Reframing the Beliefs That Hold Back AI Leadership in Higher Ed
By Dr. Claire Brady, EdD
“The future of AI in higher ed lives at the intersection of technology, pedagogy, and human development. This is not an IT project—it’s an institutional transformation that requires collaboration between faculty, student affairs, IR, HR, and academic leadership alike.”
Across campuses, AI strategy conversations are heating up: presidents are being asked for roadmaps, provosts are forming task forces, and deans are fielding daily vendor emails promising transformation. Yet for all the urgency, many leaders still feel stuck—hesitant to move forward or unsure what the next right step looks like.
The barriers, it turns out, aren’t just external. They’re often internal.
As Muriel Wilkins recently wrote in Harvard Business Review, the biggest blockers to leadership growth are hidden beliefs—assumptions that once served us well but now limit how we lead. For higher ed leaders navigating AI adoption, those beliefs show up in surprisingly familiar ways. Reframing them can unlock the clarity and courage needed to lead real, responsible progress.
“We need to move faster.” Reframe as “We need to move smarter.”
Speed can feel like the only way to keep up, but thoughtful strategy beats urgency every time. Before chasing tools, clarify your principles: How does AI align with your mission, learning outcomes, and ethical commitments? The most successful institutions aren’t racing ahead—they’re building strong foundations that prevent expensive course corrections later.
“We need to compete with other institutions.” Reframe as “We need to collaborate and learn from them.”
The AI era rewards networks, not silos. Instead of measuring your campus against the latest press release, focus on shared learning. Join consortia, compare frameworks, and open-source what works. Collaboration builds sector capacity and helps ensure equity across institutions of different sizes and resources.
“We have to be cutting-edge.” Reframe as “We have to be purpose-driven.”
It’s tempting to chase the shiniest pilot or the latest model. But innovation without purpose leads to burnout and bloat. Stay grounded in why you’re exploring AI: to strengthen student learning, extend access, and free people to do the human work that matters most.
“I should already understand this.” Reframe as I’m learning, too—and that models the right mindset for my campus.
No one has AI all figured out—not even the companies building it. Leading with curiosity instead of certainty gives your teams permission to learn alongside you. AI literacy starts with transparency, humility, and a willingness to ask good questions.
“We can’t afford to make mistakes.” Reframe as “We can’t afford not to experiment.”
Responsible innovation means piloting, assessing, and iterating—not waiting for perfect information. The only real failure is paralysis. Start small, evaluate honestly, and scale what works.
“AI belongs to IT.” Reframe as “AI belongs to all of us.”
The future of AI in higher ed lives at the intersection of technology, pedagogy, and human development. This is not an IT project—it’s an institutional transformation that requires collaboration between faculty, student affairs, IR, HR, and academic leadership alike.
Reframing these beliefs won’t just help you lead AI adoption more effectively—it will make your leadership itself more adaptive, aligned, and human. Because the future of AI in higher education isn’t about who moves first. It’s about who moves with purpose.
