Good fits: repetitive work with clear patterns
AI performs well on tasks where there is a lot of text or data and a recognisable pattern: sorting incoming requests, summarising long documents, drafting routine replies, extracting fields from forms and searching internal knowledge. In these cases it removes routine effort and leaves people to review and decide.
Poor fits: decisions that need accountability
Where a decision has legal, financial or safety consequences, AI should support a person rather than replace them. The output can be wrong while sounding confident, so a review step is part of the design, not an optional extra.
What to check before starting
Is the task clearly defined, and can we describe what a good result looks like?
Is the data the system needs available, accurate and allowed to be used for this purpose?
Where will the system run, and who can see the data it processes?
How will mistakes be noticed and corrected?
Who maintains the solution after launch?
Connect it to the way people work
An AI tool that sits in a separate window is rarely used for long. Results are better when it is built into the systems people already use, such as the CRM, the document store or the support desk, so that it fits into an existing routine.
Start small and measure
Pick one process, run a limited pilot, and compare time and quality with the current way of working. If the result is clear, extend it. If not, the cost of finding out was small.