1Which CRO workflows benefit most from agentic AI?
Top candidates include PDP personalization, checkout incentive testing, support-to-cart handoffs, and post-purchase retention nudges. These surfaces have enough data for AI to learn quickly and enough impact to justify automation.
Create a backlog ranking CRO workflows by potential lift and autonomy readiness.
2How do you structure the automations?
Each automation gets a charter (objective, KPI), signal requirements, allowed actions, and guardrails. Agents run continuous experiments, promote winners, and log decisions. Humans review dashboards and override when brand guidelines require it.
Store charters in a shared repo so new teammates inherit context instantly.
3What tools power agentic CRO automations?
Use a mix of personalization platforms (Rebuy, Nosto), experimentation engines (Convert, Eppo), and workflow orchestrators (Shopify Flow, Make, Zapier). Layer LLMs via LangChain or native app plugins to interpret signals and craft copy variations.
Avoid tool sprawl: one personalization engine, one test platform, one orchestration layer is usually enough.
4How do you govern autonomous CRO?
Set guardrails around incentives, shipping promises, tone, and compliance. Require every agent to log prompts, data inputs, and outcomes in an audit feed. Schedule weekly multi-team reviews to inspect anomalies and adjust policies.
Track override rate; rising overrides signal misaligned objectives or stale training data.
5How do you prove ROI?
Measure conversion lift, AOV, contribution profit, and manual hours saved. Compare agentic automations to legacy manual tests. Present execs with before/after dashboards and highlight a few hero experiments.
Tag each automation’s output with UTM or experiment IDs so attribution is never ambiguous.