Performance Reports, but for AI

Grants didn't just ask "did you spend the money?" They asked "did it work?" Performance reports, on a schedule, comparing actual results to what was promised. Not essays — structured updates: here's what we said we'd do, here's what happened, here's what's off track.

AI deployments need the same reporting habit.

Pick a cadence — monthly for high-stakes systems, quarterly for the rest — and ask the performance questions: Is the system doing what we bought it to do? Are the outputs still good? Have error rates, complaints, or overrides changed? Has the use drifted into territory we never approved?

This is deliberately boring. That's the point. Performance reports worked in grants because they were routine — problems surfaced in a regular meeting, not in a crisis. The same rhythm catches model drift, misuse, and quiet failures while they're still small.

Don't wait for an incident to review your AI. Report on it like a grant: on schedule, in writing, honestly.

I explain frameworks; I don't give legal counsel.