A demo that works once, in front of the team that built it, is a different thing from a system a bank will let touch a customer. I've watched this gap kill more AI initiatives than any modeling problem — the model was usually fine. What was missing was a boring, unglamorous list of production concerns nobody had budgeted time for.
The gap has a consistent shape: no answer for what happens when the model is wrong, no owner for monitoring drift once the original team moves on, and no story for a regulator or auditor asking how a decision was made. Those three gaps are why "it worked in the demo" and "it's in production" can be eighteen months apart.
Closing it means treating evaluation, logging, and a human escalation path as part of the deliverable, not an afterthought bolted on before launch. I build these into the first version of any pilot now, even when it slows the demo down — because the version that ships is the one that was designed to survive contact with production, not the one that looked best in week one.
The practical test I use: if you can't explain in one sentence who gets paged when this is wrong, it isn't ready to leave the lab.