Measurement: Tens of thousands of hours of manually intensive accounting work saved; no baseline or reporting period specified.. Limit: Adjacent software/process automation, not an AI disclosure. The $12.7 billion savings since 2019 was the broader company-wide program discussed separately and is not attributable to BlackLine..

The full prepared-remarks and Q&A sources were both screened. The call contains no relevant AI disclosure, but the CFO’s BlackLine accounting-platform implementation is a relevant adjacent-automation finding. Do not attribute ExxonMobil’s company-wide $12.7 billion savings figure to BlackLine.

## ExxonMobil · Q2 2025

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, robotics, automation, data center, and hyperscaler.

[ExxonMobil Q2 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/xom/transcripts/337949-q2-2025/)

### Enterprise AI data foundation and early opportunity prioritization

CEO said a corporate-wide ERP/data architecture was intended to make company-wide and historical data available to AI applications. Exxon was prioritizing effectiveness opportunities such as improving products and finding oil more cheaply ahead of cost efficiency, while describing the effort as early-stage and emphasizing that initial efficiencies were freeing employees from lower-value tasks rather than claiming AI savings.

Speaker: Darren Woods, Chairman and CEO. Locator: Q&A, lines 337–355; particularly 347–354.. Measurement: No quantified AI benefit or deployment count.. Limit: Data foundation and opportunity prioritization, not evidence of a scaled AI system or realized AI-specific economics..

Full-call transcript includes prepared remarks and Q&A. The AI passage is strategic/organizational and explicitly early-stage.

## ExxonMobil · Q3 2025

reviewed_relevant · Full transcript keyword search and relevant passage review; transcript page is 671 lines.

[ExxonMobil Q3 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/xom/transcripts/361820-q3-2025/)

### Internal AI use in operations

CEO said ExxonMobil was deploying AI downhole in Permian work and at operating sites, using data to support learning and production optimization; he described a focused rather than scattershot program.

Speaker: Darren Woods, Chairman and CEO. Locator: Q&A, lines 565–578.. Measurement: No isolated AI performance or financial metric.. Limit: Broad management description; workflow detail, scale and validation are not supplied in the answer..

### Low-carbon data-centre opportunity

CEO said the company saw a business opportunity in low-carbon data centres and was pursuing potential opportunities, with Denbury as a possible decarbonization enabler.

Speaker: Darren Woods, Chairman and CEO. Locator: Q&A, lines 565–578.. Measurement: No signed contract or disclosed capacity in this call.. Limit: Opportunity statement; not an executed project or internal AI-use case..

## ExxonMobil · Q4 2025

reviewed_relevant · Full transcript keyword-search and relevant passage review; transcript page is 707 lines.

[ExxonMobil Q4 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/xom/transcripts/395149-q4-2025/)

### Enterprise AI foundation

Management described an enterprise process/data transformation intended to connect business data and decisions and accelerate AI use; it characterized deployments as early and larger rollouts as future work.

Speaker: Darren Woods, Chairman and CEO. Locator: Prepared remarks lines 133–134 and Q&A lines 412–431.. Measurement: No AI-attributed savings or ROI disclosed.. Limit: Separates shared data/process foundations from realized AI outcomes; early deployment language is not evidence of enterprise-wide production scale..

### Supply-chain logistics

Management said supply-chain software is moving beyond automation toward AI-supported logistics planning, including marine movements.

Speaker: Kathryn Mikells, CFO; Darren Woods added context. Locator: Q&A lines 412–431.. Measurement: No model performance or dollar result attributed to AI.. Limit: Early software wins; no rollout scale, control method, or distinct AI economics reported..

## ExxonMobil · Q1 2026

reviewed_relevant · Full transcript searched for AI, artificial intelligence, machine learning, generative/agentic AI, predictive analytics, autonomy/automation, data-center, hyperscaler, and digital terms; Q&A confirmed present. No AI/ML/genAI hit; adjacent autonomous drilling/rig automation mention reviewed.

[ExxonMobil Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/xom/transcripts/537745-q1-2026/)

### Autonomous well construction and rig automation

CEO said ExxonMobil completed its first deepwater fully autonomous well section in Guyana using rig automation and automated downhole steering tools, describing safety and efficiency benefits. This was an autonomy/automation disclosure; the transcript did not identify the system as AI or machine learning.

Speaker: Darren Woods, Chairman and CEO. Locator: Prepared remarks, lines 128–129.. Measurement: First such well section; no quantified time, cost, or safety metric.. Limit: Do not relabel the disclosed automated/autonomous tools as AI; no quantified attributable outcome..

No relevant AI/ML or data-center demand mention found in transcript. Q&A is present, so full-call screen is complete.

## ExxonMobil · Q2 2026

reviewed_relevant · Full transcript keyword-search and relevant passage review; transcript page is 662 lines.

[ExxonMobil Q2 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/xom/transcripts/659112-q2-2026/)

### AI-assisted exploration

Management said trained AI models using prior drilling and subsurface characterization suggested four additional exploration opportunities in the Guyana block; management stressed they require more confirmation.

Speaker: Darren Woods, Chairman and CEO. Locator: Q&A, lines 140–153; related follow-up at 326–328.. Measurement: Four additional opportunities identified; none is presented as confirmed discovery.. Limit: Opportunity generation is preliminary and company-reported; no validation rate, cost, or discovery result is disclosed..

### Permian operations

CFO grouped AI/machine learning with long laterals and surfactants as contributing to Permian performance; attribution to AI alone is not separated.

Speaker: Neil Hansen, Senior VP and CFO. Locator: Q&A, lines 268–282.. Measurement: No AI-specific uplift quantified.. Limit: Bundled technology attribution; do not assign overall Permian recovery/cost results to AI alone..

No AI-related data-center demand finding captured in this call review.

## Halliburton · Q3 2024

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Halliburton Q3 2024 earnings call transcript](https://stockanalysis.com/stocks/hal/transcripts/214496-q3-2024/)

### AI-enabled remote pumping and monitoring

CEO described Intellivate AI for remotely managing pumping operations and reported expanded well monitoring across international operations.

Speaker: Jeff Miller, Chairman, President and CEO. Locator: Transcript Q&A and related Intellivate discussion. Measurement: 50% increase in international wells monitored since the start of the year.. Limit: Customer-facing product/monitoring claims; transcript did not quantify incremental revenue or customer outcomes..

### Automation and closed-loop drilling

Management discussed automation products in drilling, including continued commercialization of automated controls.

Speaker: Jeff Miller, Chairman, President and CEO. Locator: Q&A; full transcript keyword review. Measurement: Not disclosed.. Limit: Adjacent automation; AI linkage is distinct from Intellivate finding..

Full transcript with Q&A reviewed. Halliburton services/products, adjacent automation and AI-related customer power demand are separated; company did not disclose quantified internal AI adoption in the reviewed periods.

## Halliburton · Q4 2024

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Halliburton Q4 2024 earnings call transcript](https://www.fool.com/earnings/call-transcripts/2025/01/22/halliburton-hal-q4-2024-earnings-call-transcript/)

### Automation, digital solutions and closed-loop drilling

CEO said targeted technology developments included electrification, automation and digital solutions; in Q&A he cited closed-loop drilling and automation as a growth opportunity. OCTIV Auto Frac was already deployed on over half of ZEUS spreads.

Speaker: Jeff Miller, Chairman, President and CEO. Locator: Prepared remarks lines 168–170; Q&A lines 293–303, 331–337. Measurement: OCTIV Auto Frac used on over 50% of ZEUS spreads; Sensori used on more than 2,500 North American frac stages in Q4.. Limit: No explicit AI/ML disclosure; measures apply to automation/monitoring product adoption..

Full transcript with Q&A reviewed. Halliburton services/products, adjacent automation and AI-related customer power demand are separated; company did not disclose quantified internal AI adoption in the reviewed periods.

## Halliburton · Q1 2025

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Halliburton Q1 2025 earnings call transcript](https://stockanalysis.com/stocks/hal/transcripts/308655-q1-2025/)

### AI timing for stimulation technology and AI-power market

CEO described ZEUS IQ as addressing a placement problem at “AI time” and discussed rising AI-related power demand and LNG opportunities. Other passages describe frac and drilling automation products.

Speaker: Jeff Miller, Chairman, President and CEO. Locator: Transcript lines 300 and 464; automation references in prepared remarks/Q&A. Measurement: No quantified AI-related revenue.. Limit: The “AI time” reference concerns technology timing, while power demand is end-market exposure; neither demonstrates internal AI adoption..

Full transcript with Q&A reviewed. Halliburton services/products, adjacent automation and AI-related customer power demand are separated; company did not disclose quantified internal AI adoption in the reviewed periods.

## Halliburton · Q2 2025

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Halliburton Q2 2025 earnings call transcript](https://stockanalysis.com/stocks/hal/transcripts/337547-q2-2025/)

### Closed-loop drilling and completions automation

Management described LOGIX closed-loop drilling across more than 500,000 feet drilled, and discussed Intellivate remote operations, planned expansion of its installed base, and ZEUS IQ. No AI/ML terminology was found in the full transcript search.

Speaker: Jeff Miller, Chairman, President and CEO. Locator: Prepared remarks lines 124–139. Measurement: LOGIX used on more than 500,000 feet drilled; company planned to double remote-operations installed base.. Limit: Products described as automation/remote operations, not AI..

Full transcript with Q&A reviewed. Halliburton services/products, adjacent automation and AI-related customer power demand are separated; company did not disclose quantified internal AI adoption in the reviewed periods.

## Halliburton · Q3 2025

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Halliburton Q3 2025 earnings call transcript](https://stockanalysis.com/stocks/hal/transcripts/365438-q3-2025/)

### AI customer product and data-center power demand

CEO referred to Intellivate software and AI for pumping operations, alongside Halliburton’s agreement with VoltaGrid to supply behind-the-meter generation serving Oracle AI data centers.

Speaker: Jeff Miller, Chairman, President and CEO. Locator: Transcript lines 612 and 621–623; VoltaGrid partnership discussion. Measurement: VoltaGrid agreement described a 2.3 GW power-generation deployment.. Limit: Separate customer-facing software from AI data-center power demand; no quantified AI software revenue or operational impact..

### Power infrastructure supporting AI compute

Halliburton said its VoltaGrid partnership would serve AI data-center demand through behind-the-meter power generation.

Speaker: Jeff Miller, Chairman, President and CEO. Locator: VoltaGrid partnership discussion in prepared remarks. Measurement: 2.3 GW announced deployment.. Limit: Energy infrastructure opportunity, not Halliburton’s own AI adoption..

Full transcript with Q&A reviewed. Halliburton services/products, adjacent automation and AI-related customer power demand are separated; company did not disclose quantified internal AI adoption in the reviewed periods.

## Halliburton · Q4 2025

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Halliburton Q4 2025 earnings call transcript](https://stockanalysis.com/stocks/hal/transcripts/399473-q4-2025/)

### Frac fleet and drilling automation

Management described ZEUS automation, customer uptake and iCruise/LOGIX-related automation offerings; Q&A included a reported rise in ZEUS automation deployments. No AI terminology was found in the full transcript search.

Speaker: Jeff Miller, Chairman, President and CEO. Locator: Transcript lines 143–147 and Q&A. Measurement: ZEUS adoption reportedly increased 18%; exact denominator/time basis not clarified in this reference.. Limit: Automation and commercial adoption; not AI..

Full transcript with Q&A reviewed. Halliburton services/products, adjacent automation and AI-related customer power demand are separated; company did not disclose quantified internal AI adoption in the reviewed periods.

## Halliburton · Q1 2026

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Halliburton Q1 2026 earnings call transcript](https://stockanalysis.com/stocks/hal/transcripts/544129-q1-2026/)

### Frac and drilling automation deployments

Management discussed customer adoption of Octiv Auto Frac, LOGIX closed-loop drilling and Sekal automation in field deployments. No AI/ML or generative-AI terminology was found in the full transcript search.

Speaker: Jeff Miller, Chairman, President and CEO. Locator: Q&A and prepared remarks; transcript keyword review. Measurement: Not disclosed in the reviewed passages.. Limit: Automation-only disclosure; not classified as AI..

Full transcript with Q&A reviewed. Halliburton services/products, adjacent automation and AI-related customer power demand are separated; company did not disclose quantified internal AI adoption in the reviewed periods.

## Halliburton · Q2 2026

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Halliburton Q2 2026 earnings call transcript](https://stockanalysis.com/stocks/hal/transcripts/652179-q2-2026/)

### Digital, AI and automation strategy

CEO described Halliburton’s digital approach as combining software, automation, open architecture, data and strategically important AI; discussion also covered automation platforms and closed-loop drilling deployments.

Speaker: Jeff Miller, Chairman, President and CEO. Locator: Transcript lines 317–319 and Q&A references to OCTIV/LOGIX+Sekal. Measurement: Not disclosed.. Limit: Strategic positioning and product automation; no quantified AI use or outcomes..

### Closed-loop drilling and stimulation automation

Halliburton described customer deployments of OCTIV pumping control and LOGIX+Sekal closed-loop drilling systems.

Speaker: Jeff Miller, Chairman, President and CEO. Locator: Q&A, transcript lines 317–319 and related technology discussion. Measurement: Not quantified in cited passage.. Limit: Automation products; AI linkage not quantified..

Full transcript with Q&A reviewed. Halliburton services/products, adjacent automation and AI-related customer power demand are separated; company did not disclose quantified internal AI adoption in the reviewed periods.

## Helmerich & Payne · FY2024 Q4

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Helmerich & Payne FY2024 Q4 Earnings Call Transcript](https://seekingalpha.com/article/4737516-helmerich-and-payne-inc-hp-q4-2024-earnings-call-transcript)

Official archive lists an earnings release transcript, but accessible archive link did not establish full Q&A. Full Seeking Alpha transcript reviewed; full transcript and Q&A screen found no substantive relevant disclosure.

## Helmerich & Payne · FY2025 Q1

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Helmerich & Payne FY2025 Q1 Earnings Call Transcript](https://seekingalpha.com/article/4755700-helmerich-and-payne-inc-hp-q1-2025-earnings-conference-call-transcript)

Official event archive confirms date and transcript listing. Seeking Alpha full Q&A transcript reviewed; no substantive relevant disclosure located.

## Helmerich & Payne · FY2025 Q2

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Helmerich & Payne FY2025 Q2 Earnings Call Transcript](https://stockanalysis.com/stocks/hp/transcripts/314101-q2-2025/)

Full transcript including Q&A screened for AI/ML, autonomous, predictive, analytics, automation, software, digital operations, remote operations and AI/data-center demand; no substantive disclosure located.

## Helmerich & Payne · FY2025 Q3

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Helmerich & Payne FY2025 Q3 Earnings Call Transcript](https://seekingalpha.com/article/4811669-helmerich-and-payne-inc-hp-q3-2025-earnings-call-transcript)

Official archive confirms date and transcript listing. Full Seeking Alpha call transcript reviewed; no substantive AI/ML or AI-demand statement located.

## Helmerich & Payne · FY2025 Q4

reviewed_relevant · Full call, including prepared remarks and Q&A, screened for AI/artificial intelligence, ML/machine learning, generative AI, agents/copilots, predictive analytics, autonomy, automation, digital transformation, software/analytics platforms, remote operations and data-center/power-demand language.

[Helmerich & Payne FY2025 Q4 Earnings Call Transcript](https://stockanalysis.com/stocks/hp/transcripts/364508-q4-2025/)

### AI data-center demand and drilling automation strategy

CEO John Lindsay said AI and data-center power needs were creating a new source of gas demand and supporting activity in gas-rich basins. In the same call management described a 20% year-over-year increase in use of advanced digital applications and R&D focused partly on drilling automation; those operational items were not explicitly labeled AI.

Speaker: John Lindsay, CEO; Trey Adams, President; Kevin Vann, CFO. Locator: Prepared remarks, transcript lines 121, 134, 168; Q&A lines 238–240. Measurement: 20% increase in use of advanced digital solutions/applications over the year, company-reported; no AI attribution. Limit: AI mention concerns external power demand, not H&P internal AI deployment. Digital-app adoption and rig-floor automation are adjacent, not asserted to be AI..

Separate AI-related external gas demand from the adjacent digital/rig-floor automation discussion.

## Helmerich & Payne · FY2026 Q1

reviewed_relevant · Full call, including prepared remarks and Q&A, screened for AI/artificial intelligence, ML/machine learning, generative AI, agents/copilots, predictive analytics, autonomy, automation, digital transformation, software/analytics platforms, remote operations and data-center/power-demand language.

[Helmerich & Payne FY2026 Q1 Earnings Call Transcript](https://stockanalysis.com/stocks/hp/transcripts/420872-q1-2026/)

### FlexRobotics deployment

CEO John Lindsay said FlexRobotics had been deployed on three pads for a Super Major customer in the Permian, delivering results in line with or better than several operational metrics. He described it as automating routine tasks so crews can focus on performance and safety.

Speaker: John Lindsay, President and CEO. Locator: Prepared remarks, transcript line 150. Measurement: Three pads; company-reported qualitative performance vs. operational metrics. Limit: The call did not identify this robotic system as AI; no specific metric or baseline was given. Related H&P FlexRobotics source AI-116 includes a distinct AI-enabled monitoring add-on..

### AI-driven power demand supports gas drilling

CEO John Lindsay connected rising power needs from AI advances in developed nations and surging AI-led power demand to stronger gas-market outlook and the need for drilling services.

Speaker: John Lindsay, President and CEO. Locator: Prepared remarks, transcript lines 133–137. Measurement: Not quantified. Limit: This is external energy-demand context, not H&P internal AI deployment..

Separates H&P’s rig-floor robotics from external AI-led electricity and gas demand; the transcript does not say that FlexRobotics itself uses AI.

## Helmerich & Payne · FY2026 Q2

reviewed_relevant · Full call, including prepared remarks and Q&A, screened for AI/artificial intelligence, ML/machine learning, generative AI, agents/copilots, predictive analytics, autonomy, automation, digital transformation, software/analytics platforms, remote operations and data-center/power-demand language.

[Helmerich & Payne FY2026 Q2 Earnings Call Transcript](https://stockanalysis.com/stocks/hp/transcripts/552643-q2-2026/)

### FlexRobotics adoption and AI-enabled monitoring

CEO John Lindsay said FlexRobotics was deployed on a rig at a fifth pad and that additional deployments were planned, with the first three expected during calendar 2026. The product is a rig-floor robotic automation system; the associated AI disclosure is its monitoring add-on.

Speaker: John Lindsay, President and CEO. Locator: Prepared remarks, transcript lines 127–128. Measurement: First three deployments expected during calendar 2026; on one rig at a fifth pad at call date (management description). Limit: Forward-looking deployment expectation; no AI-performance metric or measured operational outcome stated. Existing source AI-116 is the related product reference..

### AI-driven electricity demand and gas drilling

CEO said advances in AI were driving higher power demand in developed nations, which he connected to opportunity for gas drilling.

Speaker: John Lindsay, President and CEO. Locator: Prepared remarks, transcript line 133. Measurement: Not quantified. Limit: Industry demand context rather than H&P internal AI use..

Includes the full transcript with Q&A; the system deployment and external power-demand statements are separate.

## Helmerich & Payne · FY2026 Q3

reviewed_relevant · Full call, including prepared remarks and Q&A, screened for AI/artificial intelligence, ML/machine learning, generative AI, agents/copilots, predictive analytics, autonomy, automation, digital transformation, software/analytics platforms, remote operations and data-center/power-demand language.

[Helmerich & Payne FY2026 Q3 Earnings Call Transcript](https://stockanalysis.com/stocks/hp/transcripts/662503-q3-2026/)

### AI-driven electricity demand and gas drilling opportunity

CEO Trey Adams connected rising power needs from AI advances to long-term oil-and-gas demand and the need for drilling solutions.

Speaker: Trey Adams, President and CEO. Locator: Prepared remarks, transcript lines 130–131. Measurement: Not quantified. Limit: External power-demand and drilling-market context, not H&P internal AI use..

### AutoSlide and rig-floor automation adoption

Management said AutoSlide drilling automation enabled zero manual slides on a record-setting Vaca Muerta well; it was completed 13% faster than the customer’s prior record and 15% under budget. Management also described broad adoption plans for its automation and drilling-technology suite.

Speaker: Trey Adams, President and CEO; Mike Lennox, EVP Western Hemisphere. Locator: Prepared remarks, transcript lines 148–150; Q&A lines 344–346. Measurement: 13% faster than prior operator record and 15% under budget (company-reported single-well comparison); zero manual slides. Limit: This was a drilling automation disclosure, not characterized as AI. Results are one company-described well and not independently verified..

The call contains an explicit AI-power-demand reference plus a distinct AutoSlide automation example. The automation performance figures are company-reported and not AI metrics.

## Imperial Oil · Q3 2024

reviewed_relevant · Full issuer transcript reviewed across prepared remarks, analyst Q&A and operator close. Screened AI/artificial intelligence, machine learning, generative/agentic AI, digital transformation, analytics, automation, robotics, remote operations, software/workflow and data-center/hyperscaler terms.

[Imperial Oil Q3 2024 earnings call transcript](https://www.imperialoil.ca/-/media/imperial/files/investor/speeches-and-presentations/imperial-q3-2024-earnings-call-transcript.pdf)

### Digital technologies, autonomous haulage and robotics

CEO Brad Corson described a broad portfolio of digital technologies, predictive maintenance, autonomous haul trucks, drones and robotic inspection. He said the portfolio had substantial potential value and that value had already been captured; the call did not assign those totals to AI or to individual tools.

Speaker: Brad Corson, CEO. Locator: PDF pages 10–12; lines 263–286. Measurement: Management said potential value was well over $1bn and roughly $500m had been captured at that time; these are aggregate digital-portfolio figures.. Limit: The amounts are not AI-only and are not allocated among the listed initiatives..

Relevant adjacent digital/automation or AI-strategy disclosure recorded; broad transformation and productivity targets are not attributed to AI absent explicit attribution.

## Imperial Oil · Q4 2024

reviewed_relevant · Full issuer transcript reviewed across prepared remarks, analyst Q&A and operator close. Screened AI/artificial intelligence, machine learning, generative/agentic AI, digital transformation, analytics, automation, robotics, remote operations, software/workflow and data-center/hyperscaler terms.

[Imperial Oil Q4 2024 earnings call transcript](https://www.imperialoil.ca/-/media/imperial/files/investor/speeches-and-presentations/imperial-oil-4q24-earnings-call-transcript.pdf)

### Autonomous haulage and digital operations

Cheryl Gomez-Smith described autonomous haul trucks and a broader set of digital initiatives across the mine and plant. She attributed productivity improvement to the autonomous fleet.

Speaker: Cheryl Gomez-Smith, SVP, Resources. Locator: PDF pages 10–11, lines 265–277. Measurement: Management attributed an 8–10% productivity improvement to autonomous haul trucks.. Limit: No AI method or separate cost/reliability outcome was quantified in this passage..

Relevant adjacent digital/automation or AI-strategy disclosure recorded; broad transformation and productivity targets are not attributed to AI absent explicit attribution.

## Imperial Oil · Q1 2025

reviewed_relevant · Full issuer transcript reviewed across prepared remarks, analyst Q&A and operator close. Screened AI/artificial intelligence, machine learning, generative/agentic AI, digital transformation, analytics, automation, robotics, remote operations, software/workflow and data-center/hyperscaler terms.

[Imperial Oil Q1 2025 earnings call transcript](https://www.imperialoil.ca/-/media/imperial/files/investor/speeches-and-presentations/2025-1q25-earnings-call-transcript.pdf)

### Technology, data and analytics in turnaround planning

Cheryl Gomez-Smith said integrated technology, data and analytics supported optimization of Kearl turnaround decisions, as the company sought a longer interval between turnarounds.

Speaker: Cheryl Gomez-Smith, SVP, Resources. Locator: PDF pages 13–14, lines 335–346. Measurement: Management was targeting a four-year turnaround interval.. Limit: No portion of the target was attributed to analytics or AI, and no AI method was named..

Relevant adjacent digital/automation or AI-strategy disclosure recorded; broad transformation and productivity targets are not attributed to AI absent explicit attribution.

## Imperial Oil · Q2 2025

reviewed_relevant · Full issuer transcript reviewed across prepared remarks, analyst Q&A and operator close. Screened AI/artificial intelligence, machine learning, generative/agentic AI, digital transformation, analytics, automation, robotics, remote operations, software/workflow and data-center/hyperscaler terms.

[Imperial Oil Q2 2025 earnings call transcript](https://www.imperialoil.ca/-/media/imperial/files/investor/speeches-and-presentations/2025-2q-earnings-call-transcript.pdf)

### Autonomous haul fleet and future mine automation

John Whelan described an autonomous haul fleet at Kearl, with robotic fueling and inspection and automation of other fleet equipment as future opportunities.

Speaker: John Whelan, CEO. Locator: PDF pages 9–10, lines 246–267. Measurement: The fleet exceeded 80 autonomous haul trucks; management said it reduced unit cash cost by about $1/bbl.. Limit: The call did not describe the system as AI or quantify separate benefits for proposed future automation..

Relevant adjacent digital/automation or AI-strategy disclosure recorded; broad transformation and productivity targets are not attributed to AI absent explicit attribution.

## Imperial Oil · Q3 2025

reviewed_relevant · Full issuer transcript reviewed across prepared remarks, analyst Q&A and operator close. Screened AI/artificial intelligence, machine learning, generative/agentic AI, digital transformation, analytics, automation, robotics, remote operations, software/workflow and data-center/hyperscaler terms.

[Imperial Oil Q3 2025 earnings call transcript](https://www.imperialoil.ca/-/media/imperial/files/investor/speeches-and-presentations/imperial-oil-3q25-earnings-call.pdf)

### Digital solutions, robotics and remote operations

Cheryl Gomez-Smith described digital solutions, automation, robotics and remote activities as tools for productivity and absolute-cost improvement.

Speaker: Cheryl Gomez-Smith, SVP, Resources. Locator: PDF pages 18–19, lines 498–500. Measurement: Not quantified. Limit: No AI technique or initiative-specific metric was stated; restructuring savings elsewhere in the call were not attributed to digital or automation..

Relevant adjacent digital/automation or AI-strategy disclosure recorded; broad transformation and productivity targets are not attributed to AI absent explicit attribution.

## Imperial Oil · Q4 2025

reviewed_relevant · Full issuer transcript reviewed across prepared remarks, analyst Q&A and operator close. Screened AI/artificial intelligence, machine learning, generative/agentic AI, digital transformation, analytics, automation, robotics, remote operations, software/workflow and data-center/hyperscaler terms.

[Imperial Oil Q4 2025 earnings call transcript](https://www.imperialoil.ca/-/media/imperial/files/investor/speeches-and-presentations/imperial-oil-4q25-earnings-call-transcript.pdf)

### Data-enabled enterprise transformation

John Whelan described transformation enabled by data availability and processing, with technology acceleration and expanded global capability centers. The company expected to reduce above-field staffing as part of the program.

Speaker: John Whelan, CEO. Locator: PDF pages 22–23, lines 639–679. Measurement: Management targeted 20% lower above-field staffing over two years and $150m annual efficiency capture starting in 2028.. Limit: These are transformation-program targets, not AI-specific savings; no relevant AI/ML/automation deployment was identified in the full-call screen..

Relevant adjacent digital/automation or AI-strategy disclosure recorded; broad transformation and productivity targets are not attributed to AI absent explicit attribution.

## Imperial Oil · Q1 2026

reviewed_relevant · Full issuer transcript reviewed across prepared remarks, analyst Q&A and operator close. Screened AI/artificial intelligence, machine learning, generative/agentic AI, digital transformation, analytics, automation, robotics, remote operations, software/workflow and data-center/hyperscaler terms.

[Imperial Oil Q1 2026 earnings call transcript](https://www.imperialoil.ca/-/media/imperial/files/investor/speeches-and-presentations/q1-2026-transcript.pdf)

### Prospective AI/ML workflow automation and digital mining

John Whelan said existing data lakes, data organization, digital twins and automation had laid a foundation for further AI/ML use through capability centers. He described AI/ML workflow automation as a future opportunity; Cheryl Gomez-Smith separately discussed mine automation and fleet optimization.

Speaker: John Whelan, CEO; Cheryl Gomez-Smith, SVP, Resources. Locator: PDF pages 19–20, lines 473–484 and 575–577. Measurement: Management described exploration of remote, semi-automated and automated mining and fleet-optimization opportunities; no amount or schedule was quantified.. Limit: AI/ML application was prospective, not a disclosed deployed result. No AI-specific savings were given; the call’s transformation targets were broader..

### Digital infrastructure and mine automation

Management discussed digital infrastructure for logistics and feedstock flexibility, and exploration of remote or automated mining and fleet optimization.

Speaker: John Whelan, CEO; Cheryl Gomez-Smith, SVP, Resources. Locator: PDF lines 249–252 and 575–577. Measurement: Not quantified. Limit: These are digital/automation initiatives; no AI-specific outcome or metric was stated..

Relevant adjacent digital/automation or AI-strategy disclosure recorded; broad transformation and productivity targets are not attributed to AI absent explicit attribution.

## Imperial Oil · Q2 2026

reviewed_no_relevant · Full transcript reviewed through prepared remarks, analyst Q&A and close. Screened AI/artificial intelligence, machine learning, automation/digital transformation, data-center/hyperscaler terms.

[Imperial Oil Q2 2026 earnings-call full transcript](https://stockanalysis.com/quote/tsx/IMO/transcripts/659225-q2-2026/)

Selected-term full-call screen found no relevant AI, digital-transformation/automation, or data-center/AI-energy-demand passage.

## Kinder Morgan · Q3 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Kinder Morgan Q3 2024 earnings call transcript](https://stockanalysis.com/stocks/kmi/transcripts/214023-q3-2024/)

Actual call date verified on transcript page and lies within disclosure window; fiscal period label is calendar quarter.

