Your AI pilot failed because nobody owned it and nothing measured it, not because the model wasn't good enough, and swapping models for the next pilot will reproduce the failure with a bigger invoice. We've watched this pattern enough times to state it as a rule: pilots without a single accountable owner and a written evaluation standard don't die dramatically, they just never become anything.
The standard autopsy
Run the post-mortem honestly and the same causes surface every time. Notice what's absent from this list: the model.
- No single owner: an 'innovation team' built it, a product team was supposed to adopt it, and neither was accountable for production.
- No evals: success was 'the demo looked impressive', which is not a bar anything can clear or fail.
- No target workflow: it automated a task nobody was blocked on, so nobody fought for it.
- No production plan: security, data access and cost questions arrived after the demo, as ambushes.
- No error budget: the first embarrassing output had no agreed process, so someone senior just turned it off.
Why evals are the difference between a demo and a product
- An eval set is a contract: here are 200 real cases, here is the score we need, here is who signs off.
- It converts 'the AI seems wrong sometimes' into a number that can improve week over week.
- It survives model swaps, you can upgrade models in an afternoon because the bar is written down.
- It forces the uncomfortable conversation, what accuracy is acceptable?, before launch instead of after the incident.
How to run the next one instead
- 1Name one owner who carries the pilot to production, with that written into their goals.
- 2Pick a workflow where someone is measurably blocked or bleeding hours today.
- 3Build the eval set before building the feature: real inputs, graded outputs, a pass bar.
- 4Agree the production criteria, score, cost ceiling, security sign-off, at kickoff.
- 5Timebox to six weeks. Passing pilots ship; failing pilots get killed and documented. No third state.