{
  "as_of": "2026-10-08",
  "title": "AI is doing operating work. The evidence says more about what runs than what it earns.",
  "subtitle": "A collection readout of company-use profiles, measured outcomes, and disclosure limits.",
  "method": {
    "source": "Canonical Energy AI Observatory research-data.json; report generated 2026-10-08; register last updated 8 October 2026.",
    "register_updated": "8 October 2026",
    "cohort": "Original screened cohort: 26 September 2024–26 September 2026. Accepted follow-ups are dated separately in the method limits.",
    "grain": "One row is one company_adoption profile, not a unique company or a market adoption unit. Each profile receives one analyst-coded primary department; records can overlap in company, asset, program, and source.",
    "result_definition": "Counted numerical result means Outcome_counted=true in the canonical data. Results are reported claims of unlike kinds (for example, production, efficiency, task accuracy, emissions, time, and financial metrics); they are not comparable performance units or all independently assured.",
    "attribution_definition": "Operator, vendor, and independent identify the source attribution coded for each record. These labels describe who reports the outcome, not a judgment of truth or independent assurance.",
    "economics_definition": "No record is coded as disclosing a complete project cash-cost/collected-cash reconciliation. Ecopetrol is a partial portfolio-level investment/benefit disclosure with a separate-period cumulative ROI, detailed in this report.",
    "classification_method": "Canonical `Record_type`, `Primary_department`, `Stage_norm`, `Outcome_counted`, `Attribution`, source dates, locators, URLs, and interpretation limits are used directly. Outcome_counted excludes overlapping portfolio aggregates AI-033 and AI-038, as documented in the collection README.",
    "limits": "This is a consolidated public-source screen, not an exhaustive global census. Original screened cohort: 26 September 2024–26 September 2026. Accepted follow-ups: 5 and 6 October 2026. Classification and research review: 8 October 2026. Public disclosure, selection, record grain, analyst classification, overlapping portfolios, and uneven source access limit industry-wide inference.",
    "method_note": "Energy AI Observatory README, 5 October evidence-method note: the register is rebuilt from baseline inputs, overrides, and accepted research records; company_adoption alone feeds headline counts; Outcome_counted excludes two overlapping portfolio aggregates. The source grades describe provenance, not independent validation."
  },
  "counts": {
    "company_adoption_profiles": 196,
    "department_count": 14,
    "operating": 112,
    "scaled": 21,
    "operating_or_scaled": 133,
    "counted_numerical_results": 54,
    "both_operating_or_scaled_and_result": 46,
    "operating_or_scaled_without_counted_result": 87,
    "counted_result_records_still_at_pilot": 8,
    "cash_implementation_cost_records_flagged": 0,
    "attribution": {
      "operator": 32,
      "vendor": 21,
      "independent": 1
    }
  },
  "exhibits": [
    {
      "id": "department-mix",
      "title": "Where company-use profiles sit",
      "unit": "Company-adoption profiles",
      "rows": [
        {
          "department": "IT, data & enterprise transformation",
          "records": 36,
          "counted_results": 9
        },
        {
          "department": "Production & field operations",
          "records": 33,
          "counted_results": 11
        },
        {
          "department": "Maintenance & reliability",
          "records": 25,
          "counted_results": 5
        },
        {
          "department": "Drilling & completions",
          "records": 22,
          "counted_results": 6
        },
        {
          "department": "HSE & emissions",
          "records": 18,
          "counted_results": 3
        },
        {
          "department": "Refining, LNG & process operations",
          "records": 14,
          "counted_results": 4
        },
        {
          "department": "Other",
          "records": 48,
          "counted_results": 16,
          "includes": [
            "Reservoir engineering & field development",
            "Exploration & geoscience",
            "Finance, investment & strategy",
            "Supply chain & logistics",
            "Trading & marketing",
            "Workforce & learning",
            "Land & legal",
            "Research & innovation"
          ]
        }
      ],
      "all_departments": [
        {
          "department": "IT, data & enterprise transformation",
          "records": 36,
          "counted_results": 9
        },
        {
          "department": "Production & field operations",
          "records": 33,
          "counted_results": 11
        },
        {
          "department": "Maintenance & reliability",
          "records": 25,
          "counted_results": 5
        },
        {
          "department": "Drilling & completions",
          "records": 22,
          "counted_results": 6
        },
        {
          "department": "HSE & emissions",
          "records": 18,
          "counted_results": 3
        },
        {
          "department": "Refining, LNG & process operations",
          "records": 14,
          "counted_results": 4
        },
        {
          "department": "Reservoir engineering & field development",
          "records": 12,
          "counted_results": 5
        },
        {
          "department": "Exploration & geoscience",
          "records": 8,
          "counted_results": 0
        },
        {
          "department": "Finance, investment & strategy",
          "records": 8,
          "counted_results": 4
        },
        {
          "department": "Supply chain & logistics",
          "records": 6,
          "counted_results": 1
        },
        {
          "department": "Trading & marketing",
          "records": 5,
          "counted_results": 2
        },
        {
          "department": "Workforce & learning",
          "records": 5,
          "counted_results": 3
        },
        {
          "department": "Land & legal",
          "records": 3,
          "counted_results": 1
        },
        {
          "department": "Research & innovation",
          "records": 1,
          "counted_results": 0
        }
      ],
      "note": "Top six primary analyst-coded departments plus Other. These are profile counts, not unique companies, spend, adoption rate, or comparative success rates. One primary department is assigned per profile; other functions can also be involved."
