There has been rapid but shallow integration of AI and generative AI in European MBA programmes, revealing that while 83% have adopted at least one AI tool, only a small fraction have achieved deep, curriculum-wide integration. AI adoption is mainly faculty-driven, with significant barriers stemming from human factors such as lack of expertise, time, and ethical clarity, rather than technical limitations. Many institutions have implemented AI for reputational reasons rather than genuine educational innovation, leading to a gap between aspirations and actual outcomes. The authors call for stronger leadership, faculty development, and robust ethical frameworks to ensure meaningful AI integration in business education. 

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“The pace of AI developments is only accelerating. Today, we are moving at a breathless pace – and largely without a roadmap. Moments to pause, reflect and ask questions can seem rare, but we must consider where we are going and if this is indeed what we want.  The speed at which generative AI technologies are being integrated into education systems in the absence of checks, rules or regulations is astonishing”
(UNESCO, 2023).

MBA programmes, as the flagship offerings of business schools, face immense pressure to lead AI initiatives even though there is no clear vision of how AI and GenAI will reshape or impact the landscape of higher education. This study extends beyond AI as a tool to examine fundamental questions of pedagogy, purpose, and positioning. MBA directors stand at the intersection of academic credibility and market relevance, where AI will inevitably change the nature of business education. Whether that change strengthens or fragments institutional teaching missions depends on deliberate leadership choices. The field owes current and future learners thoughtful, ethical AI implementation with clarity of purpose. As custodians of their institutions’ flagship programmes, MBA directors’ insights remain vital to shaping the future of AI in business education.

We surveyed 38 of these critical MBA directors who are all based at European Business Schools and who were all attendees at the EFMD MBA Directors Conference (March 2025).  The data reveals a complex landscape of AI adoption in business education, dominated by faculty-driven initiatives, and a significant gap between aspirational goals and expected outcomes. Our findings suggest that implementation is occurring slowly and is often shallow, fragmented, or symbolic. The stakes are high. How we, as educationists manage the next steps will impact in various ways, institutionally, pedagogically, and ultimately, in the learning development and readiness of MBA graduates for an AI-augmented business world. The study examined three critical dimensions: current adoption and capacity, comparative positioning and leadership, and aspirations versus realities for the current adoption of AI/GenAI in MBA programmes.

Current Adoption and Capacity: Shallow Integration Dominates

The data demonstrates widespread but shallow AI integration across MBA programmes. The prevalence of GenAI in the data shows that it dominates for many, even though AI or what could be called Analytical or Algorithm AI has been in evidence in businesses for the last decade (Iansiti and Lakhani, 2020; Davenport and Mittal, 2023). While 83% of programmes have implemented at least one AI solution, adoption depth drops precipitously: only 47% have integrated two solutions, and merely 11% have implemented four or more tools. Although participants were asked about tools, they discussed more traditional AI tools, Gen AI activities and policies, and predominantly AI modules and electives, to showcase what they had implemented. Dedicated electives and ChatGPT use dominate current implementations, with no evidence of programmes addressing a curriculum-wide strategy beyond the focus on GenAI policy. The focus was on embedding it in what is taught to students rather than it being used with students across all programmes.

This pattern reveals institutions favouring low-risk, modular approaches like AI electives or standalone tools rather than comprehensive curriculum integration. The sharp drop-off in adoption depth suggests that AI is not structurally embedded across MBA curricula, indicating institutional uncertainty about how to operationalise AI and GenAI meaningfully in pedagogy.

Who is driving AI adoption?

Four main groups were suggested, with superusers or innovators on faculty being the main group. This contrast with faculty confidence and digital capability remains highly uneven, with only 8% of schools reporting high levels of AI expertise among faculty. This creates a fundamental mismatch as the skill of embracing AI is missing even if faculty wanted to and shows a lack of comprehensive technical understanding, contributing to what our data identifies as visible implementation gaps between strategic ambition and practical delivery.

Critically, almost half of all respondents suggest that AI implementations were driven by individual faculty experts, mostly numbering in the 8% figure. Though limited in number, these faculty champions significantly outpace formal leadership initiatives from MBA directors and central administration, or the technology and innovation staff. This grassroots approach, while demonstrating innovation energy, reveals concerning gaps in strategic coordination. The more limited role of MBA directors and central leadership could suggest missed opportunities for cohesive institutional alignment. Of note is that there was some evidence of leadership ambiguity, with some directors unaware of who holds responsibility for AI direction within their institutions.

