N ow that artificial intelligence has entered our everyday professional lives, incoming MBA students are understandably asking where it fits into their studies. While it still feels uncertain for some, for others it is a tool to get more done quickly. Either way, the question is no longer simply whether students should pay attention to AI, nor is it a matter of technical competence. The better question is how to use it well. That means having the managerial judgment to know what to delegate, what to question, what to verify and what must remain a human responsibility. I present the case that AI is most helpful when MBA students think of it not as a single tool, but as something that can undertake a small set of distinct roles. Used in this way, AI can support better learning, more efficient studying, stronger leadership reflection and improved organisation. The real skill is learning which roles to assign to AI, for which task and where human judgement must remain central. A 2024 study led by Barbara Larson at Northeastern University’s D’Amore-McKim School of Business increasingly frames AI in these developmental terms: not only as a productivity enhancer, but also as a support for critical thinking, reflection and sense-making in complex work. AI, therefore, should be used to extend MBA students’ thinking, not replace it. One of the risks in any academic programme is that students start to confuse polished output with genuine understanding. While AI can generate language quickly, the purpose of an MBA is not simply to produce answers; it is to develop judgment, managerial insight and the capacity to think clearly under conditions of ambiguity. AI can be very useful in that process but only when it supports the learning rather than standing in for it. This caution is reinforced by a 2026 study led by Aarhus University’s Carsten Bergenholtz and published in the journal, Academy of Management Learning and Education . The study examined the effects of generative AI access in ill-defined, time-pressured business school exams. What it found was an “equalising effect”: lower-performing students improved, while higher‑performing students declined, producing a convergence in performance levels. Their findings suggest that AI may help some students reach an acceptable baseline more quickly, while also flattening differences in deeper thinking and analytical distinction. That’s why a small set of roles can be a helpful way to think about AI in MBA study. The five roles identified below ( and depicted in the table on page 31 ) progress from the familiar
INTEGRATING AI
role. As students’ use moves from assistant to agent, the need for oversight, ethical awareness and managerial judgement increases. This progression is precisely why the use of AI belongs in MBA education: it is not only a study skill but also a leadership capability. AI as an assistant: offering an administrative boost In the assistant role, AI helps students reduce friction in their day‑to-day work. It can summarise readings, generate first drafts of outlines, convert lecture notes into structured revision aids, create comparison tables, draft emails and help organise study plans. For MBAs who are balancing demanding jobs, family responsibilities and intensive study, this role can be especially valuable. AI can remove some of the lower-value administrative or mechanical burden around studying and free up time for higher-value thinking. There is academic evidence supporting this role. In an online experiment, MIT PhD candidates Shakked Noy and Whitney Zhang assigned occupation-specific, incentivised writing tasks to 453 college-educated professionals and randomly gave half of them access to ChatGPT. They found substantial productivity gains among those who used ChatGPT: average completion time fell by about 40 per cent, while output quality increased by 18 per cent. However, the assistant role has clear limits. A summary of a reading is not the same as understanding it; a draft paragraph is not the same as having worked through an argument yourself. Used well, AI as an assistant helps students get started or organised. Used poorly, it can create the illusion of competence without the substance underneath it. This is particularly the case for students who are entering the MBA as functional experts. MBA cohorts learn theories across a range of business subjects that they need to integrate and use across an equally wide range of use cases. When learning a subject from the ground up, therefore, it is crucial that students don’t abdicate their learning to AI. AI as a thought partner: augmenting strategic analysis MBA-level work often involves ambiguity: messy cases, incomplete information, competing stakeholder interests and difficult
trade‑offs. In such settings, AI can be useful not because it “knows the answer” but because it helps students think more broadly and more deliberately. For students, this role is especially helpful when preparing for a class discussion, case analysis or decision-focused assignment by offering alternative interpretations of a case, surfacing risks and trade-offs, testing assumptions and helping structure competing arguments. By broadening the scope of information
to the more advanced and are not interchangeable. They vary in the level of autonomy given to AI and the level of responsibility retained by the student, moving from relatively low delegation in the assistant role to much more activity in the agent
available to students, their ability to make decisions and reflect on them is enhanced because they have more options and a greater awareness of potential contingencies.
Ambition • ISSUE 5 • 2026 29
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