From POC to production: why AI pilots die and how to avoid it
Pilots die when they're built on clean demo data, have no business owner, aren't integrated into the tool people already use, have no way to measure quality, and have no budget for running costs after the demo. Plan production from week one: real data, a named owner, integration into the existing workflow, an eval set, security and legal review started early, a run-cost estimate, and a date for the go, change or stop decision.
How it usually goes
The demo impressed the board. Then the pilot met messy data, legal review and an IT queue, and quietly stopped.
A pilot should answer one question: will this work on our real data, in our real workflow? A demo that doesn't answer that only shows that the technology exists.
Pilot charter
| Item | What it says |
|---|---|
| Problem and metric | The number this moves, with a baseline |
| Real data | Including the messy part |
| Business owner | Who uses it after the pilot and owns the metric |
| Integration point | The existing tool it lives in |
| Eval set | Built with a domain expert, with pass criteria |
| Security and legal | Review started in week one |
| Run cost | Estimate and the budget owner |
| Decision date | When you decide go, change or stop |
Phases
| Phase | Goal | Exit criteria |
|---|---|---|
| Concept | Problem, metric, data access | Charter signed |
| Pilot | Works on real data with a small group | Eval pass rate and user feedback |
| Shadow or limited release | Runs alongside the current process | Metric moving, no critical incidents |
| Production | Part of the workflow | Owner, monitoring and on-call in place |
| Review or retire | Still worth running? | Metric and cost reviewed |
Ownership after launch
An AI system needs continuous ownership: someone watches quality, handles model upgrades, updates the eval set and decides when it needs to be rebuilt or retired. If nobody is named for that before launch, it degrades quietly.
Production questions for any tool or vendor
Who is using this in production, at what data volume, and for how long? Prototypes and proofs of concept are useful, and the step that counts is implementation and scaling to production.
From the floor
In the webinar on AI agents for business leaders, I described governance in three layers, executive oversight, operational teams and agent lifecycle management, and the lifecycle part is about continuous ownership: an agent goes from concept through quality testing and deployment, and at some point it's retired or parts of it are rebuilt, and without that ownership every bit of work ends up wasted.