Every Company Has 100 AI Pilots and Zero Products
A pilot cannot fail. That is exactly why it never ships. Welcome to pilot purgatory.
Walk into almost any large company right now and ask what they are doing with AI. You will get an impressive answer. There is a pilot in marketing. A proof of concept in support. An experiment in the innovation lab. A workshop, a hackathon, a vendor trial, a task force. Dozens of them, glittering with promise. Then ask a sharper question. What have your customers actually touched? And the room goes quiet, because the honest answer is almost nothing. Welcome to pilot purgatory, the defining condition of enterprise AI.
Why pilots multiply like this
Pilots multiply because pilots are safe. A pilot earns applause without ever risking accountability. You get to stand on a stage, show something that works in a controlled demo, and collect the credit for being forward-thinking. And crucially, a pilot cannot fail, because it was never required to survive contact with a real user, a real deadline, or a real bill.
A pilot cannot fail. That is exactly the problem. It was never required to survive contact with a real user.
| The pilot | The product | |
|---|---|---|
| Lives in | The comfortable ninety percent | The brutal last mile |
| Risk | None | Real, and public |
| Can it fail? | No , never required to | Yes, in front of everyone |
| Who it impresses | The room | The customer |
| What it produces | Slides | Value |
This is theater, and it feels like progress
The technical term is innovation theater, and the dangerous thing about it is how much it resembles the real thing. There are meetings. There is momentum. There are slides with encouraging numbers. Everyone is busy and everyone feels like the company is moving. But motion is not progress, and a hundred pilots that never ship is not a strategy. It is a very expensive way to feel modern.
A product means commitment: integration with messy systems, maintenance, unpredictable users, and the genuine possibility of public failure. Pilots live in the comfortable ninety percent. Products have to finish.
The incentive nobody fixes
Underneath all of this is a simple, broken incentive. Organisations reward people for starting things. The pilot gets the announcement, the budget, the kudos. Almost no one is rewarded for the slow, thankless work of dragging one thing into dependable production. So smart people rationally start a new pilot instead of finishing an old one, and the graveyard of half-built promise grows.
Kill most of the pilots. Choose the few that actually matter and force them, with real resources and real accountability, all the way to production. Measure the team on what customers use, not on what got demoed.
"But running lots of pilots is how you learn"
This is the strongest defense, and it has real merit. You cannot know in advance which idea works, so you place many small bets and let the winners reveal themselves. A venture fund does exactly this. Exploration is supposed to look wasteful from the outside. By that logic, a hundred pilots is not dysfunction. It is a healthy portfolio.
Here is where the analogy breaks. A venture fund is ruthless about the second half of the job: it kills the losers fast and pours everything into the few that work. That culling is the whole discipline. The corporate pilot graveyard does the opposite. Nothing gets killed, because killing a pilot looks like failure, and nothing gets finished, because finishing is thankless. So the bets just accumulate, none of them resolved. A real portfolio is many starts and a few ruthless finishes. A pilot graveyard is many starts and no endings at all. The number of pilots was never the problem. The missing second half is.
The scoreboard that matters
Stop counting pilots. Start counting what shipped. The companies that win the AI era will not be the ones with the most pilots. They will be the ones with the fewest, because they finished them.
I write about AI, data, and learning regularly at pinaldave.com, and I have been teaching this hands-on in my AI workshops.