This is the third installment in a series on AI adoption in energy. Click on the links to read the first piece on AI readiness and the second piece on AI explainability.
Every energy executive I talk to is thinking about artificial intelligence. Many are still watching and waiting for a clearer signal before they commit. It’s a natural instinct. What I’ve come to believe, after watching this play out across enough organizations, is that the waiting itself is a risk.
What Delayed AI Adoption Actually Costs
The energy companies that committed to AI adoption eighteen months ago are not only running faster–they’re running sprints instead of hurdles.
The adoption of every new tool takes time. People learn how to use it, and they incorporate it into their work, and over time they expand its use or discover new features. That’s true of AI, too. But what is also true of AI is that the more it is used the better it gets.
Every workflow running through an AI execution layer gets better with each run. Every decision reviewed and approved adds to a base of verified outcomes the system learns from. The gap between an organization that started building that foundation eighteen months ago and one starting today isn’t eighteen months. It’s eighteen months of accelerating institutional intelligence that late movers have to close while also trying to keep up with everything else.
Fundamentally, organizations still weighing AI adoption are making an operational choice, not a technological one. The companies that understood this early on were bold, but they also did the math on what waiting would likely cost.
Where Delayed AI Adoption Creates the Greatest Cost
1. Workflow Productivity
The first place the cost shows up is in the work.
A development engineer evaluating one drilling program where they could have evaluated four isn’t falling behind because they’re working slowly. They’re falling behind because the work around the work hasn’t been automated yet. The time isn’t lost all at once. It bleeds out one workflow at a time, across hundreds of people, quarter after quarter.
2. Institutional Knowledge and Talent
The energy industry is in the middle of a generational transition. The engineers and landmen who built decades of institutional knowledge are moving toward retirement. The organizations that have already embedded that knowledge into AI-native workflows are not only more productive today, they’re more resilient tomorrow. The ones that haven’t are watching irreplaceable expertise walk out the door with no systematic way to capture it before it’s gone.
3. AI and Competitive Advantage
In a capital-intensive industry where the difference between a good decision and a great one is measured in millions or billions of dollars, the organizations that move from analysis to action fastest have a structural advantage. Every quarter of delay is a quarter that advantage widens in someone else’s favor.
Build vs. Buy AI: What Should Energy Companies Build?
The most common reason I see organizations delay isn’t skepticism. It’s the belief that doing AI seriously means building it themselves. That belief has serious backing. Some of the most prominent voices in technology argue, loudly and often, that any company that intends to lead will build its own AI stack. For a number of our most ambitious customers, that instinct is right. They are going to build. The useful question was never build versus buy. It’s what to build, what to buy, and knowing which is which.
Here’s what I tell them: You can probably buy more than you think. The pieces that feel most proprietary are usually the ones that take years to assemble and the hardest to keep current: the data foundation, the execution layer, the workflow plumbing underneath. Buying those pieces frees your best people to do the work only your organization can do. And when you do build, build on a foundation that already understands your business so you can move fast, build powerfully, and do it economically.
There’s an honest objection to all of this. In a field moving this fast, some of whatever you build will get thrown away. That’s true, and it’s a real cost, and a visible one. But it’s the price of learning, and it’s worth paying. The cost of doing nothing never shows up on an invoice, which is exactly why it’s so easy to ignore. It’s also higher than any version of building, wasteful or not, because when you build, you’re at least learning. Standing still teaches you nothing.
The Window for AI Competitive Advantage
The energy companies that get this right in the next two years will have an advantage that is very hard to close. Not because they moved fast. Because they moved in the right direction while others were still deciding which direction to move.
The cost of doing nothing is invisible until it’s too late. But that cost is real and it grows every quarter.
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