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Why do most AI pilots never reach production?

Because nobody owns getting them there. MIT found that 95% of enterprise GenAI pilots delivered no measurable P&L impact in 2025. The model is rarely the problem. What’s missing is a named owner, a narrow scope, and the unglamorous production engineering — evaluations, monitoring, cost controls, security sign-off — that makes leadership willing to turn it on.

By Johnmicah Joseph Potter · JJP LLC

Where pilots actually die

Four things, over and over. Nobody’s name is on the outcome. Ten use cases run at once, so none of them finish. The team builds a demo that wins the room and folds under real traffic. And there’s no evaluation suite, no monitoring, no cost ceiling, so security, risk and legal have no reason to sign off. They don’t.

Notice what’s missing from that list: the model. These are ownership and engineering problems, and they cost far less to fix than the next round of vendor demos.

How to get one workflow into production

Pick one workflow with real money attached and put one person’s name on it. Then harden it the way you’d harden anything touching customers or regulated data: evals that run on every change, monitoring someone would trust at 3 a.m., a cost ceiling, a security review with an actual sign-off. Ship it inside the process people already use, not alongside it. And set the go/no-go date before you start, so leadership scales it, extends it, or kills it on evidence rather than mood.

What another quarter of pilots costs

Budget gone, nothing in production, and a board that trusts the next AI request a little less than the last one. Meanwhile the competitor who got one workflow live is compounding — their engineers are learning things yours aren’t. Getting out of this rarely takes a better model. It takes one accountable operator who ships a single thing and can point to the line it moved.

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