Define decision boundaries before release
Clarify where AI can suggest, where it can auto-act, and where human approval is required. Document this by workflow so support, product, and legal teams are aligned.
Design user trust cues into every AI flow
Show confidence level, source references when available, and a clear recovery path when output quality is uncertain. Users should always be able to edit, retry, or escalate.
Operational governance model
- Model registry with approved versions and rollback policy.
- Prompt and policy versioning with change logs.
- Audit trail for high-impact actions and support investigations.
KPI framework that balances growth and risk
Track both adoption and quality. Suggested KPI set: completion rate, user correction rate, time saved per workflow, escalation frequency, and incident severity.
Rollout strategy
Release to narrow cohorts first. Compare AI-assisted and non-assisted paths using comparable tasks. Expand only when quality and trust metrics stay stable.