Teams often arrive at an AI workshop with different experiences and expectations. Some see an opportunity to remove tedious work. Others see a new source of uncertainty. Both perspectives can help shape a better experiment.
Work with a real example
Choose a task people recognise. Map what happens today, where judgement enters and what a good result looks like. Use this shared example to discuss how an AI-assisted approach might change the work.
Compare, then discuss
Prepare a small set of representative cases. Ask people to inspect the results, including ambiguous and unsuccessful ones. Keep the discussion grounded in the task rather than in a general claim about intelligence.
Leave with a question worth testing
A productive learning session does not have to end with a deployment plan. It can end with a clear hypothesis, an owner and a modest evaluation. That is a useful move from curiosity toward evidence.