Description: Any product leaders still treating artificial intelligence like a feature upgrade ought to rethink things. Jimmy Fortuna, CPO at Enverus, holds the opinion that there’s some risky underestimation happening around AI implementation. In this episode, he explains why a different framing changes an organization's ability to capitalize on how you work, how you plan, and how you stay close to customer problems. Which (spoiler) remains one of the most worthwhile areas of focus amidst the change.
A product org with an unusual edge
If you haven’t heard of Enverus, it is the world’s largest SaaS-based data, software, and analytics company dedicated to the energy industry. What makes Enverus unusual is that the organization has a large team of intelligence analysts producing investor-grade market research. The products they build are the engine that generates the research outcomes. That tight loop between data, analysis, and software creates a competitive advantage that most product organizations simply don’t have access to. Which is part of what makes Jimmy’s perspective so interesting.
Find the market problem and everything else follows
After nearly 30 years in product across restaurants, retail automation, fleet transportation, home automation, and now energy, Jimmy keeps coming back to one core truth: if you get nothing else right, find market problems.
He uses the Pragmatic framework’s language: pervasive, compelling, urgent, with a willingness to pay. But the insight is less about concrete methodology and more about disposition.
“You do that through lots of intimate customer contact. You don’t do that in the office. You don’t do that by reading the trade rags.”
And when you’re in those customer conversations, Jimmy echoes Melissa Perry (Escaping The Build Trap): fall in love with the problem, not your product. The courage required for good product work includes being willing to hear that the customer’s most urgent problems have nothing to do with what you’re currently building.
Red, yellow, green: how to think about your product team’s time
Jimmy uses a simple framework to evaluate where product managers are spending their energy:
Red is customer support mode: someone has a problem, stop everything and fix it. If you’re here too often, something systemic is broken. Don’t just route around it, go and solve the root cause.
Yellow is involvement in active transactions: sales cycles, renewals, deals. Not bad, but not the highest use of a PM’s time. Too much yellow can be a signal that product-market fit isn’t where it should be.
Green is the good stuff: conversations with no transaction attached, no agenda, just understanding the customer’s world and what they’re worried about next.
“If you’re spending too much time in red and yellow and not enough time in green, that’s not a good future indicator.”
Culture can’t be processed into existence
Cross-functional alignment is a culture problem first and a process problem second. You cannot process your way out of a culture that has “us-versus-them” baked into it.
At Enverus, the standard is simple and non-negotiable: if another function is struggling, it’s your problem too. There’s no daylight between executive leadership and no finger-pointing across department lines. Roadmap sessions include representation from most functions in the company so that nobody is surprised. And more importantly, nobody misses the chance to contribute.
“Without [the culture], you could have all the roadmap meetings you want. It wouldn’t matter.”
AI is a foundational technology comparable to fire
There’s this category of technology called a general-purpose technology, like electricity or heat engines. They are powerful, broadly applicable, and transformational across all industries. Jimmy argues that AI clears that bar easily, and he goes further. There’s an even more fundamental category: technologies that expand what humans can do at all, like the discovery of fire.
“Artificial intelligence is a foundational technology. It’s like stone tools or fire. It’s that big of a deal.”
If AI is truly foundational, then the question “could AI change how I do this?” stops being something you ask occasionally. It becomes the default first question for near anything. And if you can’t think of an answer right now, then you keep asking.
What makes this especially hard for business leaders is the pace. Jimmy describes what his professor called the “dual prediction process” — you’re always trying to predict both where customer needs are headed and whether your organization can get there. That’s always been difficult. Now you’re trying to do it while a foundational technology is advancing with step changes in capability that appear seemingly at random.
The gap between Thanksgiving and New Year’s in AI coding capability this past year (2025), Jimmy says, was enough to make even the most skeptical, hard-edged technologists stop and say whoa!
When engineering is no longer the bottleneck
Jimmy is watching where the constraint is shifting in software development and thinks Nate Jones had it right: as AI accelerates the velocity of building, the bottleneck moves toward go-to-market. Sales, marketing, and ultimately customers themselves have a natural absorption rate, meaning there’s a ceiling on how much change they can take in at once.
That creates an imperative for product teams. Product teams need to recognize that customers are about to be inundated with new things from every direction. The bar for what earns their attention is going up.
“You better be making the thing that rises to the top.”
Which brings it back, as it always does, to market problems. The highest-quality problem you can find, for a customer who is urgently and compellingly motivated to solve it.
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