Give AI a job. And a reviewer.

Give it a bounded role

Start with a specific contribution: draft a reply from an approved knowledge base, suggest a category for an incoming request, or summarise a document for a named reader. Describe the expected output and the information it may use. These boundaries make the proposed feature easier to discuss with the team because everyone can see which part of the work remains theirs.

Make review a real step

Human review needs more detail than a confirmation button. Decide who checks the output, what they are checking and what must happen before it moves forward. A reviewer may need to confirm names, compare figures with the source or check whether a suggested response fits the customer’s request. Give them time and a clear way to reject or edit it. A nominal approval step is not the same as a considered decision.

Keep drafting separate from sending. And suggesting separate from deciding.

Put context beside the output

A polished answer can be difficult to assess without the material behind it. Where practical, show the relevant source text, the original request and any important missing information alongside the draft. Make suggested content visibly different from confirmed records. Help a reviewer check the work without hunting through other systems. If the available information does not support an answer, provide a route for requesting more context instead of pressing ahead.

Keep control after approval

If an AI agent can take actions, define those actions separately from the text it produces. Specify which systems it may change, which steps require approval and where it must stop. Record enough activity for a person to understand what happened. Provide a way to pause the workflow and route unresolved work to an owner. Begin with limited permissions, then consider wider responsibility only after reviewing its behaviour in the intended workflow.