Educating Leaders for the Age of Intelligence

• During the Age of Answers, leaders followed frameworks designed to minimize risk. But in the Age of Intelligence, they must work with incomplete information, learn through experimentation, and adapt to changing conditions in real time.
• To prepare for this work environment, students need opportunities to operate in ambiguous circumstances, reconcile competing interests, and exercise judgment.
• Students must learn to use artificial intelligence as a tool, not a provider of solutions. Instructors can show them how to reframe AI from a shortcut to a catalyst for deeper thinking.

 
For 36 years, I’ve taught at the University of Michigan’s Ross School of Business in Ann Arbor, while doing much of my innovation work in environments far outside academia—technology firms, government agencies, research labs, and public-sector organizations. This dual path emerged because, during my career, I have been drawn to places where problems were unsettled, answers were provisional, and the future arrived faster than established institutions could comfortably absorb.

Long before artificial intelligence (AI) became a dominant force, many organizations I worked with had already moved beyond what I think of as the Age of Answers—a period when expertise was relatively stable, planning cycles were deliberate, and success depended on optimizing known solutions. Instead, these companies were at the very start of the era in which we are now clearly operating: the Age of Intelligence, shaped by speed, uncertainty, contradiction, and the constant recombination of ideas, information, and technologies.

In these settings, success depends less on possessing the right answers and more on adapting thinking in real time. Are today’s business schools preparing their graduates to enter this world?

Mindsets for a World That Refuses to Sit Still

Early in my career, I worked in new media and information technology development. What struck me was how often large, capable companies were immobilized by their own planning processes. Strategic roadmaps stretched years ahead. Governance systems demanded certainty. Decisions moved through carefully sequenced cycles designed to minimize risk.

At the same time, smaller and far less resourced startups were running experiments. They weren’t trying to predict the future; they were testing it. Week by week, they learned faster because they were willing to act without complete information. Repeatedly, I watched established organizations miss emerging opportunities while newer and more agile competitors moved around them.

A similar pattern surfaced during the COVID-19 pandemic. Many large technology firms, equipped with sophisticated forecasting and planning functions, were caught off guard by the abrupt shift to remote work. Meanwhile, a relatively small company—Zoom—adapted quickly, iterated in real time, and captured a significant share of the market. The difference wasn’t superior foresight. It was speed, adaptability, and a willingness to learn through action rather than prediction.

The most important capability is not technical mastery or analytical rigor, but mindset. The individuals and teams who thrive remain effective when certainty is unavailable.

These experiences taught me that the most important capability is not technical mastery or analytical rigor—though both matter—but mindset. The individuals and teams who thrive are those who can adapt midstream, work creatively within constraints, hold competing interpretations at once, and remain effective when certainty is unavailable.

These capacities don’t come from mastering more frameworks. They emerge when ambiguity is unavoidable and learning has to happen in motion. And that’s precisely the type of learning business schools need to deliver in the Age of Intelligence.

AI Changes What and How Students Learn

In addition to dealing with ambiguity and instability, today’s leaders must understand the best ways to deploy the new tools of artificial intelligence.

Take innovation. In the past—when information was scarce, cycles were slower, and coordination was the central challenge—companies pursued innovation by determining how to take a promising idea and move it efficiently toward execution. Managers needed to understand project management, stage-gating systems, and portfolio optimization.

But today, AI can solve any problem that has a clear, well-defined answer—it can quickly and effectively devise an optimal schedule, a prioritized portfolio, or a structured plan. These activities no longer require human judgment. To prepare students for a business world in which AI performs routine analytical tasks, business schools must change what and how they teach.

In my own classrooms, I have students use AI to generate solutions to problems, but I ask them to examine those outputs critically. Where are the assumptions? What contradictions are embedded? What ambiguities remain unresolved? Students use those tensions as starting points to imagine alternative approaches, often combining AI-generated insights in ways the system itself did not anticipate.

This shift—from seeking answers to making sense of information—mirrors what I observed repeatedly outside academia. As AI accelerates decision-making, leadership becomes less about producing correct answers and more about interpreting complexity, integrating competing perspectives, and exercising judgment under conditions of uncertainty.

Students sense this intuitively. They understand that the world is nonlinear and unpredictable, and they want business schools to provide them with structured opportunities to practice thinking when clarity is incomplete. AI has exposed this need, highlighting both the limits of optimization in complex human systems and the enduring importance of human sensemaking.

How I Learned to Design for Ambiguity

As I began my career, I apprenticed in technology laboratories building experimental media with no clear precedent. Learning happened through observation, action, failure, and explanation. Employees watched someone work, tried the same approach themselves, and then taught what they had just learned to someone else. Long before I had language for it, I absorbed a simple rhythm: See one, do one, teach one.

Later, as I helped build similar labs inside large organizations, I saw how easily these environments were neutralized by traditional structures. The labs that survived focused on learning through action, not analysis.

Learning happens through observation, action, failure, and explanation. It’s a simple rhythm: See one, do one, teach one.

