What Your Pilot Graveyard Is Actually Telling You

Ask most executives how many AI pilots their company has run, and you'll get a shrug and a guess. Ask them to actually count, and the number usually surprises them. I've had this conversation with a sitting CTO who went looking and stopped counting at twenty.

Twenty pilots. A handful still technically "active." None in production at scale. And the prevailing read in the room is always the same: AI just isn't ready yet, or their people aren't ready for it, or the vendor overpromised.

I'd push back on all three. Twenty pilots isn't a technology problem. It's a pattern, and patterns are data if you're willing to look at them instead of past them.

Stop grading pilots pass/fail

The instinct after a pilot stalls is to close the folder and move to the next idea. That's the worst thing you can do with the information you just paid for. A pilot that didn't scale isn't a failure to file away — it's a diagnostic you already ran on your own organization, for free, and you're about to throw out the results.

When I go back through a graveyard of stalled pilots with a client, I'm not asking whether the AI was good. I'm asking three questions of every single one: What team owned the outcome, and did that ownership survive the handoff to production? What did the pilot NOT have to deal with — which system, which approval, which competing team — that the rest of the org deals with every day? And was the data the pilot used the same data the rest of the company actually has, or a cleaned-up version somebody quietly built for the demo?

Line up the answers across ten or twenty pilots and you stop seeing twenty separate failures. You start seeing the same three or four missing conditions, over and over, wearing different project names.

The graveyard is a map, not an obituary

Here's the reframe that changes how leadership talks about this: every pilot that didn't scale already told you exactly what your organization is missing before AI can work in it. You don't need a new diagnostic exercise. You need to go back and actually read the one you already ran.

At Angie's List and at Experian, the technology initiatives that stalled taught us more about where the organization's real seams were than the ones that succeeded on the first try. Success hides problems. Stalling exposes them — if you're willing to look at the pattern instead of the excuse.

What to do with this before you fund pilot twenty-one

Before you approve another AI pilot, pull the last five to ten that didn't make it and run them through the same three questions. You will very likely find the same missing condition showing up three, four, five times. That repeated condition is your actual roadmap — not the next flashy use case, but the organizational work nobody wanted to fund because it doesn't look like an AI project.

This is the human-centered AI argument in practice: the technology isn't the bottleneck. Your organization's willingness to name and fix its own conditions is. AI just makes the cost of skipping that step visible faster than anything that came before it.

Next in this series: the four conditions you can actually test — cheaply, in about a week — before you commit real budget to the next pilot.

Christopher Daily is a Certified AI Master Trainer and Managing Director of InnoPower LLC, and the author of Disrupt or Be Disrupted. He writes about human-centered AI — using AI to amplify human potential rather than replace it.