Purpose: the problem and the answer

In short: campuses can’t tell which AI uses are worth it. This framework gives one consistent way to find out and act.

The problem

Use is mistaken for value. Logins and license counts show access, not improvement.
Capacity is mistaken for cash savings. Freed hours cut costs only when spending actually falls.
Renewals run on habit. With no baseline, no one can show a change.
Each unit judges differently. Results can’t be compared, so the campus can’t decide what to fund.
Risk is checked once. Quality, equity, and vendor changes drift after approval.

What the framework adds

Three questions, asked against a before-AI baseline: what it costs, how it’s really used, and what changed.
Five checks that can stop a use, however good its numbers are.
One of five decisions: continue, improve, expand, restrict, or retire, with an owner and a date.
Effort scaled to the stakes, from a quick self-check to a formal review, using the same logic.
Results that feed existing reviews, such as budget and program review. No new report.

Central question

For a defined use case, does AI improve outcomes compared with the current approach or another feasible alternative, at acceptable cost and risk? And who benefits or bears the burden?

The goal is institutional value, not AI adoption.