We have watched this pattern repeat: an AI pilot impresses everyone in a meeting, then quietly disappears six weeks later. The cause is rarely a weak model. The cause is that the pilot was built to be shown, not to run.
Demo versus production
A demo needs one successful example. Production needs to handle the case nobody anticipated: the document scanned at an angle, the empty field, the user writing in mixed dialect. These are not edge cases — they are most of reality.
- Start from one use case you can measure with numbers.
- Link every inference to its source so it can be audited.
- Draw an explicit line: what does the model decide, and what stays human?
- Measure quality weekly, because performance drifts as the data changes.
A model whose output nobody trusts will not be used, however accurate it is.
Trust is built by transparency, not accuracy alone. When a reviewer can see where each number came from, they become willing to rely on the system. When they cannot, they redo the work by hand no matter what the metrics say.