Redesign the work around the capability
Adding an AI tool without removing or changing existing steps creates more work. Map the future workflow: what the system prepares, what a human decides, how exceptions are handled, and which old activities can stop.
Keep accountability understandable. Human-in-the-loop is only meaningful when the reviewer has enough information, time, and authority to challenge the system. An approval button alone does not create oversight.
Build calibrated trust
People need to know what the product does well, where it is unreliable, and how to recover. Demonstrate limitations with realistic examples. Make feedback easy and show users that their corrections improve the product.
Champions can help translate the capability into local practice. Give them support, protected time, and a route to the product team. Do not make them an unpaid substitute for reliable software and accessible help.
Measure successful work
Attendance and logins indicate exposure, not value. Track whether target users complete the intended task, whether they return, how often they correct the output, and whether the workflow outcome improves.
Combine telemetry with observation and interviews. Low usage may reveal an access problem, an unreliable product, a conflicting incentive, or simply a task that happens rarely. Diagnose the cause before prescribing another training session.
Put the idea to work.
AI Adoption Modelbusiness value
Related concepts
References & further reading
These sources provide supporting context. The operating frameworks and recommendations are editorial interpretations, not claims of endorsement.