Shipping AI features people actually keep using

The gap between an AI demo and an AI feature is trust. A demo needs to work once, in front of a friendly audience. A feature needs to be right often enough that people stop checking.

Design for the wrong answer

Assume the model will be wrong and make that survivable: show sources, make edits cheap, and never let the system take an irreversible action without a human in the loop. Features that recover gracefully get used; features that fail confidently get abandoned.

Adoption is not won by the best model. It is won by the best recovery path.

Measure habit, not delight

Track week-four retention on the feature itself, not thumbs-up ratings. If people return without prompting, the quality is sufficient. If they do not, no amount of model tuning will fix a workflow they did not need.