Competitive Positioning: Symbolic Capital Over Substance

A critical finding emerges around institutional motivations for AI adoption. There was data to show that some programmes used AI integration as symbolic capital, reinforcing reputation rather than as an institutional use to improve pedagogy. Only a few schools were perceived as leaders – these were divided between the US with 3 at the top and then followed by 3 European business schools. Many programmes described themselves as ‘lagging’. Open-text comments revealed concerning patterns of what respondents termed ‘innovation theatre’ and ‘shallow adoption’.

Particularly telling are director quotes that capture the existing competitive pressure dynamic: “Everyone talks about AI, but there’s little evidence of real transformation,” and “we’re doing it because we feel we have to—not because we have a strategy.” These responses illuminate adoption driven by external pressure rather than strategic conviction, suggesting AI implementation as reputation management rather than genuine educational innovation.

The data underscores the critical role MBA directors play as boundary spanners between pedagogy, strategy, and market positioning. This situation places them at the intersection of competing institutional logics: academic integrity versus market competitiveness, innovation versus risk management, and individual faculty autonomy versus institutional coherence. The prevalence of faculty-led initiatives, suggests that directors must navigate complex organisational dynamics to achieve meaningful AI integration while maintaining pedagogical and educational quality and institutional identity visibility in a changing market.

Barriers to Implementation: Human Factors Dominate Technical Concerns

The most significant obstacles to AI adoption cluster around human and organisational factors rather than technical limitations. Faculty time, and expertise in AI, along with speed of change and cognitive load, were the most cited barriers, emphasising the personal and systemic challenges educators face in adapting new technologies for use in the classroom. The student educational misuse and ethical clarity issues were a close second, capturing legitimate faculty concerns around plagiarism, over-reliance on AI tools by students, and a general lack of clear ethical frameworks for AI use in learning, and critically in assessments.

These findings indicate that adoption challenges are fundamentally social rather than technical, requiring investment in faculty development, more enhanced policy frameworks beyond the initial framework, and ethical guidelines that really explore the core issues.

Lower-scoring categories such as resistance to change and lack of clear policy suggest some schools are still grappling with foundational strategy and leadership clarity, reinforcing the sense that institutions are reactive at the moment and ‘wait and see’ for the future, rather than jumping into a proactive enabling of AI integration within teaching and learning.

The Aspiration-Reality Gap: Optimism Tempered by Realism

Perhaps the most telling and very interesting finding is the substantial disconnect between directorial aspirations and expected outcomes for AI and GenAI integration over the next five years. Directors aspire to transformative outcomes through enhanced student learning, personalisation, and feedback mechanisms, yet expect fragmented, uneven implementation.

While 90% of directors aspire to enhanced personalised learning through AI, only 55% expect this outcome to materialise. This gap reflects what we term ‘implementation realism’—an acknowledgment of systemic inertia, resource constraints, and organisational complexity that tempers technological optimism. Only with clear evidence of the impact on teaching and learning can the benefits be amalgamated, and it is unclear what the next steps should be. The tension between innovation optimism and operational realism reveals systemic pessimism. This could be interpreted in two ways, one as leadership and governance failure to align an AI vision with infrastructure, incentives, and staff support, or it could show real concern about next best steps cognisant of what UNESCO (2023) called the lack of ‘guardrails’ for AI and Gen AI adoption in higher education, despite the speed of widespread adoption in business and society.

Realistically, many directors also suggested that there would be piecemeal adoption and persistent inequality between well-funded and less-resourced schools, with major barriers including faculty time, ethical clarity, policy vacuum, and infrastructure issues, continuing to impede progress in many schools. All the data appeared to show major concern about what to create and to highlight educational disquiet. There was the lack of a sense of how to create the future of management education with these limitations.

Interestingly, even compounding this there could be a perception-reality gap in competitive positioning, with 51% of schools rating themselves above average in AI adoption despite limited concrete evidence supporting such assessments. Much of the adoption was a predominance of curriculum-centric approaches (teaching about AI) and a dominance of GenAI-related issues and challenges around assessment, with only six actual AI tools as evidence of educational implementation (teaching with AI). These include an in-house AI teaching bot, a custom simulation environment, department-specific chatbots, AI-powered case-solving applications, a virtual lab assistant, and a personalised learning system.

