AI in Business Education: Current Practices and Future Potential

Overview

To dig deeper into how business schools are approaching AI, GMAC interviewed stakeholders at institutions in North America, Europe, and Asia to discover and showcase innovative solutions and strategies for deploying AI in a business school environment. From these interviews, a series of six case studies were published in 2024 on the GMAC AI Hub. This paper makes use of the data and information collected during the interview and research process for this project. In addition, data is drawn from GMAC’s survey series as well as external sources to offer a more complete view of how business schools are approaching AI today, what challenges and best practices they have encountered, and what the future may hold for institutions looking to level up their AI integration.

Quick Facts

• Create a comprehensive strategy: Establish a framework and strategy for broad, institution-wide participation in selection and deployment of AI use cases.
• Leverage both bottom-up and top-down approaches: Decentralized experimentation brings many benefits such as speed, innovation, and competition; however, without central strategy and oversight, opportunities to scale and share knowledge will be missed.
• Start small, iterate, and scale through pilot projects: Test multiple ideas simultaneously in small-scale, low-cost scenarios to identify high potential opportunities for scaling.