Audit material

AI automation audit checklist

Use this checklist to decide whether a workflow is worth automating, which risks require human approval, and what a realistic first build should include.

1. Workflow fit

2. Current-state mapping

Document the trigger, owner, inputs, tools, manual decisions, outputs, delays, failure points, and handoffs. If the current workflow cannot be explained clearly, map it before automating it.

3. Data and system inventory

4. Human approval requirements

AI should stay under review when the workflow touches customer-facing messages, pricing, legal/medical/financial claims, sensitive data, irreversible system changes, or anything the client would not want sent silently.

5. Risk and failure handling

For each automated step, define what happens when data is missing, confidence is low, an integration fails, a message is ambiguous, or the output could create customer risk. Good automation fails visibly and routes work to a human.

6. Success metrics

Choose 2–4 metrics before building. Examples:

7. Initial build scope

A solid v1 has a clear trigger, restricted input sources, a visible approval point, a fallback path, and a weekly reporting loop. Avoid trying to automate every edge case in the first build.

8. Ship or pause decision

Move forward when the pain is measurable, systems are accessible, risk can be controlled with review gates, and the first build can create value in 1–3 weeks. Pause when the workflow is unclear, data is unavailable, the client wants unsafe full autonomy, or the value is too small for the cost.

Next step

Request an AI Client Ops audit

Get a workflow map, risk review, and first automation recommendation before committing to a larger build.