Why most AI pilots die in the demo
A demo only has to cover the path you planned for. Production has to handle the rest.
A pilot looks finished long before it is. You wire up the model, feed it a few realistic cases, and it does something impressive in a meeting. Everyone nods. The trouble is that the meeting is the happy path, and the happy path is maybe a tenth of what the system actually has to survive.
The demo is a sample of one. Production is the long tail you didn't rehearse.
The gap nobody scopes
What kills the pilot is rarely the model. It's the malformed input, the edge case the policy never anticipated, the handoff where a human needs to step in and there's no way for them to. These don't show up in a demo because you didn't pick them. They show up in week three of production, all at once.
What to do instead
Build the boring parts first: the measurement that tells you when it's wrong, the path for a person to take over, the logging that lets you see the failures you didn't predict. Then let the impressive part ride on top of that. It's less satisfying to demo and far more likely to still be running in a year.