KPMG outlines the optimal way to approach AI in higher ed

Higher education institutions are rapidly evolving their stance on generative artificial intelligence, shifting from viewing it as a potential threat to embracing it as a strategic tool, according to insights from KPMG consultants.

Key categorization framework: KPMG experts David Gagnon and Saravanan Subbarayan have identified four distinct approaches that higher education institutions take when integrating AI into their operations.

• Trailblazers make substantial technology investments and lead the way in AI adoption, helping guide peer institutions
• Synergists form the largest group, working collaboratively with other institutions to share resources and develop joint strategies
• Mavericks take an independent approach, developing custom AI solutions tailored to their specific institutional needs
• Stragglers move more cautiously, often constrained by limited resources, outdated technology infrastructure, or aging workforce

Internal leadership dynamics: Within institutions, AI integration is driven by three distinct types of leaders, each bringing their own priorities and perspective to the implementation process.

• Technologists, typically CIOs or IT managers, focus on infrastructure development and vendor selection decisions
• Academicians emphasize ethical considerations and research applications of AI technology
• Administrators concentrate on improving operational efficiency and workforce transformation

Resource considerations: The ability to implement AI effectively is heavily influenced by institutional resources and capabilities.

• Financial constraints often force smaller institutions into the “straggler” category
• Legacy systems and technological debt can impede AI adoption efforts
• Most institutions lack the resources to pursue independent “maverick” strategies
• Collaboration through the synergist approach has emerged as the most common solution to resource limitations

Future outlook: The integration of AI in higher education is expected to accelerate in 2025, bringing both opportunities and challenges.

• ERP and SaaS products with embedded AI capabilities will play a crucial role in adoption
• Institutions must develop comprehensive governance frameworks to address concerns about algorithmic bias and data quality
• The consensus-driven culture of higher education may slow implementation compared to commercial sectors
• Benefits of AI implementation can outweigh risks when proper controls are in place

Strategic implications: The varying approaches to AI adoption highlight a growing divide between resource-rich institutions that can lead innovation and those struggling to keep pace with technological change, potentially reshaping the competitive landscape of higher education in the coming years.