## Kinder Morgan · Q4 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Kinder Morgan Q4 2024 earnings call transcript](https://stockanalysis.com/stocks/kmi/transcripts/242717-q4-2024/)

Actual call date verified on transcript page and lies within disclosure window; fiscal period label is calendar quarter.

## Kinder Morgan · Q1 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Kinder Morgan Q1 2025 earnings call transcript](https://stockanalysis.com/stocks/kmi/transcripts/308827-q1-2025/)

Actual call date verified on transcript page and lies within disclosure window; fiscal period label is calendar quarter.

## Kinder Morgan · Q2 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Kinder Morgan Q2 2025 earnings call transcript](https://stockanalysis.com/stocks/kmi/transcripts/336613-q2-2025/)

Actual call date verified on transcript page and lies within disclosure window; fiscal period label is calendar quarter.

## Kinder Morgan · Q3 2025

reviewed_relevant · Full transcript reviewed including Q&A. AI disclosure is entirely external demand/infrastructure context; the transcript does not describe KMI internal AI deployment.

[Kinder Morgan Q3 2025 earnings call transcript](https://stockanalysis.com/stocks/kmi/transcripts/363951-q3-2025/)

### AI data-center power as natural-gas demand driver

Executive Chairman Rich Kinder said rising electricity demand, primarily to serve AI data centers, was a key natural-gas demand driver. He argued that renewable intermittency, battery cost/duration and nuclear build times increase the prospect that natural gas serves this load. This is an infrastructure demand thesis, not a claim of KMI internal AI use.

Speaker: Rich Kinder (Executive Chairman). Locator: Prepared remarks, lines 112–123. Measurement: Not quantified. Limit: Strategic market thesis; not an AI-specific customer contract, realized volume or revenue measure..

Actual call date verified on transcript page and lies within disclosure window; fiscal period label is calendar quarter.

## Kinder Morgan · Q4 2025

reviewed_relevant · Full transcript reviewed including Q&A. AI is discussed as a power-demand/customer-category context; no internal AI deployment is described.

[Kinder Morgan Q4 2025 earnings call transcript](https://stockanalysis.com/stocks/kmi/transcripts/399472-q4-2025/)

### Data-center and AI developer contracting risk

In discussing power-generation and LNG feed-gas opportunities, CEO Kimberly Dang said utility-grade take-or-pay contracts reduce risk compared with contracting directly with AI developers. Management also described data-center-driven pipeline opportunities; about 60% of the $10 billion project backlog was associated with power, not exclusively data centers.

Speaker: Kimberly Dang (CEO). Locator: Q&A, lines 373–381 and 413–421. Measurement: About 60% of the approximately $10 billion backlog was associated with power projects; not an AI- or data-center-only figure.. Limit: Demand/customer category context, not internal AI, an AI-specific project figure, or a disclosure of direct AI developer contracts..

Actual call date verified on transcript page and lies within disclosure window; fiscal period label is calendar quarter.

## Kinder Morgan · Q1 2026

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Kinder Morgan Q1 2026 earnings call transcript](https://stockanalysis.com/stocks/kmi/transcripts/544252-q1-2026/)

Actual call date verified on transcript page and lies within disclosure window; fiscal period label is calendar quarter.

## Kinder Morgan · Q2 2026

reviewed_relevant · Full transcript reviewed including Q&A. The only explicit AI reference is an analyst question about AI infrastructure; management discusses general Southeast power/gas growth, without describing KMI internal AI use.

[Kinder Morgan Q2 2026 earnings call transcript](https://stockanalysis.com/stocks/kmi/transcripts/653179-q2-2026/)

### AI data-center load as pipeline demand opportunity

Analyst Theresa Chen cited Southern Company’s OpenAI data-center agreement and AI-infrastructure gas demand, asking about resulting opportunities on SNG and the Kinder North Erin JV. CEO Kim Dang and President Sital Mody answered that Southeast expansion need is clear and that KMI is evaluating competitive projects across SNG and related assets.

Speaker: Theresa Chen (Barclays analyst); Kim Allen Dang (CEO); Sital Mody (President of Natural Gas and Pipelines). Locator: Q&A, lines 364–375. Measurement: CEO cited one Georgia utility report with over 75 GW of potential power demand through the mid-2030s; this is not KMI backlog or AI-only demand.. Limit: AI-linked power demand is analyst-framed; management response discusses general regional power/gas projects. No internal AI use or AI-attributable KMI revenue disclosed..

Actual call date verified on transcript page and lies within disclosure window; fiscal period label is calendar quarter.

## Liberty Energy · Q3 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Liberty Energy Q3 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/lbrt/transcripts/214288-q3-2024/)

Complete transcript and Q&A screened for AI/ML, generative/agentic AI, automation, software/data platforms, analytics, remote operations and power demand; no relevant disclosure located.

## Liberty Energy · Q4 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Liberty Energy Q4 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/lbrt/transcripts/237843-q4-2024/)

Complete transcript and Q&A screened for AI/ML, generative/agentic AI, automation, software/data platforms, analytics, remote operations and power demand; no relevant disclosure located.

## Liberty Energy · Q1 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Liberty Energy Q1 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/lbrt/transcripts/310954-q1-2025/)

Complete transcript and Q&A screened for AI/ML, generative/agentic AI, automation, software/data platforms, analytics, remote operations and power demand; no relevant disclosure located.

## Liberty Energy · Q2 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Liberty Energy Q2 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/lbrt/transcripts/336931-q2-2025/)

Complete transcript and Q&A screened for AI/ML, generative/agentic AI, automation, software/data platforms, analytics, remote operations and power demand; no relevant disclosure located.

## Liberty Energy · Q3 2025

reviewed_relevant · Full call, including prepared remarks and Q&A, screened for AI/artificial intelligence, ML/machine learning, generative AI, agents/copilots, predictive analytics, autonomy, automation, digital transformation, software/analytics platforms, remote operations and data-center/power-demand language.

[Liberty Energy Q3 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/lbrt/transcripts/367341-q3-2025/)

### Forge / StimCommander AI-driven frac control

CEO Ron Gusek described AI-driven automated rate/pressure-control software, StimCommander, coupled with the Forge cloud platform for continuous optimization, learning and feedback. He cited a 65% improvement in time to desired injection rate and 5–10% hydraulic-efficiency improvement. He corrected a press-release characterization: Forge was not a large-language model, but a distributed agentic-intelligence system designed for field use.

Speaker: Ron Gusek, President and CEO. Locator: Prepared remarks, transcript lines 125–132. Measurement: 65% faster time to desired injection rate; 5–10% hydraulic-efficiency improvement (management-reported). Limit: Company-reported claims; no baseline, sample size, customer/site count or independent validation stated. CEO explicitly rejected the LLM label for Forge. Existing source AI-118 covers Forge/StimCommander..

### AI-compute demand as a power-market opportunity

CEO identified AI-compute demand as a long-term power opportunity and described the company’s power pipeline, including a target of over 1 GW by 2027.

Speaker: Ron Gusek, President and CEO. Locator: Prepared remarks, transcript lines 132–135. Measurement: More than 1 GW targeted by 2027 (company target). Limit: Power business pipeline/target is not a measure of AI adoption in Liberty’s own operations..

The call separately disclosed field frac-control AI and external AI-related power demand; keep those scopes distinct.

## Liberty Energy · Q4 2025

reviewed_relevant · Full call, including prepared remarks and Q&A, screened for AI/artificial intelligence, ML/machine learning, generative AI, agents/copilots, predictive analytics, autonomy, automation, digital transformation, software/analytics platforms, remote operations and data-center/power-demand language.

[Liberty Energy Q4 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/lbrt/transcripts/396033-q4-2025/)

### AI-supported asset optimization and maintenance

CEO Ron Gusek attributed an approximately 14% reduction in total maintenance costs per unit of work to Liberty-developed AI-driven asset-optimization software alongside its digi Technologies transition. He also referred to AI-enhanced O&M systems for the distributed-power business.

Speaker: Ron Gusek, President and CEO. Locator: Prepared remarks, transcript lines 117–125. Measurement: Approximately 14% lower maintenance cost per unit of work (management-reported). Limit: The result was co-attributed to broader digi Technologies transition; no AI-isolated causal effect, site count or validation was provided..

### Data-center electricity demand for Liberty power deployments

CEO discussed AI/data-center demand as a market for the company’s distributed-power assets; he said most of the relevant capacity was expected to be deployed into data centers.

Speaker: Ron Gusek, President and CEO. Locator: Q&A, transcript line 543. Measurement: Not quantified. Limit: Market/deployment context, not an internal AI deployment metric..

Differentiated company-developed optimization/O&M claims from the external data-center market; the 14% result was not isolated to AI alone.

## Liberty Energy · Q1 2026

reviewed_relevant · Full call, including prepared remarks and Q&A, screened for AI/artificial intelligence, ML/machine learning, generative AI, agents/copilots, predictive analytics, autonomy, automation, digital transformation, software/analytics platforms, remote operations and data-center/power-demand language.

[Liberty Energy Q1 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/lbrt/transcripts/548531-q1-2026/)

### AI-enabled digital overlay for distributed power

CEO Ron Gusek connected AI-enabled productivity and hyperscaler demand with growth in on-site power, and described Liberty’s power offering as including an AI-driven technology overlay. The call emphasized testing and validating microgrid software, hardware and controller integration.

Speaker: Ron Gusek, President and CEO. Locator: Prepared remarks, transcript lines 125–136; data-center discussion in Q&A around line 483. Measurement: Not quantified. Limit: High-level strategy description; the call does not specify the AI model, workflow, deployments or measured operating benefit..

AI-driven overlay is a broad product/strategy reference and should not be conflated with a detailed named workflow.

## Liberty Energy · Q2 2026

reviewed_relevant · Full call, including prepared remarks and Q&A, screened for AI/artificial intelligence, ML/machine learning, generative AI, agents/copilots, predictive analytics, autonomy, automation, digital transformation, software/analytics platforms, remote operations and data-center/power-demand language.

[Liberty Energy Q2 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/lbrt/transcripts/653540-q2-2026/)

### Forge agentic software for power/fuel optimization

CEO Ron Gusek described Forge as a distributed agentic system used to optimize fuel, with secondary insights into fleet design, utilization and performance.

Speaker: Ron Gusek, President and CEO. Locator: Prepared remarks, transcript lines 127–128. Measurement: Not quantified. Limit: Management described product capabilities without measured fuel savings, customer counts or independently verified outcomes. Related to existing Forge/StimCommander source AI-118, although this call described power optimization..

### AI and hyperscaler/data-center power infrastructure

Liberty described its PowerBridge joint venture and a 2-GW Alpha Digital Campus for AI/hyperscaler/large-load customers, with a first phase above 300 MW and first power expected in Q4 2027. It also described an SLB alliance focused on data-center infrastructure to meet AI/HPC power demand.

Speaker: Ron Gusek, President and CEO. Locator: Prepared remarks, transcript lines 128–135. Measurement: 2 GW total campus; first phase >300 MW; first power expected Q4 2027 (company plans). Limit: Forward-looking project/market plans; no operational capacity or revenue yet established in this call..

Separates Liberty’s own Forge software from customer-market power infrastructure.

## Marathon Petroleum · Q3 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Marathon Petroleum Q3 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/mpc/transcripts/219090-q3-2024/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Marathon Petroleum · Q4 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Marathon Petroleum Q4 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/mpc/transcripts/246165-q4-2024/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Marathon Petroleum · Q1 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Marathon Petroleum Q1 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/mpc/transcripts/313246-q1-2025/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Marathon Petroleum · Q2 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Marathon Petroleum Q2 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/mpc/transcripts/342729-q2-2025/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Marathon Petroleum · Q3 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Marathon Petroleum Q3 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/mpc/transcripts/370665-q3-2025/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Marathon Petroleum · Q4 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Marathon Petroleum Q4 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/mpc/transcripts/409949-q4-2025/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Marathon Petroleum · Q1 2026

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Marathon Petroleum Q1 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/mpc/transcripts/551053-q1-2026/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Marathon Petroleum · Q2 2026

reviewed_relevant · Full transcript page and Q&A reviewed; keyword-screened for AI/ML, digital twins, automation, data center, predictive maintenance and related terms.

[Marathon Petroleum Q2 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/mpc/transcripts/660792-q2-2026/)

### Refining/logistics optimization models and generic capital prioritization for AI

Operations executives described digital twins for maintenance planning/capital reviews; wireless sensing feeding predictive reliability; and near-continuous network-wide refining/logistics models. CEO later said capital is being prioritized for AI and digital capabilities that deliver incremental EBITDA per barrel. The transcript does not explicitly link the named digital-twin, sensor or optimization workflows to AI.

Speaker: Mike (maintenance/reliability; surname not supplied in transcript excerpt), Julian Stoll, Maryann Mannen. Locator: lines 560-576. Measurement: Not quantified. Limit: AI reference is generic; named workflows are adjacent digital/predictive automation, not explicitly AI-labeled. No quantified AI-specific returns or impact..

Full Q&A review; named digital-twin/sensing/network-model workflows are not explicitly AI-labelled, while a separate management answer says AI/digital investment will be prioritized. Do not infer named workflows are AI.

## Matador Resources · Q3 2024

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, generative AI, machine learning, analytics, automation, digital, predictive, autonomous, agentic, copilot, data-center and power-demand terms.

[Matador Resources Q3 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/mtdr/transcripts/215396-q3-2024/)

### AI as a contextual gas-demand driver

CFO Brian Willey cited AI alongside LNG terminals as one source of industry interest in gas, then described Matador’s unbooked Cotton Valley gas opportunities and Delaware gas reserves as potential supply if prices support development.

Speaker: Brian J. Willey, EVP and CFO. Locator: Q&A, lines 397–400.. Measurement: Management cited more than 200–300 Bcf of Cotton Valley gas opportunities and over 1.4 Tcf of Delaware gas reserves; neither quantity was attributed to AI demand.. Limit: No AI-specific demand volume, customer, contract or development commitment disclosed..

In Q&A the CFO mentioned AI among broad gas-demand drivers; he discussed Matador gas inventory as optional future supply, without AI-linked sales.

## Matador Resources · Q4 2024

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, generative AI, machine learning, analytics, automation, digital, predictive, autonomous, agentic, copilot, data-center and power-demand terms.

[Matador Resources Q4 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/mtdr/transcripts/241783-q4-2024/)

Full transcript reviewed; no qualifying AI/ML, automation/digital or AI-linked energy-demand disclosure found.

## Matador Resources · Q1 2025

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, generative AI, machine learning, analytics, automation, digital, predictive, autonomous, agentic, copilot, data-center and power-demand terms.

[Matador Resources Q1 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/mtdr/transcripts/317228-q1-2025/)

Full transcript reviewed; no qualifying AI/ML, automation/digital or AI-linked energy-demand disclosure found.

## Matador Resources · Q2 2025

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, generative AI, machine learning, analytics, automation, digital, predictive, autonomous, agentic, copilot, data-center and power-demand terms.

[Matador Resources Q2 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/mtdr/transcripts/338251-q2-2025/)

Full transcript reviewed; no qualifying AI/ML, automation/digital or AI-linked energy-demand disclosure found.

## Matador Resources · Q3 2025

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, generative AI, machine learning, analytics, automation, digital, predictive, autonomous, agentic, copilot, data-center and power-demand terms.

[Matador Resources Q3 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/mtdr/transcripts/366226-q3-2025/)

Full transcript reviewed; no qualifying AI/ML, automation/digital or AI-linked energy-demand disclosure found.

## Matador Resources · Q4 2025

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, generative AI, machine learning, analytics, automation, digital, predictive, autonomous, agentic, copilot, data-center and power-demand terms.

[Matador Resources Q4 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/mtdr/transcripts/393923-q4-2025/)

Full transcript reviewed; no qualifying AI/ML, automation/digital or AI-linked energy-demand disclosure found.

## Matador Resources · Q1 2026

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, generative AI, machine learning, analytics, automation, digital, predictive, autonomous, agentic, copilot, data-center and power-demand terms.

[Matador Resources Q1 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/mtdr/transcripts/568605-q1-2026/)

### AI-driven operational analytics and controlled rollout

CEO Joe Foran said a cross-department committee was evaluating practical AI applications through a measured rollout. COO Glenn Stetson described AI-driven analytics across production, completions and drilling: real-time production telemetry monitoring, frac-process data, and AI-assisted lateral targeting through the MaxCom system.

Speaker: Joe Foran, CEO; Glenn Stetson, EVP and COO. Locator: Q&A, lines 280–312, especially 285–300.. Measurement: COO said the control room receives over 40 million data points daily; Matador set more than 36 drilling records in Q1, with AI assisting target placement.. Limit: The transcript did not isolate financial or production gains attributable to AI; telemetry volume and drilling records are not AI-specific savings measures..

Management described a cross-functional, measured AI program with active operations uses and drilling/completions analytics.

## Matador Resources · Q2 2026

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, generative AI, machine learning, analytics, automation, digital, predictive, autonomous, agentic, copilot, data-center and power-demand terms.

[Matador Resources Q2 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/mtdr/transcripts/662435-q2-2026/)

Full transcript reviewed; no qualifying AI/ML, automation/digital or AI-linked energy-demand disclosure found.

## Murphy Oil · Q3 2024

reviewed_no_relevant · Full available transcript keyword-screened; prepared remarks and Q&A included.

[Q3 2024 Murphy Oil Corp Earnings Call](https://ir.murphyoilcorp.com/static-files/8bd9c2d8-d94e-4b1e-9df2-14cb9bb29410)

Screened AI/artificial intelligence, machine learning, generative AI, analytics, automation, digital transformation, predictive, autonomous, agentic/copilot, data-center and power-demand terms. No qualifying disclosure found.

## Murphy Oil · Q4 2024

reviewed_no_relevant · Full available transcript keyword-screened; prepared remarks and Q&A included.

[Q4 2024 Murphy Oil Corp Earnings Call](https://ir.murphyoilcorp.com/static-files/2567ee2c-5389-461c-a8b4-ffb49de0aed1)

Screened AI/artificial intelligence, machine learning, generative AI, analytics, automation, digital transformation, predictive, autonomous, agentic/copilot, data-center and power-demand terms. No qualifying disclosure found.

## Murphy Oil · Q1 2025

reviewed_no_relevant · Full available transcript keyword-screened; prepared remarks and Q&A included.

[Q1 2025 Murphy Oil Corp Earnings Call](https://ir.murphyoilcorp.com/static-files/12312ac7-2724-4920-8c7d-a7bdd4509b7f)

Screened AI/artificial intelligence, machine learning, generative AI, analytics, automation, digital transformation, predictive, autonomous, agentic/copilot, data-center and power-demand terms. No qualifying disclosure found.

## Murphy Oil · Q2 2025

reviewed_no_relevant · Full available transcript keyword-screened; prepared remarks and Q&A included.

[Q2 2025 Murphy Oil Corp Earnings Call](https://ir.murphyoilcorp.com/static-files/713b541d-480b-4452-8723-abd6ae95c186)

Screened AI/artificial intelligence, machine learning, generative AI, analytics, automation, digital transformation, predictive, autonomous, agentic/copilot, data-center and power-demand terms. No qualifying disclosure found.

## Murphy Oil · Q3 2025

reviewed_no_relevant · Full available transcript keyword-screened; prepared remarks and Q&A included.

[Q3 2025 Murphy Oil Corp Earnings Call](https://ir.murphyoilcorp.com/static-files/d16ca386-1005-461d-9628-09e61ba1c5ee)

Screened AI/artificial intelligence, machine learning, generative AI, analytics, automation, digital transformation, predictive, autonomous, agentic/copilot, data-center and power-demand terms. No qualifying disclosure found.

## Murphy Oil · Q4 2025

reviewed_no_relevant · Full available transcript keyword-screened; prepared remarks and Q&A included.

[Murphy Oil (MUR) Q4 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/mur/transcripts/405212-q4-2025/)

Screened AI/artificial intelligence, machine learning, generative AI, analytics, automation, digital transformation, predictive, autonomous, agentic/copilot, data-center and power-demand terms. No qualifying disclosure found.

## Murphy Oil · Q1 2026

reviewed_no_relevant · Full available transcript keyword-screened; prepared remarks and Q&A included.

[Q1 2026 Murphy Oil Corp Earnings Call](https://ir.murphyoilcorp.com/static-files/61a4d721-9c8e-4735-82cf-d07f17d13d9e)

Screened AI/artificial intelligence, machine learning, generative AI, analytics, automation, digital transformation, predictive, autonomous, agentic/copilot, data-center and power-demand terms. No qualifying disclosure found.

## Murphy Oil · Q2 2026

reviewed_no_relevant · Full available transcript keyword-screened; prepared remarks and Q&A included.

[Murphy Oil (MUR) Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/mur/transcripts/662184-q2-2026/)

Screened AI/artificial intelligence, machine learning, generative AI, analytics, automation, digital transformation, predictive, autonomous, agentic/copilot, data-center and power-demand terms. No qualifying disclosure found.

## Nabors · Q3 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Nabors Q3 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/nbr/transcripts/215404-q3-2024/)

Complete transcript and Q&A keyword-screened; no substantive AI, machine-learning, agent, analytics-platform, automation or AI-power-demand disclosure located.

## Nabors · Q4 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Nabors Q4 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/nbr/transcripts/244086-q4-2024/)

Complete transcript and Q&A keyword-screened; no substantive AI, machine-learning, agent, analytics-platform, automation or AI-power-demand disclosure located.

## Nabors · Q1 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Nabors Q1 2025 Earnings Call Transcript](https://seekingalpha.com/article/4779999-nabors-industries-ltd-nbr-q1-2025-earnings-call-transcript)

StockAnalysis transcript link failed during retrieval; full Seeking Alpha Q&A transcript reviewed. No substantive AI, machine-learning, agent, analytics-platform, automation or AI-power-demand disclosure located.

## Nabors · Q2 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Nabors Q2 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/nbr/transcripts/340428-q2-2025/)

Complete transcript and Q&A keyword-screened; no substantive AI, machine-learning, agent, analytics-platform, automation or AI-power-demand disclosure located.

## Nabors · Q3 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Nabors Q3 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/nbr/transcripts/368823-q3-2025/)

Complete transcript and Q&A keyword-screened; no substantive AI, machine-learning, agent, analytics-platform, automation or AI-power-demand disclosure located.

## Nabors · Q4 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Nabors Q4 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/nbr/transcripts/393555-q4-2025/)

Complete transcript and Q&A keyword-screened; no substantive AI, machine-learning, agent, analytics-platform, automation or AI-power-demand disclosure located.

## Nabors · Q1 2026

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Nabors Q1 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/nbr/transcripts/556004-q1-2026/)

Complete transcript and Q&A keyword-screened; no substantive AI, machine-learning, agent, analytics-platform, automation or AI-power-demand disclosure located.

## Nabors · Q2 2026

reviewed_relevant · Full call, including prepared remarks and Q&A, screened for AI/artificial intelligence, ML/machine learning, generative AI, agents/copilots, predictive analytics, autonomy, automation, digital transformation, software/analytics platforms, remote operations and data-center/power-demand language.

[Nabors Q2 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/nbr/transcripts/657019-q2-2026/)

### Automated rig-floor wrench system

CEO Tony Petrello said Canrig introduced Titan, described as its first advanced, fully automated rig-floor wrench, and that it was in commercial service with strong early field results. The call did not characterize Titan as AI or machine learning.

Speaker: Tony Petrello, Chairman, President and CEO. Locator: Prepared remarks, transcript line 128. Measurement: Not quantified. Limit: No unit count, customer deployment scale, comparative performance metric or AI claim was given..

AI/ML terms were not present as a substantive management disclosure. Titan automation in prepared remarks is captured separately as adjacent automation.

## Northern Oil and Gas (NOG) · Q3 2024

reviewed_no_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Northern Oil and Gas (NOG) Q3 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/nog/transcripts/217399-q3-2024/)

No qualifying disclosure found after full-call screening.

## Northern Oil and Gas (NOG) · Q4 2024

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[NOG Q4 2024 Earnings Call Transcript](https://www.roic.ai/quote/NOG/transcripts/2024-year/4-quarter)

### AI tools and data/demand context

NOG said it was investing in financial, land, data-science and engineering teams; separately, management attributed screening 500 ground-game opportunities to proprietary data and technology. No AI or ML method was identified.

Speaker: Nick O’Grady, CEO; Adam Dirlam, President. Locator: Prepared remarks lines 254–255; lines 283–288.. Measurement: No AI-specific financial, production or contracted-volume outcome quantified.. Limit: Data-science and proprietary-technology references are not treated as AI; AI demand is not treated as a company deal..

The disclosure is classified only to the specificity stated; no financial value or AI-specific production outcome inferred.

## Northern Oil and Gas (NOG) · Q1 2025

reviewed_no_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Northern Oil and Gas (NOG) Q1 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/nog/transcripts/317682-q1-2025/)

No qualifying disclosure found after full-call screening.

## Northern Oil and Gas (NOG) · Q2 2025

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Northern Oil and Gas (NOG) Q2 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/nog/transcripts/340315-q2-2025/)

### AI tools and data/demand context

CEO said AI/data-center demand could draw on low-cost stranded gas, but that it had not yet materially affected markets; discussion was about industry demand, not NOG-specific commitments.

Speaker: Nick O’Grady, CEO. Locator: Q&A lines 485–487.. Measurement: No AI-specific financial, production or contracted-volume outcome quantified.. Limit: Data-science and proprietary-technology references are not treated as AI; AI demand is not treated as a company deal..

The disclosure is classified only to the specificity stated; no financial value or AI-specific production outcome inferred.

## Northern Oil and Gas (NOG) · Q3 2025

reviewed_no_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Northern Oil and Gas (NOG) Q3 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/nog/transcripts/369683-q3-2025/)

No qualifying disclosure found after full-call screening.

## Northern Oil and Gas (NOG) · Q4 2025

reviewed_no_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Northern Oil and Gas (NOG) Q4 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/nog/transcripts/401908-q4-2025/)

No qualifying disclosure found after full-call screening.

## Northern Oil and Gas (NOG) · Q1 2026

reviewed_no_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Northern Oil and Gas (NOG) Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/nog/transcripts/571789-q1-2026/)

No qualifying disclosure found after full-call screening.

## Northern Oil and Gas (NOG) · Q2 2026

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Northern Oil and Gas (NOG) Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/nog/transcripts/656898-q2-2026/)

### AI tools and data/demand context

CEO said NOG built custom AI-powered management and evaluation tools and uses advanced tracking systems to analyze acquisitions pursued, passed on and won. No operating or financial outcome was attributed.

Speaker: Nick O’Grady, CEO. Locator: Prepared remarks lines 148–152.. Measurement: No AI-specific financial, production or contracted-volume outcome quantified.. Limit: Data-science and proprietary-technology references are not treated as AI; AI demand is not treated as a company deal..

The disclosure is classified only to the specificity stated; no financial value or AI-specific production outcome inferred.

## NOV · Q3 2024

reviewed_relevant · Full transcript reviewed; searched AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital transformation; Q&A present.

[NOV Inc. Q3 2024 earnings call full transcript](https://stockanalysis.com/stocks/nov/transcripts/216390-q3-2024/)

### Internal AI for manufacturing and supply chain

CEO said NOV applied AI to machine-tool monitoring for manufacturing scheduling/efficiency and to supply-chain management and forecasting.

Speaker: Clay Williams, CEO. Locator: Transcript lines 573–574. Measurement: No quantified savings or measured operating impact disclosed.. Limit: Management description without outcome metric; internal use only..

### Customer digital/AI products and drilling automation

CEO described NOV’s MaxEdge data portal, sensor-connected equipment and Kaizen/Drilling Performance applications as a digital foundation for AI and greater automation, including ATOM RTX.

Speaker: Clay Williams, CEO. Locator: Transcript lines 574–579. Measurement: MaxEdge users rose about 25% sequentially to over 5,000; connected assets rose about 9% sequentially.. Limit: User/asset metrics measure portal adoption, not AI-model usage, revenue or customer outcomes..

Quartr transcript hosted by StockAnalysis. Full call text and Q&A checked; findings distinguish NOV internal use from customer-facing offerings and adjacent automation.

## NOV · Q4 2024

reviewed_relevant · Full transcript reviewed; searched AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital transformation; Q&A present.

[NOV Inc. Q4 2024 earnings call full transcript](https://stockanalysis.com/stocks/nov/transcripts/249734-q4-2024/)

### Drilling equipment automation orders

In discussing offshore rig upgrades, management cited orders for pipe handling and automation packages.

Speaker: Jose Bayardo, President and COO. Locator: Transcript line 319. Measurement: Not disclosed.. Limit: Automation-only reference; no AI linkage stated..

Quartr transcript hosted by StockAnalysis. Full call text and Q&A checked; findings distinguish NOV internal use from customer-facing offerings and adjacent automation.

## NOV · Q1 2025

reviewed_relevant · Full transcript reviewed; searched AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital transformation; Q&A present.

[NOV Inc. Q1 2025 earnings call full transcript](https://stockanalysis.com/stocks/nov/transcripts/313160-q1-2025/)

### Internal manufacturing AI and customer drilling algorithms

Management said NOV applies automation and artificial intelligence in manufacturing operations. CEO also described control systems, machine learning and AI algorithms used with drilling systems to improve efficiency and safety.

Speaker: Rodney Reed, CFO for manufacturing disclosure; Clay Williams, CEO for product/operations discussion. Locator: Transcript lines 176 and 185–188. Measurement: 22 automation upgrades in process; management did not attribute this count specifically to AI.. Limit: Internal use was not quantified; customer-facing drilling claims are product/technology claims, not verified operator outcomes..

Quartr transcript hosted by StockAnalysis. Full call text and Q&A checked; findings distinguish NOV internal use from customer-facing offerings and adjacent automation.

## NOV · Q2 2025

reviewed_relevant · Full transcript reviewed; searched AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital transformation; Q&A present.

