Based on interviews with 33 institutional leaders across 2- and 4-year colleges, our research highlighted that scaling AI in higher education requires more than enthusiasm or pilot projects. Leaders consistently pointed to a set of prerequisites, spanning funding, infrastructure, governance, and human capacity, that must be in place for adoption to be effective, equitable, and sustainable.

AI is no longer an emerging technology at the margins, but reshaping how colleges and universities operate, teach, and serve students. Leaders recognize that AI will become increasingly pervasive in higher education. This creates a dual imperative: institutions must grapple with the threats AI poses to their institutions and the mission of higher education, while also seizing the opportunities to reimagine student learning, services, and institutional effectiveness.

When institutional leaders discuss AI adoption, the conversation often starts with technology budgets and licensing fees. But the real story is broader: adopting AI at scale requires both visible financial investments and hidden resource commitments in time, people, and culture. Understanding this full spectrum of costs is essential for moving from experimentation to sustainable, enterprise-level adoption.

As institutions experiment with artificial intelligence (AI), one consistent barrier is ensuring faculty and staff have the knowledge, skills, and confidence to use these tools effectively. Training is not only a matter of technical knowledge but also of trust, credibility, and sustainability.

These Prompt Battle Challenge Cards invite teams to design, test, and refine AI prompts across high-stakes institutional scenarios—from curriculum redesign and student success to faculty development, finance, and assessment. This in-depth deck helps leaders build hands-on AI fluency through collaborative problem-solving and real-world practice.

This scenario deck is designed to support colleges and universities as they explore the opportunities, risks, and practical considerations of adopting AI across institutional functions.

This conversation guide is designed to help presidents and boards have focused, high-value discussions about AI and the institution’s future. It is not a prescriptive strategy or a technical plan. Instead, it offers an adaptable structure and set of prompts that boards can use to clarify priorities, surface risks, and align on what responsible progress looks like in your institutional context.

AI is already reshaping how students learn, how organizations work, and what “career readiness” requires. At the same time, colleges face mounting pressure—from vendors, peers, policymakers, and public narratives—to “do something” about AI quickly. Without a shared frame for decision-making, that pressure can lead to disconnected pilots, one-off purchases, or reactive commitments that outpace governance, capacity, and strategy.

Artificial intelligence tools are becoming everyday companions in higher education, shaping how we learn, teach, and work. Yet, the quality of what these tools produce depends heavily on how we communicate with them. Crafting effective prompts (the instructions we give to an AI system) is a core skill for unlocking value while ensuring responsible use.

Based on our research with institutional leaders, many colleges and universities are still at early stages of AI adoption, where experimentation is driven by individuals rather than coordinated strategy. This hesitancy was partially due to a lack of knowledge and confidence. Faculty and staff often lack comprehensive training on how to effectively use AI tools, leading to shallow implementation in classrooms and student services or, in some cases, not engaging at all. This results in missed opportunities: those closest to student learning and institutional operations are also those best positioned to identify innovative solutions, yet without AI literacy they cannot fully leverage these tools.

This rubric was developed to help institutions, researchers, and practitioners assess where an organization falls along a continuum of AI adoption. It provides a structured way to categorize practices into three levels (Experimental/Emerging, Scaling, and Transforming) based on qualitative, observable institutional practices, behavioral indicators, and commitments.

This survey pack helps institutions assess AI readiness across multiple stakeholder groups. Rather than relying on assumptions or anecdotal evidence, these instruments provide structured data to inform AI strategy, resource allocation, and support planning. Structured instruments to assess AI readiness across leadership, departments, staff, and students.

This in-depth report offers a comprehensive analysis of how colleges and universities across the country are adopting artificial intelligence—uncovering national patterns, institutional challenges, and emerging practices from interviews with leaders at 33 diverse institutions. It provides a detailed, data-rich foundation for understanding the uneven landscape of AI adoption and the opportunities ahead for higher education.

Since generative artificial intelligence (AI) burst into mainstream awareness in late 2022, it has quickly evolved from an emerging trend to a paradigm-shifting reality in higher education. As a sector, higher education institutions are striving to catch up to rapidly evolving systems. These individual efforts have resulted in rapid yet uneven shifts in response to the rise of artificial intelligence. Across the thousands of colleges and universities in the United States, there is an uneven capacity to respond to the changes brought about by AI (driven by gaps in financial resources, technical infrastructure, governance and policy, human capital, and change management and culture), which now defines the landscape of AI adoption. Colleges and universities across the United States are actively exploring how AI might enhance teaching, learning, student services, and operations. Yet despite this widespread interest, adoption at the institutional level remains uneven1. Most institutions remain in early or scaling stages, where AI use is driven by individual champions, pilot projects, or department-level experimentation. This landscape reflects both a growing awareness of AI’s potential and the persistent technical, financial, ethical, and cultural barriers that institutions must address. This report examines those barriers and highlights how institutional leaders are beginning to overcome them. Drawing on interviews with leaders from 33 institutions (both 2- and 4-year) nationwide, it provides a concise synthesis of adoption patterns, equity gaps, and emerging strategies for responsible AI integration. This summary report distills the insights of the full report— Adopting AI in Higher Education: Patterns, Challenges, and Emerging Practices— into key findings and implications designed for quicker reference and strategic use. A snapshot of how colleges and universities are experimenting and strategizing their way into the age of AI.

Recorded on February 9, 2026
Two and a half years after ChatGPT's release, only 17% of institutions in our study show indicators of strategic AI integration—the rest remain in earlier phases, navigating uneven depth and speed of adoption. This session introduces findings from T3 Advisory's national study of 33 institutions and presents the practical frameworks emerging from our research. You'll learn what distinguishes institutions that are transforming from those still experimenting, and walk away with a diagnostic tool to assess where your campus stands. This session is designed for presidents, provosts, CIOs, deans, and strategic planning leads seeking a research-grounded foundation for AI decision-making.

Implementation Tools for Higher Education July 2025

A Practical Playbook for Postsecondary Intermediaries June 2025

Accelerating Student Success in Higher Education April 2025

Insights from the Complete College America Convening January 2025

Charting a Path to Institutional AI Adoption for Student Success in Higher Education

Early supplementary research related to larger research on Initiative Fatigue in Higher Education.

Could AI Help Overcome the Scarcity Mindset and Transform the Higher Education Landscape?