Why most AI projects never reach production
The failure is almost never the model. It’s scope, data access and the absence of anyone who owns the thing after launch.
A pilot that works in a demo and dies before production is the most common outcome in corporate AI. It’s worth being precise about why, because the reasons are boring and fixable.
The pilot was scoped to impress, not to run
Demos are built against clean data, a happy path and a friendly audience. Production is the opposite of all three. If the pilot never handled the exceptions, the exceptions are the project.
Nobody had the access required to ship it
The technical work is often done long before anyone establishes who can approve a service account on the CRM. Access is a procurement problem wearing an engineering costume, and it should be resolved in week one.
There was no owner after launch
A system that changes behaviour when the underlying data changes needs someone watching it. If no named person owns the output, the first bad week ends the project.
What to do instead
- Scope the exceptions before the happy path
- Resolve system access in the first week, before any build
- Run in parallel with the manual process until output matches
- Name the owner and the escalation path before go-live
- Instrument everything, so a regression is visible before a client notices
None of this is about model choice. It’s about treating an AI project as an operations change that happens to involve software.