Choose a task with a clear output

Summarising internal documents, classifying support requests and suggesting drafts are examples with definable evaluation criteria. Before choosing a tool, describe its inputs, expected outputs and cases requiring human review.

A small experiment needs representative examples of real work. Include missing, contradictory and confusing inputs. Good results on a few ideal examples are not enough to justify broad adoption.

Keep people at decision points

AI-generated content can be wrong even when it sounds confident. External messages, important updates and customer-facing actions need appropriate review. Limit data access to what the tool's task requires.

Do not send sensitive documents to an external service before understanding its terms, data handling and access controls. The integration should record processing status without exposing secrets.

Measure value rather than usage

Compare completion time, correction rates and output quality with the previous process. If reviewers spend more time than the tool saves, narrow the scope or change the approach. Expand step by step after demonstrating benefits in the team's real context.