[NOV Inc. Q2 2025 earnings call full transcript](https://stockanalysis.com/stocks/nov/transcripts/339472-q2-2025/)

### Rig automation and robotics

Management cited commissioned automation packages, hands-free robotic tripping, and a pipeline of additional installations; NOVOS and Kaizen Drilling Optimizer were named in a surface-automation order.

Speaker: Jose Bayardo, CEO; Jose Bayardo, President and COO in Q&A. Locator: Transcript lines 183–186 and 428–433. Measurement: Robotics active on four rigs with 11 additional systems in the pipeline.. Limit: No explicit AI link in the call..

### Predictive analytics for electric service rigs

CEO said electrification could enable real-time monitoring and condition-based monitoring/predictive analytics to improve maintenance and extend asset life.

Speaker: Jose Bayardo, CEO. Locator: Transcript line 466. Measurement: Not disclosed.. Limit: Predictive analytics opportunity, not explicitly described as AI or deployed at scale..

Quartr transcript hosted by StockAnalysis. Full call text and Q&A checked; findings distinguish NOV internal use from customer-facing offerings and adjacent automation.

## NOV · Q3 2025

reviewed_relevant · Full transcript reviewed; searched AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital transformation; Q&A present.

[NOV Inc. Q3 2025 earnings call full transcript](https://stockanalysis.com/stocks/nov/transcripts/368236-q3-2025/)

### Drilling automation and robotics

NOV described its NOVOS system as a platform for repetitive drilling automation and multi-machine/rig-floor control, with ATOM RTX robotic systems commercialized on a customer rig.

Speaker: Jose Bayardo, CEO. Locator: Transcript line 210. Measurement: One commercialized rig cited; additional adoption not detailed here.. Limit: Automation and robotics reference; AI was not found in the transcript search..

Quartr transcript hosted by StockAnalysis. Full call text and Q&A checked; findings distinguish NOV internal use from customer-facing offerings and adjacent automation.

## NOV · Q4 2025

reviewed_relevant · Full transcript reviewed; searched AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital transformation; Q&A present.

[NOV Inc. Q4 2025 earnings call full transcript](https://stockanalysis.com/stocks/nov/transcripts/397031-q4-2025/)

### Drilling automation, robotics and AI strategy

CEO described NOV’s drilling equipment stack as combining data capture, downhole transmission, control systems, automation, robotics and AI, alongside a multi-customer ATOM RTX robotics pilot/commercialization program.

Speaker: Jose Bayardo, CEO. Locator: Transcript lines 449–458. Measurement: Three land rigs, three offshore rigs and roughly 27–30 robot arms cited for the robotics platform.. Limit: AI application and outcomes were not specified; platform adoption numbers refer to robotics, not AI..

### AI-enabled subsurface interpretation

Prepared remarks said operators use artificial intelligence and newer processor chips for faster iteration and improved subsurface interpretation in deepwater exploration.

Speaker: Jose Bayardo, CEO. Locator: Transcript line 159. Measurement: Not disclosed.. Limit: Describes technology available to customers; no NOV AI revenue or realized operator outcome quantified..

Quartr transcript hosted by StockAnalysis. Full call text and Q&A checked; findings distinguish NOV internal use from customer-facing offerings and adjacent automation.

## NOV · Q1 2026

reviewed_relevant · Full transcript reviewed; searched AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital transformation; Q&A present.

[NOV Inc. Q1 2026 earnings call full transcript](https://stockanalysis.com/stocks/nov/transcripts/552641-q1-2026/)

### Rig automation and robotics adoption

In response to an analyst, management discussed digital capabilities, automation and robotics upgrades for service rigs, including a rapid emergency disconnect system. No AI use was disclosed in the searched transcript.

Speaker: Jose Bayardo, Chairman, President and CEO. Locator: Transcript line 445. Measurement: Not disclosed.. Limit: Adjacent automation/robotics only; no AI linkage or adoption count in this passage..

Quartr transcript hosted by StockAnalysis. Full call text and Q&A checked; findings distinguish NOV internal use from customer-facing offerings and adjacent automation.

## NOV · Q2 2026

reviewed_relevant · Full transcript reviewed; searched AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation and digital transformation; Q&A present.

[NOV Inc. Q2 2026 earnings call full transcript](https://stockanalysis.com/stocks/nov/transcripts/656206-q2-2026/)

### AI model deployed for process-system customer

NOV said its process systems business deployed an AI model on the NOV Max Platform for a North Sea operator's sulfate-removal optimization program.

Speaker: Rodney Reed, SVP and CFO. Locator: Transcript lines 177–179. Measurement: One deployment described; no value or measured result disclosed.. Limit: Customer-facing solution; transcript gives no quantified outcome or recurring revenue..

Quartr transcript hosted by StockAnalysis. Full call text and Q&A checked; findings distinguish NOV internal use from customer-facing offerings and adjacent automation.

## Occidental · Q3 2024

reviewed_relevant · Full issuer-hosted speaker-labelled transcript PDF including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center, power and related terms.

[Occidental Q3 2024 Earnings Call Transcript](https://www.oxy.com/siteassets/documents/investors/quarterly-earnings/oxy3q24transcript.pdf)

### AI-associated power demand and carbon-management opportunity

CEO linked growth in data centers and AI with a rising need for reliable, inexpensive, lower-emissions power. She said Oxy could participate through its Net Power investment and by supplying direct-air-capture solutions to data centers and power generators seeking carbon-removal credits.

Speaker: Vicki Hollub, President and CEO. Locator: Prepared remarks, pp. 4–5, lines 139–147. Measurement: No load, gas volume, contract or AI-attributed revenue quantified.. Limit: Forward-looking strategic positioning; does not establish an executed data-center power or carbon-removal contract..

Official quarterly archive lists this call and a complete transcript; source is issuer-hosted. Management statements and performance claims are not independently validated.

## Occidental · Q4 2024

reviewed_relevant · Full issuer-hosted speaker-labelled transcript PDF including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center, power and related terms.

[Occidental Q4 2024 Earnings Call Transcript](https://www.oxy.com/siteassets/documents/investors/quarterly-earnings/oxy4q24transcript.pdf)

### Cross-functional AI portfolio and Gulf use cases

CEO described ongoing AI initiatives in Gulf of America supply-chain management, asset integrity and reservoir characterization. She also said Oxy had established an AI Center of Excellence to coordinate company initiatives and accelerate business value.

Speaker: Vicki Hollub, President and CEO. Locator: Prepared remarks, p. 4, lines 131–138. Measurement: No quantified AI-specific outcome.. Limit: Management-described use cases; individual models, deployment scale and attributable economics were not disclosed..

Official quarterly archive lists this call and a complete transcript; source is issuer-hosted. Management statements and performance claims are not independently validated.

## Occidental · Q1 2025

reviewed_relevant · Full issuer-hosted speaker-labelled transcript PDF including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center, power and related terms.

[Occidental Q1 2025 Earnings Call Transcript](https://www.oxy.com/siteassets/documents/investors/quarterly-earnings/oxy1q25transcript.pdf)

### AI work in Gulf subsurface evaluation

In Q&A about the resource base, the CEO said the company’s existing conventional-resource estimate might be conservative given AI work under way in the Gulf of America. This was described as an active technical workstream, not a booked reserve or quantified AI result.

Speaker: Vicki Hollub, President and CEO. Locator: Q&A, pp. 11–12, lines 410–423. Measurement: No AI-derived reserve addition quantified.. Limit: The CEO’s comparison does not show that AI has added resources; subsurface scope and validation were not detailed..

Official quarterly archive lists this call and a complete transcript; source is issuer-hosted. Management statements and performance claims are not independently validated.

## Occidental · Q2 2025

reviewed_relevant · Full issuer-hosted speaker-labelled transcript PDF including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center, power and related terms.

[Occidental Q2 2025 Earnings Call Transcript](https://www.oxy.com/siteassets/documents/investors/quarterly-earnings/oxy2q25transcript.pdf)

### Internal AI program and developing Gulf subsurface project

CEO described an internally built AI capability, with use in subsurface work in the Permian and other U.S. onshore areas, as well as operational efficiency. Separate teams were working on operations, a complex Gulf of America application, and logistics/supply chain. The Gulf project was still in development, with execution anticipated to begin in a later one-to-two-year window.

Speaker: Vicki Hollub, President and CEO. Locator: Q&A, p. 11, lines 370–383. Measurement: No AI-attributed volume, cost or recovery result.. Limit: Current onshore assistance is distinct from the future Gulf project; execution timing is forward-looking..

### AI, sensors and automation in routeless field operations

CEO said AI, field sensors and automation were being combined to prioritize lease-operator routes, supporting a transition to routeless operations. The call also identified work centers using AI across operating and supply-chain activities.

Speaker: Vicki Hollub, President and CEO; Ken Dillon, SVP International Oil and Gas Operations. Locator: Prepared remarks, p. 3, lines 64–70; Q&A, p. 13, lines 436–443. Measurement: About 40% of onshore production was in routeless operations.. Limit: The coverage measure refers to production, not wells, field staff or AI-only deployment; route and cost effects were not isolated..

Official quarterly archive lists this call and a complete transcript; source is issuer-hosted. Management statements and performance claims are not independently validated.

## Occidental · Q3 2025

reviewed_relevant · Full issuer-hosted speaker-labelled transcript PDF including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center, power and related terms.

[Occidental Q3 2025 Earnings Call Transcript](https://www.oxy.com/siteassets/documents/investors/quarterly-earnings/oxy3q25transcript.pdf)

### Adjacent production analytics and artificial-lift efficiency

Richard Jackson said base production outperformance partly reflected artificial-lift work, including use of analytics to improve efficiency; uptime also supported new-well results.

Speaker: Richard Jackson, COO; response to Matthew Portillo, TPH. Locator: Q&A, printed p. 13, lines 445–453.. Measurement: Not quantified. Limit: Management did not explicitly identify AI or machine learning, nor quantify an analytics-only effect. This is adjacent analytics, not an AI claim..

Full issuer-hosted transcript screened. Prior no-relevant status corrected after parent independently verified Q&A; analytics term qualifies under broad adjacent-automation scope, but AI attribution is absent.

## Occidental · Q4 2025

reviewed_relevant · Full issuer-hosted speaker-labelled transcript PDF including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center, power and related terms.

[Occidental Q4 2025 Earnings Call Transcript](https://www.oxy.com/siteassets/documents/investors/quarterly-earnings/oxy4q25transcript.pdf)

### AI-enabled remote operations centers

Management said Oxy launched a Gulf of America Remote Operations Command Center, complementing Rockies and Permian centers, using advanced AI and remote monitoring to support safety, reliability and operating efficiency. In Q&A, COO Richard Jackson described routeless coverage and resolving operational issues remotely during a winter storm, and said AI would help deploy staff more effectively.

Speaker: Vicki Hollub, President and CEO; Richard Jackson, COO. Locator: Prepared remarks, p. 3, lines 74–77; Q&A, pp. 13–14, lines 489–498. Measurement: Management reported about 40% of U.S. production under remote-center coverage and roughly 300 issues resolved remotely per day in the Rockies during a winter storm.. Limit: These figures describe the broader remote-operations model; the transcript does not attribute them solely to AI or quantify AI-specific financial/production results..

Official quarterly archive lists this call and a complete transcript; source is issuer-hosted. Management statements and performance claims are not independently validated.

## Occidental · Q1 2026

reviewed_relevant · Full issuer-hosted speaker-labelled transcript PDF including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center, power and related terms.

[Occidental Q1 2026 Earnings Call Transcript](https://www.oxy.com/siteassets/documents/investors/quarterly-earnings/oxy1q26transcript.pdf)

### Advanced analytics and AI for development optimization

CEO said Oxy planned to combine its data and technical foundation with advanced analytics and AI to improve development and performance. The prepared remarks framed this as a capability extension, not a measured AI outcome.

Speaker: Vicki Hollub, President and CEO. Locator: Prepared remarks, p. 3, lines 68–75. Measurement: No AI-specific production or financial metric.. Limit: Forward-looking capability statement; deployment scope was not quantified..

### AI within workforce-efficiency agenda

In Q&A, incoming CEO Richard Jackson included AI technologies among options for workforce efficiency and process prioritization. Elsewhere in the call, management said AI was taking a broader role across company disciplines.

Speaker: Richard Jackson, COO and incoming CEO; Vicki Hollub, President and CEO. Locator: Q&A, pp. 7–8, lines 291–295; p. 12, lines 449–459. Measurement: No specific workflow, headcount impact or savings attributed to AI.. Limit: Strategic statements only; no deployment detail or realized benefit was reported..

Official quarterly archive lists this call and a complete transcript; source is issuer-hosted. Management statements and performance claims are not independently validated.

## Occidental · Q2 2026

reviewed_relevant · Full issuer-hosted speaker-labelled transcript PDF including prepared remarks and Q&A reviewed; searched for AI/artificial intelligence, generative AI, machine learning, agentic/copilot, predictive, autonomous, automation/digital, data-center, power and related terms.

[Occidental Q2 2026 Earnings Call Transcript](https://www.oxy.com/siteassets/documents/investors/quarterly-earnings/oxy2q26transcript.pdf)

### AI among core operating-technology priorities

CEO grouped AI with CO2, power and water technologies that Oxy seeks to integrate into basin- and well-specific development planning. The call did not describe a named AI model or deployed workflow.

Speaker: Richard Jackson, President and CEO. Locator: Prepared remarks, p. 3, lines 68–81. Measurement: Management referenced a $4 billion incremental sustainable-cash-flow objective by 2030 for the broader program, not an AI-specific contribution.. Limit: Do not attribute the company-wide objective to AI; no implementation or AI-specific result was quantified..

### Data-center power build-out as a carbon-capture opportunity

In Q&A, Jackson said growing power generation and data-center construction in the Permian could create an opportunity to capture CO2 from those facilities, alongside Oxy’s DAC and EOR activities. The transcript describes data centers but does not expressly attribute their demand to AI.

Speaker: Richard Jackson, President and CEO. Locator: Q&A, p. 11, lines 379–392. Measurement: Management said CO2 and power together account for 30% of EOR barrel operating cost; no data-center volume or contract was disclosed.. Limit: Adjacent power/data-center context, not an explicit AI-demand estimate or contracted project..

Official quarterly archive lists this call and a complete transcript; source is issuer-hosted. Management statements and performance claims are not independently validated.

## OMV · Q3 2024

reviewed_no_relevant · Full prepared remarks and the issuer-hosted edited Q&A transcript were reviewed together; screened AI/artificial intelligence, ML, GenAI, agentic, predictive analytics, digitalization/transformation, automation, software, data-center and hyperscaler terms.

[OMV Q3 2024 Conference Call Q&A transcript, with companion presentation speech](https://www.omv.com/downloads/2024/11/ea006066-c61c-0434-61b2-98c501a5857f/Transcript_OMV_Q3_2024.pdf)

Issuer quarterly-publications page lists the presentation speech and conference-call Q&A transcript for Results January–September and Q3 2024. No relevant AI, analytics, automation or data-center disclosure was found across the full call materials. Prepared remarks: https://www.omv.com/downloads/2024/10/6f057d01-72ce-b3a9-4eb4-f4f8110490d8/presentation-speech-q3-2024.pdf

## OMV · Q4 2024

reviewed_no_relevant · Full prepared remarks and the issuer-hosted edited Q&A transcript were reviewed together; screened AI/artificial intelligence, ML, GenAI, agentic, predictive analytics, digitalization/transformation, automation, software, data-center and hyperscaler terms.

[OMV Q4 2024 Conference Call Q&A transcript, with companion presentation speech](https://www.omv.com/downloads/2025/02/f01ab3c8-359a-e32d-9b63-6a1f92860714/Transcript_OMV_Q4_2024.pdf)

Issuer quarterly-publications page lists the presentation speech and conference-call Q&A transcript for Results January–December and Q4 2024. No relevant AI, analytics, automation or data-center disclosure was found across the full call materials. Prepared remarks: https://www.omv.com/downloads/2025/02/ef0f0b71-2c79-aac5-55fa-7cff0af9c36d/OMV%20Q4%202024%20Analyst%20Conference%20Call%20Speech.pdf

## OMV · Q1 2025

reviewed_no_relevant · Full prepared remarks and the issuer-hosted edited Q&A transcript were reviewed together; screened AI/artificial intelligence, ML, GenAI, agentic, predictive analytics, digitalization/transformation, automation, software, data-center and hyperscaler terms.

[OMV Q1 2025 Conference Call Q&A transcript, with companion presentation speech](https://www.omv.com/downloads/2025/05/e71dc975-6aaa-044b-b501-ce4e60fc7d88/Transcript_OMV_Q1_2025.pdf)

Issuer quarterly-publications page lists the presentation speech and conference-call Q&A transcript for Results January–March 2025. No relevant AI, analytics, automation or data-center disclosure was found across the full call materials. Prepared remarks: https://www.omv.com/downloads/2025/04/83e96f5f-4a14-d64d-8a4f-eb950c176932/OMV%20Q1%202025%20Analyst%20Conference%20Call%20Speech.pdf

## OMV · Q2 2025

reviewed_no_relevant · Full prepared remarks and the issuer-hosted edited Q&A transcript were reviewed together; screened AI/artificial intelligence, ML, GenAI, agentic, predictive analytics, digitalization/transformation, automation, software, data-center and hyperscaler terms.

[OMV Q2 2025 Conference Call Q&A transcript, with companion presentation speech](https://www.omv.com/downloads/2025/08/58ae6d3e-c092-3767-bcc3-b3eadabb0935/Transcript_OMV_Q2%202025%20Conference%20Call.pdf)

Issuer quarterly-publications page lists the presentation speech and conference-call Q&A transcript for Results January–June and Q2 2025. No relevant AI, analytics, automation or data-center disclosure was found across the full call materials. Prepared remarks: https://www.omv.com/downloads/2025/07/92220be7-ebcf-1560-fda8-fd1a18a8edfe/OMV%20Q2%202025%20Analyst%20Conference%20Call%20Speech.pdf

## OMV · Q3 2025

reviewed_no_relevant · Full prepared remarks and the issuer-hosted edited Q&A transcript were reviewed together; screened AI/artificial intelligence, ML, GenAI, agentic, predictive analytics, digitalization/transformation, automation, software, data-center and hyperscaler terms.

[OMV Q3 2025 Conference Call Q&A transcript, with companion presentation speech](https://www.omv.com/downloads/2025/11/03739e1d-ea39-ad48-d887-b848fdcd26af/Transcript_OMV_Q3%202025%20Conference%20Call.pdf)

Issuer quarterly-publications page lists the presentation speech and conference-call Q&A transcript for Results January–September and Q3 2025. No relevant AI, analytics, automation or data-center disclosure was found across the full call materials. Prepared remarks: https://www.omv.com/downloads/2025/10/0ff51f0f-e14a-a27b-5c6a-9fea957050aa/OMV_Q3_2025_Analyst_Conference_Call_CEO_Speech.pdf

## OMV · Q4 2025

reviewed_no_relevant · Full transcript reviewed, including prepared remarks, analyst Q&A and operator close; screened AI/artificial intelligence, ML, GenAI, agentic, predictive analytics, digitalization/transformation, automation, software, data-center and hyperscaler terms.

[OMV Q4 2025 earnings call transcript](https://stockanalysis.com/quote/vie/OMV/transcripts/551555-q4-2025/)

No relevant AI, analytics, automation or data-center disclosure was found in the full call. Q&A and operator close are present.

## OMV · Q1 2026

reviewed_no_relevant · Full transcript reviewed, including prepared remarks, analyst Q&A and operator close; screened AI/artificial intelligence, ML, GenAI, agentic, predictive analytics, digitalization/transformation, automation, software, data-center and hyperscaler terms.

[OMV Q1 2026 earnings call transcript](https://stockanalysis.com/quote/vie/OMV/transcripts/408704-q1-2026/)

No relevant AI, analytics, automation or data-center disclosure was found in the full call. Q&A and operator close are present.

## OMV · Q2 2026

reviewed_no_relevant · Full transcript reviewed, including prepared remarks, analyst Q&A and closing remarks. Selected AI/artificial intelligence, machine-learning, GenAI, agentic, predictive analytics, digitalization/transformation, automation, software, data-center and hyperscaler terms screened.

[OMV Aktiengesellschaft Q2 2026 earnings-call full transcript](https://stockanalysis.com/quote/vie/OMV/transcripts/550727-q2-2026/)

Full call reviewed through operator close; no relevant AI, analytics, automation or data-center disclosure found.

## ONEOK · Q3 2024

reviewed_relevant · AI mentions appear in Q&A; transcript includes full question-and-answer session through close.

[ONEOK Q3 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/oke/transcripts/219091-q3-2024/)

### AI-linked gas-market opportunity

CEO identified AI-related industrial growth as part of natural-gas demand opportunities in ONEOK’s Louisiana and intrastate footprint.

Speaker: Pierce Norton, President and CEO. Locator: Q&A lines 410 and 454. Measurement: Not quantified. Limit: Opportunity context only; no AI-specific contract or attributable revenue disclosed..

Call date and quarter confirmed on ONEOK/Stock Analysis transcript archive. Q&A presence confirmed. Keyword screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## ONEOK · Q4 2024

reviewed_relevant · Full transcript reviewed, including Q&A; AI-linked gas-demand discussion appears in prepared remarks and Q&A.

[ONEOK Q4 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/oke/transcripts/247610-q4-2024/)

### AI-related natural-gas demand outlook

CEO cited data-center growth associated with AI as a demand tailwind and described projected natural-gas demand growth.

Speaker: Pierce Norton, President and CEO. Locator: Prepared remarks lines 132-133; Q&A lines 246-247. Measurement: CEO referenced an industry projection that AI could add 3–6 Bcf/day of gas demand; described as an estimate, not ONEOK guidance.. Limit: Market-level estimate only; no specific customer project or AI revenue quantified in this call..

Call date and quarter confirmed on ONEOK/Stock Analysis transcript archive. Q&A presence confirmed. Keyword screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## ONEOK · Q1 2025

reviewed_relevant · Full transcript reviewed, including Q&A; AI demand appears in analyst Q&A.

[ONEOK Q1 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/oke/transcripts/319267-q1-2025/)

### AI-driven gas demand estimate

CEO said AI data centers could increase natural-gas demand by an estimated 3–8 Bcf/day over the next five years, with many opportunities located in ONEOK’s operating areas.

Speaker: Pierce Norton, President and CEO. Locator: Q&A lines 183-189. Measurement: 3–8 Bcf/day estimated incremental AI data-center demand over five years.. Limit: Industry opportunity estimate, not ONEOK forecast or contracted throughput..

Call date and quarter confirmed on ONEOK/Stock Analysis transcript archive. Q&A presence confirmed. Keyword screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## ONEOK · Q2 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[ONEOK Q2 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/oke/transcripts/340404-q2-2025/)

Call date and quarter confirmed on ONEOK/Stock Analysis transcript archive. Q&A presence confirmed. Keyword screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## ONEOK · Q3 2025

reviewed_relevant · Full transcript reviewed, including Q&A; management discusses AI-driven data-center projects and analyst Q&A.

[ONEOK Q3 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/oke/transcripts/370666-q3-2025/)

### AI data-center gas supply opportunity

Management said it was in active discussions on numerous AI-driven data-center projects and had been contacted about more than 30 data-center opportunities; assets near some projects may give ONEOK a speed-to-market advantage.

Speaker: Pierce Norton, President and CEO; Sheridan Swords, EVP and Chief Commercial Officer. Locator: Prepared remarks line 159; Q&A lines 334-340. Measurement: Management said it had been contacted by well over 30 projects.. Limit: Opportunities under discussion; no individual project FID, contracted volumes or EBITDA disclosed in the cited discussion..

Call date and quarter confirmed on ONEOK/Stock Analysis transcript archive. Q&A presence confirmed. Keyword screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## ONEOK · Q4 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[ONEOK Q4 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/oke/transcripts/395877-q4-2025/)

Call date and quarter confirmed on ONEOK/Stock Analysis transcript archive. Q&A presence confirmed. Keyword screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## ONEOK · Q1 2026

reviewed_relevant · Full transcript reviewed, including Q&A; explicit AI and power-demand discussion in analyst Q&A.

[ONEOK Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/oke/transcripts/566584-q1-2026/)

### AI-related gas demand and infrastructure

Management said it was in advanced discussions on AI and power projects; CFO said larger hyperscaler volume requirements may require longer, larger pipelines than early project assumptions.

Speaker: Sheridan Swords, EVP and Chief Commercial Officer; Walter Hulse, CFO. Locator: Q&A lines 448-458. Measurement: Not quantified. Limit: Projects remain in discussion/queue; no finalized contracted volumes or revenue given in this passage..

Call date and quarter confirmed on ONEOK/Stock Analysis transcript archive. Q&A presence confirmed. Keyword screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## ONEOK · Q2 2026

reviewed_relevant · Full transcript reviewed, including Q&A; management directly addresses AI data-center/power projects and a secured 1-GW gas-supply contract.

[ONEOK Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/oke/transcripts/657023-q2-2026/)

### AI data-center power generation gas contract

Chief Commercial Officer said ONEOK secured a contract to supply natural gas to a 1-GW power-generation system discussed in response to a question on AI data centers; he also said late-stage talks were underway to supply other AI data centers.

Speaker: Sheridan Swords, Chief Commercial Officer; Walt Hulse, CFO. Locator: Analyst Q&A, lines 331-343. Measurement: 1 GW power-generation supply contract; management said project capex would exceed $100 million and generate a return supported by firm demand.. Limit: Management did not name the customer or explicitly say the 1-GW plant was dedicated to a specific AI data center; additional AI projects remained under discussion..

Call date and quarter confirmed on ONEOK/Stock Analysis transcript archive. Q&A presence confirmed. Keyword screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## Ovintiv · Q3 2024

reviewed_relevant · Full third-party Quartr transcript hosted on MarketBeat reviewed end to end (opening, prepared remarks, analyst Q&A and operator/IR close); original issuer audio unavailable and not independently reviewed. Screened AI/artificial intelligence, ML, generative AI, agents/copilots, analytics, automation, digital transformation, predictive/autonomous systems, software/technology, and data-center/power-demand terms.

[Ovintiv Q3 2024 Earnings Call Transcript](https://www.marketbeat.com/earnings/reports/2024-11-7-ovintiv-inc-stock/)

### AI/ML automation in drilling, completions and production

CEO Brendan McCracken said Ovintiv’s North American private well-data set supported analytical, engineering and geoscience work. He said “AI machine learning automation” had been deployed in drilling and completion and production operations across assets, and associated those capabilities collectively with drilling/completion speed and costs and lower portfolio base decline. The call did not identify a model, vendor, workflow control, deployment denominator or AI-specific measured result; performance attribution is qualitative and combined with automation and broader operating changes.

Speaker: Brendan McCracken, President and CEO. Locator: Q&A, lines 579–597; 00:15:04–00:17:27.. Measurement: Not quantified. Limit: Third-party transcript only; original audio unavailable. AI, machine learning and automation are grouped together and no AI-only outcome is quantified..

Full-call transcript review closes prior inaccessible gap. Chronologically relevant to later Ovintiv AI disclosures; cross-check AI-092/AI-093 before counting as a distinct initiative. Not issuer-audio verified.

## Ovintiv · Q4 2024

reviewed_relevant · Full third-party Quartr transcript hosted on MarketBeat reviewed end to end (opening, prepared remarks, analyst Q&A and operator/IR close); original issuer audio unavailable and not independently reviewed. Complete transcript screened for AI/artificial intelligence, ML, generative AI, agents/copilots, analytics, automation, digital/digital transformation, predictive/autonomous systems, software/technology, optimization/algorithms, data-center/hyperscaler and power/electricity demand. Operating-use terms were absent in transcript body; a data-center gas-demand passage was present.

[Ovintiv Q4 2024 Earnings Call Transcript](https://www.marketbeat.com/earnings/reports/2025-2-26-ovintiv-inc-stock/)

### Data-center gas-demand optionality

CEO Brendan McCracken described data-center demand optionality in Western Canada and said Ovintiv had held conversations with interested counterparties about supplying gas to data-center or petrochemical projects. He framed these as possible local incremental-demand outlets within gas-market diversification. The passage does not explicitly connect the demand to AI, identify a customer or contract, or disclose volume, price or revenue. It is energy-market context, not evidence of Ovintiv’s internal AI use.

Speaker: Brendan McCracken, President and CEO. Locator: Q&A, lines 1072–1091 (00:40:35–00:42:27); related portfolio discussion lines 1122–1142 (00:42:38–00:44:32).. Measurement: Not quantified. Limit: Third-party transcript only; original audio unavailable. Data-center demand is not explicitly described as AI-driven and is only optionality/conversations, not contracted demand..

Do not mark reviewed_no_relevant under the broader ledger taxonomy: call contains data-center gas-demand discussion, although no explicit AI link. No operating AI/ML, analytics, automation or digital-transformation mention found in full transcript. Original issuer audio unavailable.

## Ovintiv · Q1 2025

reviewed_relevant · Full speaker-labelled transcript reviewed including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Ovintiv (OVV) Q1 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/ovv/transcripts/320334-q1-2025/)

### Digital workflows and remote operations

Management said acquired Montney wells were integrated into the operations control center, enabling remote operations and use of established digital workflows to optimize individual-well cash flow.

Speaker: Greg Givens, EVP and COO. Locator: Prepared remarks, line 156.. Measurement: About 10 days removed from drilling cycle time in acquired assets; sub-15-day spud-to-rig-release average.. Limit: No explicit AI/ML disclosure in the call; cycle-time figure is not attributed to digital workflows alone..

Digital workflows/remote operations; full transcript screened.

## Ovintiv · Q2 2025

reviewed_relevant · Full speaker-labelled transcript reviewed including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Ovintiv (OVV) Q2 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/ovv/transcripts/343439-q2-2025/)

### AI-supported drilling, completions and production workflows

CEO said the company was deploying AI across the portfolio through its AI drill center, AI completion center and production operations control room, using private well data; he characterized the work as nascent/early with running room. In Q&A, COO cited operations-control-center machine-learning/AI tools for gas-lift optimization and automation across acquired assets to restore production faster after disruptions.

