Put AI to work on the right tasks

Start with a small job that has a clear input and a useful, checkable output. An inquiry summary, a proposal outline, a release checklist, or a monthly report built from verified data can be a practical place to begin.

Decide what the assistant should prepare and what your team should approve. Give a reply-drafting assistant access to the context it needs, while leaving the final send with a person. An invoice draft likewise needs no access to change bank details or move money.

Keep the context focused. Share only the information needed for the task, keep credentials and sensitive records out of prompts, and decide which data is approved for the model provider. Separate each client's or project's information from unrelated work.

Measure the whole job. Include the time spent reviewing and correcting the output, the cost of model calls, and the errors a person catches. That gives you a useful basis for deciding whether the workflow deserves a place in the business.

Build on the tasks your team finds useful in its day-to-day work. Keep people responsible for scope, quality, communication, and release decisions as the system grows. The aim is a team with better information and more room to focus on the customer.