AI support automation readiness checklist

Demand readiness

Automation starts with enough repeated, coherent demand to justify a reusable response. If each case is unique or the underlying product changes weekly, a fixed asset may create more review work than it removes.

• The issue appears repeatedly in recent customer messages
• The correct outcome is consistent across customers
• The required facts are available to the workflow
• The response can be maintained by a named owner

Risk readiness

Define explicit red-risk categories before generation. Privacy deletion, security incidents, fraud, unsafe events, legal threats, chargebacks, and unexplained changes to authentication or payment data should not rely on a general confidence score.

Yellow cases may be draftable but still require live state and approval. Refunds, cancellations, billing changes, identity checks, and order edits usually belong here.

• Red cases are blocked from automatic drafting or sending
• Yellow cases require live state and human approval
• Green cases are stable and informational
• Reviewers can report a safety miss

Evaluation readiness

Test the complete process on representative history. Measure whether every message is covered, whether themes remain coherent, whether known red cases are recalled, and whether repeated runs produce stable groupings. Record tokens and cost so quality decisions include economics.

• Use hidden labels for evaluation
• Run at least two independent passes
• Inspect false negatives individually
• Re-run after changing model, prompt, provider, or policy

Operational readiness

Require an approval queue, immutable audit history, duplicate-safe write-back, and an adoption event. The system should prove what was generated, who reviewed it, where it was exported, and whether the team confirmed real use.