What it means
An AI operational readiness review verifies that ownership, support, monitoring, incident response, continuity, capacity, user guidance, and rollback are ready before production use.
Why it matters
A technically functional model can still fail operationally when teams cannot recognize degradation, explain decisions, or respond to unsafe outputs.
Practical focus
- Define the system boundary, accountable owner, approved purpose, material risks, and decision authority for AI Operational Readiness Review
- Review service ownership, runbooks, telemetry, thresholds, escalation, staffing, support windows, resilience, vendor contacts, user training, and change controls.
- Collect and protect this evidence: readiness checklists, test results, unresolved risks, owner acceptance, tabletop outcomes, capacity evidence, and launch conditions.
- Exercise this failure scenario as a planned test: the system launches without an on-call owner or a tested process for disabling automated actions.
Common mistakes
- Using the label AI Operational Readiness Review without defining the exact systems, data, people, decisions, and time period in scope
- Treating a policy statement or tool demonstration as proof that AI Operational Readiness Review operates consistently in production
- Failing to retain readiness checklists, test results, unresolved risks, owner acceptance, tabletop outcomes, capacity evidence, and launch conditions in a form that lets an independent reviewer reconstruct decisions and changes
- Testing only the normal path and never rehearsing what happens when the system launches without an on-call owner or a tested process for disabling automated actions.
Certification relevance
This subject appears in or supports the following certification bodies of knowledge: