One of the most common enterprise AI mistakes is treating excitement during a demo as evidence of adoption.
A meeting-room demo usually has curated inputs, a skilled operator, and no real consequence when it fails. Everyday work has none of those protections.
It never entered the existing workflow
If users must leave the system they know, reformat their inputs, and manually carry the output back, the operating cost can erase the time saved by AI.
Users do not know when to trust it
“Usually correct” is not enough for a high-risk workflow. People need to know what the answer is based on, where it can fail, and how to correct it.
Nobody owns the final action
An agent may recommend an action, but production still requires explicit ownership: who has permission, who is accountable for errors, and when a human must confirm.
Evaluation was detached from use
Offline accuracy cannot answer every question. A real evaluation also covers latency, recovery, review effort, behavioral change, and continued use.
When adoption stalls, ask: is the model not good enough, or did we never design it as part of the work?