    },
    {
      "id": "deployment-and-results",
      "title": "Operating stage and numerical evidence overlap",
      "unit": "Company-adoption profiles",
      "operating_scaled": 133,
      "both": 46,
      "without_result": 87,
      "counted_results": 54,
      "result_op_scaled": 46,
      "result_pilots": 8,
      "bars": [
        {
          "label": "Operating or scaled",
          "count": 133,
          "denominator": 196
        },
        {
          "label": "Counted numerical results",
          "count": 54,
          "denominator": 196
        },
        {
          "label": "Both",
          "count": 46,
          "denominator": 196
        }
      ],
      "note": "Three separate bars use the same 196-profile denominator. The sets overlap and are not a funnel. Stage does not independently establish active reach or impact, and a missing counted metric does not show absence of benefit."
    },
    {
      "id": "result-attribution",
      "title": "Who reports the 54 counted results?",
      "unit": "Counted numerical-result profiles",
      "rows": [
        {
          "attribution": "operator",
          "records": 32,
          "share_pct": 59.3
        },
        {
          "attribution": "vendor",
          "records": 21,
          "share_pct": 38.9
        },
        {
          "attribution": "independent",
          "records": 1,
          "share_pct": 1.9
        }
      ],
      "note": "Attribution categories identify who reported a result, not source reliability or independent assurance. Outcomes have unlike units and evidence designs."
    }
  ],
  "implications": [
    {
      "audience": "Operators",
      "headline": "Tie deployment to an operating decision and a measured baseline.",
      "text": "The register has 133 profiles classified operating or scaled and 54 with counted numerical results; 46 appear in both sets. That pattern supports a disclosure-level takeaway: described use is more common than a comparable result. For a rollout, name the accountable operator, baseline, exposure denominator, override/decision rights, and value measure before scale-up.",
      "counterexample": "ExxonMobil/XTO reports closed-loop optimization on 1,300+ Permian wells and about 2% average oil uplift (AI-235); the control phases and cohort details still need care."
    },
    {
      "audience": "Suppliers and service partners",
      "headline": "Make the operating handoff part of the product.",
      "text": "Suppliers should fit into existing systems, support the result after launch, and show full costs. The case register includes 21 vendor-attributed outcomes among 54 counted results; attribution identifies who reports a result, not independent assurance.",
      "counterexample": "Ecopetrol reports portfolio investment and benefits plus a separate cumulative ROI measure (AI-147), but periods and bases are unmatched. It illustrates the data investors need and the bridge still missing."
    },
    {
      "audience": "Evidence readers",
      "headline": "Keep attribution, reach, and assurance separate.",
      "text": "Of 54 result-bearing profiles, 32 are operator-attributed, 21 vendor-attributed, and one independent-attributed. These source labels do not grade the truth of a claim. Pair every result with its source, denominator, period, counterfactual, and review status.",
      "counterexample": "Petrobras reports 10,000 tCO₂/year lower emissions from AI industrial-flare control (AI-146), but its investor slide omits plant count and calculation method. Snam/Engineering Group reports strong task-specific extraction scores (AI-208), not independently verified operating economics."
