8 AI initiative profiles for TotalEnergies across 7 departments, with evidence grades and source links. Strongest evidence: A · Operator filing — Industrial data and AI foundation across operated upstream.
A · Operator filing · Announced · Production & field operations
Target scope: 40 upstream and 16 refining/chemical sites; 10x asset data points; full deployment end-2027. India center: at least 500 engineers by 2027 across all named programs. No quantified AI outcome.
Source (2026-02-11)
B · Primary publication · Announced · Research & innovation
No quantified results disclosed
Source (2025-06-12)
B · Primary publication · Operating · IT, data & enterprise transformation
Digital Factory team: 300 developers, data scientists and digital experts (Company-reported); Factory solutions: 100+ total; 60 use ML-to-GenAI methods (Company-reported)
Source (2025-06-12)
B · Primary publication · Announced · Exploration & geoscience
Announced three-year joint development program representing more than €100m investment; no deployed-model result or realized return disclosed.
Source (2026-09-15)
B · Primary publication · Pilot · Refining, LNG & process operations
Five potential events forecast an average of 12 minutes before alarm incidents; company says timely corrective action helped minimize downtime and flaring emissions.
Source (2025-11-11)
B · Primary publication · Announced · HSE & emissions
35 fugitive methane emissions detected since MethaneLive's early-2026 launch (company-reported; attributed to real-time data analysis, not specifically to AI)
Source (2026-06-18)
B · Primary publication · Announced · IT, data & enterprise transformation
Issuer-disclosed infrastructure investment >€100m and sixfold compute-capacity increase. It forecasts approximately 40% lower energy consumption at equal performance and a fivefold reduction in cooling-system consumption.
Source (2026-05-06)
C · Vendor-reported · Operating · Maintenance & reliability
Fault cause identification: 25%-45% improvement (company-reported) (Vendor-reported customer statement); Savings target for selected procurement: 10% target, not realized result (Company-reported target)
Source (2025-10-22)
Company and vendor statements are attributed, not independently validated. Evidence grades: A operator filing, B operator release, C vendor-reported, D secondary or unverified.
Later research and source checks
Supplementary notes can update existing programs or describe adjacent work. They are not extra adoption counts.
One useful warning model did not make every coker use case useful
A named TotalEnergies engineer said a 75%-accuracy drum-level model was not retained because switching also depended on conditions outside its scope.
No test denominator or definition for 75% accuracy. At least two acted-on alerts is not a total-alert count, avoided-trip count or reliability rate.
Limits: Named remarks in sponsored conference coverage; original slides and later company confirmation not located. Other uses continued. No quantified loss, avoided flaring, downtime benefit or full costs; do not call the whole pilot a failure.
Honeywell AI pilot aids coker unit operations at TotalEnergies refinery · 2026-06-11
TotalEnergies uses generative AI to check trading data
AWS describes a TotalEnergies Trading & Shipping system that matches structured and unstructured transaction data.
A named customer manager reports more reliable controls and fewer financial losses, without amounts, error rates, a baseline or an observation period.
Limits: Vendor-published customer testimony, not independent validation. No costs or net ROI. Matching records does not establish authority to place trades, release payments or change books. No specific model provider can be inferred from the Bedrock service description.
TotalEnergies puts Gen AI to work on its trading operations · Undated
TotalEnergies reportedly dropped one drum-cycling use case
Control Global quotes TotalEnergies engineer Thomas Klein saying a Port Arthur drum-level model achieved 75% prediction accuracy, but the drum-cycling use case was not retained.
The report does not define the accuracy denominator or test set, or quantify cost or loss. Other pilot work continued; the entire program was not reported abandoned.
Limits: Original conference reporting, labeled Sponsored. We could not retrieve the presentation or a later company statement. The earlier company release confirms the broader pilot, not the decision to drop this use case. Keep these figures separate from its five-event result.
Honeywell AI pilot aids coker unit operations at TotalEnergies refinery · 2026-06-11
TotalEnergies and Honeywell pilot AI-assisted control room · 2025-11-11
Q3 call: what we checked
Full transcript screen · call 2026-10-29
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.
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.
Limits: This is customer-demand context, not evidence of internal AI adoption, realized software savings or a failed deployment.
TotalEnergies Q3 2026 earnings-call transcript · PDF p.12 AI power-demand context; p.19 call close.
Read the call record and coverage notes