Next Steps and Actionable Strategies

Based on these findings, we provide three actionable strategies for bridging the aspiration-reality gap in ways that enhance education and the MBA student’s academic development.

Faculty development and support plan: Given the central role of faculty champions and widespread confidence gaps, systematic approaches to AI literacy and pedagogical integration require immediate attention. The dominance of faculty-related barriers suggests that technical infrastructure alone cannot drive meaningful adoption. If faculty gaps cannot be managed then the hiring of educational technologists to assist faculty could provide major support. Conscious of the workload and multiple demands on faculty, dedicated resources could provide the necessary support, without detracting from the faculty focus on research and their other required deliverables.

Strategic leadership decisions: The current bottom-up implementation pattern necessitates research into effective governance structures that balance faculty autonomy with institutional coherence. The low percentage of school leadership involvement could indicate weak strategic coordination in institutional AI and GenAI planning leaving this role to the faculty champions or the MBA directors to manage.

More developed educationally-focused ethical integration protocols: With ethical concerns being the dominating barrier this captures the educational worries about academic integrity in the learning and assessment process. There must be more development of practical in-depth frameworks for responsible AI use in assessment and pedagogy. The urgency for clear in-depth institutional policies on how educational deliverables are impacted emerges as a critical implementation prerequisite. Without solid empirical underpinning, it is hard to move forward and provide clear direction. This must be an industry-wide concern.

Implications for Management Education

The stakes of this transition extend beyond individual institutions to the broader credibility and relevance of business education in an AI-augmented world. The data suggests that MBA programmes stand at a critical juncture: they must decide whether there is a need for meaningful integration even while navigating legitimate concerns about ethics, educational quality, and institutional identity. How this balance is struck will fundamentally shape the future of management education and the readiness of graduates for an AI-augmented business world.

As AI/GenAI increasingly enters the management education landscape (see Shan and Wade, 2025) the challenges for management educators are immense (Fellenz et al., (2022)), as are those for individual MBA directors who must manage AI initiatives without losing institutional soul—ensuring that AI integration enhances rather than undermines the pedagogical and educational quality and integrity that defines excellence in management education. Our data reveals clear evidence of dual concerns: pedagogical and educational on the one hand, and marketplace and rhetoric demands for more AI on the other. This tension manifests itself as institutions attempt to maintain both effective teaching practices and overall programme excellence while integrating AI—a complex tightrope balancing act that requires careful navigation between bold steps and incremental changes, in a constantly changing and uncertain world.

As the EFMD “Learning 2035” article concludes:

“The future belongs to those who embrace collaboration, build thriving ecosystems, and drive real change. Yet, as bold as these steps may seem, they risk being just incremental shifts rather than the seismic change our contributors argue is essential.”

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References

Davenport, T. H., and Mittal (2023). All-in On AI: How Smart Companies Win Big with Artificial Intelligence.
Harvard Business Review Press

Fellenz, M., Hoidn, S. and Brady, M. (2022). The Future of Management Education. Abingdon, UK: Routledge.

Iansiti, M. and Lakhani K. R. (2020) Competing in the Age of AI, Harvard Business Review, Jan-Feb, pp. 61-67

Pohlenz, P. (2022). Innovation, professionalisation and evaluation: implications for quality management in higher education.
Quality in Higher Education 28 (1). https://doi.org/10.1080/13538322.2021.1951452

Salmon, M. (2024). Representation of the academic workforce in English university strategy-making: an exploratory study.
High Education, 88, pp. 2101-2121. https://doi.org/10.1007/s10734-024-01203-3

Shan, J., and Wade, M., (2025) The Age of AI in education: Transforming the Learning Landscape, IMD, Tonomus Global Centre for Digital and AI Transformation, https://www.imd.org/news/the-age-of-ai-in-education-transforming-the-learning-landscape/

UNESCO report by Sabzalieva, E.and Valentini, A. (2023). ChatGPT and artificial intelligence in higher education: Quick start guide. UNESCO International Institute for Higher Education in Latin America and the Caribbean. https://unesdoc.unesco.org/ark:/48223/pf0000385146