A pilot is a bounded learning exercise with a decision at the end. Define its audience, duration, tasks, and stop conditions. Keep the existing manual workflow available so a model or integration outage does not block the customer’s work.
Roll out in stages
Start with supervised users and human-reviewed outputs. Gather feedback tied to individual cases so failures can be reproduced. Expand access only after the agreed quality and operating criteria are met. Separate measured outcomes from projected savings.
Record model, prompt, adapter, and retrieval configuration versions. A rollback must restore a compatible configuration, not just an older application binary.
Prepare the operator
The handoff should include setup, ownership, expected metrics, common failures, support contacts, data handling, and a kill switch. Explain how to trace a request, disable AI features, and continue the manual process.
| Handoff exercise | Evidence |
|---|---|
| Start the sandbox | Successful sample request |
| Diagnose a failed dependency | Correct failure stage identified |
| Disable model calls | Manual workflow remains usable |
| Restore the prior configuration | Regression cases pass |
Exercise
Ask someone who did not build the prototype to follow the runbook. Observe without supplying missing steps. Revise the document where they become stuck.
Check: present a pilot review that recommends expansion, revision, or stopping. Include sample size, actual results, unresolved issues, and named ownership for the next step. Do not present simulated business impact as measured customer ROI.