    }
  ],
  "examples": [
    {
      "id": "AI-235",
      "company": "ExxonMobil",
      "initiative": "Closed-loop machine-learning gas-lift optimization across 1,300+ Permian wells",
      "role": "Reach counterexample · production",
      "what": "ExxonMobil / XTO describes closed-loop gas-lift optimization across 1,300+ Permian wells and reports about 2% average oil uplift in its operator-authored SPE abstract.",
      "why": "A material operating example, with counterfactual and control-boundary detail still limited; JPT article is member-exclusive beyond its accessible summary.",
      "reported_metric": "Operator-authored SPE abstract reports about 2% average oil uplift. JPT’s October 2024 interview reports 1.87% in a three-month human-approval stage and about 2.2% in the next three-month, mostly automatic stage. These summaries are not additive; periods and cohorts are incompletely disclosed.",
      "interpretation_limit": "The primary paper's accessible abstract states the full paper is unavailable, so full technical validation is not possible. The operator-reported uplift lacks a disclosed counterfactual or confidence interval. Gas-lift performance can drift when water cut or productivity changes; JPT says an existing setting may remain suboptimal for years without frequent monitoring. The control-boundary details and automatic-versus-human exception rates are undisclosed.",
      "source_date": "2025-10-01",
      "source_url": "https://jpt.spe.org/gas-lift-optimization-in-the-permian-uses-machine-learning-artificial-intelligence-restricted",
      "source_locator": "Accessible abstract and first paragraph; lines 592–620",
      "source_type": "SPE Journal of Petroleum Technology paper highlight",
      "evidence_status": "Accessible text read in full. The JPT page is marked member-exclusive after the opening summary; this supports 1,300+ deployment and workflow framing, not details beyond the accessible text.",
      "attribution": "operator"
    },
    {
      "id": "AI-146",
      "company": "Petrobras",
      "initiative": "AI industrial-flare control and Smart Tocha monitoring",
      "role": "Reach counterexample · emissions",
      "what": "Petrobras attributes 10,000 tCO2/year lower emissions to real-time AI industrial-flare control.",
      "why": "Issuer presentation does not disclose baseline, plant count, measurement period, or calculation method; it does not name Smart Tocha as the source of the full result.",
      "reported_metric": "10,000 tCO2/year reduction attributed to real-time AI industrial-flare control.",
      "interpretation_limit": "Do not assign the full result specifically to Smart Tocha: the slide does not name it. No baseline, measurement period, plant count or emissions calculation method. P-52 is newly identified scope, not proof that the strategy-deck 10,000 tCO2/year result belongs to P-52 or Smart Tocha.",
      "source_date": "2025-11",
      "source_url": "https://petrobras.com.br/documents/2677942/12513210/PN%2B2026-30_27nov_final_ing.pdf/3d500254-cbf1-c5ce-56b6-008b4a28a733?download=true&t=1765223843000&version=1.0",
      "source_locator": "Slide 158, Accelerate value generation through digital technologies",
      "source_type": "Issuer investor presentation",
      "evidence_status": "Original 163-page issuer PDF downloaded; slide 158 visually inspected to verify each metric-to-workflow mapping.",
      "attribution": "operator"
    },
    {
      "id": "AI-147",
      "company": "Ecopetrol",
      "initiative": "AI and advanced analytics ecosystem; intelligent upstream planning",
      "role": "Economics counterexample · partial bridge",
      "what": "Ecopetrol reports US$1.88m AI investment and US$3.50m benefits in 2025, plus an estimated 7% cumulative ROI since 2021.",
      "why": "The annual portfolio investment/benefit and cumulative ROI use different periods and bases. No all-in project cost or cash-conversion bridge is disclosed.",
      "reported_metric": "AI investment US$1.88m in 2025; benefits US$3.5m (revenue +US$2.06m, costs −US$1.44m); cumulative ROI since 2021 estimated at 7% (company-reported); 4,609 labor hours released by intelligent planner",
      "interpretation_limit": "Company-reported AI portfolio investment and revenue/cost benefits, not audited project cash ROI. No all-in project cost, cash-conversion bridge or comparable asset-level denominator; the cumulative ROI uses a different period.",
      "source_date": "2026-03-04",
      "source_url": "https://www.ecopetrol.com.co/wps/wcm/connect/4fec4771-90bd-446d-a107-cbe140583b88/Circular-031-esp-anexo-vf.pdf?CVID=pP5vUHj&MOD=AJPERES",
      "source_locator": "2025 sustainability annex, PDF page 209 (printed page 209); issued 4 March 2026",
      "source_type": "annual report",
      "evidence_status": "partially verified primary (audit: source supports some, not all, profile claims)",
      "attribution": "operator"
    },
    {
      "id": "AI-208",
      "company": "Snam",
      "initiative": "AI extraction of gas-plant design data and equipment relationships",
      "role": "Independent-attribution example · task test",
      "what": "Snam / Engineering Group’s technical preprint reports 95% component identification and 90% correct assignment to drawing sections.",
      "why": "This is a task-specific document extraction evaluation, not independent verification of operating savings or field impact; complex diagrams remain a limitation.",
      "reported_metric": "Table 3 reports 95% component identification and 90% correct assignment to sections; these are task-specific evaluation metrics.",
      "interpretation_limit": "The abstract gives different aggregate accuracy figures. Complex diagrams remain a weakness. No independently validated performance, cash saving, measured time saving or fleet rollout is established; the 11,000-plant fleet is contextual.",
      "source_date": "2025-11-05",
      "source_url": "https://arxiv.org/html/2511.08609v1",
      "source_locator": "Sections 3.1, 4 (Tables 1–3) and 5; abstract uses different summary accuracy figures",
      "source_type": "Operator/provider-authored technical preprint",
      "evidence_status": "Full original preprint reviewed; not represented as a peer-reviewed deployment study.",
      "attribution": "independent"
    }
  ],
  "csv": {
    "path": "industry-takeaways.csv",
    "row_count": 196,
    "grain": "One row per company_adoption profile; original canonical source URL, date, locator, and interpretation limit are carried where present."
  }
}