Speaker: Brendan McCracken, President and CEO; Greg Givens, EVP and COO. Locator: Q&A, lines 445–452 and 595–596.. Measurement: Not quantified. Limit: No quantified AI-specific benefit; CEO said technology was not fully baked and early..

### Emerging data-center demand for natural gas

CEO said the market was seeing early arrangements for demand pull on the data-center side and viewed it as part of a strong North American gas market.

Speaker: Brendan McCracken, President and CEO. Locator: Q&A, line 347 (data-center response).. Measurement: Not quantified. Limit: Early arrangements only; transcript did not explicitly attribute demand to AI or disclose an Ovintiv contract, volume, price or revenue..

In-house AI drill/completion centers; ML/AI gas-lift optimization; automation; early data-center market demand arrangements.

## Ovintiv · Q3 2025

reviewed_relevant · Full speaker-labelled transcript reviewed including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Ovintiv (OVV) Q3 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/ovv/transcripts/367356-q3-2025/)

### AI and automation in acquired-asset operations

Management said wells were being connected to the Grand Prairie Operations Control Center, using automation and in-house AI to optimize production and reduce downtime. For acquired assets, executives said rigs would be connected to the DRIVE optimization center for AI-assisted drilling efficiency, while production would use AI algorithms to optimize well output and artificial-lift set points.

Speaker: Brendan McCracken, President and CEO; Corey Code, EVP and CFO. Locator: Prepared remarks, line 153; Q&A, lines 394–399.. Measurement: CEO expected several days removed from drilling times; no AI-only share or realized metric stated.. Limit: Forward-looking acquired-asset integration benefits; synergy categories combine multiple actions and are not attributable solely to AI..

In-house AI/automation for production optimization/downtime reduction; scaling acquired assets.

## Ovintiv · Q4 2025

reviewed_relevant · Full speaker-labelled transcript reviewed including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Ovintiv (OVV) Q4 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/ovv/transcripts/395878-q4-2025/)

### In-house AI tools for drilling and acquired-well integration

CEO said several in-house AI tools helped reduce drilling cycle times, failures and improve efficiency. In Q&A, management said acquired producing wells had been connected to the Operations Control Center and rigs to DRIVE, where AI would support drilling optimization.

Speaker: Brendan McCracken, President and CEO. Locator: Prepared remarks, line 159; Q&A, line 381.. Measurement: Management cited $1 million per well of savings already incorporated for acquired assets, but the transcript does not isolate AI’s contribution.. Limit: AI is part of a broader technology/integration synergy package; no AI-attributed portion of the per-well amount..

In-house drilling AI tools and AI optimization center for acquired rigs; reported per-well synergies, not all AI-attributable.

## Ovintiv · Q1 2026

reviewed_relevant · Full speaker-labelled transcript reviewed including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Ovintiv (OVV) Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/ovv/transcripts/566530-q1-2026/)

### Private-data AI for production/lift optimization and real-time frac design

CEO described use of AI paired with Ovintiv’s private data set and in-house algorithms in operations centers to improve uptime/artificial-lift optimization and to tune roughly 70 frac-design input factors in real time. Management also discussed predictive models and digital remote-well workflows.

Speaker: Brendan McCracken, President and CEO; Greg Givens, EVP and COO. Locator: Q&A, lines 350–355; prepared remarks line 160; Q&A line 329.. Measurement: About 70 frac-design input factors mentioned; no AI-only savings or production gain quantified in the located passages.. Limit: Call describes capabilities and workflow; no isolated AI performance metric..

AI and private data for production/lift optimization and real-time frac-design tuning; digital remote operations.

## Ovintiv · Q2 2026

reviewed_relevant · Full speaker-labelled transcript reviewed including prepared remarks and analyst Q&A; searched AI, artificial intelligence, machine learning, analytics, automation, digital, predictive, autonomous, data-center/hyperscaler and power-demand terms.

[Ovintiv (OVV) Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/ovv/transcripts/666661-q2-2026/)

### AI-powered drilling, completion and production operating workflows

CEO said frontier innovations were AI-powered and used across the portfolio for technical workflows and real-time execution, citing faster cycles, enhanced production, reduced downtime and significant cost savings. COO attributed a good portion of the base-production outlook beating plan by 3% to remote operations using AI and automation for artificial-lift settings, downtime reduction and decline flattening. CEO also described in-house AI and automation for rod pumps reducing failures/zero-production days.

Speaker: Brendan McCracken, President and CEO; Greg Givens, EVP and COO. Locator: Prepared remarks, lines 137 and 161; Q&A, lines 284 and 330.. Measurement: Base production expected 3% above original plan; COO attributed a “good portion” to AI/automation remote operating capability, not a precise allocation.. Limit: AI/automation contribution not quantified separately; company also described numerous non-AI completion innovations..

### Data centers as an emerging Western Canada gas outlet

CEO called Western Canada data-center build-out an emerging outlet that could take some Ovintiv gas over time, supporting diversification away from AECO.

Speaker: Brendan McCracken, President and CEO. Locator: Q&A, lines 446–452.. Measurement: Not quantified. Limit: No specific customer, contracted volume, commercial terms or revenue disclosed; not explicitly labeled AI-driven in this exchange..

AI-powered operating workflows and AI/automation artificial lift; base-production and cost claims; emerging data-center gas outlet.

## Patterson-UTI · Q3 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Patterson-UTI Q3 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/pten/transcripts/215891-q3-2024/)

Complete transcript and Q&A keyword-screened; no substantive AI, machine-learning, analytics-platform, automation or AI-power-demand disclosure located.

## Patterson-UTI · Q4 2024

reviewed_relevant · Full call, including prepared remarks and Q&A, screened for AI/artificial intelligence, ML/machine learning, generative AI, agents/copilots, predictive analytics, autonomy, automation, digital transformation, software/analytics platforms, remote operations and data-center/power-demand language.

[Patterson-UTI Q4 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/pten/transcripts/248348-q4-2024/)

### Cortex automation and performance-data analysis

CEO Andy Hendricks described integrating automation and performance-data analysis in drilling operations and increased demand for the Cortex Automation system. The transcript did not explicitly identify Cortex Automation as AI/ML.

Speaker: Andy Hendricks, President and CEO. Locator: Prepared remarks, transcript lines 128–130 and 169–171. Measurement: Not quantified. Limit: No AI method, adoption count or quantified operational outcome was attributed specifically to Cortex in this passage. It may relate to, but is not expanded into, existing Cortex/REX source AI-117..

### AI-driven data-center electricity demand and power solutions

In discussing the company’s power business, CEO Andy Hendricks referred to electricity demand from data centers and hyperscalers and identified AI as a driver. He said Patterson-UTI was concentrating on integrated power solutions for oil-and-gas E&P, where it saw a more attractive fit than competing directly for data-center projects.

Speaker: Andy Hendricks, President and CEO. Locator: Prepared remarks, transcript lines 174–193. Measurement: Not quantified. Limit: Market context and management strategy; no specific AI-data-center contract or quantified power demand was disclosed..

Transcript’s AI/data-center discussion is an energy-demand/strategy disclosure, not evidence that PTEN itself deployed AI.

## Patterson-UTI · Q1 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Patterson-UTI Q1 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/pten/transcripts/313650-q1-2025/)

Complete transcript and Q&A keyword-screened; no substantive AI, machine-learning, analytics-platform, automation or AI-power-demand disclosure located.

## Patterson-UTI · Q2 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Patterson-UTI Q2 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/pten/transcripts/338261-q2-2025/)

Complete transcript and Q&A keyword-screened; no substantive AI, machine-learning, analytics-platform, automation or AI-power-demand disclosure located.

## Patterson-UTI · Q3 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Patterson-UTI Q3 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/pten/transcripts/366828-q3-2025/)

Complete transcript and Q&A keyword-screened; no substantive AI, machine-learning, analytics-platform, automation or AI-power-demand disclosure located.

## Patterson-UTI · Q4 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Patterson-UTI Q4 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/pten/transcripts/401982-q4-2025/)

Complete transcript and Q&A keyword-screened; no substantive AI, machine-learning, analytics-platform, automation or AI-power-demand disclosure located.

## Patterson-UTI · Q1 2026

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Patterson-UTI Q1 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/pten/transcripts/552583-q1-2026/)

Complete transcript and Q&A keyword-screened; no substantive AI, machine-learning, analytics-platform, automation or AI-power-demand disclosure located.

## Patterson-UTI · Q2 2026

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Patterson-UTI Q2 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/pten/transcripts/653693-q2-2026/)

Complete transcript and Q&A keyword-screened; no substantive AI, machine-learning, analytics-platform, automation or AI-power-demand disclosure located.

## Permian Resources · Q3 2024

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; keyword scan: AI, artificial intelligence, machine learning, generative AI, LLM, analytics, automation, digital, predictive, autonomous, agentic, copilots, data-center and power-demand terms.

[Permian Resources Q3 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/pr/transcripts/219604-q3-2024/)

### AI data-center demand and Permian gas positioning

CEO James Walter said the company expected U.S. AI data-center demand to become real and viewed the Permian and its gas inventory as positioned to benefit through stronger in-basin gas demand and prices.

Speaker: James Walter, Co-CEO. Locator: Q&A, lines 172–182, especially 178–180.. Measurement: Not quantified. Limit: Strategic demand expectation only: no specific data-center project, signed contract, AI-attributable gas volume or price impact was quantified..

Management explicitly discussed AI data-center demand as a potential tailwind for Permian gas and in-basin prices; no committed project or AI-specific volume was disclosed.

## Permian Resources · Q4 2024

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; keyword scan: AI, artificial intelligence, machine learning, generative AI, LLM, analytics, automation, digital, predictive, autonomous, agentic, copilots, data-center and power-demand terms.

[Permian Resources Q4 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/pr/transcripts/237127-q4-2024/)

Full transcript review completed; no qualifying AI/ML, adjacent automation/digital or AI-linked energy-demand disclosure found.

## Permian Resources · Q1 2025

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; keyword scan: AI, artificial intelligence, machine learning, generative AI, LLM, analytics, automation, digital, predictive, autonomous, agentic, copilots, data-center and power-demand terms.

[Permian Resources Q1 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/pr/transcripts/319663-q1-2025/)

### Data-center-related gas-marketing opportunity

In response to a question about Permian data-center deals, Co-CEO James Walter said gas marketing had been a focus for six to nine months and the company was working to improve value across gas, oil and NGL molecules, with possible updates later in the year.

Speaker: James Walter, Co-CEO. Locator: Q&A, lines 397–408, especially 402–408.. Measurement: Not quantified. Limit: No named customer, signed data-center deal, AI linkage, volume or realized value disclosed; do not treat the analyst’s framing as a company AI claim..

Q&A discussed data-center participation and gas-marketing work. The transcript did not explicitly establish AI as the driver; this is recorded as broader energy-demand context.

## Permian Resources · Q2 2025

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; keyword scan: AI, artificial intelligence, machine learning, generative AI, LLM, analytics, automation, digital, predictive, autonomous, agentic, copilots, data-center and power-demand terms.

[Permian Resources Q2 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/pr/transcripts/344176-q2-2025/)

Full transcript review completed; no qualifying AI/ML, adjacent automation/digital or AI-linked energy-demand disclosure found.

## Permian Resources · Q3 2025

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; keyword scan: AI, artificial intelligence, machine learning, generative AI, LLM, analytics, automation, digital, predictive, autonomous, agentic, copilots, data-center and power-demand terms.

[Permian Resources Q3 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/pr/transcripts/372149-q3-2025/)

### Internal LLM workflow for subsurface information sharing

CEO James Walter said large language models help move internal drilling, production, log and seismic information across A&D, land, drilling, completion and engineering teams in minutes rather than the prior weeks or months, supporting faster screening and development decisions.

Speaker: James Walter, Co-CEO. Locator: Q&A, lines 629–639, especially 634–636.. Measurement: Qualitative workflow time: weeks or months reduced to minutes.. Limit: No financial, production or inventory-addition outcome isolated to LLM use; the analyst introduced the AI framing and management confirmed LLM-enabled workflows..

CEO described internal large-language-model workflow use in response to an analyst question about AI and inventory expansion.

## Permian Resources · Q4 2025

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; keyword scan: AI, artificial intelligence, machine learning, generative AI, LLM, analytics, automation, digital, predictive, autonomous, agentic, copilots, data-center and power-demand terms.

[Permian Resources Q4 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/pr/transcripts/399915-q4-2025/)

Full transcript review completed; no qualifying AI/ML, adjacent automation/digital or AI-linked energy-demand disclosure found.

## Permian Resources · Q1 2026

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; keyword scan: AI, artificial intelligence, machine learning, generative AI, LLM, analytics, automation, digital, predictive, autonomous, agentic, copilots, data-center and power-demand terms.

[Permian Resources Q1 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/pr/transcripts/571851-q1-2026/)

Full transcript review completed; no qualifying AI/ML, adjacent automation/digital or AI-linked energy-demand disclosure found.

## Permian Resources · Q2 2026

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; keyword scan: AI, artificial intelligence, machine learning, generative AI, LLM, analytics, automation, digital, predictive, autonomous, agentic, copilots, data-center and power-demand terms.

[Permian Resources Q2 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/pr/transcripts/662328-q2-2026/)

Full transcript review completed; no qualifying AI/ML, adjacent automation/digital or AI-linked energy-demand disclosure found.

## Petrobras · Q3 2024

reviewed_no_relevant · Full transcript reviewed: management presentation, analyst Q&A and closing/operator remarks; selected explicit AI and adjacent digital/automation/data-center terms screened.

[Petrobras Q3 FY2024 Earnings Call Transcript](https://www.roic.ai/quote/PBR:US/transcripts/2024-year/3-quarter)

Full English interpretation includes management presentation, analyst Q&A, closing remarks, and operator sign-off. Searched AI/artificial intelligence, machine learning, digital/digitalization, automation/autonomy, analytics, software, data-center/hyperscaler terms; no relevant company disclosure found. English speech was interpreted live from Portuguese. Actual call date Nov. 8, 2024, consistent with issuer transcript archive.

## Petrobras · Q4 2024

reviewed_no_relevant · Full transcript reviewed: management presentation, analyst Q&A and closing/operator remarks; selected explicit AI and adjacent digital/automation/data-center terms screened.

[Petrobras Q4 2024 Earnings Call Transcript](https://www.roic.ai/quote/PBR/transcripts/2024/4)

Full transcript includes presentation, analyst Q&A and close. Searched AI/artificial intelligence, machine learning, digital/digitalization, automation/autonomy, analytics, software, data-center/hyperscaler terms; no relevant disclosure found. Marketscreener’s archive lists actual call date Feb. 27, 2025; ROIC archive labels this record Feb. 28 in the navigation, so the row retains issuer/third-party archive call date Feb. 27.

## Petrobras · Q1 2025

reviewed_no_relevant · Full transcript reviewed: management presentation, analyst Q&A and closing/operator remarks; selected explicit AI and adjacent digital/automation/data-center terms screened.

[Petrobras Q1 FY2025 Earnings Call Transcript](https://www.roic.ai/quote/PBR-A:UN/transcripts/2025-year/1-quarter)

Full transcript includes management presentation, analyst Q&A, final remarks and operator close. Searched AI/artificial intelligence, machine learning, digital/digitalization, automation/autonomy, analytics, software, workflow and data-center/hyperscaler terms; no relevant disclosure found. English text is an interpreted transcript of a Portuguese call.

## Petrobras · Q2 2025

reviewed_no_relevant · Full transcript reviewed: management presentation, analyst Q&A and closing/operator remarks; selected explicit AI and adjacent digital/automation/data-center terms screened.

[Petrobras Webcast Transcript 2Q 2025](https://www.marketscreener.com/news/petrobras-webcast-transcript-2q-2025-ce7c51d9dc8cf426)

Full English interpretation of issuer webcast, including presentation, Q&A and close. Searched AI/artificial intelligence, machine learning, digital/digitalization, automation/autonomy, analytics, software/workflow, data-center/hyperscaler and Portuguese AI terms; no relevant disclosure found. MarketScreener archive corroborates call date Aug. 8, 2025; its mirror page was published Aug. 14.

## Petrobras · Q3 2025

reviewed_no_relevant · Full transcript reviewed: management presentation, analyst Q&A and closing/operator remarks; selected explicit AI and adjacent digital/automation/data-center terms screened.

[Petrobras Webcast Transcript 3Q 2025](https://www.marketscreener.com/news/petrobras-webcast-transcript-3q-2025-ce7d5edbdc8bf020)

Full English interpretation of issuer webcast. Screened AI/artificial intelligence, machine learning, IA/inteligência artificial, digital/digitalization, automation/autonomy, analytics, software/workflow, data-center/hyperscaler, and broad technology/optimization language. No relevant AI, adjacent digital/automation, or AI-energy-demand disclosure found; ordinary production optimization/engineering and reservoir management were not treated as AI. Issuer transcript itself states call was Nov. 7, 2025; MarketScreener page published Nov. 18.

## Petrobras · Q4 2025

reviewed_no_relevant · Full transcript reviewed: management presentation, analyst Q&A and closing/operator remarks; selected explicit AI and adjacent digital/automation/data-center terms screened.

[Webcast – 4th Quarter Results 2025](https://api.mziq.com/mzfilemanager/v2/d/25fdf098-34f5-4608-b7fa-17d60b2de47d/00435526-7399-429e-1186-8899e06d6c07?origin=2)

Official full English interpretation includes management remarks, Q&A, executive closing remarks and operator close. Searched AI/artificial intelligence, machine learning, digital/digitalization, automation/autonomy, analytics, software/workflow, data-center/hyperscaler terms; no relevant disclosure found. Cover identifies event date Mar. 6, 2026; Marketscreener mirror is published Mar. 11.

## Petrobras · Q1 2026

reviewed_no_relevant · Full transcript reviewed: management presentation, analyst Q&A and closing/operator remarks; selected explicit AI and adjacent digital/automation/data-center terms screened.

[Webcast – 1st Quarter Results 2026](https://api.mziq.com/mzfilemanager/v2/d/25fdf098-34f5-4608-b7fa-17d60b2de47d/f1a812ff-3fc8-5b09-1709-ddb46cb91add?origin=2)

Official full English interpretation includes management presentation, analyst Q&A and final/operator remarks. Searched AI/artificial intelligence, machine learning, digital/digitalization, automation/autonomy, analytics, software/workflow, data-center/hyperscaler terms; no relevant disclosure found. PDF identifies the call as Q1 2026 and the call was held May 12, 2026. English text is translated from Portuguese.

## Petrobras · Q2 2026

reviewed_no_relevant · Full translated English transcript reviewed through presentation, analyst Q&A, management closing remarks, and replay notice. Screened AI/artificial intelligence, Portuguese inteligência artificial/IA/aprendizado de máquina, digital/automation, and data-center terms.

[Petrobras Q2 2026 earnings-call full transcript (English interpretation)](https://stockanalysis.com/quote/bvmf/PETR4/transcripts/553885-q2-2026/)

Selected-term screen of the full English interpretation found no relevant AI or AI-energy-demand disclosure. Because the accessible transcript is translated from Portuguese, exact original-language wording is not independently searchable here.

## Phillips 66 · Q3 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Phillips 66 Q3 2024 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2024/10/29/phillips-66-psx-q3-2024-earnings-call-transcript/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Phillips 66 · Q4 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Phillips 66 Q4 2024 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2025/01/31/phillips-66-psx-q4-2024-earnings-call-transcript/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Phillips 66 · Q1 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Phillips 66 Q1 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/psx/transcripts/311337-q1-2025/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Phillips 66 · Q2 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Phillips 66 Q2 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/psx/transcripts/339059-q2-2025/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Phillips 66 · Q3 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Phillips 66 Q3 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/psx/transcripts/367404-q3-2025/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Phillips 66 · Q4 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Phillips 66 Q4 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/psx/transcripts/405012-q4-2025/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Phillips 66 · Q1 2026

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Phillips 66 Q1 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/psx/transcripts/551946-q1-2026/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Phillips 66 · Q2 2026

reviewed_relevant · Full transcript reviewed, including prepared remarks and Q&A; keyword-screened for AI/artificial intelligence, ML/machine learning, generative AI, agents/copilots, predictive analytics, autonomy, automation, digital transformation and data-center/AI load references.

[Phillips 66 Q2 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/psx/transcripts/657024-q2-2026/)

### Enterprise AI deployment and established ML in maintenance/turnarounds

CEO Mark Lashier said Phillips 66 has a company-wide AI program that identifies use cases and scales them across the organization. Frontline engineers use AI to extract operational data and deploy solutions faster, augmenting asset performance. He separately said machine learning has been used for years to improve maintenance and shorten turnarounds.

Speaker: Mark Lashier, Chairman and CEO. Locator: Prepared remarks line 159; analyst Phillip Jungwirth Q&A and CEO answer, transcript lines 547-555.. Measurement: Not quantified. Limit: No user, refinery, adoption, cost, throughput or reliability counts were disclosed; the speed comparison is qualitative. The transcript does not quantify AI-attributable performance..

CEO described an enterprise program normalizing AI, frontline engineering use to extract data and deploy solutions faster, and longstanding machine learning for maintenance and turnaround improvement. Q3 2025 contained only an analyst rhetorical AI aside, without substantive management AI disclosure.

## Presidio Production · Q3 2024

reporting_exception · Archive reviewed; predecessor private-company calls not surveyed.

[Presidio financial results archive](https://ir.bypresidio.com/financial-information/financial-results)

The public-company series starts in 2026. This is a reporting exception, not proof that no predecessor call occurred.

## Presidio Production · Q4 2024

reporting_exception · Archive reviewed; predecessor private-company calls not surveyed.

[Presidio financial results archive](https://ir.bypresidio.com/financial-information/financial-results)

The public-company series starts in 2026. This is a reporting exception, not proof that no predecessor call occurred.

## Presidio Production · Q1 2025

reporting_exception · Archive reviewed; predecessor private-company calls not surveyed.

[Presidio financial results archive](https://ir.bypresidio.com/financial-information/financial-results)

The public-company series starts in 2026. This is a reporting exception, not proof that no predecessor call occurred.

## Presidio Production · Q2 2025

reporting_exception · Archive reviewed; predecessor private-company calls not surveyed.

[Presidio financial results archive](https://ir.bypresidio.com/financial-information/financial-results)

The public-company series starts in 2026. This is a reporting exception, not proof that no predecessor call occurred.

## Presidio Production · Q3 2025

reporting_exception · Archive reviewed; predecessor private-company calls not surveyed.

[Presidio financial results archive](https://ir.bypresidio.com/financial-information/financial-results)

The public-company series starts in 2026. This is a reporting exception, not proof that no predecessor call occurred.

## Presidio Production · Q4 2025

reporting_exception · Archive reviewed; predecessor private-company calls not surveyed.

[Presidio financial results archive](https://ir.bypresidio.com/financial-information/financial-results)

The public-company series starts in 2026. This is a reporting exception, not proof that no predecessor call occurred.

## Presidio Production · Q1 2026

reviewed_relevant · Full transcript keyword-screened, including prepared remarks and Q&A; relevant passages reviewed. Editorial summary excluded.

[Presidio Q1 2026 earnings call transcript](https://www.investing.com/news/transcripts/earnings-call-transcript-presidio-production-reports-q1-2026-results-amid-merger-impact-93CH-4693187)

### Well agent and human control

Management said DOUG reaches all field operators; intervention correctness rose from roughly 50% to 70% within weeks. Operators retain the decision and feed back their actions.

Speaker: Will Ulrich, Co-CEO. Locator: Q1 Q&A with Neal Dingmann, lines 183–188. Measurement: ~50% → ~70% correct interventions. Limit: Correctness definition, sample size and independent validation are absent..

### Weekend production and acquisition screening

Management attributed ~1% uplift through April to weekend prioritization and targeted 3–5% for 2026. It also described applying production-gap models to acquisition diligence.

Speaker: Will Ulrich, Co-CEO. Locator: Q1 prepared remarks 116–119; Q&A 179–198. Measurement: ~1% reported; 3–5% target. Limit: Company-reported uplift is distinct from the target. Diligence depends on seller data; no deal return is quantified..

### Gas demand

Management connected gas marketing to data-center growth.

Speaker: Will Ulrich, Co-CEO. Locator: Q1 prepared remarks 89; Q&A 176–178. Measurement: Not quantified. Limit: Demand context, not internal AI adoption..

First public earnings call. Third-party text says AI-supported editorial production; primary recording was not independently replayed.

## Presidio Production · Q2 2026

reviewed_relevant · All filed prepared remarks keyword-screened; document ends before Q&A. Full-call review remains incomplete.

[Presidio Q2 2026 SEC-filed earnings remarks](https://www.sec.gov/Archives/edgar/data/2083125/000121390026089284/ea030206301ex99-2.htm)

### DOUG surveillance

Management reported ~2.3% AI-linked production uplift, including 1.4 percentage points and ~400 boe/d from DOUG.

Speaker: Will Ulrich and Chris Hammack, Co-CEOs. Locator: Prepared remarks, printed pp. 2–3. Measurement: ~2,000 platform wells; $4.5m annualized revenue across initiatives. Limit: Reported attribution lacks an independent counterfactual. Revenue is annualized across initiatives, not net cash or DOUG alone..

### Weekend scheduling

AI prioritizes weekend pumper routes; weekend production increased 2.5%.

Speaker: Will Ulrich and Chris Hammack, Co-CEOs. Locator: Prepared remarks, printed p. 3. Measurement: 2.5% weekend-production increase. Limit: Different population from company-wide uplift; do not add the percentages..

### Plunger controls

Adaptive plunger boxes on 24 wells raised aggregate gas from 2,680 to 2,946 Mcf/d.

Speaker: Will Ulrich and Chris Hammack, Co-CEOs. Locator: Prepared remarks, printed p. 3. Measurement: ~10% on 24 wells; ~0.2% company-wide. Limit: Before/after comparison, no matched control or implementation cost disclosed..

Filed document omits Q&A. Keep partial review visible despite relevant prepared-remarks findings.

## Range Resources · Q3 2024

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Range Resources (RRC) Q3 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/rrc/transcripts/215354-q3-2024/)

### AI and regional data-center/power demand

Q&A discussed whether utilities, independent power producers and data centers would seek long-term gas agreements; conversations were early and pricing/terms still developing. No AI linkage.

Speaker: Mark Scucchi, CFO. Locator: Q&A lines 453–459.. Measurement: No AI-specific contracted volume or financial value quantified.. Limit: Future projects, discussions and demand forecasts are not treated as executed supply deals..

Energy-demand context is classified separately where the call did not explicitly link demand to AI. No unannounced deals are inferred.

## Range Resources · Q4 2024

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Range Resources (RRC) Q4 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/rrc/transcripts/237805-q4-2024/)

### AI and regional data-center/power demand

Management anticipated in-basin power, AI and data-center growth opportunities and said existing transport could support future supply, without a commitment.

Speaker: Range management. Locator: Q&A lines 243–244 and 321.. Measurement: No AI-specific contracted volume or financial value quantified.. Limit: Future projects, discussions and demand forecasts are not treated as executed supply deals..

Energy-demand context is classified separately where the call did not explicitly link demand to AI. No unannounced deals are inferred.

## Range Resources · Q1 2025

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Range Resources (RRC) Q1 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/rrc/transcripts/319626-q1-2025/)

### AI and regional data-center/power demand

Management discussed AI data centers and power projects as drivers of future regional gas demand and considered future dedicated pricing structures for existing supply.

Speaker: Range management. Locator: Q&A lines 557–558 and 568–569.. Measurement: No AI-specific contracted volume or financial value quantified.. Limit: Future projects, discussions and demand forecasts are not treated as executed supply deals..

Energy-demand context is classified separately where the call did not explicitly link demand to AI. No unannounced deals are inferred.

## Range Resources · Q2 2025

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Range Resources (RRC) Q2 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/rrc/transcripts/337940-q2-2025/)

### AI and regional data-center/power demand

CEO tied Pennsylvania gas demand and potential long-term supply to announced AI-power and infrastructure investment. Management discussed data-center supply opportunities and pricing, but no executed contract.

Speaker: Dennis Degner, CEO; Mark Scucchi, CFO. Locator: Prepared remarks lines 121–124; Q&A lines 526–541.. Measurement: No AI-specific contracted volume or financial value quantified.. Limit: Future projects, discussions and demand forecasts are not treated as executed supply deals..

Energy-demand context is classified separately where the call did not explicitly link demand to AI. No unannounced deals are inferred.

## Range Resources · Q3 2025

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Range Resources (RRC) Q3 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/rrc/transcripts/366121-q3-2025/)

### AI and regional data-center/power demand

Management discussed regional gas-fired power/data-center projects, Fort Cherry and an estimated 2.5 Bcf/d of Northeastern data-center demand potential by decade-end. No explicit AI link.

Speaker: Range management. Locator: Prepared remarks lines 128–129; Q&A lines 392–393.. Measurement: No AI-specific contracted volume or financial value quantified.. Limit: Future projects, discussions and demand forecasts are not treated as executed supply deals..

Energy-demand context is classified separately where the call did not explicitly link demand to AI. No unannounced deals are inferred.

## Range Resources · Q4 2025

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Range Resources (RRC) Q4 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/rrc/transcripts/404970-q4-2025/)

### AI and regional data-center/power demand

Management supported prospective power-generation and data-center projects across its transport footprint. No explicit AI link.

Speaker: Range management. Locator: Prepared remarks line 127.. Measurement: No AI-specific contracted volume or financial value quantified.. Limit: Future projects, discussions and demand forecasts are not treated as executed supply deals..

Energy-demand context is classified separately where the call did not explicitly link demand to AI. No unannounced deals are inferred.

## Range Resources · Q1 2026

reviewed_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Range Resources (RRC) Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/rrc/transcripts/568494-q1-2026/)

### AI and regional data-center/power demand

CEO described ongoing dialogue with a dozen-plus data-center/power projects, including a Midwest power-link structure and Fort Cherry. No explicit AI link.

