Governance Guide
AI A/B Test Governance Framework for AI Teams
AI teams need a repeatable governance process for A/B test standards. This framework structures hypothesis validation, result tracking, and deployment approval controls.
Implementation Steps
- Define test hypothesis with measurable success criteria and minimum sample size.
- Set approval gates: quality threshold, cost impact limit, and reviewer sign-off.
- Track test outcomes and publish results with statistical significance evidence.
- Escalate unresolved quality issues with owner-assigned remediation deadlines.
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- Provider and model split recommendations
- Budget guardrail design by traffic stage
- KPI plan for spend, quality, and conversion