Designing AI workflows with a clear human review step

Start with the work, not the model

When a workflow feels repetitive, it is tempting to begin by asking which AI tool to use. Start by writing down what actually happens: what information arrives, what someone does with it, what decisions they make, and what happens when the information is incomplete.

This first map is a working hypothesis. Check it with the people who do the work before designing around it.

Separate preparation from decisions

Some steps prepare information for a person: grouping items, drafting a response, or suggesting a route. Other steps make a consequential decision. Treat these differently. A useful assistant can make its input, proposed output, and uncertainty visible so a person can check the work before it moves on.

• Show what the assistant received and what it produced.
• Make edit, approve, reject, and retry actions easy to distinguish.
• Define what should happen when required information is missing.

Design for exceptions and recovery

A happy-path diagram is not enough. Include a path for ambiguous inputs, unavailable services, and results that do not match the task. Make it possible to pause, correct the input, or return to the existing manual process.

The right level of automation depends on the task, its consequences, and the team's ability to review it. Validate the proposed workflow with representative examples before relying on it in everyday work.

Measure the workflow you actually have

Before implementation, agree what a useful outcome would mean for this team: fewer handoffs, less repeated entry, clearer review, or another observable change. Record a baseline, then compare the same task under comparable conditions. Do not treat a faster draft as proof that the full workflow improved.