The Pulse of Higher Ed

Across higher education, the conversation about artificial intelligence has moved rapidly from curiosity to consequence. Institutions are no longer asking only whether AI will affect their work; they are asking how to lead through that change responsibly, strategically, and in ways that advance mission. 

That shift is at the center of UPCEA’s AI Study Groups, a six-week pilot designed to help member institutions build community, exchange practical insights, and develop actionable approaches to AI implementation. Due to strong interest, UPCEA launched two cohorts of 25 participants each, with more than 30 additional members on a waitlist—an early signal of the urgency and appetite for peer learning around AI. 

The Study Groups are part of UPCEA’s broader effort to help members move from AI awareness to applied implementation. UPCEA’s AI Hub serves as a central resource for practical guidance, research, primers, training, and member examples focused on the use of AI in online and professional continuing education. The Hub emphasizes areas especially relevant to UPCEA members, including marketing and enrollment, learner support, teaching and learning design, governance and operational readiness, credential innovation, workforce alignment, and institutional strategy. 

Over the first three weeks, participants have surfaced a clear theme: AI implementation is not primarily a technology challenge. It is a leadership challenge. 

Moving Beyond Experimentation 

Many institutions represented in the cohorts are somewhere between experimentation and institutionalization. Some are still working through early pilots and informal exploration, while others have established AI councils, task forces, enterprise tool deployments, competency requirements, or significant investments in AI-enabled projects. 

Examples shared across the groups included coordinated investment in more than 100 AI projects, use of Glean for enterprise data integration, an AI competency requirement for graduating students, a structured Microsoft 365 Copilot beta program, and the integration of AI into professional studies curriculum and team workflows. 

While the scale and maturity of these efforts vary, participants consistently emphasized the same underlying challenge: moving from pockets of innovation to coordinated institutional practice. 

That requires more than enthusiasm. It requires governance, communication, professional development, shared language, and a realistic sense of what institutions can actually implement in the near term. 

Governance Is Emerging, but Not Yet Settled 

One of the most active areas of discussion has been AI governance. Participants described a wide range of approaches, including AI advisory groups, councils, readiness task forces, working groups, acceptable use policies, data policies, procurement processes, and guiding principles for teaching and learning. 

A recurring insight is that governance structures must fit institutional context. A large public system, a continuing education unit, a professional school, and a centralized campus may all need different models. But regardless of structure, participants identified several common questions: 

• How should institutions balance faculty agency with the need for guidance? 

• Who should make decisions about AI tools, data use, procurement, and access? 

• How should academic and administrative use cases be governed differently? 

• What policies need to change before AI-enabled processes can scale? 

• How will institutions measure whether AI initiatives are successful? 

The conversations also highlighted a tension many campuses are facing; waiting for perfect policy can delay necessary learning, but moving too quickly without guardrails can create risk. The study groups have encouraged participants to think in terms of roadmaps rather than static answers. 

“Crawl, Walk, Run” Is Resonating 

During week two, the facilitator introduced a framework that resonated strongly across both cohorts: distinguishing what is possible, plausible, and probable. 

AI often invites expansive thinking. Leaders may want to hear what is possible. But implementation teams and end users need clarity about what is plausible within the next 9 to 12 months and what is probable given current staffing, systems, policies, and organizational readiness. 

This distinction helped participants reframe AI work from broad aspiration to disciplined sequencing. Several examples illustrated why this matters. Projects that initially appeared simple—such as chatbots, AI agents, marketing tools, or AI-assisted web development—often required more human oversight, process documentation, testing, accessibility review, or scope refinement than expected. 

The “crawl, walk, run” approach offered a practical way forward. Start with a contained use case. Build trust. Document the process. Demonstrate value. Learn from the deployment. Then scale. 

This was especially important in conversations about AI agents. Participants noted that AI agents can be easier to understand when framed as digital team members with clearly defined roles, limited decision-making authority, and specific tasks. But the group also emphasized that agents are only as effective as the processes they are built to support. If a process is undocumented or inconsistent, AI may amplify confusion rather than reduce it. 

