“The role of the economist is not to make simple things complicated, but to make complicated things simple.” Milton Friedman said that and, yes, he may have oversimplified when framing the purpose of business. But Friedman’s point about clarity is worth holding onto even if his views about role of business in society are not. I’ve always believed the best professors are those who can take complex concepts and make them accessible.
The bad news for even the most talented professors is that today I’m able to get this clarity from AI. Lately I’ve been asking ChatGPT about things I struggled with in graduate school, like Myerson’s proof of the Revelation Principle and the differences between Brouwer’s Fixed Point Theorem and Kakutani’s. The truth is I don’t recall enough math for the conversations to be helpful. But I can easily see AI as an indispensable tool for PhD students today.
My little experiment points to a larger question on the minds of business school leaders, “What are the implications of AI for the future of management education?” It is the question I’m most often asked these days. So here is a summary version of my usual response.
Business schools have built a large part of their value around content (the ideas, concepts, models, and frameworks). They have highly trained faculty teaching rigorous analytical frameworks in well-structured curricula. Expertise and explanations are a big part of the product. But technology—AI in particular—now delivers insights and clarity about concepts on demand, instantly and at scale. The question for business schools is what to do about it. The answer, I believe is clear. It lies in moving higher up the value chain. And leveraging technology to move fast.
When AI takes more of the content, it frees up time and energy for experiences. When students can access explanations instantly, business education becomes less about what happens at the front of the room and more about what students do with the ideas. Learning shifts from absorbing information to applying it to make decisions, reflecting on outcomes, understanding power dynamics, and navigating uncertainty. AI becomes the scaffold that allows instructors to design richer, more immersive learning environments where students practice leadership, not just study it.
Technology also enables something educators have struggled to provide, personalization. Every group (class) contains students who learn differently and have varying interests, strengths, and aspirations. But the traditional structure of the education forces everyone into the same pace and rhythm. AI changes that. It gives each learner the opportunity for a more responsive path, one that adapts to their pace and priorities. Instead of being pressed by the need to “cover the material,” students can pull themselves ahead through curiosity.
One of the biggest opportunities to leverage AI is to make management education more continuous. By this I’m talking not only about moving from discrete interventions to continuous engagement, but also about extending learning over the career lifecycle. Business is changing too quickly for a one or two-year degree to sustain someone for three to five years, much less a career. With the help of AI, a business school’s contribution to learning doesn’t have to end with graduation. Students can carry intelligent learning companions with them, receiving ongoing coaching, updates, and practice tailored to their careers. Business schools can become lifelong partners rather than one-time providers, supporting alumni as industries, roles, and technologies change.
Finally, the rise of AI provides the opportunity for management education to become more social, not less. When content delivery takes less time, the classroom (physical or virtual) becomes a place for conversation, debate, teamwork, and reflection, enabling the messy, relational experiences where leadership actually develops. Students must learn to collaborate not only with peers, but also with intelligent systems. Prompting, evaluating AI output, questioning its assumptions, and deciding when not to trust it (and how to build trust with it) become essential managerial skills. Future leaders will manage hybrid teams of humans and machines, and business schools have a central role to play in preparing them.
I’ve been particularly interested in this last point because of my own experience. Today, I still value the clarity economics brings to my understanding of the world. The models help me to bring order to complexity and offer a shared language for thinking about choices. But they are simplifications. The real work of organizations happens in the messy space between people, in the way they communicate, build (or lose) trust, perceive others, and handle their emotions. I save this topic for another blog.
When you bring all this together, the future becomes clear. AI isn’t undermining business schools, it is pushing them toward their true value, building on the human, relational, and developmental aspects of education that have been overshadowed by content delivery. In short, the future of management education is more experiential, personalized, continuous, and social. Fundamentally, it’s about transforming how we teach more than what we teach.
The business schools that thrive will be the ones that lean into this shift. They will treat AI not as a competitor but as a catalyst, one that frees them and enables them to focus on the things that matter most. That includes designing powerful experiences, cultivating judgment, developing human capabilities, and supporting learners across their entire careers.
Competitor or Catalyst? The Role of AI in Shaping the Future of Management Education
