In education, learning progression matters. Students do not develop mastery simply because they were shown the final system or given the answer key. Competence develops through repetition, adjustment, feedback, recognition of patterns, and gradually learning how to evaluate their own thinking along the way. Many organizations unintentionally skipped that stage.

The problem with the race to get to the result is that it cuts out all the intentional interaction needed with AI to learn how to evaluate those results. Workflows should be something that develop naturally — not something forced. Users running similar scenarios through AI multiple times begin to recognize patterns and effects that different interactions have on outputs. This is essential to what we call ‘operational literacy with AI.’

This past weekend, I conducted training for a group of business leaders, and we started with a prompt I had designed for instructional purposes. The question came up almost immediately: “Aren’t we all going to get the same answer?” Everyone was using the same tool and the same prompt, so it was a fair question. Given how often AI is framed as a matter of finding the “right” prompt, the expectation of consistent output makes sense. In case anyone reading may not know, they we