The most useful starting point for an AI conversation is often an ordinary question: what keeps taking up your team’s time?
It might be finding information across documents, preparing a recurring report, or sorting incoming requests. Describing that task precisely creates a better basis for experimentation than choosing a tool first.
Choose a small, observable task.
A good first experiment has a clear input, a clear output, and someone who can judge the result. Define what a useful response looks like before building the prototype. Include examples where the correct behaviour is to ask for help.
Keep review in the workflow.
Decide which outputs need a person’s approval and make that review easy. A draft, a suggested classification, and a final decision have different consequences. The interface should make those differences clear.
Compare it with the simpler option.
Sometimes a better form, a search function, or a straightforward rule solves the problem. Consider those options alongside AI. A useful experiment tells you whether the extra complexity earns its place.
At MeDomot, this is the starting point we propose for an automation conversation: understand the work, define a small test, and decide what to do next using the results.