Speaker: Dennis Degner, CEO. Locator: Q&A lines 190–197.. Measurement: No AI-specific contracted volume or financial value quantified.. Limit: Future projects, discussions and demand forecasts are not treated as executed supply deals..

Energy-demand context is classified separately where the call did not explicitly link demand to AI. No unannounced deals are inferred.

## Range Resources · Q2 2026

reviewed_no_relevant · Full transcript reviewed and keyword-screened; prepared remarks and Q&A included.

[Range Resources (RRC) Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/rrc/transcripts/652916-q2-2026/)

No qualifying disclosure found in the full call.

## Repsol · Q3 2024

reviewed_relevant · Full 20-page transcript reviewed; prepared remarks and Q&A to operator close; screened AI, artificial intelligence, machine learning, analytics, digital, automation, software and data-center terms.

[Q3 2024 Repsol SA Earnings Call (LSEG edited transcript)](https://www.repsol.com/content/dam/repsol-corporate/en_gb/accionistas-e-inversores/resultados/2024/transcript-webcast-3q24.pdf)

### Waylet digital retail app scale

CEO Josu Jon Imaz said Repsol’s Waylet app continued to expand its multi-energy offering. Digital clients were nearly 9 million at end-September; the app added more than 3,000 users per day and had reached 230,000 daily transactions. No AI, ML or automated decision capability was described.

Speaker: Josu Jon Imaz San Miguel, Chief Executive Officer. Locator: Prepared remarks, PDF p.4, lines 135–138.. Measurement: Nearly 9m digital clients; >3,000 new users/day; 230,000 daily transactions.. Limit: Digital-channel scale only; no AI method, operational workflow, economics or attribution to earnings disclosed..

Full edited transcript with presentation and Q&A; no explicit AI, ML or data-center disclosure. Strategy finding records broad digital-channel scale only.

## Repsol · Q4 2024

reviewed_relevant · Full 24-page transcript reviewed, prepared remarks and Q&A to close; searched AI, ML, data-center, digital, analytics, automation and software terms.

[Q4 2024 Repsol SA Earnings Call (LSEG edited transcript)](https://www.repsol.com/content/dam/repsol-corporate/en_gb/accionistas-e-inversores/resultados/2024/transcript-webcast-4q24.pdf)

### AI/data-center-related U.S. power and gas demand; Spanish data-center site option

CEO Josu Jon Imaz cited data centers as one structural source of U.S. demand growth, alongside industrial demand, supporting renewable PPAs and the need for dispatchable gas. Separately, he said Repsol received 402 MW of interconnection capacity in Aragón and was preparing a package of land/water, a potential PPA and renewables to monetize with a third-party data-center investor. The call did not explicitly connect either demand item to AI.

Speaker: Josu Jon Imaz San Miguel, Chief Executive Officer. Locator: Q&A PDF pp.11–12 lines 408–429; PDF pp.16–17 lines 582–591; prepared remarks PDF p.5 lines 163–168.. Measurement: 402 MW authorized interconnection capacity; no customer, signed PPA, power capacity, gas volume or contract disclosed.. Limit: Market commentary and a site monetization option, not an executed data-center supply deal; AI was not mentioned or attributed as workload driver; Repsol said the planned data-center investment would be by a third party..

### Waylet customer-channel reach

CEO said Repsol had 9.3 million digital clients at year-end 2024, up 17% year-on-year; the customer count contributed to retail sales at service stations.

Speaker: Josu Jon Imaz San Miguel, Chief Executive Officer. Locator: Prepared remarks PDF p.4, lines 137–139.. Measurement: 9.3m digital clients; +17% year-on-year.. Limit: Digital-channel metric only; no AI or operational automation described..

Full transcript reviewed through Q&A close. Data-center/gas demand comments are not AI-specific; Spanish development is a third-party investment opportunity, not a Repsol-funded project.

## Repsol · Q1 2025

reviewed_relevant · Full 496-line transcript reviewed from operator opening through Q&A close; screened AI, artificial intelligence, Spanish IA terms, machine learning, data centers, analytics, digital, automation and software.

[Repsol (BME:REP) Q1 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/bme/REP/transcripts/296308-q1-2025/)

### Digital retail-client scale

CEO Josu Jon Imaz reported 9.6 million digital clients, 16% above the year-earlier period, with users of the Waylet app contributing. Repsol also reported 2.6 million power and gas retail customers. No AI or ML capability was connected to these metrics.

Speaker: Josu Jon Imaz San Miguel, Chief Executive Officer. Locator: Prepared remarks lines 138–142.. Measurement: 9.6m digital clients, +16% year-on-year; 2.6m power and gas customers.. Limit: Customer-channel growth, not an AI deployment or quantified technology return..

Full call text includes prepared remarks and Q&A. Screen found no explicit AI/ML/data-center mention; broad digital-client metric is recorded separately.

## Repsol · Q2 2025

reviewed_relevant · Full 22-page transcript reviewed through final Q&A close; screened AI, artificial intelligence, IA/inteligencia, ML, data centers, analytics, automation, software and digital.

[Q2 2025 Repsol SA Earnings Call (LSEG edited transcript)](https://www.repsol.com/content/dam/repsol-corporate/es/accionistas-e-inversores/resultados/2025/2t/transcripcion-webcast-2t25.pdf)

### AI and data-center-driven U.S. renewable-power demand

CEO Josu Jon Imaz said AI, data centers and industrial activity were increasing U.S. power needs and renewable-PPA appetite; he viewed renewables as the near-term resource able to meet growth while new gas-fired capacity faced equipment and construction bottlenecks. Repsol had 5 GW of U.S. renewable pipeline expected to benefit from tax credits, but no portion was assigned to AI/data-center customers.

Speaker: Josu Jon Imaz San Miguel, Chief Executive Officer. Locator: Q&A PDF pp.6–7, lines 224–243.. Measurement: 5 GW renewables pipeline supported by U.S. tax credits; no AI/data-center allocation, signed PPA, customer or contracted volume disclosed.. Limit: Market-demand commentary and company pipeline context; no AI-specific project or sales attributable to Repsol..

### Digital customer-channel reach

Management said digital clients, including wallet users, reached 10.1 million at end-June, 17% above the comparable 2024 period.

Speaker: Josu Jon Imaz San Miguel, Chief Executive Officer. Locator: Prepared remarks PDF p.4, lines 143–147.. Measurement: 10.1m digital clients; +17% year-on-year.. Limit: Digital-channel scale only; no AI method or workflow described..

Full edited transcript, including Q&A. AI/data-center demand appears in CEO answer; company pipeline is not represented as contracted AI load.

## Repsol · Q3 2025

reviewed_relevant · Full 19-page transcript reviewed, prepared remarks and Q&A to close; searched English and Spanish AI terms plus digital, analytics, automation, software, platforms and data centers.

[REP.MC - Q3 2025 Repsol SA Earnings Call (LSEG edited transcript)](https://www.repsol.com/content/dam/repsol-corporate/en_gb/accionistas-e-inversores/resultados/2025/transcript-webcast-3q-2025.pdf)

### Digital retail app scale

CEO Josu Jon Imaz cited nearly 10 million users of Repsol’s Waylet app as evidence of digital leadership in retail. He also referenced almost 4,000 sales points and 24 million customers across Spain and Portugal. The transcript did not connect this retail channel to AI, machine learning, predictive analytics or automation.

Speaker: Josu Jon Imaz San Miguel, Chief Executive Officer. Locator: PDF p.17, lines 627–630.. Measurement: Nearly 10m app users; almost 4,000 sales points; 24m customers in Spain and Portugal.. Limit: Digital-channel scale only; no AI technology, workflow integration, economics or causal link to earnings disclosed..

Official Repsol archive lists Q3 2025 webcast transcript; source PDF is hosted by Repsol and LSEG edited. Full call transcript includes prepared remarks and Q&A.

## Repsol · Q4 2025

reviewed_relevant · Full 20-page transcript reviewed from prepared remarks through last Q&A; screened English/Spanish AI, digital, analytics, data-center, automation, software and platform terms.

[Q4 2025 Repsol SA Earnings Call (LSEG edited transcript)](https://www.repsol.com/content/dam/repsol-corporate/es/accionistas-e-inversores/resultados/2025/4t/transcripcion-webcast-4t25.pdf)

### Renewable power positioned for a third-party data center

CEO Josu Jon Imaz said Repsol had developed an 805 MW wind pipeline in Spain to hybridize its Escatrón combined-cycle generation and secure power for a future data center built by a third party. The transcript did not say the facility serves AI workloads.

Speaker: Josu Jon Imaz, Chief Executive Officer. Locator: Prepared remarks, PDF p.5, lines 178–180.. Measurement: 805 MW wind pipeline; no data-center capacity, buyer, signed supply contract or commissioning date disclosed.. Limit: Future third-party data-center project and energy option; not AI-specific and no customer contract described..

### Waylet digital retail engagement

CEO reported 10.8 million digital clients in December 2025, up 16% year-on-year; Waylet reached 89 million transactions during 2025, 10% above 2024.

Speaker: Josu Jon Imaz, Chief Executive Officer. Locator: Prepared remarks, PDF p.4, lines 153–155.. Measurement: 10.8m digital clients; 89m app transactions in 2025, +10% year-on-year.. Limit: Digital retail-channel metrics only; no AI or workflow automation disclosed..

Official Repsol archive lists recorded webcast and transcript. Source is issuer-hosted, LSEG edited; call date is event date (19 February), not later transcript publication date. Full Q&A close verified.

## Repsol · Q1 2026

reviewed_relevant · Full 681-line transcript reviewed from operator opening through Q&A close; screened AI/IA, artificial intelligence, digital, analytics, data-center, automation, software and technology terms.

[Repsol (BME:REP) Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/bme/REP/transcripts/532560-q1-2026/)

### Digital customer-channel scale

CEO Josu Jon Imaz said Repsol’s digital-client base reached 11.2 million at quarter-end, up 17% year-on-year, with Waylet the main contributor. Management also linked app discounts to customer-value initiatives but did not describe AI or automated decision systems.

Speaker: Josu Jon Imaz, Chief Executive Officer. Locator: Prepared remarks, lines 147–153.. Measurement: 11.2m digital clients; +17% year-on-year.. Limit: Digital retail channel only; no AI, analytics workflow or quantified technology return described..

Full transcript and Q&A reviewed. No AI/data-center references; included digital channel metric under broad adjacency screen.

## Repsol · Q2 2026

reviewed_relevant · Full 646-line transcript reviewed through the Q&A close; screened AI, artificial intelligence, IA/inteligencia, digital, analytics, data centers, automation and software.

[Repsol (BME:REP) Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/bme/REP/transcripts/547577-q2-2026/)

### Waylet digital retail client reach

CEO Josu Jon Imaz reported 11.6 million digital clients in Q2 2026, up 15% from Q2 2025, with Waylet as the main contributor. The transcript did not describe AI, ML, or automation in the app.

Speaker: Josu Jon Imaz, Chief Executive Officer. Locator: Prepared remarks, line 153.. Measurement: 11.6m digital clients; +15% year-on-year.. Limit: Digital-channel reach only; no AI functionality or financial result attributed..

Full third-party StockAnalysis/Quartr transcript reviewed, including Q&A. The prior no-relevant status was tightened to relevant for the adjacent digital-channel metric.

## Santos · H2 2024

reviewed_relevant · Partial transcript text reviewed. The public page states that it displays the first half of the call; full remainder is gated behind a paid API. Relevant prepared remarks and early Q&A passages are visible; no complete-call screening claimed.

[Santos 2024 Full-Year Results Webcast transcript (partial public text)](https://earningscalls.dev/transcripts/santos-limited_sto_earnings_call_transcript_2025-02-19)

### AI for field surveillance, production optimization and remote operations

CEO Kevin Gallagher said Santos was integrating AI and related advanced technologies to optimize field surveillance and production, support remote operations, and pursue efficiency improvements across the business.

Speaker: Kevin Gallagher, Managing Director and Chief Executive Officer. Locator: Prepared remarks, lines 84–88.. Measurement: No deployment count, saving or production uplift was quantified.. Limit: The remarks state focus areas and intended outcomes but give no implementation detail, date, measured result or AI-attributed economics. The public transcript page exposes only the first half of the call, so this is a partial screen..

### AI opportunity for corporate-center process automation

In Q&A on the cost-out program, Gallagher identified AI and other technologies as an opportunity to automate labor-intensive corporate-center processes. The wider annual structural-savings target covered operating cost and sustaining capital and was not allocated to AI.

Speaker: Kevin Gallagher, Managing Director and Chief Executive Officer. Locator: Q&A with Dale Koenders, lines 101–111; CFO target context, lines 51–55.. Measurement: Management targeted US$100–150 million in annual structural savings over 1–2 years.. Limit: The target covered a broad cost review, including operating cost and sustaining CapEx; no portion, timing or realized savings was attributed to AI. This is a partial transcript screen..

Santos issuer archive confirms the Feb. 19 2025 full-year results webcast and publishes the presentation and Q&A audio, but no transcript PDF was located. The accessible third-party page exposes only the first half and gates the rest behind a paid API; findings are therefore partial and do not imply full-call screening.

## Santos · H1 2025

reviewed_relevant · Full transcript reviewed, including prepared remarks, analyst Q&A and operator close; screened AI/artificial intelligence, ML, GenAI, agentic, analytics, predictive, automation, software, digital, data-center and hyperscaler terms.

[Santos H1 2025 results full earnings-call transcript](https://stockanalysis.com/quote/asx/STO/transcripts/318992-h1-2025/)

### AI for field surveillance, production optimization and remote operations

CEO Kevin Gallagher said Santos was integrating AI and related advanced technologies to optimize field surveillance and production, support remote operations, and pursue efficiency improvements across the business. In Q&A he also identified corporate-center process automation as an AI opportunity.

Speaker: Kevin Gallagher, CEO and Managing Director. Locator: Prepared remarks, lines 84–88; Q&A with Dale Koenders, lines 101–111.. Measurement: The wider annual structural-savings target was US$100–150 million over 1–2 years.. Limit: The savings target covers operating cost and sustaining CapEx broadly; no portion or realized savings was attributed to AI. Management gave no AI deployment count, timing or measured operating uplift..

### Cooper Basin modernization and production-data flow

COO Brett Darley described a Moomba Central optimization project to modernize older, less reliable Cooper Basin facilities, reduce operating costs and staffing needs, and improve data flow from constrained producing areas.

Speaker: Brett Darley, Chief Operating Officer, Santos. Locator: Q&A, lines 534–545, especially 537–540.. Measurement: No headcount or savings amount was given for the modernization work.. Limit: No specific digital system, automation deployment or AI capability was identified; this is a broader operations-modernization disclosure..

Full transcript and Q&A through close reviewed. AI/automation remarks are qualitative; broad cost target is not treated as an AI-attributed result. No data-center demand disclosure found.

## Santos · H2 2025

reviewed_relevant · Full transcript reviewed, including prepared remarks, analyst Q&A and operator close; screened AI/artificial intelligence, ML, GenAI, agentic, analytics, predictive, automation, software, digital, data-center and hyperscaler terms.

[Santos 2025 Full-Year Results full earnings-call transcript](https://stockanalysis.com/quote/asx/STO/transcripts/400120-h2-2025/)

### Barossa commissioning software issue

CEO Kevin Gallagher said a heat-sensor configuration issue required a reset across Barossa facilities and caused a two-to-three-week delay; he described it as a software issue. He also noted a separate systemic review of safety-water connections and a precautionary half-rate period while compressors were repaired.

Speaker: Kevin Gallagher, Managing Director and Chief Executive Officer. Locator: Q&A with Henry Meyer, lines 495–505.. Measurement: Two to three weeks; settings were reset on 300 Barossa facilities.. Limit: The operational incident was a commissioning reliability issue. No AI/ML capability, software economics or quantified financial impact was disclosed..

Full transcript includes Q&A and operator close. Software-reliability issue is logged as adjacent digital/operations context, not as an AI disclosure; no data-center demand mention found.

## Santos · H1 2026

reviewed_no_relevant · Full half-year results transcript reviewed through prepared remarks, analyst Q&A and closing remarks. Screened AI/artificial intelligence, ML, automation, digital transformation, data-center/hyperscaler terms.

[Santos H1 2026 earnings-call full transcript](https://stockanalysis.com/quote/asx/STO/transcripts/559432-h1-2026/)

Santos reports semiannually; no Q3/Q1 trading-call rows are inferred. Selected-term full-call screen found no relevant AI or AI-energy-demand passage.

## Shell · Q3 2024

reviewed_no_relevant · Full transcript reviewed, including Q&A; searched AI, artificial intelligence, machine learning, automation, data center, hyperscaler, and digital transformation.

[Shell PLC Q3 2024 Earnings Call Transcript](https://earningscall.biz/e/nyse/s/shel/y/2024/q/q3)

A complete third-party transcript with Q&A was located after the prepared-remarks-only Alpha Spread page. Full-call term search found no relevant AI or AI-demand evidence. The transcript provider disclaims accuracy; no relevant passage was found to corroborate against issuer text.

## Shell · Q4 2024

reviewed_relevant · Full transcript reviewed, including Q&A; searched AI, artificial intelligence, machine learning, automation, data center, hyperscaler, and digital transformation.

[Shell PLC Q4 2024 Earnings Call Transcript](https://earningscall.biz/e/nyse/s/shel/y/2024/q/q4)

### AI among technology options for process-safety improvement

CEO Wael Sawan grouped AI with sensors and robotics as newer technologies already beginning to affect Shell’s effort to reverse process-safety trends. He disclosed no specific AI system, site, incident-detection result, or quantified safety outcome.

Speaker: Wael Sawan, CEO. Locator: Prepared remarks, lines 32–38; Q&A follows at lines 65–74.. Measurement: No AI-specific metric or outcome.. Limit: High-level CEO attribution to emerging safety technologies; transcript does not isolate AI’s role or substantiate a result. Computer-generated third-party transcript may contain errors..

Full transcript and Q&A located through EarningsCall. The provider labels the transcript computer-generated and cautions about accuracy; passage is paraphrased and limited to the management statement. Actual issuer results-announcement date is 2025-01-30; a separate ROIC listing dated 2025-01-01 is inconsistent and was not used.

## Shell · Q1 2025

reviewed_relevant · Full transcript read/search reviewed for AI, artificial intelligence, machine learning, agentic, automation, data-center, hyperscaler, and digital-transformation terms; Q&A confirmed present.

[Shell plc (LON:SHEL) Q1 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/lon/SHEL/transcripts/252544-q1-2025/)

### AI use and disclosure

CEO described AI as one element of Shell’s productivity/cost agenda and tied it to efforts to improve productivity and supply-chain performance.

Speaker: Wael Sawan, CEO. Locator: Q&A, lines 430–451. Measurement: No AI-specific quantified outcome stated.. Limit: No AI-specific savings, deployment scope, or outcome quantified..

Full transcript was searched; no additional relevant energy-demand finding was identified except the separate hyperscaler/power disclosure summarized for Q3 2025.

## Shell · Q2 2025

reviewed_relevant · Full transcript read/search reviewed for AI, artificial intelligence, machine learning, agentic, automation, data-center, hyperscaler, and digital-transformation terms; Q&A confirmed present.

[Shell plc (LON:SHEL) Q2 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/lon/SHEL/transcripts/311998-q2-2025/)

### AI use and disclosure

CEO said functions were simplifying and applying AI, with supply-chain standardization and procurement highlighted as future opportunity areas.

Speaker: Wael Sawan, CEO. Locator: Q&A, lines 229–238. Measurement: No AI-specific quantified outcome stated.. Limit: No AI-specific measured savings or implementation scale disclosed; overall cost-reduction target is not attributable to AI..

Full transcript was searched; no additional relevant energy-demand finding was identified except the separate hyperscaler/power disclosure summarized for Q3 2025.

## Shell · Q3 2025

reviewed_relevant · Full transcript read/search reviewed for AI, artificial intelligence, machine learning, agentic, automation, data-center, hyperscaler, and digital-transformation terms; Q&A confirmed present.

[Shell plc (LON:SHEL) Q3 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/lon/SHEL/transcripts/395219-q3-2025/)

### AI use and disclosure

CEO said AI/data signals were being used to detect operating issues before platform trips, and that AI was increasingly used in trading. Separately, Shell said it was in discussions with hyperscalers, citing its UK Google renewable-power deal and potential U.S. operations through Savion.

Speaker: Wael Sawan, CEO. Locator: Q&A, lines 174–185. Measurement: Qualitative operating and trading use; no AI-attributed metric.. Limit: Operational benefit described qualitatively without AI-specific availability or financial attribution. Google deal is a cited power supply arrangement, not evidence of a new AI-related contract in this call..

Full transcript was searched; no additional relevant energy-demand finding was identified except the separate hyperscaler/power disclosure summarized for Q3 2025.

## Shell · Q4 2025

reviewed_relevant · Full transcript read/search reviewed for AI, artificial intelligence, machine learning, agentic, automation, data-center, hyperscaler, and digital-transformation terms; Q&A confirmed present.

[Shell plc (LON:SHEL) Q4 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/lon/SHEL/transcripts/394530-q4-2025/)

### AI use and disclosure

CEO linked AI and agentic AI to the $5–7bn structural-cost-reduction program and described data cleanup and ERP/energy-trading-system modernization as foundations for organization-wide scaling; upstream uses include subsurface interpretation, exploration, proactive technical monitoring, and maintenance.

Speaker: Wael Sawan, CEO. Locator: Q&A, lines 392–417. Measurement: No AI-specific quantified outcome stated.. Limit: Management said it was still learning and was not yet booking cost reductions from agentic AI; actual bottom-line impact remained unquantified..

Full transcript was searched; no additional relevant energy-demand finding was identified except the separate hyperscaler/power disclosure summarized for Q3 2025.

## Shell · Q1 2026

reviewed_relevant · Full transcript read/search reviewed for AI, artificial intelligence, machine learning, agentic, automation, data-center, hyperscaler, and digital-transformation terms; Q&A confirmed present.

[Shell plc (LON:SHEL) Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/lon/SHEL/transcripts/415692-q1-2026/)

### AI use and disclosure

Management said it was embedding AI in data-enabled workflows for existing reservoirs and using similar workflows to unlock frontier-exploration opportunities; the CEO said timing of success was too early to call. AI was also mentioned as one component of national digital strategies dependent on energy.

Speaker: Wael Sawan, CEO. Locator: Q&A, lines 174–182; additional digital strategy context around line 550. Measurement: No AI-specific quantified outcome stated.. Limit: No validated reserve additions, exploration success rate, or AI-related revenue/energy demand estimate disclosed..

Full transcript was searched; no additional relevant energy-demand finding was identified except the separate hyperscaler/power disclosure summarized for Q3 2025.

## Shell · Q2 2026

reviewed_relevant · Full transcript read/search reviewed for AI, artificial intelligence, machine learning, agentic, automation, data-center, hyperscaler, and digital-transformation terms; Q&A confirmed present.

[Shell plc (LON:SHEL) Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/lon/SHEL/transcripts/550257-q2-2026/)

### AI use and disclosure

Management framed AI as a potential lever for future value creation and asked the organization to explore additional value opportunities. This was a strategic prompt, not a reported implementation or measured outcome.

Speaker: Wael Sawan, CEO. Locator: Q&A, lines 250–260. Measurement: No AI-specific quantified outcome stated.. Limit: No implementation, project, quantified value, or delivery timing was specified..

Full transcript was searched; no additional relevant energy-demand finding was identified except the separate hyperscaler/power disclosure summarized for Q3 2025.

## SLB · Q3 2024

reviewed_relevant · full transcript reviewed; keyword-screened for artificial intelligence/AI, machine learning, GenAI/generative, agentic, autonomy, automation, predictive analytics, copilots and digital transformation

[Q3 2024 SLB earnings call edited transcript](https://investorcenter.slb.com/static-files/be2cf86b-8f90-4bb9-ba88-21073bc1b316)

### Lumi platform and AI/ML-enabled service portfolio

SLB said it offered roughly 150 AI/ML capabilities in its products and solutions, launched the Lumi data-and-AI platform, and announced a generative-AI collaboration with NVIDIA.

Speaker: Olivier Le Peuch, CEO. Locator: pp. 2-3, 11-12. Measurement: ~150 capabilities stated by CEO. Limit: Company/product claims; not evidence of operator-internal AI adoption or quantified outcomes..

### Offshore drilling automation/autonomous operations

CEO said SLB was already deploying well-construction automation/optimization and autonomous operations on some offshore rigs.

Speaker: Olivier Le Peuch, CEO. Locator: pp. 6-7. Measurement: some offshore rigs; count not disclosed. Limit: Automation scope; not explicitly stated to be AI in this passage..

Full official IR-hosted Refinitiv transcript reviewed by keyword. SLB said it offered about 150 AI/ML capabilities across products/solutions, launched Lumi data-and-AI platform, announced NVIDIA generative-AI collaboration, and described automation/autonomous operations in some offshore rigs. Product/vendor and broad customer solution claims, not a quantified internal adoption or realized-impact disclosure.

## SLB · Q4 2024

reviewed_relevant · full transcript reviewed; keyword-screened for artificial intelligence/AI, machine learning, GenAI/generative, agentic, autonomy, automation, predictive analytics, copilots and digital transformation

[Q4 2024 SLB earnings call edited transcript](https://investorcenter.slb.com/static-files/2ca00ce1-37f5-4246-ac40-c7d1d750cc47)

### Lumi AI platform customer pilots

CEO described early customer interest and pilots following Lumi launch; he said it was too early to disclose revenue/growth.

Speaker: Olivier Le Peuch, CEO. Locator: pp. 12-14, Q&A. Measurement: Not quantified. Limit: Unquantified early tests/pilots; no named operator workflow or outcome established here..

Full official IR-hosted Refinitiv transcript reviewed. CEO described Lumi AI platform, early customer interest/pilots and a few customer examples to follow; he said it was weeks after launch and too early to disclose revenue/growth. This is vendor/product strategy; named operating deployments and outcomes were not established in the passage reviewed.

## SLB · Q1 2025

reviewed_relevant · full transcript reviewed; keyword-screened for artificial intelligence/AI, machine learning, GenAI/generative, agentic, autonomy, automation, predictive analytics, copilots and digital transformation

[Q1 2025 SLB earnings call edited transcript](https://investorcenter.slb.com/static-files/119710f3-76e7-4bee-9a3f-1434226c7845)

### AI data-center demand and Digital growth strategy

CEO framed AI as a growth driver for SLB’s data-center solutions and a digital opportunity.

Speaker: Olivier Le Peuch, CEO. Locator: p. 3. Measurement: Not quantified. Limit: Demand-side/strategic framing; no AI deployment or AI-specific sales metric in passage..

Full official IR-hosted Refinitiv transcript reviewed. AI is framed as a digital growth opportunity and demand driver for SLB’s data-center solutions. No named customer AI workflow or AI-specific revenue/impact metric surfaced in keyword screening; keep AI-energy-demand separate from operational AI adoption.

## SLB · Q2 2025

reviewed_relevant · full transcript reviewed; keyword-screened for artificial intelligence/AI, machine learning, GenAI/generative, agentic, autonomy, automation, predictive analytics, copilots and digital transformation

[Q2 2025 SLB earnings call edited transcript](https://investorcenter.slb.com/static-files/d1fa7696-b0a7-40bb-a77f-b6ca62def018)

### Lumi platform early AI/GenAI adoption

CEO described early tests/adoption of Lumi as a customer platform for data and AI workflows, with AI tools from partners.

Speaker: Olivier Le Peuch, CEO. Locator: p. 14, Q&A. Measurement: Not quantified. Limit: Early-stage, qualitative; no deployment count, named workflow, AI revenue or outcomes..

Full official IR-hosted Refinitiv transcript reviewed. CEO described early tests/adoption and customer use of Lumi as an open data/AI platform with partner AI tools and GenAI/AI workflows; he characterized adoption as early-stage. No deployment counts, named end-user workflows, revenue or outcomes disclosed in the surfaced passage.

## SLB · Q3 2025

reviewed_relevant · full transcript reviewed; keyword-screened for artificial intelligence/AI, machine learning, GenAI/generative, agentic, autonomy, automation, predictive analytics, copilots and digital transformation

[Q3 2025 SLB earnings call edited transcript](https://investorcenter.slb.com/static-files/1139ee84-e9dd-4cd2-abb9-cddc095b1c23)

### AIQ ENERGYai for ADNOC; Lumi-enabled customer deployment

SLB announced collaboration to deploy AIQ’s agentic ENERGYai solution for ADNOC, powered by Lumi; CEO characterized broader adoption as early.

Speaker: Olivier Le Peuch, CEO. Locator: pp. 2, 14-15. Measurement: CEO said only a few of 1,500 customers had adopted platform/AI. Limit: Vendor/customer product deployment, not SLB internal AI. Broader Digital Operations/asset metrics are not AI-specific..

### Digital Operations automation scale

SLB reported automated drilling footage growth and connected field assets as indicators of digital operations expansion.

Speaker: Olivier Le Peuch, CEO. Locator: pp. 2, 5, 8. Measurement: automated drilling footage +50% y/y; >20,000 connected assets. Limit: Digital/automation metrics, not an AI-only adoption, revenue or outcome measure..

Full official IR-hosted Refinitiv transcript reviewed. SLB reported an AIQ collaboration to deploy its ENERGYai agentic-AI solution for ADNOC, powered by SLB Lumi; it also discussed AI-enabled products/platforms and automation. CEO said adoption was early: 1,500 customers, only a few adopting Lumi/AI at that point. Digital Operations revenue rose 39% sequentially and connected assets exceeded 20,000, but these are digital-business metrics and not AI-only adoption/revenue/outcomes.

