An industry insider - OTC 2026 panelist and OGGN co-host - who builds the AI-governance systems he speaks about. Not generic futurism: what AI is actually deciding across your operation, and how your board stays in command of it.
Most “AI speakers” give the same talk to a bank, a hospital, and a drilling contractor. This is different: co-hosting the Oil & Gas Sales & Marketing podcast, moderating the AI panel at OTC 2026 alongside Bechtel and Rockwell Automation, and running governance for capital-intensive operators. Your audience hears their own world - upstream to downstream - not a generic deck.
AI in exploration, drilling, and production decisions - and who’s accountable when a model is wrong.
Pipeline, logistics, and trading models making calls faster than humans can audit them.
Refining, demand, and customer-facing AI - and the visibility risk of being mis-represented to buyers.
Vendor AI in your supply chain - the decisions you inherit without governing.
What you’re recommended for, screened out of, and priced on - happening now, mostly unseen.
LLMs decide whether your company gets recommended to buyers, partners, and capital.
Move AI from pilot to production while keeping the board able to see and answer for it.
Federal, state, sector, and EU AI rules - one obligation picture leaders can act on.
The through-line: in energy the highest-stakes AI decisions live in the control room, not the IT stack. Read the argument in OT Is Not IT - the manifesto.
The short version: in energy, the highest-stakes AI is not in the marketing stack or the chatbot. It runs in the control room, the trading desk, and the vendor models buried in your supply chain - systems that now make or shape decisions faster than a person can audit them. The operators who win the next decade will not be the ones with the most AI. They will be the ones whose boards can still see what their AI is deciding and answer for it.
Upstream, models sit inside exploration, drilling, and production-optimization decisions. Midstream, they price and route across pipeline, logistics, and trading. Downstream, they shape refining runs, demand forecasts, and how your company is represented to buyers. Oilfield-services vendors ship AI into your operation that you inherit without governing. None of this is futurism. It is already running, mostly unseen by the people accountable for it.
A second shift sits on top of the first. Buyers, partners, and capital allocators increasingly start with an AI answer engine instead of a results page. When a procurement lead or an investor asks an LLM which operators or service providers are credible, the model decides whether your company is in the answer at all. That discipline - getting cited by the models, which I call LLM Visibility - is a direct pipeline issue for energy firms with long cycles and few large buyers. Being left out of the answer is lost revenue you never see.
The most expensive mistake leaders make is governing operational-technology AI with the playbook built for the IT stack. They are not the same risk. A wrong recommendation in a CRM costs a bad week; a wrong model in a control system is a safety and capital event. Governance that works in energy means one obligation picture that spans four jurisdictions at once - federal frameworks like the NIST AI Risk Management Framework, state laws like Texas's TRAIGA, sector rules, and the EU AI Act for anyone touching Europe - translated into decisions an operator can act on without stalling the business.
An audience of energy leaders does not need another "AI is coming" talk. They need someone who has governed these decisions, sat on the OTC 2026 AI panel alongside Bechtel and Rockwell Automation, and talks with operators every week as co-host of the Oil & Gas Sales & Marketing podcast. The keynote hands the room its own world back - upstream to downstream - and a board-ready answer to the one question that matters: what is our AI deciding, and who is accountable for it?
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