The Human Side Cannot Be an Afterthought 

Across the first three weeks, participants repeatedly returned to the people most affected by AI implementation: faculty, instructional designers, students, IT staff, advisors, administrative assistants, marketing teams, enrollment management professionals, and continuing education leaders. 

The conversations made clear that AI adoption is uneven. Some faculty and staff are enthusiastic early adopters. Others are skeptical, fatigued, fearful, or unsure what is being asked of them. Students, too, are navigating mixed messages: concerns about academic integrity, uncertainty about acceptable use, anxiety about employment readiness, and fears about job displacement. 

A key learning from the cohorts is that AI literacy cannot be limited to a single department or optional professional development opportunity. It needs to reach across roles and functions. Participants discussed the need for training that helps people understand not only how to use tools, but also when to use them, what risks to watch for, and how AI connects to institutional mission. 

Several participants also emphasized the importance of meeting people where they are. For some, the first step may be learning basic terminology. For others, it may be redesigning workflows, testing agents, or building AI into curriculum. Effective leadership requires recognizing that different groups need different forms of support. 

AI Leadership Looks a Lot Like Good Leadership 

By week three, the cohorts began focusing more directly on institutional AI roadmaps. One framing that stood out was the idea that AI leadership is not necessarily a separate category of leadership. In many ways, it is good leadership applied to a fast-moving context. 

Participants explored three leadership components: people, processes, and progress. 

The people component asks leaders to consider who will be affected, what fears or hopes they bring, and how institutions can build trust. The process component asks leaders to examine workflows, policies, procurement, governance, and documentation. The progress component asks institutions to move incrementally, measure outcomes, and align AI efforts with mission rather than novelty. 

This framing helped move the conversation beyond tools. The central question became not “Which AI platform should we adopt?” but “What institutional work are we trying to improve, and how do we lead that change responsibly?” 

Community Is Part of the Infrastructure 

Another early lesson from the study groups is that institutions need peer communities as much as they need technical resources. Participants have shared policies, frameworks, use cases, lessons learned, and candid stories about what has not worked. 

UPCEA’s AI Hub and CORe community are emerging as important spaces for this exchange. Participants were encouraged to submit AI use cases, contribute resources, and continue sharing examples that can help others move from isolated experimentation to informed action. As UPCEA continues developing the AI Hub, these member-generated examples and lessons learned can help build a shared evidence base for responsible, practical AI implementation across online and professional education. 

This kind of community matters because many institutions are wrestling with similar questions, but few have complete answers. The study groups offer a space to compare approaches, learn from missteps, and build momentum together. 

Early Takeaways 

After the first three weeks, several key takeaways are emerging: 

• AI implementation must be grounded in institutional reality. Ambition is important, but successful projects require realistic timelines, clear scope, and attention to staffing, policy, and culture. 

• Governance is essential, but it should enable learning rather than freeze action. Institutions need structures that provide guidance while allowing room for iteration. 

• Small wins matter. Contained projects can build trust, demonstrate value, and create the foundation for broader implementation. 

• Process documentation is a prerequisite for effective AI use. Institutions cannot automate or augment workflows they do not fully understand. 

• AI literacy must be broad-based. Faculty, staff, students, administrators, and support teams all need opportunities to understand and shape AI use. 

• Leadership must center people. Fear, fatigue, curiosity, resistance, and enthusiasm are all part of the change process. 

• Most importantly, AI work should be mission-aligned. The goal is not to adopt AI for its own sake, but to strengthen learning, improve operations, support students, and help institutions adapt thoughtfully to a changing environment. 

As the AI Study Groups continue, participants will hear from additional institutional examples, refine their own next steps, and continue developing roadmaps for AI implementation. The early conversations suggest that higher education’s AI future will not be built through tools alone. It will be built through shared learning, disciplined implementation, and the willingness to lead through uncertainty together. 

To explore additional resources, research, primers, and member insights, visit UPCEA’s AI Hub. 

 

Content for this resource was refined with the assistance of AI. All text has been thoroughly reviewed, edited, and approved by UPCEA staff with subject matter expertise. References and links have been verified for accuracy and reliability.