Most institutions have a rough sense of who on their faculty is "good with AI" and who isn't — based on hallway conversation, not any actual measurement. That's not enough to plan training investment.
Assess awareness, not just usage
Someone who's never opened an AI tool but understands the landscape conceptually may be closer to ready than someone using tools daily without understanding their limitations.
Separate confidence from competence
Self-reported comfort with AI tools is a weak signal on its own — some of the most confident staff have the shallowest actual understanding. Pair self-assessment with a practical check.
Look at variance across departments, not just averages
An institution-wide average readiness score hides the departments that are dangerously behind. Department-level breakdowns are where the actionable insight lives.
Re-measure after training, not just before
Training without a before-and-after comparison makes it impossible to know if it worked. Treat readiness measurement as an ongoing cycle, not a one-time diagnostic.