The Pulse of Higher Ed

Each spring, campuses quietly rehearse a familiar transition. The cadence shifts. Energy returns. Commencement ceremonies are scheduled, and multiple beautification processes are underway, from reenergized flower beds to window washing and clean walkways.  What was dormant begins to move again. In higher education, this seasonal rhythm offers more than symbolism. It provides a useful leadership lens for how institutions can approach one of the most significant disruptions of our time: the integration of artificial intelligence.  Just as campus teams intentionally plan seasonal improvements, institutional leaders must be equally deliberate in preparing for AI-driven change. 

AI is not a future-state consideration. It is already reshaping academic work, student services, enrollment operations, and institutional decision-making. Yet, like any meaningful transformation, the barrier is not access to tools. It is the human experience of change that ultimately determines success. 

Leadership, therefore, must move beyond advocacy for innovation and toward a disciplined, strategic approach to managing this transition. 

Recognize That Resistance Signals Loss, Not Opposition 

Even in seasons of renewal, not all aspects of change are experienced positively.  A consistent finding in change management literature is that resistance is rarely about the change itself. It is about perceived loss. As William Bridges (2009) argues in his transition model, individuals do not resist change; they resist endings. This transition model leans heavily on ensuring people have a purpose, a plan, and a part to play.   

In the context of AI adoption, these losses are tangible: 

• Established workflows that provided predictability and safety 
• Professional identity tied to expertise that is now augmented by AI 
• Familiar student engagement practices that are no longer meeting needs 
• Decision-making processes increasingly incorporate AI-supported insights 

To help build the purpose, plan, and part others will play, Kathleen Ives, Marie Cini, and Ray Schroeder, in AI Applications in Online Higher Education (2026), emphasize that institutional transformation is less about technological deployment and more about human adaptation to new modes of work and interaction. Effective AI strategy begins with acknowledging what faculty, staff, and administrators believe they are losing. Creating structured space to process this transition is critical to leading a campus toward AI integrations. 

Focus Institutional Energy on What Can Be Controlled 

Periods of disruption tend to expand institutional anxiety. AI introduces uncertainty around policy, ethics, workforce implications, and academic integrity. Attempting to resolve all of these simultaneously often results in organizational inertia. 

Here, a principle aligned with modern leadership theory becomes useful: focus on controllable actions. John Kotter’s (2012) work on leading change reinforces the importance of creating short-term wins and actionable steps to sustain momentum. 

For institutions, this translates into: 

• Identifying 2–3 high-impact AI use cases (e.g., student communications, advising triage, enrollment analytics) 
• Establishing clear governance and ethical guardrails 
• Measuring operational improvements (time saved, responsiveness, engagement rates) 

Rather than pursuing comprehensive AI transformation or introducing an overwhelming array of tools, institutions benefit from disciplined, incremental progress that builds confidence and organizational learning. 

Anchor Stability While Introducing Innovation 

One of the more overlooked aspects of change-leadership is the need to preserve continuity. Excessive simultaneous change, such as new tools, new policies, and new expectations, can destabilize even high-performing institutions. 

In Leading Change, Kotter highlights the importance of maintaining core cultural anchors while introducing new behaviors. 

Within higher education, these anchors include: 

• Institutional mission and access commitments 
• Shared governance structures 
• Core academic values (quality, rigor, integrity) 

AI should not replace these anchors; it should enhance their execution.  Leaders should frame AI not as a disruption to institutional identity, but as an extension of existing commitments particularly around access, student success, and operational effectiveness. 

Normalize Discomfort as Part of the Process 

A common leadership misstep is overemphasizing optimism and enthusiasm. While positive framing has value, it can inadvertently invalidate legitimate concerns. 

Heifetz, Grashow, and Linsky (2009), in their work on adaptive leadership, argue that productive change requires maintaining a level of “constructive disequilibrium” with enough discomfort to prompt growth, but not so much that institutional systems shut down. 

AI adoption inherently creates this tension: 

• Faculty questioning academic integrity implications 
• Staff concerned about role evolution 
• Leaders balancing innovation with risk management 

Institutions should not attempt to eliminate discomfort, but rather structure it through dialogue, professional development, and clear expectations. This requires a continued focus on people, their roles in implementation, and the purpose for which AI is introduced into specific functions. 

Shift from Certainty to Curiosity in Institutional Culture 

Perhaps the most critical leadership shift is cultural. AI introduces a level of uncertainty that traditional higher education governance models are not historically designed to manage efficiently. 

In this context, curiosity becomes a strategic asset. Amy Edmondson’s (2018) research on psychological safety underscores that organizations learn faster when individuals feel safe to experiment, question, and iterate. 

The Ives, Cini, and Schroeder framework similarly points to the need for institutions to move toward: 

• Continuous experimentation 
• Iterative implementation 
• Cross-functional collaboration 

Leaders must explicitly model curiosity by asking better questions, encouraging exploration, and rewarding learning rather than perfection. 

A Seasonal Model for AI Change Leadership 

Spring offers a useful metaphor, but also a practical framework: 

• Prepare the ground (Acknowledgment): Identify losses and concerns 
• Plant deliberately (Action): Launch focused AI use cases through a planned structure 
• Stabilize roots (Continuity): Reinforce institutional anchors of mission and vision 
• Allow growth tension (Adaptation): Normalize discomfort and environmental safety 
• Cultivate curiosity (Culture): Encourage experimentation and create space for informed risk-taking 

This approach aligns with both classic change management theory and emerging guidance on AI integration in higher education. 

AI will continue to reshape higher education, regardless of institutional readiness. The differentiator will not be which institutions adopt AI, but how they lead through the transition toward success. 

Spring reminds us that change is not inherently disruptive; it is developmental. With intentional leadership, AI can follow the same pattern: not a force to be managed reactively, but a season to be led strategically. 

References 

• Bridges, W. (2009). Managing Transitions: Making the Most of Change. Balance. 
• Edmondson, A. (2018). The Fearless Organization. Wiley. 
• Heifetz, R., Linsky, M., & Grashow, A. (2009). The Practice of Adaptive Leadership. 
  Harvard Business Review Press. 
• Kotter, J. P. (2012). Leading Change. Harvard Business Review Press.  
• Ives, J., Cini, M., & Schroeder, R. (2024). AI Applications in Online Higher Education. Routledge.  

Vickie Cook is a nationally recognized higher education leader specializing in enrollment strategy, online and digital learning, organizational transformation, team development, and leadership growth. She currently serves as a Senior Fellow and Strategic Advisor for UPCEA.  To learn more about UPCEA Research and Consulting, please contact [email protected].