Most AI projects do not fail because the model cannot do the task. The demo usually works. They fail in the gap between a demo that impresses a room and a system people depend on every day.
The pilot is the easy part
A pilot proves feasibility on a clean, narrow slice of the problem. Production means messy real data, edge cases, real users who do unexpected things, uptime, security, and a team that has to run the thing after the builders leave. Those are different problems, and the second set is where the work actually is.
Where pilots die
The pattern repeats across industries. The data was hand-cleaned for the demo and nobody built the pipeline to keep it clean. The edge cases were waved away. There was no security or access model. Cost per use was never calculated, so the economics fall apart at volume. The output had nowhere to go, no path into a workflow a real person uses. And when the pilot ended, no one owned what came next.
- Data was prepared by hand for the demo, with no pipeline behind it
- Edge cases and failure modes were never handled
- No security, access control, or audit trail
- Cost per use was never modeled, so volume breaks the economics
- The output had no path into a real workflow
- No owner after the pilot ended
Scope for production from day one
The fix is to decide the production target before you build the pilot, then build the pilot as the first real slice of that system rather than a throwaway demo. Pick one workflow end to end instead of a flashy fragment. Model cost per use early. Build the data path, not a one-time export. Name the owner before launch, not after.
A pilot proves the model can do the task. Production proves your business can depend on it. They are not the same project.
This is how we build at Optume: the production system is scoped first, and the pilot is its first slice, so there is no cliff between proof and product.