The white paper describes higher education as being well into an active adoption phase for generative AI, not just early exploration. Based on IDC’s March 2025 Vertical AI Use Case Survey of 150 higher education respondents: - 60% report they have already introduced several generative AI–enhanced applications or services into production. - 32% say they are investing significantly in GenAI with plans to launch applications or services in the next 12 months. - 9% are in early testing and proof-of-concept stages. - 0% report having no plans to leverage GenAI. This marks a shift for a sector that has historically moved slowly with new technology. Institutions are now: - Aligning AI investments with long-term strategic goals such as student success, research innovation, and operational efficiency. - Investing in AI initiatives across major functions: teaching and learning, research, student services, and administration. - Building internal AI skills and capacity, including training and change management programs. - Modernizing data, processes, and infrastructure to be “AI ready.” The paper highlights four institutions—Auburn University, Babson College, Georgia Tech, and the University of North Carolina at Chapel Hill—as examples of campuses that are already putting GenAI into practice and using those experiences to guide broader institutional strategies.

The white paper identifies six foundational characteristics that show up consistently in more advanced AI strategies in higher education: 1. Differentiators Institutions are using AI to strengthen what makes them unique, not just to automate tasks. - Examples include specialized research data sets, distinctive degree programs, and unique learning experiences. - UNC Chapel Hill, for instance, focuses on using its extensive research data to enable advanced AI-driven analysis and discoveries, positioning itself as a leader in AI-enabled research. 2. Guardrails Campuses are starting with guidelines for responsible use rather than rigid, top-down governance. - Auburn University emphasizes clear guidelines and guardrails for faculty, staff, and students. - The goal is to balance ethical use and risk mitigation with room for experimentation, then evolve toward more formal governance as institutions learn from early use. 3. Collaborative Communities AI progress is driven by cross-campus collaboration and knowledge sharing. - Babson College’s interdisciplinary AI lab, “The Generator,” brings together faculty, staff, and students to share ideas, co-develop AI projects, and learn from each other. - These communities help build a culture of experimentation and make it easier to scale successful practices. 4. Vendor Partnerships Institutions are leaning on external partners for advanced AI capabilities. - Georgia Tech works closely with Microsoft, OpenAI, NVIDIA, and others. - Its AI Makerspace, built with NVIDIA, provides students access to a cluster of 300+ GPUs in a safe learning environment. - Leaders see these partnerships as essential for accessing tools, expertise, and infrastructure they cannot easily build alone. 5. Change Management and Training Ongoing upskilling and structured change management are treated as core to AI strategy. - Auburn University offers a “Teaching with AI” course to help faculty integrate AI into their curriculum. - Institutions are rethinking roles so that AI elevates human work toward more strategic, creative, and collaborative tasks. - Centralized resources and support help faculty, staff, and students adopt AI tools more confidently. 6. Leadership Effective AI strategies blend top-down support with bottom-up innovation. - Senior leaders provide vision, resources, and clear encouragement to use AI. - Grassroots efforts from faculty, staff, and students generate practical use cases and innovation. - At UNC, leadership sets expectations and provides tools, while explicitly encouraging the campus community to drive AI innovation. Strategically, the paper also recommends that IT and academic leaders: - Align AI investments with the institution’s broader mission and long-term goals. - Invest in “AI for all” by democratizing access to AI tools for all students, faculty, and staff. - Use persona-based approaches (e.g., advisors, researchers, instructors, administrators) to tailor AI solutions. - Adopt an iterative, flexible strategy that prioritizes progress over perfection while maintaining a long-term vision.

The white paper uses four U.S. institutions as concrete examples of how AI-ready campus strategies look in practice. 1. Auburn University Focus areas: - Responsible use and guardrails - Training and change management What they are doing: - Prioritizing guidelines for responsible AI use for faculty, staff, and students, rather than starting with restrictive policies. - Emphasizing ethical standards while still allowing experimentation. - Offering a “Teaching with AI” course to help faculty integrate AI into their courses. - Providing ongoing support and resources to upskill faculty, staff, and students. Key takeaway: Start with clear, practical guidelines and invest early in training so people feel supported, not constrained. 2. Babson College Focus areas: - Collaborative communities - Strategic alignment and broad access What they are doing: - Running an interdisciplinary AI lab called “The Generator,” where faculty, staff, and students collaborate on AI projects and share best practices. - Aligning AI investments with strategic priorities in teaching and learning, research, student success, and operations. - Providing generative AI tools to all students, staff, and faculty. - Offering an AI grant process for more advanced use of AI agents and bots. - Encouraging an iterative, “fail fast and adapt” approach to pilots. Key takeaway: Build a visible, cross-functional hub for AI experimentation and ensure broad access to tools so innovation is not limited to a small group. 3. Georgia Tech Focus areas: - Vendor partnerships - AI for all and persona-based design What they are doing: - Forming deep partnerships with Microsoft, OpenAI, NVIDIA, and others. - Creating an AI Makerspace with access to 300+ GPUs in a cluster built with NVIDIA, giving students a safe environment to explore AI. - Pursuing an “AI for all” strategy that exposes the entire campus community to diverse AI tools. - Using a persona-based approach to implement AI across student success, teaching, research, and administration, tailoring solutions to real workflows. Key takeaway: Use strategic partnerships to extend your capabilities and design AI solutions around the daily realities of different campus roles. 4. University of North Carolina at Chapel Hill (UNC) Focus areas: - Differentiation through research - Balanced leadership model What they are doing: - Leveraging unique research data sets as a core differentiator, using AI to enable advanced analysis and new discoveries. - Integrating AI into multiple research departments to enhance research capabilities. - Combining strong top-down support from leadership with encouragement for grassroots innovation. Key takeaway: Identify where your institution has unique strengths—such as research data—and use AI to deepen those strengths while empowering the community to shape how AI is used. Across all four institutions, the practical lessons are consistent: - Tie AI to clear institutional goals (student success, research, operations). - Provide campus-wide access to AI tools, with appropriate guardrails. - Invest in training and change management, not just technology. - Partner with vendors to access advanced capabilities. - Allow strategies to evolve iteratively as the AI landscape and campus needs change.