Making AI useful at work starts with a better question.

There is a predictable way for an AI project to become expensive without becoming useful: start with the technology.

Someone sees a new model, a new assistant or a new automation tool. The team gets excited. A trial begins. A few impressive examples appear. Then the project meets the ordinary working day and runs out of road.

Start with the repeated bit

A better first question is simple: what part of the work is taking too long, happening too often or relying on one person’s memory?

That might be writing the same kind of report every week, checking information across several systems, answering the same customer question or turning rough notes into something the rest of the team can use.

Good first projects are close enough to the work that people can tell whether they have helped.

Then ask three more questions

• What would “better” look like in a normal week?
• Who will use the result, and who needs to trust it?
• What information should never be put into the process?

These questions do more than narrow the brief. They make the work easier to test, easier to explain and easier to hand over.

Useful is a better measure than impressive

The first win does not need to be dramatic. Saving a few hours, reducing rework or making a decision easier to check is enough if the team keeps using the change.

That is where training matters. People need to understand what the system is doing, where it can go wrong and how to make a sensible call when it does.

At WAIT, we help teams find that first useful piece of work, build the skills around it and put a workable process in place.