## SLB · Q4 2025

reviewed_relevant · full transcript reviewed; keyword-screened for artificial intelligence/AI, machine learning, GenAI/generative, agentic, autonomy, automation, predictive analytics, copilots and digital transformation

[Q4 2025 SLB earnings call edited transcript](https://investorcenter.slb.com/static-files/377e2aaf-38e0-451d-a3c3-84818ac2dc5f)

### Tela agentic AI assistant and ADNOC Production System Optimization

CEO announced Tela, an agentic AI assistant for upstream energy, and a partnership with ADNOC to launch an AI-powered production-system optimization platform.

Speaker: Olivier Le Peuch, CEO. Locator: p. 3. Measurement: Digital ARR $1bn, +15% y/y (not AI-specific). Limit: Vendor/product launches; broad Digital ARR cannot be attributed to AI. Existing source S04 already linked to AI-008/AI-009..

Full official IR-hosted LSEG transcript reviewed. CEO disclosed launch of Tela, an agentic-AI assistant for upstream energy, and an ADNOC partnership to launch an AI-powered Production System Optimization platform. Digital ARR reached $1bn (+15% y/y), but not identified as AI revenue. Existing source S04 has AI-008 and AI-009 source claims; avoid duplicate initiative records.

## SLB · Q1 2026

reviewed_relevant · full transcript reviewed; keyword-screened for artificial intelligence/AI, machine learning, GenAI/generative, agentic, autonomy, automation, predictive analytics, copilots and digital transformation

[Q1 2026 SLB earnings call edited transcript](https://investorcenter.slb.com/static-files/6746a7fc-c01f-40fb-b091-c368ffa4f784)

### Lumi/Tela partnership and customer AI workflows

CEO described S&P Global Energy partnership using its datasets with Lumi/Tela and domain foundation models, plus adoption of AI/software workflows including drilling automation and autonomous ESP production.

Speaker: Olivier Le Peuch, CEO. Locator: p. 10; pp. 12-13. Measurement: Not quantified. Limit: Vendor/product and customer-adoption narrative; no customer count, AI-specific revenue or measured operational result..

Full official IR-hosted LSEG transcript reviewed. SLB described a strategic partnership with S&P Global Energy combining datasets with Lumi/Tela and domain foundation models, and customer adoption of AI/software workflows such as drilling automation and autonomous ESP production. These are vendor/product/customer-adoption statements; no adoption count, AI-specific revenue, or measured operational outcome disclosed in reviewed passages.

## SLB · Q2 2026

reviewed_relevant · full transcript reviewed; keyword-screened for artificial intelligence/AI, machine learning, GenAI/generative, agentic, autonomy, automation, predictive analytics, copilots and digital transformation

[Q2 2026 SLB earnings call edited transcript](https://investorcenter.slb.com/static-files/b5556335-a86a-48f3-8ab5-6f7470178112)

### AI/digital operations and AI infrastructure demand

CEO described AI/digital solutions across upstream workflows and customer adoption of autonomous/automated drilling and production; separately discussed SLB data-center infrastructure exposure to AI demand.

Speaker: Olivier Le Peuch, CEO. Locator: pp. 3-4, 14-15. Measurement: Data Center Solutions targeted >$2bn annualized run rate exiting 2027 (business-wide, AI-demand infrastructure). Limit: AI infrastructure revenue is not operator AI adoption; no AI-only revenue or quantified operating outcomes disclosed..

Full official IR-hosted LSEG transcript reviewed. CEO positioned Digital Operations and AI as growth drivers and described workflows from subsurface interpretation/well delivery to production optimization and autonomous operations; Q&A referred to customer adoption of autonomous/automated drilling and production solutions. SLB separately said Data Center Solutions would exceed a $2bn annualized revenue run rate exiting 2027 and called itself an infrastructure partner to the AI economy; this is AI-energy-demand exposure, not evidence of its own operational AI use. No AI-only revenue or quantified customer operating outcome disclosed.

## SM Energy · Q3 2024

reviewed_relevant · Full prepared remarks and full Q&A reviewed together; searched AI/artificial intelligence, GenAI, machine learning, analytics, automation, digital transformation, predictive/autonomous/agentic/copilot and data-center/power-demand terms.

[SM Energy Q3 2024 Results Q&A Transcript; paired with Q3 prepared remarks](https://stockanalysis.com/stocks/sm/transcripts/220122-q3-2024-q-a/)

### Operational technology and geoscience capability

Management cited operational technology and innovation, including technical/geoscience expertise used to develop resource opportunities and inventory.

Speaker: Herbert Vogel, President and CEO. Locator: Prepared remarks, lines 358–359, 386.. Measurement: No technology-specific result attributed.. Limit: No AI/ML or automated workflow specified..

Reviewed paired full prepared remarks and full live Q&A. Prepared remarks aired Oct. 31; Q&A call was Nov. 1. Prepared remarks transcript: https://www.marketbeat.com/earnings/reports/2024-10-31-sm-energy-stock/. Issuer archive prepared-remarks transcript PDF and separate Q&A PDF were also located but returned cache-miss errors; accessible paired transcripts were used.

## SM Energy · Q4 2024

reviewed_relevant · Full prepared remarks and full Q&A reviewed together; searched AI/artificial intelligence, GenAI, machine learning, analytics, automation, digital transformation, predictive/autonomous/agentic/copilot and data-center/power-demand terms.

[SM Energy Q4 2024 Results Q&A Transcript; paired with full-year prepared remarks](https://stockanalysis.com/stocks/sm/transcripts/314837-q4-2024-q-a/)

### Broad electricity-demand context

CEO said energy and electricity demand were expected to rise and cited growing technology needs among the drivers.

Speaker: Herbert Vogel, President and CEO. Locator: Prepared remarks transcript, line 351.. Measurement: Cited an EIA outlook of up to 50% growth in primary energy demand by 2050.. Limit: Broad demand forecast, not AI-specific or an SM customer/volume commitment..

Reviewed paired full prepared remarks and full live Q&A. Prepared remarks aired Feb. 19; Q&A call was Feb. 20. Prepared remarks transcript: https://www.marketbeat.com/earnings/reports/2025-2-19-sm-energy-stock/.

## SM Energy · Q1 2025

reviewed_relevant · Full prepared remarks and full Q&A reviewed together; searched AI/artificial intelligence, GenAI, machine learning, analytics, automation, digital transformation, predictive/autonomous/agentic/copilot and data-center/power-demand terms.

[SM Energy Q1 2025 Results Q&A Transcript; paired with Q1 prepared remarks](https://stockanalysis.com/stocks/sm/transcripts/317704-q1-2025-q-a/)

### ML subsurface and development applications; company-wide GenAI

An advanced-analytics team uses ML for acquisition evaluation, completion design and well spacing. SM said company-wide generative AI was enabled, with custom solutions in development.

Speaker: Beth McDonald, COO. Locator: Prepared remarks, lines 148–151.. Measurement: No AI/ML-attributed financial or operating outcome disclosed.. Limit: Custom GenAI use cases and deployment depth were not specified..

### Robotic process automation for back office

Management said robotic process automation reduces risk and increases back-office efficiency.

Speaker: Beth McDonald, COO. Locator: Prepared remarks, line 151.. Measurement: No time or cost savings quantified.. Limit: RPA scope and systems were not specified..

Reviewed full prepared remarks and Q&A together. Prepared remarks aired May 1; live Q&A was May 2. Prepared remarks transcript: https://stockanalysis.com/stocks/sm/transcripts/341964-q1-2025-prepared-remarks/.

## SM Energy · Q2 2025

reviewed_relevant · Full prepared remarks and full Q&A reviewed together; searched AI/artificial intelligence, GenAI, machine learning, analytics, automation, digital transformation, predictive/autonomous/agentic/copilot and data-center/power-demand terms.

[SM Energy Q2 2025 Results Q&A Transcript; paired with Q2 prepared remarks](https://stockanalysis.com/stocks/sm/transcripts/341364-q2-2025-q-a/)

### ML well-design workflow and well performance

COO said the technical team advanced ML models to refine well designs, with the workflow delivering stronger wells and higher cash flow. Howard County wells were reported over 30% above peer-operated performance.

Speaker: Beth McDonald, COO. Locator: Prepared remarks, lines 410–419.. Measurement: Howard County wells over 30% better than peer-operated wells.. Limit: No ML-attributed share of the uplift or higher cash flow was isolated..

Reviewed full prepared remarks and full live Q&A together. Prepared remarks aired July 31; Q&A was Aug. 1. Prepared remarks transcript: https://www.marketbeat.com/earnings/reports/2025-7-31-sm-energy-stock/. Issuer archive transcript URLs for both pieces were found but the fetcher returned cache-miss errors; accessible paired transcripts were used.

## SM Energy · Q3 2025

inaccessible · Full-call transcript page identified in search; page fetch returned a cache-miss error, so text and Q&A were not reviewed.

[SM Energy Q3 2025 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2025/11/05/sm-energy-sm-q3-2025-earnings-call-transcript/)

Issuer 8-K said the originally scheduled Nov. 5 webcast/call was canceled and replaced by a call on Nov. 3, 2025. A Motley Fool full-transcript page was located but its fetch failed with a cache-miss error; this quarter remains unreviewed, not a negative screen.

## SM Energy · Q4 2025

reviewed_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, generative AI, machine learning, analytics, automation, digital, predictive, autonomous, agentic, copilot, data-center and power-demand terms.

[SM Energy Q4 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/sm/transcripts/398558-q4-2025/)

### Multivariate and geomechanical analysis for stacked-pay optimization

CEO Beth McDonald said the combined technical team was applying multivariate analysis and geomechanical modeling in the southern Midland Basin to optimize stacked-pay development.

Speaker: Beth McDonald, President and CEO. Locator: Q&A, lines 235–242, especially 240–242.. Measurement: Not quantified. Limit: No AI/ML attribution, model specifications, or quantified outcome disclosed..

Management described multivariate analysis and geomechanical modeling as inputs to optimization of stacked-pay development. No AI/ML or automated process was disclosed.

## SM Energy · Q1 2026

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, generative AI, machine learning, analytics, automation, digital, predictive, autonomous, agentic, copilot, data-center and power-demand terms.

[SM Energy Q1 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/sm/transcripts/571779-q1-2026/)

Full transcript reviewed; no qualifying AI/ML, predictive analytics, adjacent automation/digital or AI-linked energy-demand disclosure found.

## SM Energy · Q2 2026

reviewed_no_relevant · Full speaker-labelled transcript reviewed, including prepared remarks and analyst Q&A; searched AI, artificial intelligence, generative AI, machine learning, analytics, automation, digital, predictive, autonomous, agentic, copilot, data-center and power-demand terms.

[SM Energy Q2 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/sm/transcripts/662208-q2-2026/)

Full transcript reviewed; no qualifying AI/ML, predictive analytics, adjacent automation/digital or AI-linked energy-demand disclosure found.

## Suncor Energy · Q3 2024

reviewed_relevant · Full transcript reviewed across presentation, analyst Q&A and close. Screened AI/artificial intelligence, machine learning, digital, analytics, automation, software, data-center/hyperscaler and predictive terms; autonomous haulage terms reviewed as adjacent automation.

[Suncor Energy Q3 2024 earnings-call full transcript](https://equibles.com/stocks/su/calls/2024-q3)

### Autonomous haulage fleet deployment

CEO Rich Kruger described a mine strategy using fewer, larger trucks, including autonomous trucks, operated for efficiency. He said 35 of 55 new 400-ton trucks were already operating, with the remainder scheduled for early 2025.

Speaker: Rich Kruger, CEO. Locator: Equibles transcript lines 288–291.. Measurement: The full 55-truck replacement program was expected to reduce annual operating costs by more than $300m.. Limit: The cost target covers the larger-truck fleet replacement, not autonomous operation alone; no AI method or autonomous-only result was disclosed..

Adjacent automation, not AI: AI-099 is the exact Suncor autonomous-haulage program. Call source contains Q&A and operator close.

## Suncor Energy · Q4 2024

reviewed_relevant · Full transcript reviewed across prepared remarks, analyst Q&A and close. Screened AI/artificial intelligence, machine learning, digital, analytics, automation, software, data-center/hyperscaler and robotics terms.

[Suncor Energy Q4 2024 earnings-call full transcript](https://www.roic.ai/quote/SU:UN/transcripts/2024-year/4-quarter)

### Autonomous haulage deployment

Peter Zebedee said autonomous trucks delivered all ore to Suncor’s base-plant crushers by year-end, and the company achieved its target to convert 91 trucks to autonomous operation.

Speaker: Peter Zebedee, EVP, Oil Sands; Rich Kruger, CEO. Locator: ROIC transcript lines 434–452.. Measurement: 91 trucks converted; all base-plant ore deliveries were made by autonomous trucks by December 2024.. Limit: This is autonomous trucking, with no AI method or AI-attributed productivity measure disclosed. Management said 2024 reflected only a partial-year ramp and expected a full-year benefit in 2025..

Actual call date confirmed as 6 February 2025; the original archive row date of 20 February was incorrect. AI-099 is the exact Suncor autonomous-haulage register match; no AI inference is made.

## Suncor Energy · Q1 2025

reviewed_relevant · Full transcript reviewed: prepared remarks, analyst Q&A, and close.

[Suncor Energy Q1 FY2025 Earnings Call Transcript](https://www.roic.ai/quote/SU/transcripts/2025-year/1-quarter)

### Autonomous haulage, data analytics and fleet software

CEO Rich Kruger said the base-plant autonomous fleet exceeded 100 trucks and was moving toward 140. He said it reached staffed-operation performance in nine months versus a typical two-year industry benchmark, and described data analytics in the AHS command center and a software upgrade with Komatsu and SMS for all-weather “mud mode.”

Speaker: Rich Kruger, CEO. Locator: ROIC transcript lines 250–263; corroborating Q&A on Mine Connect data/insights at Equibles lines 344–355.. Measurement: More than 100 autonomous trucks, targeted fleet of 140; staffed-operation-equivalent performance in nine months versus a typical two-year benchmark.. Limit: No AI/ML method or autonomous-only financial return was disclosed. The productivity projection stated as “10/3, 400-ton” in the transcript is unclear and excluded..

Full secondary transcript includes presentation, Q&A and close. Screened AI/artificial intelligence, machine learning, digitalization, analytics, automation/autonomy, software, remote operations and data-center/hyperscaler terms. Relevant findings concern adjacent operational automation/data/software; no explicit AI disclosure found. Issuer event announcement and ROIC transcript confirm call date May 7, 2025. Equibles transcript was used for corroborating Q&A context; its “Concluded Mar 31” field is quarter-period metadata, not the call date.

## Suncor Energy · Q2 2025

reviewed_relevant · Full transcript reviewed: prepared remarks, analyst Q&A, executive close, and operator disconnect.

[Earnings call transcript: Suncor Energy Q2 2025 results show mixed performance](https://www.investing.com/news/transcripts/earnings-call-transcript-suncor-energy-q2-2025-results-show-mixed-performance-4173855)

### Autonomous haulage and operational controls

In Q&A, CEO Rich Kruger and Oil Sands EVP Peter Zebedee described scaling autonomous haulage at the base plant and plans to implement it at Syncrude in 2026, citing safety and productivity as expected benefits. The CEO separately described automated controls in a coke-drum replacement project.

Speaker: Rich Krueger, CEO; Peter Zebedee, EVP Oil Sands. Locator: Transcript lines 479–484 and 637–644.. Measurement: Management said the autonomous fleet had grown from 20 to 120 trucks and expected 150 or more by year-end; the transcript’s unit context is unclear, so no fleet metric is relied on.. Limit: No AI/ML method or realized autonomy-only result was disclosed. Automated controls relate to a separate refinery project..

Full transcript includes prepared remarks, analyst Q&A, executive close and operator disconnect. Screened explicit AI/ML, digitalization, analytics, software, autonomy, automation, remote operations and data-center terms. Investing.com’s surrounding summary article was AI-assisted; only its full call transcript was reviewed for company disclosures. Actual call date Aug. 6, 2025.

## Suncor Energy · Q3 2025

reviewed_no_relevant · Full transcript reviewed: prepared remarks, analyst Q&A, and operator close.

[Suncor Energy Inc. Q3 2025 Earnings Call Transcript](https://equibles.com/stocks/su/calls/2025-q3)

Full transcript contains prepared remarks, analyst questions and operator close. Searched AI/artificial intelligence, machine learning, digital/digitalization, analytics, autonomous/automation, software, remote operations, data-center/hyperscaler and broader technology terms; no relevant disclosure found. Equibles page labels the call period Q3 2025 but its “Concluded Sep 30” field is quarter-end metadata; issuer/archive event date Nov. 5, 2025 is retained.

## Suncor Energy · Q4 2025

reviewed_relevant · Full transcript reviewed; Q&A is present and closes at lines 575–585. AI and related terms searched. The transcript also describes autonomous haul-system operations.

[Suncor Energy Q4 2025 earnings call transcript](https://stockanalysis.com/quote/tsx/SU/transcripts/394291-q4-2025/)

### Autonomous haulage / operational automation

Management described the autonomous haulage system at Base Plant as fully deployed, with as many as 140 haul trucks running autonomously. EVP Oil Sands Peter Zebedee discussed a supplier-developed “Mud Mode” technology to reduce truck slippage and stoppages in soft conditions, with a next iteration planned. This is operational autonomy/automation; the transcript does not characterize it as AI or machine learning.

Speaker: Peter Zebedee, EVP of Oil Sands. Locator: Lines 182–190. Measurement: Up to 140 haul trucks running autonomously at Base Plant; 1.4 billion tons of material moved across Suncor mining portfolio in 2025, up 12% year over year at essentially same cost base.. Limit: Material-movement/efficiency figures are not attributed solely to autonomous trucks or Mud Mode; no AI/ML architecture or isolated realized financial benefit disclosed..

Complete transcript text was accessible on the cited third-party page; review includes the Q&A and recorded call close.

## Suncor Energy · Q1 2026

reviewed_no_relevant · Full issuer transcript reviewed: prepared remarks, Q&A, and operator close.

[Suncor Energy First Quarter 2026 Financial Results Call Transcript](https://www.suncor.com/-/media/project/suncor/files/investor-centre/transcripts/2026-q1-suncor-energy-financial-results-call-transcript-en.pdf?created=20260513141838&modified=20260513141902)

Issuer full transcript includes presentation, Q&A and operator close. Searched AI/artificial intelligence, machine learning, digital/digitalization, analytics, automation/autonomy, software/workflow, remote operations and data-center/hyperscaler terms; no relevant disclosure found. Official investor archive confirms webcast date May 6, 2026.

## Suncor Energy · Q2 2026

reviewed_relevant · Full transcript reviewed through prepared remarks, analyst Q&A and call close. Screened artificial intelligence, AI, machine learning, automation/autonomy, digital transformation, data-center/hyperscaler terms.

[Suncor Energy Q2 2026 earnings-call full transcript](https://stockanalysis.com/quote/tsx/SU/transcripts/661427-q2-2026/)

### Integrated value-chain optimization described as an AI tool

EVP Downstream said Suncor runs a linear-programming model integrating Edmonton refinery with oil-sands operations and broader markets, and characterized it as an AI tool used to optimize the region. He described reallocating crude/intermediate streams during operating upsets and balancing refinery, upstream and market needs.

Speaker: Dave Oldreive, EVP Downstream. Locator: Lines 261–268.. Measurement: Management said the integrated operation had capability to route 10,000–15,000 barrels/day in intermediate streams; this is operational capability, not an AI-attributed or AI-measured benefit.. Limit: The transcript does not disclose model architecture, whether machine learning is involved, independent validation, or a counterfactual. Do not treat broader record sales/throughput as AI-caused..

The AI mention is a management characterization of a linear-programming optimization model; no ML or genAI component is disclosed. Separate “mud mode 2.0” truck software and reduced slippage were not labeled AI in this call.

## Targa Resources · Q3 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Targa Resources Q3 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/trgp/transcripts/217965-q3-2024/)

Call date and fiscal quarter verified on issuer/Stock Analysis earnings transcript archive. Term screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## Targa Resources · Q4 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Targa Resources Q4 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/trgp/transcripts/238006-q4-2024/)

Call date and fiscal quarter verified on issuer/Stock Analysis earnings transcript archive. Term screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## Targa Resources · Q1 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Targa Resources Q1 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/trgp/transcripts/317780-q1-2025/)

Call date and fiscal quarter verified on issuer/Stock Analysis earnings transcript archive. Term screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## Targa Resources · Q2 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Targa Resources Q2 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/trgp/transcripts/340963-q2-2025/)

Call date and fiscal quarter verified on issuer/Stock Analysis earnings transcript archive. Term screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## Targa Resources · Q3 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Targa Resources Q3 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/trgp/transcripts/372172-q3-2025/)

Call date and fiscal quarter verified on issuer/Stock Analysis earnings transcript archive. Term screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## Targa Resources · Q4 2025

reviewed_relevant · Full transcript reviewed, including Q&A; analyst attributes better Permian recovery to operator AI and other technology, and management responds.

[Targa Resources Q4 2025 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/trgp/transcripts/400224-q4-2025/)

### Producer AI-enhanced recovery commentary

An analyst cited Targa’s large producer customers discussing improved Permian recoveries using more science, including AI, lightweight proppants, and surfactants. Management said technological developments may be one of several factors behind more gas production than forecast and deferred detailed assessment to producer customers.

Speaker: Manav Gupta, UBS analyst; Jen Kneale, President. Locator: Q&A lines 388-395. Measurement: Not quantified. Limit: Third-party/producer operational developments discussed by an analyst; not Targa’s own AI deployment, quantified outcome, or Targa product revenue..

Call date and fiscal quarter verified on issuer/Stock Analysis earnings transcript archive. Term screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## Targa Resources · Q1 2026

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Targa Resources Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/trgp/transcripts/560395-q1-2026/)

Call date and fiscal quarter verified on issuer/Stock Analysis earnings transcript archive. Term screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## Targa Resources · Q2 2026

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Targa Resources Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/stocks/trgp/transcripts/662443-q2-2026/)

Call date and fiscal quarter verified on issuer/Stock Analysis earnings transcript archive. Term screen included AI, artificial intelligence, machine learning, automation, data center and predictive analytics.

## TC Energy · Q3 2024

reviewed_relevant · Full transcript reviewed, including Q&A; transcript page includes the complete operator question-and-answer session through call close.

[TC Energy Q3 2024 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/tsx/TRP/transcripts/217772-q3-2024/)

### AI-driven energy demand

CEO explicitly included electricity consumption from AI and data centers among the drivers of incremental North American gas demand and related pipeline infrastructure opportunities.

Speaker: François Poirier, President and CEO. Locator: Prepared remarks, lines 125-127. Measurement: Not quantified. Limit: Demand-side exposure only; not evidence of TC Energy using AI internally or selling an AI product..

Call date verified against issuer transcript archive. No quote reproduced.

## TC Energy · Q4 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[TC Energy Corporation Q4 2024 Earnings Call Transcript](https://www.roic.ai/quote/TRP.TO/transcripts/2024-year/4-quarter)

Call date verified against issuer archive. The full call discusses data-center load and power demand without explicitly linking it to AI; not counted as an AI mention.

## TC Energy · Q1 2025

inaccessible · Transcript page located and call date confirmed, but full transcript could not be fetched for complete keyword/Q&A review.

[TC Energy Corporation Q1 2025 Earnings Call Transcript](https://www.roic.ai/quote/TRP.TO/transcripts/2025-year/1-quarter)

Issuer transcript archive lists the May 1 call. Search-result excerpt and an attempted full-page fetch were insufficient for a full-call review, so no absence claim is made.

## TC Energy · Q2 2025

inaccessible · Transcript page located and call date confirmed; page fetch returned a cache miss, preventing complete full-call keyword/Q&A review.

[TC Energy Corporation Q2 2025 Earnings Call Transcript](https://www.roic.ai/quote/TRP%3AUN/transcripts/2025-year/2-quarter)

Issuer archive lists the July 31 call. Roic search-result transcript excerpt is extensive but page fetch failed, so it is retained as a lead rather than represented as a complete review.

## TC Energy · Q3 2025

reviewed_relevant · Full transcript reviewed, including Q&A; operator opens Q&A at lines 370-376 and closes it at lines 934-953.

[TC Energy Corporation Q3 2025 Earnings Call Transcript](https://www.roic.ai/quote/TRP.TO/transcripts/2025-year/3-quarter)

### Internal AI deployment

TC Energy described an integrity-focused AI platform automating document verification and compliance workflows, plus Agentic AI for short-term marketing and algorithms for real-time pipeline configuration/capacity recommendations. Management said review times fell from hours to minutes; no financial amount was quantified.

Speaker: François Poirier, President and CEO. Locator: Prepared remarks, lines 283-288. Measurement: Document review time reduced from hours to minutes (management statement).. Limit: No dollars, EBITDA uplift, deployment scale or independently verified outcome disclosed..

Issuer archive and MarketScreener date the call November 6, 2025; this third-party transcript page labels it November 8, 2025. Used issuer-archive call date and retained transcript page date discrepancy.

## TC Energy · Q4 2025

reviewed_relevant · Full transcript reviewed, including Q&A; the text starts with operator opening and includes Q&A from line 58 onward through call close.

[TC Energy Q4 2025 Earnings Call Transcript](https://eulerpool.com/stock/CA87807B1076/earning-calls/11404-q4-2025-earnings-call-transcript)

### Internal AI strategy

CEO said the company would use AI among its commercial and technological innovation priorities; in Q&A he described early pilot projects that monetize previously unrecognized capacity using complex algorithms.

Speaker: François Poirier, President and CEO. Locator: Prepared remarks line 54; Q&A lines 124-126. Measurement: Not quantified. Limit: AI initiative/pilot description without quantified AI-attributed financial benefit or detailed model architecture..

### AI and data-center energy demand

CEO identified AI and data-center energy needs as drivers of sustained North American electricity demand; management discussed serving utility and data-center-related gas demand.

Speaker: François Poirier, President and CEO; Tina Faraca, EVP and COO. Locator: Prepared remarks line 33; Q&A lines 64-69 and 105-109. Measurement: Not quantified. Limit: Customer/infrastructure opportunity, not AI product revenue..

Issuer archive dates this call Feb. 13, 2026. Third-party transcript is readable and includes the Q&A.

## TC Energy · Q1 2026

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[TC Energy Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/tsx/TRP/transcripts/556719-q1-2026/)

Call date and transcript confirmed on issuer and transcript archives. Data centers, grid load, and robotics are discussed but no explicit AI/ML/genAI link was found.

## TC Energy · Q2 2026

reviewed_relevant · Full transcript reviewed, including Q&A; AI initiative is in analyst Q&A and full transcript contains the complete call.

[TC Energy Q2 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/tsx/TRP/transcripts/659122-q2-2026/)

### Pipeline optimization AI pilot

Management described proof-of-concept AI applications on small pipeline segments, with regional teams competing for capital through business cases and outcome commitments. CEO said they were about halfway to a $100 million AI-related incremental EBITDA target for 2026.

Speaker: François Poirier, President and CEO. Locator: Analyst Q&A, lines 218-226. Measurement: 2026 target: $100 million AI-related incremental EBITDA; management said approximately halfway achieved after two quarters.. Limit: CEO said pilots covered selected small segments (about 100 km at a time) and benefits cannot be linearly extrapolated; early-stage and company-reported..

### AI-related power/data-center demand

Management discussed accelerating power and data-center demand as drivers of incremental gas infrastructure opportunities; this is customer demand rather than AI operations.

Speaker: François Poirier, President and CEO; Tina Faraca, EVP and COO. Locator: Prepared remarks lines 122-131; Q&A lines 386-420.. Measurement: Not quantified. Limit: No AI-specific customer revenue or contracted volumes quantified in these passages..

Call date verified on issuer/transcript archive.

## TotalEnergies · Q3 2024

reviewed_no_relevant · Full 19-page issuer transcript reviewed, including prepared presentation and analyst Q&A through operator close. Searched AI/artificial intelligence, machine learning, generative/agentic, predictive, analytics, software, data-platform, remote, automation/autonomy, digital-transformation/workflow and data-center/hyperscaler terms.

[Q3 2024 Conference Call transcript](https://totalenergies.com/sites/g/files/nytnzq121/files/documents/totalenergies_TotalEnergies_3Q24_conference_call_transcript_pdf.pdf)

The issuer-hosted transcript is dated 31 October 2024, matching the archive call date. Selected explicit-AI, adjacent digital/analytics/automation, and AI-energy-demand terms returned no relevant matches. This is a transcript-content finding only, not a claim that the issuer has no AI activity.

## TotalEnergies · Q4 2024

reviewed_relevant · Issuer-hosted 31-page transcript screened from presentation through Q&A close, including AI/ML, data-center/hyperscaler, digital, automation, predictive, analytics, software, workflow, and remote-operation terms.

[2024 Results & 2025 Objectives – Transcript](https://totalenergies.com/sites/g/files/nytnzq121/files/documents/totalenergies_2024-results-2025-objectives-transcript_2025_en.pdf)

### Digital plant and planned AI models for industrial process control

CEO described plans to organize a dedicated digital/AI line in OneTech, connect real-time data from refineries and FPSOs, and develop reverse models for advanced process control. The Executive Committee had decided to invest in the area; management said near-term delivery timing was uncertain.

Speaker: Patrick Pouyanné, Chairman & CEO. Locator: PDF P27, lines 967–984 (the CEO answers the analyst’s AI-adoption question).. Measurement: No investment amount, savings or measured operational outcome disclosed.. Limit: Program described as planned/developing; management was not sure whether value would be delivered within two to three years. No named model, system rollout or realized result..

### Data-center/hyperscaler electricity demand as a prospective market

In discussing future electricity needs, CEO said data centers and hyperscalers wanted power soon and framed the demand as a prospective opportunity for integrated power investment.

Speaker: Patrick Pouyanné, Chairman & CEO. Locator: PDF P27, lines 1003–1007.. Measurement: No AI-specific load, capacity, customer contract, or delivered volume disclosed in this passage.. Limit: Qualitative market commentary; the call does not attribute demand to AI alone or provide a committed project/capacity. The 3 TWh Clean Firm Power contracted to large industrials and big tech elsewhere in the presentation is not represented here as AI-specific or exclusively data-center supply..

