The Biggest Lie in Energy AI Right Now

I sat on a panel at EMC 2025 in Houston last month with some of the sharpest minds building technology for retail energy. We were supposed to talk for 45 minutes about AI. We could have gone for four hours. But the moment that stuck with me wasn’t about some breakthrough capability. It was a show of hands.

Greg Leflar, our moderator, asked the room: who has funded at least one AI project in the last 18 months? Nearly every hand went up. Then he asked: keep your hand up if it delivered on expectations.

The drop was significant. And it’s consistent with what I see across our customer base every day.

The Myth: Just Vibe Code It

Here’s the pitch making the rounds right now: take a team of analysts, give them AI coding tools, and in a few months they’ll replicate the functionality of your existing enterprise software at a fraction of the cost.

I heard this firsthand from a customer’s chief strategy officer. Their company had just partnered with a major cloud AI vendor, and there was a committee identifying SaaS vendors to replace through vibe coding.

My response: welcome that conversation. Because the idea that you can vibe code decades of deeply vertical energy expertise into a new application in a couple of months is, to put it plainly, a lie.

At ennrgy.com, we’ve spent 200+ combined years building the operational intelligence that lives inside Risk360. Load forecasting models averaging 94–97% accuracy. Real-time settlement reconciliation across every major ISO. Gas nominations that run 365 days a year without missing a beat. None of that was built in a hackathon. It was built through years of operating in the markets we serve, hitting every edge case, every market anomaly, every operational curveball energy throws at you.

AI-assisted coding can get you 80% of the way there. That’s exactly far enough to be dangerous. The remaining 20% is where the scars are—the hard-won knowledge that separates a demo from a production system.

The Time-to-Value Illusion

There’s a related trap. The first time you use AI and it generates something impressive in seconds, it feels like magic. That wow moment creates a false sense that production-grade solutions are right around the corner.

They’re not. Data still has to be clean. Integrations still have to be built. Edge cases still have to be handled. And your organization still has to change how it works.

We believe this deeply at ennrgy.com—it’s why our tagline is “Systems of Record → Systems of Action.” The gap between having data and acting on it is where most AI projects die. We’ve spent years building the infrastructure to close that gap: the data layer, the operational context, the expert team that validates every output against real market outcomes.

What This Means for You

The takeaway isn’t to avoid AI. We’re an AI-native company—decision intelligence is one of our three core competencies. The takeaway is to approach it with the rigor it deserves:

• Respect the domain. AI is a powerful accelerant, but it doesn’t replace institutional knowledge. Before you rip out a vendor, understand what you’re actually replacing.
• Budget for the last mile. If your project plan assumes the same speed from prototype to production, your timeline is wrong.
• Keep operators in the loop. AI generates confident-looking results even when they’re wrong. You need experienced people who can spot the errors. That’s why we design for humans-in-the-loop—not humans-out-of-the-loop.

AI is transformative. We see it every day in what Headroom Intel and our ennrgy.ai products deliver for customers. But transformation isn’t magic. It’s engineering, domain expertise, and operational discipline.

The myth isn’t that AI is powerful. The myth is that power alone is enough.