Operational Guardrails: A Pre-Use Checklist for Non-Engineers

Use this checklist before you deploy AI outputs into customer-facing work, published content, or automated workflows.

1. Clear goal — Is the desired outcome and the success metric defined in one sentence?
2. Representative test cases — Have you created at least 5 real examples to test the AI on before use?
3. Source verification — Can every factual claim be traced to a source you control or have validated?
4. Sensitivity check — Does the data include PII, health, legal, or other sensitive info? If yes, scrub or get approval.
5. Human-in-the-loop — Is a human explicitly required to approve outputs for high-risk categories?
6. Failure mode list — Have you listed the top 3 ways the AI could be wrong and what to do in each case?
7. Logging and traceability — Will you save prompts, outputs, and a short rationale for decisions?
8. Reviewer sign-off — Who has to review and sign off before changes reach customers?
9. Roll-back plan — If something goes wrong, can you quickly revert to the previous process?
10. Measurement plan — Have you defined how and when you'll measure success and review results?

Keep a short, shared record of completed checklists for each experiment; over time this becomes your team's practical governance history.