Reporting exception: this is the issuer’s 5 February 2025 combined 2024 Results and 2025 Objectives meeting, serving as the Q4/FY results event rather than a separate Q4-only transcript. The issuer PDF confirms the date and says Q&A follows the presentations (P1 lines 26–36); the transcript includes the Q&A through its end. Inherited ROIC/third-party transcript was used only as a lead; issuer-hosted transcript reviewed. Search metadata had conflicting Q3 2024 label; issuer PDF title and date control.

## TotalEnergies · Q1 2025

reviewed_relevant · Issuer PDF full transcript searched; presentation, Q&A and close reviewed. AI/ML, automation/digital, data-centre and related terms checked.

[TotalEnergies 1Q2025 Earnings Call transcript](https://totalenergies.com/sites/g/files/nytnzq121/files/documents/totalEnergies_q1-2025-results-transcript_2025_en.pdf)

### AI/data-centre electricity demand

CEO Patrick Pouyanné linked the growth in electricity demand to technology companies, data centres and AI, and positioned TotalEnergies’ Integrated Power business as a strategic growth/resilience pillar. The statement was a broad market-demand rationale, not a customer contract or quantified AI-specific load forecast.

Speaker: Patrick Pouyanné, Chairman and CEO. Locator: Transcript lines 616–633 (PDF page 18; PDF page count offset: printed page 19).. Measurement: CEO cited 4% global electricity-demand growth in 2024; no data-centre/AI demand share or company-specific volume quantified.. Limit: Do not interpret the 4% global demand statistic as attributable to AI/data centres; statement gives no attribution split or forecast..

The issuer-hosted PDF URL includes q1-2025 and its contents identify 1Q2025 (April 30, 2025), but the browser metadata/title mistakenly labels it Q3 2024/Oct. 31. Content and call date verified from PDF cover (P0) and transcript lines; date/link-title discrepancy retained here.

## TotalEnergies · Q2 2025

reviewed_relevant · Issuer PDF full transcript reviewed through Q&A; AI, machine learning, automation, digital, data-centre and related terms searched.

[TotalEnergies 2Q2025 Earnings Call transcript](https://totalenergies.com/sites/g/files/nytnzq121/files/documents/totalenergies_2q2025-results-transcript_2025_en.pdf)

### Enterprise digital and AI platform for asset operations

CEO said TotalEnergies is moving from discussion to deployment, with Emerson on a real-time data platform to structure asset data worldwide across upstream and downstream. The company was developing software and advanced process control applications, including AI-enabled downstream asset monitoring, and a further AI platform for an asset was planned.

Speaker: Patrick Pouyanné, Chairman and CEO. Locator: Transcript lines 325–342 (PDF pages 9–10).. Measurement: No deployment count, operating metric, savings or quantified return disclosed.. Limit: The transcript describes program scope and intention; no AI model type, named second platform partner, achieved operational benefit or quantified economics are disclosed..

Issuer transcript is a 22-page PDF with Q&A section. Screened full call. Q&A question at lines 316–324 calls the initiative an announced “digital AI deployment”; response at 325–342 provides management scope.

## TotalEnergies · Q3 2025

reviewed_relevant · Issuer full-call PDF reviewed, including Q&A; screening includes explicit AI, data platforms, digitalization, software and AI-application terms.

[TotalEnergies 3Q2025 Earnings Call transcript](https://totalenergies.com/sites/g/files/nytnzq121/files/documents/totalenergies_3q2025-results-transcript_2025_en.pdf)

### Global operational data platform and AI scale-up

CEO described two global data initiatives: Emerson’s Inmation platform to connect site/refinery operational data over a 2.5–3-year rollout, and a worldwide E&P program with Cognite intended to accelerate AI use. Existing Digital Factory had 300 data experts/data scientists. Management characterized value creation as revenue enhancement, and gave 1% production uplift as a hypothetical example rather than a forecast or realized result.

Speaker: Patrick Pouyanné, Chairman and CEO. Locator: Lines 442–466; detailed investment/talent Q&A at 580–605 (PDF P12–13 and P16–17).. Measurement: CEO estimated roughly €300 million ($350 million) worldwide platform-program investment; Digital Factory has 300 data experts/data scientists; illustrative 1% production increase is hypothetical.. Limit: No deployed asset count, verified production uplift, savings or realized return disclosed; the €300m amount relates to the global data platform program, not a specific AI model alone..

Unlike the earlier bounded AI mentions, Q&A details data architecture, named platform providers, intended AI use, scale and investment. AI-030 company match checked (existing register describes TotalEnergies Digital Factory portfolio); linkage is contextual, not proof these deployments are identical.

## TotalEnergies · Q4 2025

reviewed_relevant · Full transcript keyword search and relevant passage review; transcript page is 1,003 lines. This call was also present in inherited primary-source leads.

[TotalEnergies Q4 2025 Results & 2026 Objectives Transcript](https://stockanalysis.com/quote/epa/TTE/transcripts/375810-q4-2025/)

### Data-centre power products

Management described corporate renewable PPAs, clean firm power, and land/grid access solutions for data-centre customers; it named Microsoft and AWS PPAs in 2025 and a Google Texas agreement.

Speaker: Stéphane Michel, President Gas, Renewables & Power. Locator: Presentation lines 229–235.. Measurement: Management stated an approximately 10% PPA premium versus comparable industrial sales.. Limit: Company-reported commercial framing; contract economics and realized margin are not independently verified here..

### Internal AI operating strategy

Management described a data foundation expanding asset data points and adding a real-time AI layer across 40 upstream and 16 refining/chemical sites; a 500-engineer India competency center was planned by 2027.

Speaker: Stéphane Michel, President Gas, Renewables & Power. Locator: Presentation lines 245–255; Q&A lines 442–476.. Measurement: Target: at least 500 engineers by 2027; deployment completion target stated for end of 2026.. Limit: Roadmap and resource plan, not realized production uplift. Examples and system-wide deployment remain in progress..

## TotalEnergies · Q1 2026

reviewed_no_relevant · Full issuer transcript (26-page PDF) reviewed; presentation and Q&A through call close keyword-screened.

[TotalEnergies 1Q26 Results - Earnings Call Transcript](https://totalenergies.com/sites/g/files/nytnzq121/files/documents/totalEnergies_q1-2026-results-transcript_2026_en.pdf)

Issuer transcript covers presentation and Q&A through conference-call close. Searches for AI, artificial intelligence, machine learning, automation, data center and hyperscaler returned no match.

## TotalEnergies · Q2 2026

reviewed_relevant · Full transcript keyword search; relevant passage opened.

[TotalEnergies Q2 2026 Earnings Call Transcript](https://stockanalysis.com/quote/epa/TTE/transcripts/546385-q2-2026/)

### AI-related power demand

Management cited data centres and AI growth as support for continued investment in integrated power.

Speaker: Patrick Pouyanné, Chairman and CEO. Locator: Q&A/power discussion, transcript lines 424–429.. Measurement: No call-specific AI demand volume or customer contract quantified.. Limit: Demand rationale only; do not infer an executed project from this passage..

## Tourmaline Oil · Q3 2024

reviewed_no_relevant · Full transcript reviewed across presentation, analyst Q&A and close. Screened AI/artificial intelligence, machine learning, genAI/agentic, digitalization, analytics, automation, software/workflow, data-center/hyperscaler, autonomous, predictive and remote-operations terms.

[Tourmaline Oil Q3 2024 earnings-call full transcript](https://www.gurufocus.com/stock/TRMLF/transcripts/2591378)

No relevant AI, adjacent digital/automation, or AI-data-center-demand disclosure found in this full-call screen.

## Tourmaline Oil · Q4 2024

reviewed_no_relevant · Full transcript reviewed across prepared remarks, analyst Q&A and close. Screened AI/artificial intelligence, machine learning, digital, analytics, automation, software, data-center/hyperscaler, technology, predictive and remote-operation terms.

[Tourmaline Oil Q4 2024 earnings-call full transcript](https://www.investing.com/news/transcripts/earnings-call-transcript-tourmaline-oils-q4-2024-highlights-strong-growth-93CH-3912982)

Investing.com transcript metadata contains an erroneous 2024 event-year in the recorded-call line; issuer/archive and other archive records place the call on 6 March 2025. Actual call date retained as 2025-03-06.

## Tourmaline Oil · Q1 2025

reviewed_no_relevant · Full transcript screened, including management remarks, Q&A, closing remarks, and operator close. AI, artificial intelligence, machine learning, automation, analytics, software, digital, and data-center/power-demand terms checked.

[Tourmaline Oil Q1 2025 earnings call transcript](https://www.alphaspread.com/security/tsx/tou/investor-relations/earnings-call/q1-2025)

Alpha Spread’s generated summary mentions data-center growth as a gas-demand driver, but the full transcript body does not provide a corresponding speaker passage. Not counted as a verified disclosure.

## Tourmaline Oil · Q2 2025

reviewed_no_relevant · Full transcript screened, including prepared remarks, Q&A, closing remarks, and operator close. AI, artificial intelligence, machine learning, automation, analytics, software, digital, and data-center terms checked.

[Earnings call transcript: Tourmaline Oil Q2 2025 beats EPS but stock dips](https://www.investing.com/news/transcripts/earnings-call-transcript-tourmaline-oil-q2-2025-beats-eps-but-stock-dips-4164226)

Transcript records the call as July 31, 2025. Investing.com transcript body contains Q&A and operator close; no relevant AI/digital/data-center mention found.

## Tourmaline Oil · Q3 2025

reviewed_relevant · Full transcript screened, including prepared remarks, Q&A, closing remarks, and operator close. AI, artificial intelligence, machine learning, automation, analytics, software, digital, and data-center/power-demand terms checked.

[Earnings call transcript: Tourmaline Oil Q3 2025 misses EPS forecasts](https://www.investing.com/news/transcripts/earnings-call-transcript-tourmaline-oil-q3-2025-misses-eps-forecasts-93CH-4339507)

### data-center gas demand and commercialization

Management discussed evaluating direct gas supply and co-location opportunities for data-center power generation, alongside conversations with builders. It described potential behind-the-fence projects as an additional gas-marketing channel and noted that power demand could tighten basin supply-demand.

Speaker: Unconfirmed management respondent (Investing.com excerpt does not label the speaker). Locator: Q&A, Investing.com transcript lines 155–162; specific discussion of data-center gas and power opportunities.. Measurement: One efficient 1-GW power plant was described as consuming roughly 150 MMcf/d; management said ten could be developed quickly while the basin remained balanced.. Limit: Exploratory opportunity and industry-level estimate; no named customer, committed project, or explicit link to AI workloads..

Data-center power/gas discussion is demand-side and not evidence that Tourmaline itself uses AI. Speaker attribution is unconfirmed because the available Q&A passage lacks a respondent label.

## Tourmaline Oil · Q4 2025

reviewed_relevant · Full transcript screened, including prepared remarks, Q&A, closing remarks, and operator close. AI, artificial intelligence, machine learning, automation, analytics, software, digital, and data-center/power-demand terms checked.

[Earnings call transcript: Tourmaline Oil Q4 2025 sees strong cash flow despite EPS miss](https://www.investing.com/news/transcripts/earnings-call-transcript-tourmaline-oil-q4-2025-sees-strong-cash-flow-despite-eps-miss-93CH-4544866)

### data-center gas demand and commercialization

CEO Mike Rose said Tourmaline had spent a year evaluating co-location near one of its gas plants. VP of Capital Markets Jamie Heard described behind-the-meter data centers and potential firm gas supply as local and regional marketing opportunities.

Speaker: Mike Rose, President and CEO; Jamie Heard, Vice President of Capital Markets. Locator: Q&A, Investing.com transcript lines 168–177.. Measurement: Management estimated at least 1.5 Bcf/d of in-basin gas consumption by 2030 from the combined behind-the-fence and two on-grid projects it was tracking.. Limit: Aggregate market estimate, not Tourmaline’s committed volume or a sanctioned project; no explicit connection to AI workloads..

Investing.com transcript confirms the actual call date as March 5, 2026; Q4 2025 results were published in 2026.

## Tourmaline Oil · Q1 2026

reviewed_relevant · Full transcript screened from prepared remarks through Q&A, closing remarks, and operator close. AI, artificial intelligence, machine learning, automation, analytics, software, digital, and data-center/power-demand terms checked.

[Tourmaline Oil Q1 2026 Earnings Call Transcript & Audio](https://stockanalysis.com/quote/tsx/TOU/transcripts/583895-q1-2026/)

### AI and data-enabled operating / exploration tools

CEO Mike Rose said Tourmaline is applying learning software to optimize plunger-lift production and using behind-the-bit drilling technology to improve drilling speed. He also described AI bots reviewing 3D seismic horizons to complement exploration, and an Ambyint artificial-lift optimization partnership being deployed across the fleet.

Speaker: Mike Rose, President and CEO. Locator: Q&A, StockAnalysis/Quartr transcript lines 263–270 and 302–306.. Measurement: Rose said optimized well calls could add 10,000 BOE/d; he said productivity gains were too early to quantify when first asked.. Limit: The 10,000 BOE/d is a potential estimate, not realized output; specific AI systems, deployment scale, and realized productivity were not quantified..

### data-center power and gas demand

Rose said the company had spent about a year evaluating co-location near a gas plant. He described possible firm gas supply or direct participation in Alberta data-center power projects; VP Jamie Heard discussed behind-the-meter build-outs in western U.S. markets accessible to Tourmaline gas.

Speaker: Mike Rose, President and CEO; Jamie Heard, Vice President of Capital Markets. Locator: Q&A, StockAnalysis/Quartr transcript lines 219–227; discussion of broader data-center AI adoption separately at lines 263–269.. Measurement: No gas-load forecast or contracted consumption volume was disclosed in this call.. Limit: Exploratory participation and regional estimate only; no project or supply contract announced, and management did not expressly attribute data-center load to AI..

No productivity uplift was confirmed as realized; CEO called measurement too early before offering the Ambyint potential estimate.

## Tourmaline Oil · Q2 2026

reviewed_relevant · Full transcript keyword search and relevant passage review; transcript page is 435 lines.

[Tourmaline Oil Q2 2026 Earnings Call Transcript](https://stockanalysis.com/quote/tsx/TOU/transcripts/662471-q2-2026/)

### Machine-learning-assisted well design

Management attributed part of well-performance improvement to an in-house multi-discipline data integration capability assisted by machine learning.

Speaker: Jamie Heard, VP Capital Markets. Locator: Prepared remarks, lines 133–136.. Measurement: NE BC Montney first-half performance 28% above prior five-year average on 25 wells at IP90; Alberta Deep Basin 14% above five-year average on 30 wells.. Limit: Performance uplift is attributed only in part to the ML-assisted capability; no standalone AI contribution or validation methodology was given..

### Data-centre gas demand and supply opportunity

Management described data-centre build-out as a potential source of gas demand and said it was pursuing a co-location prospect near Banshee plant, while preferring a gas supply contract over owning a data centre.

Speaker: Jamie Heard, VP Capital Markets; Mike Rose, President and CEO. Locator: Q&A lines 157–161 and 234–245, 283–290.. Measurement: CEO estimated potential incremental in-basin demand could reach up to 1 Bcf/day; one Banshee co-location pursuit had been underway for over a year.. Limit: Potential demand estimate and early-stage site pursuit only; no firm contract or project announcement..

## Transocean · Q3 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Transocean Q3 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/rig/transcripts/217977-q3-2024/)

Complete transcript and Q&A screened for AI/ML, software/platform, analytics, autonomous systems, automation, remote operations and AI-power-demand language; no relevant disclosure located.

## Transocean · Q4 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Transocean Q4 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/rig/transcripts/245471-q4-2024/)

Complete transcript and Q&A screened for AI/ML, software/platform, analytics, autonomous systems, automation, remote operations and AI-power-demand language; no relevant disclosure located.

## Transocean · Q1 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Transocean Q1 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/rig/transcripts/316839-q1-2025/)

Complete transcript and Q&A screened for AI/ML, software/platform, analytics, autonomous systems, automation, remote operations and AI-power-demand language; no relevant disclosure located.

## Transocean · Q2 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Transocean Q2 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/rig/transcripts/335631-q2-2025/)

Complete transcript and Q&A screened for AI/ML, software/platform, analytics, autonomous systems, automation, remote operations and AI-power-demand language; no relevant disclosure located.

## Transocean · Q3 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Transocean Q3 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/rig/transcripts/370535-q3-2025/)

Complete transcript and Q&A screened for AI/ML, software/platform, analytics, autonomous systems, automation, remote operations and AI-power-demand language; no relevant disclosure located.

## Transocean · Q4 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Transocean Q4 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/rig/transcripts/398989-q4-2025/)

Complete transcript and Q&A screened for AI/ML, software/platform, analytics, autonomous systems, automation, remote operations and AI-power-demand language; no relevant disclosure located.

## Transocean · Q1 2026

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Transocean Q1 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/rig/transcripts/562154-q1-2026/)

Complete transcript and Q&A screened for AI/ML, software/platform, analytics, autonomous systems, automation, remote operations and AI-power-demand language; no relevant disclosure located.

## Transocean · Q2 2026

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Transocean Q2 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/rig/transcripts/660693-q2-2026/)

Complete transcript and Q&A screened for AI/ML, software/platform, analytics, autonomous systems, automation, remote operations and AI-power-demand language; no relevant disclosure located.

## Valero Energy · Q3 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Valero Energy Q3 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/vlo/transcripts/215872-q3-2024/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Valero Energy · Q4 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Valero Energy Q4 2024 Earnings Call Transcript](https://stockanalysis.com/stocks/vlo/transcripts/237689-q4-2024/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Valero Energy · Q1 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Valero Energy Q1 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/vlo/transcripts/310962-q1-2025/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Valero Energy · Q2 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Valero Energy Q2 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/vlo/transcripts/338803-q2-2025/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Valero Energy · Q3 2025

reviewed_relevant · Full Q&A transcript on The Motley Fool reviewed and cross-checked against StockAnalysis/Quartr full transcript; keyword-screened for AI/ML, robotics/automation, digital, data center and predictive capability.

[Valero Energy Q3 2025 Earnings Call Transcript](https://www.fool.com/earnings/call-transcripts/2025/10/23/valero-energy-vlo-q3-2025-earnings-call-transcript/)

### Refinery AI/ML exploration and robotic inspection automation

Greg Bram said Valero was evaluating where AI could create tangible value, had deployed some new techniques in a few applications, and saw good operational data quality as a prerequisite. He described cautious optimism and possible availability improvement. In a separate answer he said robotic automation and AI techniques were being expanded for efficiency and equipment inspection; examples included tank-cleaning robots and drones for FCC-structure inspection.

Speaker: Greg Bram, SVP Refining Operations; Lane Riggs, Chairman/CEO. Locator: Lines 283-287, 327-332. Measurement: Not quantified. Limit: No use-case inventory, sites, deployment scale, quantified benefit, or causal attribution was supplied. Transcript host is third party; company IR archive did not yield a primary transcript in this review..

Previously inherited lead R065 called this “cautiously optimistic” on AI. Full third-party transcript now directly reviewed; retain the distinction between AI experimentation and adjacent robotics/drone inspection. Full transcript and Q&A reviewed with the stated keyword screen. Full transcript cross-checked against StockAnalysis/Quartr; the AI/ML and robotic inspection disclosures map to existing AI-102.

## Valero Energy · Q4 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Valero Energy Q4 2025 Earnings Call Transcript](https://stockanalysis.com/stocks/vlo/transcripts/402311-q4-2025/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Valero Energy · Q1 2026

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Valero Energy Q1 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/vlo/transcripts/548539-q1-2026/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Valero Energy · Q2 2026

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Valero Energy Q2 2026 Earnings Call Transcript](https://stockanalysis.com/stocks/vlo/transcripts/658074-q2-2026/)

Full transcript and Q&A reviewed and keyword-screened. No qualifying management disclosure in the reviewed call was identified; this result does not establish that the company had no AI/automation activity outside the call.

## Weatherford · Q3 2024

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Weatherford International Q3 2024 earnings call transcript](https://weatherford.gcs-web.com/static-files/1dc3d08a-b565-4eff-9acd-1e366fadea74)

### Customer digital platforms and AI/ML models

CEO described Weatherford’s Datagration/PetroVisor data-integration offering and its ability to deliver customer-specific optimization platforms and AI/ML models through customer-selected channels.

Speaker: Girishchandra Saligram, President and CEO. Locator: Transcript lines 526–536. Measurement: No deployment or revenue metric disclosed.. Limit: Product capability and commercial thesis; no evidence of customer outcomes or internal AI adoption..

### Digital production optimization opportunity

CEO described customer demand to combine fragmented data and use production-optimization platforms, SCADA and a unified data model to improve operational efficiency.

Speaker: Girishchandra Saligram, President and CEO. Locator: Transcript lines 487–502. Measurement: Not disclosed.. Limit: Digital offering/strategy; AI is addressed separately above..

Q3/Q4 2024 use issuer-hosted Refinitiv edited transcripts; later periods use Quartr full-text pages hosted by StockAnalysis. Claims are categorized as internal AI, customer product, or digital strategy; no inference of quantified financial impact beyond disclosures.

## Weatherford · Q4 2024

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Weatherford International Q4 2024 earnings call transcript](https://weatherford.gcs-web.com/static-files/af3af14b-1c75-4937-b21e-05907c93badb)

### Digital offerings and systems modernization strategy

CEO identified digital offerings as a growth vector and described a multi-year cost/productivity program using technology, lean processes and systems enhancements.

Speaker: Girish Saligram, President and CEO. Locator: Transcript lines 156–172. Measurement: Not disclosed.. Limit: No explicit AI or automation claim in the transcript; included for the stated digital-transformation scope..

Q3/Q4 2024 use issuer-hosted Refinitiv edited transcripts; later periods use Quartr full-text pages hosted by StockAnalysis. Claims are categorized as internal AI, customer product, or digital strategy; no inference of quantified financial impact beyond disclosures.

## Weatherford · Q1 2025

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Weatherford International Q1 2025 earnings call transcript](https://stockanalysis.com/stocks/wfrd/transcripts/313490-q1-2025/)

### Digital products and AIQ partnership

CEO discussed a partnership with Abu Dhabi AIQ and positioned Weatherford’s SCADA, edge and production-optimization products as digital tools to help customers improve production from existing reservoirs.

Speaker: Girish Saligram, President and CEO. Locator: Transcript lines 449–456. Measurement: Not disclosed.. Limit: No specific AIQ product, deployment count, AI capability detail or revenue was disclosed in this discussion..

Q3/Q4 2024 use issuer-hosted Refinitiv edited transcripts; later periods use Quartr full-text pages hosted by StockAnalysis. Claims are categorized as internal AI, customer product, or digital strategy; no inference of quantified financial impact beyond disclosures.

## Weatherford · Q2 2025

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Weatherford International Q2 2025 earnings call transcript](https://stockanalysis.com/stocks/wfrd/transcripts/337746-q2-2025/)

### Generative AI for productivity

CEO said the company was using shared services, automation and generative AI to improve long-term productivity, supported by continued investment in infrastructure systems.

Speaker: Girish Saligram, President and CEO. Locator: Transcript lines 156–157. Measurement: Not disclosed.. Limit: Broad management statement with no use-case detail or measured impact..

Q3/Q4 2024 use issuer-hosted Refinitiv edited transcripts; later periods use Quartr full-text pages hosted by StockAnalysis. Claims are categorized as internal AI, customer product, or digital strategy; no inference of quantified financial impact beyond disclosures.

## Weatherford · Q3 2025

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Weatherford International Q3 2025 earnings call transcript](https://stockanalysis.com/stocks/wfrd/transcripts/366118-q3-2025/)

### Generative AI for internal productivity

Management said shared services, automation technology and generative AI were being used to improve productivity and bottom-line performance, and described future systems infrastructure with AI-driven workflows.

Speaker: Girish Saligram, President and CEO. Locator: Transcript lines 143 and 157–159. Measurement: Not disclosed.. Limit: No quantified benefit or deployment scale disclosed..

Q3/Q4 2024 use issuer-hosted Refinitiv edited transcripts; later periods use Quartr full-text pages hosted by StockAnalysis. Claims are categorized as internal AI, customer product, or digital strategy; no inference of quantified financial impact beyond disclosures.

## Weatherford · Q4 2025

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Weatherford International Q4 2025 earnings call transcript](https://stockanalysis.com/stocks/wfrd/transcripts/401744-q4-2025/)

### AI-enabled corporate systems and process productivity

Management described use of AI and digital platforms to improve cost-base productivity and said a new ERP system is being designed for automation and AI enablement.

Speaker: Girish Saligram, President and CEO. Locator: Transcript lines 144 and 474–475. Measurement: Not disclosed.. Limit: Planned/enabling infrastructure and broad productivity claims; no measured impact disclosed..

Q3/Q4 2024 use issuer-hosted Refinitiv edited transcripts; later periods use Quartr full-text pages hosted by StockAnalysis. Claims are categorized as internal AI, customer product, or digital strategy; no inference of quantified financial impact beyond disclosures.

## Weatherford · Q1 2026

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Weatherford International Q1 2026 earnings call transcript](https://stockanalysis.com/stocks/wfrd/transcripts/551661-q1-2026/)

### Internal AI-enabled process automation

CEO said AI is being used to improve efficiency and margins, with tools automating accounts-receivable, accounts-payable and inventory workflows; a separate tool identifies idle inventory for reuse.

Speaker: Girish Saligram, President and CEO. Locator: Transcript lines 157 and 469–471. Measurement: Not disclosed.. Limit: Management described use cases but gave no measured savings or quantified inventory recovery..

Q3/Q4 2024 use issuer-hosted Refinitiv edited transcripts; later periods use Quartr full-text pages hosted by StockAnalysis. Claims are categorized as internal AI, customer product, or digital strategy; no inference of quantified financial impact beyond disclosures.

## Weatherford · Q2 2026

reviewed_relevant · Full transcript reviewed, including Q&A; screened AI, artificial intelligence, machine learning, generative, agentic, copilot, predictive, autonomous, automation, digital and transformation.

[Weatherford International Q2 2026 earnings call transcript](https://stockanalysis.com/stocks/wfrd/transcripts/652911-q2-2026/)

### AI in customer offerings and internal productivity

CEO described using AI in production optimization, drilling and tubular-running offerings for customers, alongside early internal productivity work and an AI-first approach to ERP design.

Speaker: Girish Saligram, President and CEO. Locator: Transcript lines 470–479. Measurement: Not disclosed; CEO said monetization approaches remain under development.. Limit: Mixes customer-facing applications and early internal-productivity exploration; no adoption or financial impact metric..

Q3/Q4 2024 use issuer-hosted Refinitiv edited transcripts; later periods use Quartr full-text pages hosted by StockAnalysis. Claims are categorized as internal AI, customer product, or digital strategy; no inference of quantified financial impact beyond disclosures.

## Williams · Q3 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Williams Q3 2024 earnings call transcript](https://stockanalysis.com/stocks/wmb/transcripts/219007-q3-2024/)

Quarter label follows company reporting quarter; actual call date is within the requested disclosure window. Source page is a full transcript and includes Q&A.

## Williams · Q4 2024

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Williams Q4 2024 earnings call transcript](https://stockanalysis.com/stocks/wmb/transcripts/237750-q4-2024/)

Quarter label follows company reporting quarter; actual call date is within the requested disclosure window. Source page is a full transcript and includes Q&A.

## Williams · Q1 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Williams Q1 2025 earnings call transcript](https://stockanalysis.com/stocks/wmb/transcripts/315553-q1-2025/)

Quarter label follows company reporting quarter; actual call date is within the requested disclosure window. Source page is a full transcript and includes Q&A.

## Williams · Q2 2025

reviewed_relevant · Full transcript reviewed (StockAnalysis/Quartr), including Q&A. Earlier inherited issuer-hosted URL returned 403 in this pass; accessible full third-party transcript corroborates the call content and exact date. The issuer URL/source discrepancy is retained in notes.

[Williams Q2 2025 earnings call transcript](https://stockanalysis.com/stocks/wmb/transcripts/342590-q2-2025/)

### AI model test in Sequent gas marketing

CEO Chad Zamarin described a one-month Dallas–Fort Worth market test in which a team of young programmers built AI models to market energy; one modeler scored 96% against the CEO’s stated perfect-execution benchmark while a ten-year trader scored 94%. Management also said the organization was in early stages of applying AI to cost/efficiency, market intelligence, project execution, and maintenance decision support.

Speaker: Chad Zamarin (CEO); Larry Larsen (COO). Locator: Q&A, lines 549–568. Measurement: 96% modeler score vs 94% trader score on the stated test; no scaled deployment or realized savings reported.. Limit: Single test case and management characterization; not evidence of broad production deployment. The related Socrates/data-center references are separate AI-energy-demand context..

Quarter label follows company reporting quarter; actual call date is within the requested disclosure window. Source page is a full transcript and includes Q&A.

## Williams · Q3 2025

reviewed_no_relevant · Full transcript, including prepared remarks and Q&A, reviewed and screened for AI/ML, generative/agentic AI, predictive analytics, autonomy, automation, digital/software platforms, data/analytics, remote operations and AI-related power/data-center demand.

[Williams Q3 2025 earnings call transcript](https://stockanalysis.com/stocks/wmb/transcripts/370619-q3-2025/)

Quarter label follows company reporting quarter; actual call date is within the requested disclosure window. Source page is a full transcript and includes Q&A.

## Williams · Q4 2025

no_public_call · Issuer event archive checked: February 10, 2026 event is 2026 Analyst Day; Q4 2025 earnings release and an Analyst Day transcript are listed, with no separate Q4 2025 earnings call/webcast listed.

[Williams 2026 Analyst Day / quarterly archive](https://investor.williams.com/events-and-presentations)

Williams reported Q4 2025 alongside its 2026 Analyst Day rather than holding a quarterly earnings call. No call transcript slot exists to review for the disclosure window.

