Business schools may be preparing students for the AI era by teaching them more AI. I am increasingly convinced that is the smaller part of the job.
AI is already very good at the layer we have traditionally rewarded most: producing things. Research summaries, market analyses, business models, recommendations, slides. A competent first pass can now appear before a student has finished choosing a template.
The interesting part is what comes before and after.
Someone still has to decide which problem is worth solving, what assumptions deserve challenging, which evidence matters, which tradeoff to accept, and whether the answer makes sense outside the screen. Then someone has to defend the decision and live with the consequences.
And that shift matters because value does not disappear when execution becomes easier. It moves. When producing an answer becomes abundant, the scarce part becomes deciding which answer deserves attention, which direction deserves resources, and which consequences we are prepared to accept. AI may reduce the cost of doing the work while increasing the value of knowing what work should be done.
That’s where business education should be moving.
I am trying to push my courses upstream. Less reward for polished output alone. More emphasis on problem framing, judgment under constraint, difficult choices, argument, iteration, and ownership. AI is welcome in the room. In many cases, I want students using it aggressively.
But I do not want them confusing the ability to generate an answer with the ability to know what should be done.
The future of work may not be about humans finding a shrinking collection of tasks machines cannot perform. It may be about humans moving upstream, toward the questions, choices, and responsibilities that give those tasks direction.
When execution becomes cheap, judgment becomes the job.