Based on these experiences, I have altered my approach to teaching. For instance, partnering with furniture company Haworth, I have created a learning lab environment for my students—a conservatory-like space where capability is developed through practice rather than instruction. This environment draws less from management science and more from John Dewey’s philosophy of experiential and democratic education—particularly his belief that learning emerges through inquiry, reflection, and shared problem-solving rooted in real situations. Medical education still relies on this approach. Business education rarely does.

I also have stopped opening my classes by introducing students to frameworks. Frameworks are useful, but they assume the world is intelligible. Increasingly, it is not.

Instead, I begin my classes by presenting students with lived situations that require interpretation. For instance, I might show a short video depicting a homeless encampment in a major city. I present it as a situation, not a problem to be solved. Students first describe what they see. Then they identify the paradoxes—competing values that are all legitimate and mutually constraining. From those tensions, they derive provisional insights and propose small experiments to test them.

Only after students have wrestled with ambiguity do I introduce formal methods or frameworks. At that point, the tools no longer dominate perception; they serve it. Students learn where frameworks illuminate a situation, where they fall short, and how to adapt their thinking when conditions change. This sequence—experience first, structure later—mirrors how learning occurred in the innovation environments where I spent much of my career.

Small Experiments That Build Adaptive Mindsets

Over the years, I’ve learned that meaningful change in education rarely comes from large redesigns or sweeping reforms. It comes from small, intentional experiments that quietly alter how students experience learning. These experiments work not because they are innovative in themselves, but because they change the conditions under which thinking occurs. Each of these experiments shifts the role of the student—from answer producer to sensemaker.

Below, I share five experiments that I have used in my classes. None requires new funding, new centers, or new governance structures. Faculty can adapt these ideas to fit any course or local context:

Introduce contradiction deliberately. In many innovation environments, the most productive moments come when people are forced to manage competing demands at once. Translating this into the classroom can be as simple as asking students to design solutions that must satisfy two or more legitimate but conflicting goals—such as efficiency and equity, standardization and customization, or speed and safety. The learning comes not from resolving the tension, but from reasoning within it.

Start with the situation, not the framework. Instead of opening a module with a model or method, begin with a news story or a short video that presents a contemporary challenge that students can recognize. Ask students first to describe what they see, then to compare interpretations. Only later introduce formal frameworks as tools for sensemaking rather than lenses that determine what counts as relevant.

Use AI as a provocation, not a solution. In my classes, I ask students to generate AI-based answers early in an assignment and then treat those outputs as raw material rather than conclusions. I ask students to identify assumptions, paradoxes, omissions, and blind spots in the AI’s response, and then to build alternative approaches that deliberately work against those limitations. This compels students to reframe their perceptions of AI—they now view AI not as a shortcut, but as a catalyst for deeper thinking.

Meaningful change in education comes from small experiments that alter how students experience learning and change the conditions under which thinking occurs.

Run a short ambiguity lab. This approach creates an environment where problems cannot be fully specified in advance. Give students an underdefined challenge and ask them to frame the problem, identify tensions, propose experiments, and reflect on what they have learned. Evaluate their work based on how well they frame inquiries, iterate solutions, and formulate judgments—not on whether they produce polished solutions. Students often find this uncomfortable, but the discomfort is precisely what develops adaptive capacity.

Ask students to teach what they just learned. The “see one, do one, teach one” rhythm I encountered in technology labs remains one of the most effective learning accelerators I know. After completing an exercise, ask students to explain their insights to others—sometimes peers, sometimes external audiences. Teaching forces them to clarify assumptions, confront gaps in understanding, and integrate experience into coherent insight.

Individually, these experiments may seem modest. Collectively, they begin to change how students understand learning itself—from acquiring answers to developing judgment.

Why Business Schools Will Produce Tomorrow’s Leaders

In the Age of Intelligence, business schools sit at the intersection of theory and practice, analysis and judgment, public purpose and private enterprise. Few institutions have comparable reach across sectors—or the same opportunity to shape how future leaders learn to think.

What has changed is not the importance of business education, but the conditions under which it operates. When answers were scarce, education emphasized mastery of established frameworks. Today, when answers are abundant and increasingly automated, the central task is helping students develop judgment—the ability to interpret complexity, integrate competing values, and act responsibly when clarity is incomplete.

This is not a call for wholesale reinvention. In my experience, durable change comes from cumulative, small-scale experimentation rather than grand design. Faculty do not need permission to try new ways of framing problems, sequencing learning experiences, or designing assignments that surface ambiguity. Students are already encountering complexity outside the classroom; education can help them make sense of it inside.

The practices described here—beginning with lived situations, working through paradoxes, using AI as a provocation, and learning through small experiments—did not originate as theories of education. I observed them over my career as I saw organizations struggle to support creative work internally. I saw these strategies being employed in business environments where waiting for certainty was not an option.

In a world defined by ambiguity and acceleration, these same approaches translate naturally to business education—especially as AI reshapes what it means to know, decide, and lead. But a business school’s work does not begin by showing students how to find answers. It begins by teaching them how to think when answers are no longer enough.