A promising demonstration answers “Could this work?” A pilot should help answer “Does this improve a defined task here?” Write the task down before choosing the measures, and keep the first scope narrow enough to inspect.
The public introduction to EDUCAUSE’s 2025 AI Landscape Study describes institutional AI across strategy, policy, use cases, and workforce. The scorecard below is our suggested operational exercise, not a framework validated by that study.
Record the starting point
Choose a task such as directing routine public-information inquiries. Review a small, appropriate sample of current work. Note the time required, unresolved questions, and common errors. Protect personal information and use approved access procedures throughout.
Measure quality alongside effort
A faster answer is useful only if it is sufficiently accurate and the reader can act on it. Define who checks examples, how they distinguish a harmless wording issue from a consequential error, and how uncertain cases reach a person.
Include the receiving team
If a pilot moves work from one office to another, a single department’s time saving may be misleading. Ask both teams to record rework and handoff problems. Invite feedback from the people expected to use the revised process.
Decide at a scheduled review
Agree in advance on the review date and the outcomes that would prompt continuation, revision, or a pause. A small sample may reveal useful problems without proving a broad improvement. Document that limitation when presenting the result.
Task · baseline · quality examples · staff effort · unresolved cases · decision owner · review date. End with a decision and its reasons, rather than a slide of activity counts.
Sources & further reading
Sources checked September 19, 2026. The exercises and recommended next steps are The School Guru’s editorial suggestions. Check the linked authority for current requirements and institution-specific applicability.