By Prof Radhika Shrivastava
Management education has leapt before. The case method, simulations, data and analytics were all leaps. Each one gave us something we didn’t have. AI is being treated as the next leap in that same line, another capability bolted on, another fluency to teach. I don’t think that’s quite right.
Every leap before this one assumed a human was still doing the thinking, just with better inputs. Better cases gave us richer context to reason through. Better data gave us sharper evidence to reason with. AI is the first leap where the reasoning itself can be outsourced. That’s a different kind of shift, not just a bigger one, and it changes what management education is actually for.
If intelligence can now be generated on demand, the real question isn’t how we teach more of it. It’s what do we protect in its place. Fluency is the floor now. Judgment is the leap.
Some of the world’s leading business schools have already started making that shift. Kellogg now offers executive programmes focused on human-AI judgment, while Berkeley Haas has repositioned its strategic vision around what it calls The Human Edge of AI. Both are pursuing the same scarce capability, and it isn’t artificial intelligence.
Much of the industry’s response, however, has gone in another direction. The instinct has been to teach fluency through tools, prompts, and platforms. That instinct isn’t wrong. It’s just not enough. Treating AI literacy as the destination risks missing the larger transformation that management education now demands.
I don’t think this is only a classroom problem, though. I see it earlier, inside the organizations our classrooms are meant to prepare people for.
My own doctoral research looked at high-growth firms, the kind scaling fast enough that yesterday’s structures stopped fitting today’s headcount. Almost every breakdown I came across was the same shape, even when the company didn’t. The founder’s instinct never quite transferred to the managers hired to scale the business. Talent decisions were often made quickly, without a clear framework for judgment, while organizational systems struggled to keep pace with growth.
What separated the firms that scaled successfully from those that didn’t wasn’t the absence of pressure. Every fast-growing company faces it. The difference lay in whether judgment was something they built deliberately in their managers, or something they assumed would emerge on its own. Systems mattered too. But systems without judgment cracked under pressure, and judgment without systems never made it past a certain size. The two had to grow together on purpose.
I believe AI is creating the same pattern today, only at a much larger scale. What holds up in organizations, and equally in classrooms, has less to do with how much AI gets added and more to do with how deliberately judgment gets built alongside it.
So what does that judgment look like? For me, it begins with acumen. Acumen is noticing what the data doesn’t say, reading a room, a market, or a moment in ways that nothing trained only on the past can fully anticipate.
Then comes judgment, perhaps the hardest capability to define because it appears precisely when evidence runs out. AI can present ten well-reasoned options, each backed by data and each entirely defensible. Yet none of them can tell you which path to choose when the situation is genuinely new and no precedent exists.
I’ve seen experienced professionals who are exceptional at gathering information freeze at exactly this point because collecting more information had always been how they bought themselves time. Judgment doesn’t buy time. It spends it. It is the willingness to commit to a decision that no model, however sophisticated, was ever trained to make because the situation itself did not exist when the training happened.
Finally, there is agency. Agency is acting on that judgment and owning what follows. It remains the one responsibility no technology can assume, regardless of how polished its recommendations may appear. None of these qualities are new. In fact, they are the oldest currency management has ever traded in. What is changing is how easily they can become dormant when we allow AI to draft not just our documents, but increasingly, our thinking.
This is why we are rethinking both ends of management education at FIIB. For our MBA students, AI fluency is now table stakes, not a differentiator. The real work begins after that. For professionals in our executive programmes, the challenge appears even earlier. Many already use AI every day, and some have quietly shared that they feel less certain about their decisions now, not more. It isn’t AI doing that to them. It’s a judgment muscle that has gradually gone quiet through disuse.
The next leap in management education won’t be measured by how much AI we teach. It will be measured by how deliberately we protect what AI cannot do.
That’s the harder leap. It’s also the one that matters.
Prof Radhika Shrivastava is the President & CEO, Fortune Institute of International Business (FIIB).
DISCLAIMER: The views expressed are solely of the author and ETEDUCATION does not necessarily subscribe to it. ETEDUCATION will not be responsible for any damage caused to any person or organisation directly or indirectly.
