Most AI pilots prove that a demo works. A useful pilot proves that a real workflow gets better, with numbers you'd defend to a skeptical stakeholder.
1. Pick one high-friction use case
Choose a library where a stale rule causes real cost: compliance, support escalation, product launches. One library, one owner.
2. Document today's workflow
How long does a draft take? How long from a policy change to updated training? Who reviews? Write the baseline down before you change anything.
3. Define an evidence checklist
Decide in advance what counts as success: hours saved per draft, time from change to update, review findings per course, learner errors on the changed rule.
4. Design a controlled pilot
Run real changes through the new workflow. Keep human review exactly as rigorous as before, or more so.
5. Evaluate independently
Have someone outside the pilot team check a sample of outputs against sources.
6. Expand only after proof
If the numbers hold, add a second library. If they don't, you've learned cheaply. Want help scoping one? Book a walkthrough.