Artificial intelligence (AI) is not a death knell for the case method. As students prepare for an AI-enabled workplace, the technology allows them to access information and analyze cases more efficiently in preparation for classroom discussion. At the same time, judgment, discernment, and human connection have become more critical than ever before.
AI might be able to extend what students and instructors can see and test, and change how they prepare, but it cannot replace the human work of deciding, listening, disagreeing, revising, and accepting responsibility with integrity.
In the book Leading into the Age of Wisdom: Reimagining the Future of Work, author Brian Gorman draws a distinction particularly relevant to how case learning can prepare students for an AI-enabled workplace: “Intelligence expands the range of possible answers. Wisdom determines which answer is worth choosing.”
A Rehearsal Space for the AI-Enabled Workplace
As AI becomes embedded into everyday work, the case classroom can provide a rehearsal space for navigating it. Students need to know not only how to consult and work with AI, but when to trust it, when to challenge it, when to ignore it, and when to slow a decision down.
That means students can use AI for research, comparison, analysis, and idea generation while still questioning its assumptions, identifying its limits, and rejecting its recommendations when needed.
AI can be treated not simply as a preparation tool for the workplace, but as part of the management environment students need to understand. In a finance case discussion, students might ask what an AI forecast has missed. In an operations case discussion, they might consider what happens when optimization removes human engagement. In a leadership case discussion, they might examine who gains authority when a system begins structuring decisions.
Using AI to create more dynamic learning experiences can give students opportunities to grapple with changing conditions, competing perspectives, and the consequences of their decisions. Euvin Naidoo, Distinguished Professor of Global Accounting, Risk and Agility at Thunderbird School of Global Management, sees an important role for AI in enriching that learning environment without displacing the human interaction at the heart of case learning.
“AI enriches context and introduces dynamism, but judgment is still forged through conversation, debate and reflection. The instructor remains the steward of insight. The classroom remains a community of inquiry,” Naidoo says.
“In a world where agility matters and answers are rarely black and white, this evolution is not about adding technology for its own sake. It is about strengthening our ability to prepare leaders who can ask better questions, make sound decisions and act with clarity in uncertain conditions,” he adds.
This builds on the case method’s core teaching purpose. Cases have always put students in the role of decision-maker; AI simply adds a new layer of context they will need to navigate in the workplace.
Practising Adaptability When Conditions Change
In the workplace, problems rarely come with fixed information, predictable conditions, or a single source of analysis. Case learning can provide opportunities for students to practise responding when circumstances change, new information emerges, or an initial recommendation no longer holds.
During the Ivey Publishing webinar AI in Case Teaching: From Faculty Fears to New Possibilities, Kyle Maclean, Associate Professor of Management Science at Ivey Business School, and Mazi Raz, Assistant Professor of Strategy at Ivey Business School, explored how AI can be used to deepen case-based learning while pushing students beyond easy answers.
Maclean’s own teaching offers one example of how AI can create more room for the complex judgment students will need beyond the classroom. In one course, he used a custom AI agent to support student research-project checkpoints. The AI was prompted with issues students commonly faced, allowing teams to work through early challenges on demand before submitting the conversation for Maclean’s review. The result was a way to move attention toward the more complex issues that required human judgment.
For Raz, creating that kind of learning environment requires a similar adaptability from the instructor.
“What excites me the most is that it's constantly keeping me awake,” Raz says. “I’m a lot more excited because every time I look at what I've been doing, I have to think very carefully how I'm going to do this differently this time.”
Raz's approach emphasizes fluidity, improvisation, and continuous reinvention. His case teaching looks to surface the expected AI analysis early, then shift the discussion as new perspectives and considerations emerge. A class might begin with the protagonist’s decision, then shift to the customer’s view, the competitor’s response, or an absent stakeholder’s concerns.
That kind of unpredictability mirrors the conditions students will encounter beyond the classroom, where decisions often need to be revisited as new information, perspectives, and consequences emerge.
Practising the Process, Not Just the Answer
In the workplace, a polished AI-generated recommendation is rarely enough. Leaders need to explain how they reached a decision and understand the consequences of the choices they make. Case learning can provide a space to practise that process.
Antoine Duvauchelle, lecturer at INSEAD and co-founder of LiveCase, sees particular value in learning experiences that require students to make decisions and respond to consequences in real time. “Real time decision making tends to expose assumptions very quickly,” he says. Rather than relying on a prepared answer, students must consider what they noticed, what they missed, why they hesitated, and how they responded.
“It becomes less about who can sound smartest, and more about how decisions are made,” Duvauchelle adds.
That shift also changes what instructors can examine after the decision has been made. Rather than discussing a hypothetical answer, Duvauchelle says instructors can focus the debrief on “what the class actually did, the tradeoffs they chose, and the patterns that emerged across the cohort.”
Raz offers a related direction: process-oriented assessment. Instead of grading only whether students reached a particular conclusion, instructors can examine the reasoning path behind it: the assumptions students began with, how those assumptions changed, the evidence and rebuttals they considered, and where they resisted an easy answer.
AI can also introduce new perspectives that challenge that reasoning.
“We could be very creative with our use of AI to offer us views and perspectives that are not currently available in the classroom. It could be the voice that is missing, it could be the adversarial voice that is necessary, it could be the supplementary perspective that many people don't get to,” Raz says. “There are lots of opportunities to deepen and enrich our classroom conversations with AI, and the trick is leaning into those experiments.”
The opportunity for case learning, then, is not to have students compete with AI to produce the best answer, but to give them opportunities to practise what happens after an answer is available: questioning it, defending a different position, responding to new information, understanding consequences, and ultimately taking responsibility for a decision.
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PRACTICAL APPLICATION: Redesigning the Case Classroom
For instructors looking to put these ideas into practice, Kyle Maclean and Mazi Raz offer concrete approaches in their session, Redesigning the Case Classroom: A Diagnostic Framework for the Gen AI Era (presented at the 2026 Academy of Management Annual Meeting). Their framework helps shift the classroom focus from fast AI preparation to what technology cannot replace: testing judgment, adapting under pressure, and revising thinking in real time.
- Design the case sequence so reasoning cannot be skipped
Ask students to make an initial decision before class, then introduce new exhibits, variables, or information after they have committed to a position. The changing information requires students to revisit their reasoning rather than rely on an analysis prepared in advance.
- Increase the complexity of the problem
Introduce conflicting evidence, competing stakeholder demands, new constraints, or adjusted numerical values during the discussion. Students must respond to an evolving managerial problem and consider whether their original recommendation still holds.
- Make managerial reasoning publicly accountable
Ask students to defend recommendations aloud, respond directly to competing arguments, and revise their positions after hearing alternatives. This puts the focus on the reasoning behind an answer rather than the quality of the answer students arrive with. Use AI to analyze discussion patterns after the class.
- Deepen managerial deliberation with AI
Introduce unexpected stakeholders and surface ethical or political tensions. Use AI to generate counterarguments or board interventions to add complexity and deepen classroom deliberation.
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Students Used AI to Prepare for Your Case Class. Now What?
Practical strategies for preserving productive struggle and pushing students beyond AI-generated preparation.
Why Case Instructors Matter More Than Ever in the Age of AI
Why judgment, discussion, accountability, and human facilitation become more important as AI capabilities grow.
What AI-Native Cases Could Mean for Business Education
How designing cases with AI in mind could create more dynamic, responsive learning experiences.
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