The challenge of academic innovation: how artificial intelligence is reshaping management education in the global landscape

This study examines how business schools are responding to rapid technological change, with a particular focus on how Artificial Intelligence (AI) is being incorporated into master’s-level management curricula. The research is anchored in the CEMS Global Alliance in Management Education, which brings together 33 business schools worldwide and more than 70 corporate partners, and whose Master in International Management (MiM) is highly ranked in the QS rankings (2026). The main objective is to understand how CEMS member institutions are adapting their curriculum to remain relevant amid shifts in global management education and evolving market expectations. Empirically, the study investigates how AI is being integrated into the MiM curriculum. It draws on qualitative evidence from semi-structured, in-depth interviews with 12 academic directors and program managers from CEMS member schools who are directly involved in curriculum and program development. Participants were selected through purposive sampling to ensure representation across all continents. The interview data were analyzed to identify the main challenges institutions face and the practices they are adopting to prepare students for an AI-enabled future. Findings suggest that one of the earliest and most visible responses to generative AI has been changes in assessment and academic integrity policies. Many institutions are moving toward more authentic and verifiable formats, such as oral defenses, in-class assessments, and presentations. At the same time, rather than prioritizing tool-specific training, schools increasingly emphasize students’ critical thinking, evaluative judgment, and responsible AI use, treating AI as an assistant that should be used transparently and critically. The analysis also highlights that progress often results from a mix of bottom-up faculty experimentation and top-down institutional initiatives (e.g., committees, training workshops, and pedagogical innovation units). Even so, implementation remains uneven, as schools try to keep pace with rapid developments by introducing new courses and embedding AI-related approaches within existing ones, while reinforcing the idea of AI as a learning support tool when used responsibly. Overall, the study offers practical and theoretical insights into academic innovation in the AI era and outlines implications for academic leaders and program designers who seek to preserve educational quality while preparing graduates for complex, technology-infused global challenges.