This is the second installment in a series on AI adoption in energy. If you missed the first piece, start here.
The question I hear most often from energy teams evaluating AI: “How do we know it’s right?”
It deserves a straight answer. Here’s mine.
Deterministic AI: The Tools Energy Teams Already Trust
The models engineers and planners have built careers on are deterministic. Same inputs, same output, every time. A decline curve model returns the same estimated ultimate recovery whether you run it on Monday or Friday. An economic simulator returns the same net present value given the same assumptions. You can trace every number back to an equation, defend it in a meeting and hand it to your CFO with confidence.
When your name is on a recommendation, you need to be able to show your work.
Deterministic tools make that possible by design.
How Probabilistic AI Works
AI models don’t calculate. They pattern-match.
A large language model processes an enormous volume of examples (measured in billions) and learns the statistical relationships between them. When it produces an output, it isn’t running a set equation. It’s returning the most likely response given everything it was trained on. Ask it the same question twice and you may get slightly different answers. Ask it about something at the edge of its training data and it fills the gap with something plausible that isn’t necessarily true.
That’s where hallucinations come from. The LLM isn’t malfunctioning. It’s doing what it was built to do: provide an answer based on probability derived from a vast but incomplete corpus of knowledge. It has no internal mechanism to flag the error because it doesn’t know what it doesn’t know.
Which makes it all the more important to understand what you’re working with when using AI, and it’s why every major AI company has a disclaimer telling you their LLM makes mistakes and you should double-check its work.
AI Explainability: Can the Tool Show Its Work?
Can the tool show you how it arrived at its output?
The actual data sources, the assumptions made, the places where the model is working near the edge of what it knows. If a vendor can’t show you that, they’re asking you to trust a black box. In energy, where a wrong answer can have far-reaching consequences, that’s not a standard worth accepting.
Systems returning answers without showing their work erode trust faster than they build it. Business decisions require traceability, not black-box outputs. As one supermajor put it: “An automated answer that’s wrong is worse than no automation at all.”
The gap between a generic model and one built on 25 years of structured energy intelligence isn’t just about accuracy. It’s about what the model was trained to know. A model that has processed 7 million wells, 350 million land records and the actual data structures and workflows of energy work isn’t pattern-matching across the internet. It’s pattern-matching across the decisions this industry has been making for decades. That narrows the hallucination surface significantly and gives the reviewer something real to check against.
AI doesn’t have to be unverifiable. The governance around the tool has to be built to compensate for what the model can’t guarantee on its own.
