A better first question
“Where can we use AI?” is an understandable starting point. But it puts a technology before the people and processes it is meant to serve. A more useful question is: where does important work become unnecessarily difficult?
Look for the places where people repeatedly gather the same information, switch between disconnected tools, or wait for a decision that lacks context. Describe that work clearly before deciding what should change.
Understand the whole workflow
A task rarely exists in isolation. A document arrives from somewhere. A recommendation informs someone’s decision. An automated action creates a consequence downstream. Good discovery follows that chain from beginning to end.
Map the inputs, handoffs, exceptions, and outcomes. Ask the people doing the work what makes a normal day difficult. A small integration or a clearer interface may be more useful than a complex model.
Make success observable
Choose a baseline before building. It might be time spent reviewing a request, the number of corrections, or the ease with which a colleague can find reliable information. The measure should reflect the actual problem.
Then test a small, representative workflow. Keep a human decision point where judgment matters. Expand only when the system performs well enough to earn a larger role.
Build for everyday use
A promising demonstration is only a beginning. Useful AI needs a place in the tools people already understand, clear limits, and a way to recover when something goes wrong.
The goal is not to add intelligence everywhere. It is to make a specific part of the working day better—and make that improvement dependable.