## Williams · Q1 2026

reviewed_relevant · Full transcript reviewed (StockAnalysis/Quartr), including Q&A. AI references concern customer/data-center load and the power infrastructure response; no internal AI use was identified in the transcript.

[Williams Q1 2026 earnings call transcript](https://stockanalysis.com/stocks/wmb/transcripts/554430-q1-2026/)

### Data-center power solutions accommodate AI loads

CEO Chad Zamarin described working with customers on load-following and understanding AI loads; the company positioned natural-gas generation and battery storage as solutions for data-center power and grid protection. He said batteries primarily buffer rapid, dynamic AI load changes.

Speaker: Chad Zamarin (CEO). Locator: Q&A, lines 550–564 and 686–692. Measurement: Not quantified. Limit: AI is the customer load driver; this is infrastructure serving AI demand, not Williams internal AI deployment..

Quarter label follows company reporting quarter; actual call date is within the requested disclosure window. Source page is a full transcript and includes Q&A.

## Williams · Q2 2026

reviewed_relevant · Full transcript reviewed (StockAnalysis/Quartr), including Q&A. Mentions concern AI-driven demand/load and Socrates commissioning, not operator-internal AI.

[Williams Q2 2026 earnings call transcript](https://stockanalysis.com/stocks/wmb/transcripts/660690-q2-2026/)

### Socrates AI data-center power load commissioning

CEO Chad Zamarin tied the company’s infrastructure role to the AI revolution. COO Larry Larsen said Socrates load-testing checked that AI load-following worked as intended; Phase 1 began delivering power to the facility, with ramp-up expected over the month.

Speaker: Chad Zamarin (CEO); Larry Larsen (COO). Locator: Prepared remarks, lines 173–175; Q&A, lines 312–320. Measurement: First power delivery underway at call date; no AI-specific revenue or utilization metric.. Limit: AI is the served customer load; load following is power-system commissioning, not Williams internal AI use..

Quarter label follows company reporting quarter; actual call date is within the requested disclosure window. Source page is a full transcript and includes Q&A.

## Woodside · Q3 2024

not_located · Archive discrepancy checked: issuer/ASX archives show the Q3 report released 2024-10-16 without a Q3 earnings-call transcript; StockAnalysis lists an Oct. 15 Q3 call but its summary describes H1 2024 results. Its linked transcript URL returned cache miss.

[Woodside archive discrepancy: Q3 2024 label conflicts with H1 2024 transcript summary](https://stockanalysis.com/quote/asx/WDS/transcripts/)

Retained as a not-located archive discrepancy, not counted as an actual Q3 results call and not screened for mentions. The actual H1 2024 results teleconference was outside the disclosure window (2024-08-27). Issuer/ASX calendars show Q1 and Q3 operating reports without earnings-results teleconference transcripts during the window; Q2 is covered in H1 calls and Q4 in H2/FY calls.

## Woodside · H2 2024

reviewed_no_relevant · Full transcript reviewed, including prepared remarks, analyst Q&A and operator close; screened AI/artificial intelligence, ML, GenAI, agentic, analytics, predictive, automation, software, digital transformation, data-center and hyperscaler terms.

[Woodside H2 2024 results full earnings-call transcript](https://stockanalysis.com/quote/asx/WDS/transcripts/239707-h2-2024/)

Issuer/ASX publication and local call date is 2025-02-25; StockAnalysis labels the full transcript Feb. 24, reflecting a timezone date difference. Full transcript includes Q&A and closing. No relevant AI, AI-linked data-center, analytics, automation or software disclosure found. Reporting-cadence check: issuer/ASX archives show Q1 and Q3 operating reports but no results-call transcript for those interim updates; Q2 is covered in H1 and Q4 in H2/FY calls. Separate investor/status events are outside results-call scope.

## Woodside · H1 2025

reviewed_no_relevant · Full transcript reviewed, including prepared remarks, analyst Q&A and operator close; screened AI/artificial intelligence, ML, GenAI, agentic, analytics, predictive, automation, software, digital transformation, data-center and hyperscaler terms.

[Woodside H1 2025 results full earnings-call transcript](https://stockanalysis.com/quote/asx/WDS/transcripts/318357-h1-2025/)

Full transcript includes Q&A and closing. No relevant AI, AI-linked data-center, analytics, automation or software disclosure found. Reporting-cadence check: issuer/ASX archives show Q1 and Q3 operating reports but no results-call transcript for those interim updates; Q2 is covered in H1 and Q4 in H2/FY calls. Separate investor/status events are outside results-call scope.

## Woodside · H2 2025

reviewed_relevant · Full transcript reviewed, including prepared remarks, analyst Q&A and close; screened AI/artificial intelligence, ML, GenAI, agentic, analytics, predictive, automation, software, digital-transformation, data-center and hyperscaler terms.

[Woodside H2 2025 results full earnings-call transcript](https://stockanalysis.com/quote/asx/WDS/transcripts/401946-h2-2025/)

### U.S. data-center gas demand context for Louisiana LNG

In response to analyst Sarah Kerr’s question about power demand from U.S. data centers, Acting CEO Liz Westcott described Louisiana LNG as able to access U.S. domestic gas supply and referenced available gas volumes and the project’s contracted transport and supply position.

Speaker: Liz Westcott, Acting CEO, Woodside Energy Group. Locator: Q&A, lines 546–551.. Measurement: Westcott cited more than 1.1 trillion cubic feet of gas available to LNG projects and other users.. Limit: The data-center demand context came from the analyst; management did not link it specifically to AI, quantify incremental data-center load, identify a data-center customer, or disclose an associated contract..

Issuer local publication/call date is 2026-02-24; StockAnalysis labels the full transcript Feb. 23, reflecting a timezone date difference. Data-center reference arose in Q&A but is not AI-specific. Full call includes prepared remarks and Q&A. Reporting-cadence check: issuer/ASX archives show Q1 and Q3 operating reports but no results-call transcript for those interim updates; Q2 is covered in H1 and Q4 in H2/FY calls. Separate investor/status events are outside results-call scope.

## Woodside · H1 2026

reviewed_no_relevant · Full half-year results transcript reviewed through prepared remarks, analyst Q&A and conference close. Screened AI/artificial intelligence, ML, automation, digital transformation, data-center/hyperscaler terms.

[Woodside Energy Group H1 2026 earnings-call full transcript](https://stockanalysis.com/quote/asx/WDS/transcripts/657658-h1-2026/)

Woodside reports semiannually; no Q3/Q1 trading-call rows are inferred. Selected-term full-call screen found no relevant AI or AI-energy-demand passage. Reporting cadence audit: issuer and ASX archive searches show Q1 and Q3 quarterly reports but no earnings-result call transcript/webcast for those interim trading updates in the review window; Q2 reporting is subsumed into H1 and Q4 into FY/H2 earnings calls. The unresolved 15 Oct 2024 StockAnalysis Q3 label is noted in its separate not_located row and is not evidence of a verified call. Separate strategy/investor/status events are outside earnings-results-call scope. Reporting-cadence check: issuer/ASX archives show Q1 and Q3 operating reports but no results-call transcript for those interim updates; Q2 is covered in H1 and Q4 in H2/FY calls. Separate investor/status events are outside results-call scope.


## Targeted Q3 2026 extension · checked 2 November 2026

Seven targeted source records; only ExxonMobil and SLB have complete original-transcript topical screens. Other prepared remarks, selected original Q&A captions, releases and access attempts are partial. Other Q3 cells were not assessed in this extension.

### ExxonMobil · reviewed_relevant

Complete 30-page issuer-hosted transcript downloaded and text-screened through prepared remarks, Q&A and conference close; AI, machine learning, digital, automation and autonomous terms searched; relevant passages read.

[ExxonMobil third-quarter 2026 earnings-call transcript](https://d1io3yog0oux5.cloudfront.net/_46d510d0cb3425a46e5af22ca2415bcf/exxonmobil/db/2288/22725/webcast_transcript/Transcript_-_ExxonMobil_3Q_2026_Earnings_Call.pdf) · 2026-10-30

Operating automation population is disclosed. No isolated AI result or rig-normalized cost comparison.

Exxon’s Q3 call identifies three automated drilling rigs operating in the Permian. About 25% of the Permian fleet next year and approximately half by 2028 are rollout expectations, not current adoption or achieved savings. No measured benefit, full cost, net ROI or control/approval boundary. Company-wide structural savings must not be credited to these rigs.

Darren Woods · PDF pp.3–4, prepared remarks

### SLB · reviewed_relevant

Complete 18-page issuer-hosted edited transcript topical-screened; relevant prepared remarks and Q&A read. This is not a line-by-line factual audit.

[SLB Q3 2026 edited earnings-call transcript](https://investorcenter.slb.com/static-files/b578b6d9-7008-4e3d-9317-7112b7c08493) · 2026-10-23

Existing SLB thesis already covers the platforms. No new named customer or AI-only outcome credit.

Management describes early Lumi/Tela adoption and further potential in production optimization. More than 750,000 automated drilling feet in the quarter, nearly 50% higher year-on-year; DrillOps monitors more than 20% of active international rigs. These are supplier scale measures, not AI-only productivity or ROI. Customer populations, definitions and control boundaries remain unspecified. Digital financial results cover more than AI.

Olivier Le Peuch · PDF p.3 prepared remarks; pp.15–16 Q&A with Goldman Sachs and TD Cowen

### Chevron · reviewed_relevant

Original prepared remarks and slides read through the operational, exploration and closing AI passages. Full original Q&A not verified. Partial review only.

[Chevron Q3 2026 slides and prepared remarks](https://chevroncorp.gcs-web.com/static-files/01bc23b1-43e8-4c09-9e47-6e24614391ce) · 2026-10-30

Original prepared remarks verify the >$100m aggregate and ApEx/ScoutAI details. Secondary Q&A descriptions of a 24/7 production agent across Permian/Bakken/PZ and early well-design tools remain held. No GEORGE identity or same-workflow mapping inferred.

Prepared remarks report DJ downtime prediction, ApEx drawing on four petabytes and a century of company knowledge, and ScoutAI deployed in ten new basins. Basin information and assessments reportedly take hours instead of months and weeks respectively. Chevron reports over $100m of AI value captured year-to-date 2026; Gulf of America pattern recognition identified over 150 leads, with 16 prospects entering evaluation. The repeated 50% reduction in DJ lost-production events is already W31AM-CHEVRON-DJ. The aggregate value has no defined gross/net basis, cost bridge or project attribution. Leads are not discoveries or reserves. Portfolio discoveries are not isolated AI outcomes. No automatic drilling control or new result count inferred.

Mark Nelson, operational/exploration remarks; Mike Wirth, closing remarks · Physical PDF pp.5,7,12; slides 5,7,12. Q&A begins at p.13 and is not included.

### Shell · reviewed_relevant

Original five-page prepared speech read; official Shell Q&A captions exported and selected passage 21:00–25:55 read by editor. Partial original Q&A review, not a full-call negative or line-by-line audit.

[Shell Q3 2026 results Q&A — official Shell video and captions](https://www.youtube.com/watch?v=NWQxNJ1VSDA&t=1260s) · 2026-10-29

Session held 29 October 2026; official video uploaded 2 November. Caption wording may contain transcription errors. Wael identified by the original turn transition at 23:44. The broader performance limitation is not an AI-specific failure. Prepared speech is a separate source.

In response to a refinery-maintenance question, Wael Sawan describes using data and AI to anticipate problems, intervene earlier and coordinate people. He also emphasizes maintenance investment and closer integration of trading with assets. No isolated AI benefit is quantified. The separately mentioned 99% year-to-date utilization reflects wider refining-team performance. No named model, AI deployment site, population, baseline, cost or ROI. The statement that more remains to do concerns broader refining/manufacturing work; it does not establish a failed AI deployment. Approval and control permissions are unspecified.

Wael Sawan; original caption turn transition at 23:44 · 21:00–23:44, especially 23:07–23:17 and 23:28–23:39

### Halliburton · reviewed_relevant

Original release and technology highlights read; full original call unavailable in this review. Release-only, not a transcript screen.

[Halliburton Q3 2026 earnings release](https://www.halliburton.com/en/about-us/press-release/halliburton-announces-third-quarter-2026-results.html) · 2026-10-20

The call’s secondary back-office AI fragment remains unverified. SAP migration expense and corporate results are not AI ROI.

Release highlights LOGIX automation and remote operations for Eni Indonesia and planned bp Bumerangue work. No incremental measured result admitted from this release. Contract scope, operating deployment and outcomes remain separate. This partial release check cannot establish what the full call said.

Halliburton issuer release; no call speaker assigned · Technology highlights: bp and Eni bullets

### Baker Hughes · reviewed_relevant

Original SEC-hosted release relevant awards and technology passages read. Secondary full-call lead is not counted as an original transcript screen.

[Baker Hughes Q3 2026 release, SEC Exhibit 99.1](https://www.sec.gov/Archives/edgar/data/1701605/000170160526000033/earningsreleaseex991093020.htm) · 2026-10-27

Existing W28PM-KOC and W28PM-PETROBRAS retain their original contract/deployment limits. Equipment sold into AI infrastructure is energy-demand context.

Release describes Petrobras Cordant optimization across seven offshore units and SBM remote/condition monitoring in a service agreement. SBM agreement covers 27 LM2500 turbines across four FPSOs; Petrobras scope already covered. Awards are not proof of accepted fleet rollout or measured benefit. No AI-specific performance, cost or net ROI is disclosed.

Baker Hughes issuer release; no call speaker assigned · Printed p.3, IET bullets

### NOV · inaccessible

Luna read original release; parent repeat request returned 403. Full original call not located. No retained passage admitted on the inaccessible parent retrieval.

[NOV Q3 2026 release and call-material lead](https://investors.nov.com/news-releases/news-release-details/nov-reports-third-quarter-2026-earnings) · 2026-10-27

Global offshore data/monitoring award duplicates W27PM-NOV. No accepted automation rollout or outcome inferred.

## Additional Q3 screens · 5 November 2026

Two complete Q3 original-transcript topical screens. Eni has no qualifying internal AI passage; Equinor discusses external data-center demand. Other Q3 gaps remain open.

### Eni

Complete 13-page original transcript topically screened, prepared remarks and Q&A through the call close. AI, artificial intelligence, machine learning, digital, automation, software and data-center terms checked.

No qualifying AI or internal software passage found in this transcript screen. Separate release disclosures about external supercomputing access and an exploratory robotics agreement do not become call passages. This does not establish company inactivity.

[Eni Q3 2026 results transcript](https://www.eni.com/content/dam/enicom/documents/eng/investor/presentations/2026/2026-third-quarter-results/2026-third-quarter-results-Transcript.pdf) · 2026-10-23

### Equinor

Complete 16-page issuer-hosted LSEG edited transcript downloaded and text-screened, prepared remarks and Q&A through call close. AI, artificial intelligence, machine learning, digital, automation, autonomous, software, robotics and data-center terms checked; matched passage read in context.

The AI passage concerns customers’ data-center demand and energy partnerships. No internal AI workflow or economic result is disclosed in that passage. Original issuer download read; no separate replay listening claimed.

[Equinor Q3 2026 analyst call and Q&A, issuer-hosted LSEG edited transcript](https://cdn.equinor.com/files/h61q9gi9/global/2ff6e386bf6b09782133ac2e90a8a0d18a412a7b.pdf?EQNR-OSL-transcript-2026-10-28.pdf) · 2026-10-28

## Chevron original Q&A resolved · 6 November 2026

Complete 17-page Chevron-edited original transcript topically screened, including prepared remarks and Q&A through call close. Relevant AI, software, automation and power-demand passages read in context. Text reader succeeded through the official event-page link; direct fetch and rendering failed. No replay listening claimed.

Replaces the earlier prepared-only screen. Production-assistant deployment and early well-design tests are now issuer-verified. No GEORGE identity, automatic-control permission or project-level allocation of aggregate AI value inferred. Prior partial record retained in the dated research ledger.

Prepared remarks describe DJ downtime prediction, ApEx data retrieval and ScoutAI assessments across ten basins. Reported AI value exceeds $100m year-to-date; over 150 exploration leads yielded 16 prospects for evaluation. Aggregate gross/net basis and costs unspecified. Leads are not discoveries. DJ event reduction already logged separately.

Mark Nelson and Eimear Bonner · PDF/printed pp. 3–5 and 10.

In-house production assistant deployed in Permian, Bakken and Partitioned Zone; well-design and capital-scoping tools remain in early tests. No project-specific outcome disclosed. No well count, control permissions, comparison or costs.

Eimear Bonner · Q&A with James West, PDF/printed p. 10.

Human-agent teams support turnaround and refinery planning; AI also informs reliability and yield analysis. No isolated benefit disclosed. Catalyst-driven yield gains are separate from AI.

Eimear Bonner and Mark Nelson · Q&A, PDF/printed pp. 9–10.

Kilby has a Microsoft power agreement; permits, financing and final investment decision remain pending. 2.67 GW; 20-year PPA. Contracted power is not operating AI adoption or cash return.

Eimear Bonner · Q&A, PDF/printed pp. 11–12.

[Chevron Q3 2026 earnings call: Chevron-edited transcript](https://chevroncorp.gcs-web.com/static-files/000e7741-422d-4e9f-854e-07c4ccee0ffb) · 2026-10-30

## Shell and remaining Q3 sources · 6 November 2026

Shell’s full official Q&A captions and ConocoPhillips’ complete issuer-linked transcript topically screened. Baker Hughes’ prepared remarks checked; its Q&A remains open. No new AI-attributed measured result or failed rollout verified. Other Q3 gaps remain.

### Shell

Complete official Shell Q&A caption export topically screened from 0:12 through the 55:18 call close, including AI/digital, software, automation, pilot, scaling and failure terms; matching passage read in context. Earlier five-page prepared-speech screen retained. Caption-based screen, not audio replay listening or word-for-word accuracy validation.

Upgrades the prior 21:00–25:55 selected-caption review. No additional quantified AI result or named failed rollout established by this topical screen. The 99% utilization, Pernis/Scotford product records and broader remaining-work statement are not isolated AI outcomes. Caption wording may contain transcription errors; source upload 2 November versus call 29 October retained.

[Shell Q3 2026 results Q&A — official Shell video and captions](https://www.youtube.com/watch?v=NWQxNJ1VSDA&t=1260s) · call 2026-10-29

In response to a refinery-maintenance question, Wael Sawan describes using data and AI to anticipate problems, intervene earlier and coordinate people. He also emphasizes maintenance investment and closer integration of trading with assets. No isolated AI benefit is quantified. The separately mentioned 99% year-to-date utilization reflects wider refining-team performance. No named model, AI deployment site, population, baseline, cost or ROI. The statement that more remains to do concerns broader refining/manufacturing work; it does not establish a failed AI deployment. Approval and control permissions are unspecified.

Wael Sawan; original caption turn transition at 23:44 · 21:00–23:44, especially 23:07–23:17 and 23:28–23:39

### Baker Hughes

All 18 pages of issuer prepared remarks downloaded and topically screened, with relevant digital, AI and remote-monitoring passages read in context. Document ends when questions are invited. Q&A remains unscreened; this is a partial call review despite the issuer index labeling the download Transcript.

Corrects the previous record’s 27 Oct release date to the actual 28 Oct call date, verified by the original release schedule. Prior SEC release retained as additional source. Existing KOC/Petrobras contract notes are not recounted; power orders remain external-demand context. No new accepted deployment, measured outcome or complete net ROI. The actual prepared-remarks posting date is unknown.

[Baker Hughes Q3 2026 earnings conference call prepared remarks](https://s21.q4cdn.com/548121662/files/doc_earnings/2026/q3/transcript/Baker-Hughes-3Q26-Earnings-Call-Prepared-Remarks.pdf) · call 2026-10-28

Prepared remarks describe an SBM service agreement combining maintenance with iCenter remote monitoring and diagnostics. Agreement covers 27 LM2500 gas turbines across four FPSOs. Contract scope, not verified commissioning or AI-attributed improvement; no measured benefit or full costs.

Lorenzo Simonelli · PDF/printed p.6.

### ConocoPhillips

Complete 17-page issuer-linked LSEG edited transcript downloaded and topically screened through call close. AI, artificial intelligence, machine learning, digital, software, automation, diagnostics, trial, pilot and rollout terms checked; relevant passages read in context. No replay listening claimed.

No qualifying internal AI/software deployment or AI economic result found in this topical screen. Conventional technology result retained as context, not an AI success or failure. Measured rollout pace does not establish failed scaling. Parent independently checked PDF/printed pages 10–11. The call was 5 November; the edited document has a 6 November timestamp, and the issuer posting time is unknown.

[ConocoPhillips Q3 2026 earnings call, issuer-hosted LSEG edited transcript](https://static.conocophillips.com/files/resources/cop_transcript_2026-11-05.pdf) · call 2026-11-05

Nick Olds reports surfactant-treated well results while describing continued tests and a measured pace of further treatments in 2027. Reported 20% oil-productivity uplift; about 1.5 years of data across several well groups, with a range of outcomes. Conventional treatment, not AI-attributed. Well count, common control/baseline, comparison period and full costs unspecified; neither failure nor net ROI established.

Nick Olds · PDF/printed p.11; real-time frac diagnostics discussed separately p.10.

## Chord and Q3 availability · 6 November 2026

Chord Q3 release verifies AI screening, without an isolated benefit; full call and deck remain open. Devon Q3 deck confirms the earlier gross lift result and illustrative subsurface comparison; original Q&A remains open. EQT explicitly canceled its Q3 call. Earlier complete Shell captions and ConocoPhillips transcript screens are retained; other Q3 gaps remain.

### Chord Energy

Original 4 November Q3 release read, especially the AI-screening, base-production and program-savings passages. Release-only source check; Q&A and investor deck have not been verified. Partial call-period coverage, not a full-call screen.

Call date verified from the original 19 October issuer schedule. AI screening is distinct from the earlier rod-lift metric. Company savings, frac productivity and chemical treatments are not AI-isolated. Original Q3 deck download failed; no transcript screen claimed.

[Chord Energy Q3 2026 financial and operating results](https://ir.chordenergy.com/2026-11-04-Chord-Energy-Reports-Third-Quarter-2026-Financial-and-Operating-Results,-Declares-Base-Dividend,-and-Updates-2026-Outlook)

### EQT

Original 27 October issuer results release checked for the explicit call-cancellation notice. No Q3 call screen claimed; accompanying presentation and10-Q remain unchecked.

EQT states it canceled the Q3 earnings call after announcing the Range transaction. This is a call-availability fact, not a technology failure or evidence of inactivity. The original presentation is a separate coverage gap.

[EQT Q3 2026 results and earnings-call cancellation notice](https://ir.eqt.com/investor-relations/news/news-release-details/2026/EQT-Reports-Third-Quarter-2026-Results/default.aspx)

### Devon Energy

Original 28-page Q3 deck downloaded in the prior review; parent re-read PDF/printed pages 18 and 21 on 6 November. Those pages were visually inspected in the prior review. Call date verified from issuer release. Q&A original transcript/captions not located; no complete call screen claimed.

These deck findings already appear in W05PM-DVN-LIFT-Q3 and W05PM-DVN-SUBSURFACE. This adds call-period source coverage, not a new result or deployment. Third-party call text is a lead awaiting original-source verification; its qualitative AI claims are not admitted here.

[Devon Energy Q3 2026 earnings presentation](https://s2.q4cdn.com/462548525/files/doc_financials/quarterly/2026/q3/Q3-2026-DVN-Earnings-Presentation.pdf)

## Deeper Q3 sources · 6 November 2026

Weatherford’s complete issuer-hosted Q3 transcript topically screened; digital strategy and conventional completion-technology adoption remain distinct from AI outcomes. PTTEP’s two original Q3 decks checked; ARV inspection offerings are not customer-deployment proof. Devon’s official Q&A is behind guest registration; its shareholder letter corroborates existing workflows. No new measured AI result or failed AI rollout verified in this bounded check.

### Devon Energy

Original 28-page Q3 deck downloaded in the prior review; parent re-read PDF/printed pages 18 and 21 on 6 November. Those pages were visually inspected in the prior review. Call date verified from issuer release. Q&A original transcript/captions not located; no complete call screen claimed. Fresh 6 November issuer/archive search and official Q4 replay UI check reached guest registration requiring contact details and Terms of Use acceptance; no registration submitted or audio reviewed.

These deck findings already appear in W05PM-DVN-LIFT-Q3 and W05PM-DVN-SUBSURFACE. This adds call-period source coverage, not a new result or deployment. Third-party call text is a lead awaiting original-source verification; its qualitative AI claims are not admitted here. The 5 November shareholder letter independently corroborates the two existing AI workflows; it provides no new isolated outcome. Official Q&A remains unverified. Third-party transcripts were not promoted to original-call evidence.

[Devon Energy Q3 2026 earnings presentation](https://s2.q4cdn.com/462548525/files/doc_financials/quarterly/2026/q3/Q3-2026-DVN-Earnings-Presentation.pdf) · call 2026-11-06

Devon reports around 1,000 self-optimizing wells and another 1,900 ready for smart gas lift. Reported +5% gross uplift on wells using smart gas lift. Ready is not active. No measured-well denominator, comparison, period or full costs; not a fleet-wide net return.

Devon issuer presentation; no call speaker assigned · PDF/printed p.18.

A proprietary AI model screens spacing, frac design and acquisitions; experts can inspect forecasts. The +46% well comparison is labeled illustrative. No model-attributed realized result, evaluated cohort or capital-approval authority disclosed.

Devon issuer presentation; no call speaker assigned · PDF/printed p.21.

[Devon Q3 results and conference-call schedule](https://www.devonenergy.com/news/2026/Devon-Energy-Reports-Third-Quarter-2026-Results) · Original release call-date paragraph read.

[Devon Energy letter to shareholders](https://www.devonenergy.com/news/2026/LETTER-TO-SHAREHOLDERS) · Original full letter read; section 1 technology-forward paragraph corroborates subsurface models and expanding closed-loop AI gas lift. Not a Q&A transcript.

[Devon official Q3 webcast — registration boundary](https://events.q4inc.com/attendee/958533107) · Official Q4 event UI reached guest registration after Continue without a Q4 account. Contact fields and acceptance of Terms of Use required; no form submitted, replay or captions reviewed.

### Weatherford

Complete 18-page original issuer-hosted edited transcript downloaded and topically screened through Q&A close. Whole-document AI, intelligence, ML, software, digital, automation, autonomous, pilot, scale and failure searches; matching context and adoption discussion read. Not a replay-audio verification.

Digital is part of a broad portfolio strategy, without a named AI result. Management describes customer risk aversion and integration/track-record requirements for offshore completions, not a failed AI deployment. Company margins, acquisition synergies and managed-pressure-drilling penetration are not AI-attributed benefits. Call date is on cover; exact transcript publication date unknown.

[Weatherford Q3 2026 earnings call — issuer-hosted LSEG edited transcript](https://weatherford.gcs-web.com/static-files/d0474707-60dd-48ef-844e-7b387e191369) · call 2026-10-21

Weatherford includes digital in its portfolio expansion. In Q&A, management says offshore-completion adoption takes integration, a track record and customer willingness to try new technology. No isolated AI operating measure or financial return. The adoption discussion concerns completion technology, not a named AI rollout. It establishes neither AI failure nor disappointing AI ROI. Scope, comparison, cost and AI control boundaries are undisclosed.

Girish Saligram, President and CEO · PDF/printed p.8; pp.15–16.

### PTTEP

Original 48-page analyst meeting deck and 47-page OppDay deck downloaded through issuer-linked viewers and completely text-screened for AI, robotics, software, automation and scale/failure terms. ARV portfolio slide read and visually inspected at analyst p.34. Presentation-only review; no Q&A transcript or replay reviewed.

New targeted Q3 source slot, not a retrospective screen of all PTTEP quarters. ARV offerings are separate from internal X.brain and its planned agents. No new measured result, customer rollout, failed AI deployment or complete net ROI established.

[PTTEP Q3 2026 analyst meeting presentation](https://hub.optiwise.io/storage/129/presentation/2026/pttep-am-3q2026.pdf) · call 2026-11-06

PTTEP maps ROVULA’s subsea vehicles and XSPECTOR inspection-data software, alongside SKYLLER’s aerial monitoring and asset-integrity software. Portfolio capabilities; no customer outcome measure. Product descriptions do not establish commissioned customer deployments, repeatability, full costs or net return. They do not update X.brain’s rollout.

PTTEP issuer presentation; no call speaker assigned · Analyst PDF/printed p.34; OppDay p.33.

[PTTEP Q3 2026 OppDay presentation](https://hub.optiwise.io/storage/129/presentation/2026/pttep-oppday-3q2026.pdf) · All 47 pages topically screened; matching ARV portfolio slide p.33.

## TotalEnergies original Q3 source · 6 November 2026

TotalEnergies’ complete original Q3 transcript is now topically screened. Its AI remark concerns power demand, not internal adoption or a measured software result. Earlier Q3 reviews are retained; other company and document gaps remain.

Original 19-page issuer-hosted PDF downloaded and all text topically screened for AI, intelligence, ML, software, digital, automation, pilot, scale and failure terms through call close. Matching demand passage and surrounding Q&A read. No replay-audio verification.

Resolves the previously inaccessible original transcript. AI appears as demand context for Integrated Power, not an internal technology result. Production, refining throughput, cost discipline and German wind-concession write-offs are not AI-attributed outcomes or failed AI projects. Cover verifies 29 October call date; exact transcript posting date unknown.

[TotalEnergies Q3 2026 earnings-call transcript](https://totalenergies.com/sites/g/files/nytnzq121/files/documents/totalenergies_3q26-results-transcript_2026.pdf) · call 2026-10-29

The CEO refers to AI activity when discussing the power business in the United States and Europe. No AI-specific demand volume or internal operating measure. This is customer-demand context, not evidence of internal AI adoption, realized software savings or a failed deployment.

Patrick Pouyanné, Chairman and CEO · PDF p.12; reply to Alastair Syme on downstream strategy.