1What foundation do you need before AI CRO?
Centralize product, customer, and analytics data. Clean tags, unify attribution, and capture zero-party traits. Without reliable data, AI models optimize toward noise. Build a signal matrix so every agent knows which inputs are trustworthy and how fresh they are.
Audit analytics tracking quarterly; AI agents magnify existing tracking mistakes.
2How do you prioritize AI experiments?
Target high-impact surfaces: PDPs, cart, checkout, onboarding flows. Use ICE (Impact, Confidence, Effort) scoring but add “Autonomy Readiness”—can the workflow run with minimal human touch? Start with low-risk components like hero copy or badges before letting AI alter pricing or shipping.
Limit concurrent experiments per surface so your analytics team can interpret results.
3What guardrails keep AI CRO safe?
Set policy guardrails (brand tone, compliance), technical guardrails (latency, rollback), and business guardrails (margin floors, inventory caps). Require every AI decision to log its prompt, data inputs, and outcome. Publish a kill-switch runbook so on-call staff can revert with one click.
Run chaos drills monthly—feed the agent bad data and confirm it pauses gracefully.
4How do you integrate AI with human teams?
AI suggests hypotheses, drafts copy, and launches microtests. Humans review strategy, creative direction, and compliance. Hold weekly syncs where AI-generated insights inform the broader roadmap. Document learnings in a shared knowledge base so insights compound.
Assign ‘AI experiment owners’ who ensure outputs align with brand and measurable goals.
5How do you report AI CRO performance?
Track conversion rate, AOV, profit, time to launch, and manual hours saved. Compare AI-led tests vs traditional experiments. Build an executive dashboard that highlights wins, fails, overrides, and ROI. Transparency keeps leadership confident while you scale autonomy.
Pair quantitative results with screen captures or transcripts so stakeholders see what changed.
Scorecard snapshot
- • Conversion lift vs baseline
- • Experiments launched per month
- • Manual hours saved
- • Override rate and issues resolved