AI Pilots Are a Form of Procrastination
Every Fortune 500 is running AI pilots. MIT Sloan put the failure rate at 95%. The story we tell ourselves is that pilots are how we learn. After 17 years inside transformations, I have come to a more uncomfortable conclusion. The pilot is not a learning mechanism. It is a decision-deferral mechanism dressed as one. Here is how to tell the difference, and the forcing-function alternative.
Key takeaways
- The 95% AI pilot failure number is not a sign that pilots are broken. It is a sign that pilots are working exactly as designed. They were built to defer decisions, not to make them.
- Pilots perform four hidden functions inside large organisations: budget protection, career insurance, vendor evaluation theatre, and decision avoidance. None of those four functions require the pilot to ever ship.
- AI specifically breaks the pilot model in three ways: capability moves faster than the pilot timeline, the model you piloted is not the model you would deploy, and scope drift accelerates with each extension.
- The grown-up replacement is not bigger pilots. It is forcing functions. Production-first deployment, sunset clauses on legacy processes, P&L-tied OKRs, and irreversible commitments that close the off-ramp.
- A simple five-question diagnostic separates a real pilot from procrastination. If the answer to any of them is no, you are not piloting. You are delaying.
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