In The Age Of AI, IESE Bets The MBA’s Future On Judgment

For years, business schools have responded to artificial intelligence the same way: launch a new elective, add an analytics track, create a concentration, maybe ink a partnership with a tech company for certifications.

At IESE Business School at the university of Navarra in Barcelona, Spain, one of Europe’s premier business schools, that approach no longer fit the scale of what’s coming.

Starting in September 2026, IESE will roll out a comprehensive redesign of its full-time MBA, embedding AI throughout every first-year course and reshaping the program around what school leaders describe as the central managerial challenge of the next decade: learning to lead organizations where humans and AI systems work side by side.

“We made a deliberate choice not to bolt AI onto a couple of electives and call it a transformation,” says Marc Badia, IESE’s deputy dean. “AI now runs through every first-year course, because in two years our graduates will be in roles where the question is not ‘Can you use AI?’ but ‘Can you lead and manage in a human-plus-AI organization?'”

FROM ‘FRAGMENTED’ AI EFFORTS TO A FULL REDESIGN

The redesign is among the most ambitious MBA overhauls announced in response to generative AI. But IESE leaders say it didn’t emerge from ChatGPT panic or competitive pressure. They describe it instead as the culmination of years of faculty development, internal experimentation, and increasingly urgent conversations about how AI is changing not just business, but the nature of managerial work itself.

Badia traces the school’s AI push to 2019, when IESE launched its AI and the Future of Management Initiative and introduced a course called AI for Executives that has since become one of its best sellers.

By the time generative AI exploded into public view in late 2022, the school already had a cohort of faculty working across multiple AI-related projects. IESE then accelerated development, bringing in outside thinkers including Wharton professor Ethan Mollick, running faculty seminars, and launching large-scale internal experimentation.

“One of the things that, to me, is remarkable is how all the faculty understood that this is serious, it’s different, and no one can miss this train,” Badia says.

EVERYONE WHO COULD BE THERE WAS THERE

That urgency culminated in a faculty hackathon last fall that drew roughly 85 of the school’s 120 full-time professors.

“Everyone who could be there was there,” Badia says.

The result was less a single initiative than what IESE Associate Dean for Academic Affairs and Innovation Evgeny Káganer describes as the convergence of many previously disconnected efforts.

“We had a number of somewhat fragmented things that we had been working on,” Káganer says. “Faculty innovation, learning innovation, understanding the impact of AI on business and education. We were looking for something that would bring it all together and really engage the entire school.”

WHY EDUCATION IS ESPECIALLY EXPOSED

Historically, he says, IESE had been cautious about major curricular changes, typically testing new approaches in smaller or more peripheral programs before pushing them into the flagship MBA.

“This time felt different,” Káganer says.

Part of that urgency stems from Káganer’s view that education is among the industries most profoundly disrupted by AI – because the technology strikes at both the product and the process at once.

“It affects your core value offering because the competencies students need are changing fast,” he says. “And at the same time, your core operating model – how we actually facilitate learning – is also being changed.”

THREE LAYERS OF AI COMPETENCY

That dual disruption is why IESE chose not merely to add AI content but to rethink the architecture of the MBA itself.

The redesign is built around three progressively developed competency areas: personal AI fluency, or the ability to work effectively with AI tools and systems; AI-enabled workflow and operating model redesign; and strategic AI acumen, including business model transformation and system-level decision-making.

Káganer argues that most organizations have already moved past simple task automation and are now wrestling with much larger questions about how entire workflows should be rebuilt around AI.

“We’re going back almost 30 years to business process reengineering,” he says. “Students need to understand current workflows, understand what different forms of AI can do, and then think about what the future architecture of those workflows should look like.”

THE HARDEST QUESTION IN THE ROOM

That means grappling with questions companies themselves haven’t answered: Which tasks belong to humans? Which to AI? Where do the handoffs happen? What risks emerge? And perhaps most difficult: how do leaders persuade employees to embrace systems that may ultimately replace parts of their own jobs?

“You have to engage humans to start doing things differently,” Káganer says. “But those same humans understand that some of these systems may eventually replace them.”

The uncertainty surrounding those questions, Badia says, is already showing up in MBA enrollment trends.

“I think part of the drop in MBA candidates across the market comes from this situation,” he says. “People think, ‘I’d rather stay put in my workplace because I don’t know what’s going to happen.'”

THE COST OF MISSING THE TRAIN

Staying put carries its own risks, he argues — particularly for workers at organizations not actively experimenting with AI.

“There’s a cost of opportunity of missing the train,” Badia says.

To anchor the redesign, IESE faculty developed what Káganer calls “the seven questions” – a framework meant to guide professors as they rethink the managerial dilemmas at the core of each first-year course.

Among them: How should humans and AI collaborate to create value? Who is accountable when AI causes harm? How do we govern AI responsibly? How must leadership evolve for AI-mediated organizations?

Faculty were then asked to revisit the core dilemmas already embedded in their courses and reconsider them through the lens of AI.

‘AI AS THE CONTEXT, NOT THE TOOL’

“This is not about AI as a tool,” Káganer says. “It’s AI as the context.”

That distinction is central to how IESE frames the redesign. The school is not primarily trying to teach prompt engineering or technical skills in isolation. The real work, leaders argue, is preparing people to operate amid ambiguity, shifting organizational structures, and a changing definition of what managers actually do.

The redesign also includes a pre-program AI course developed with Google that introduces students to large language models, AI agents, and retrieval-augmented generation before classes begin. AI boot camps run through the first year. And in one of the more inventive elements of the program, students will build portfolios of AI-related work – audits, agentic workflow blueprints, vibe-coded MVPs – that they can bring into recruiting conversations with employers.

“These are no different from what architects do,” Káganer says. “Architects don’t just show up with a CV. They show up with a portfolio.”

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