The Leadership Moves That Make AI Adoption Work

Artificial intelligence (AI) adoption in higher education is accelerating, and the institutions making the most progress share a common approach. They recognize that success comes from pairing technology with the right foundation.

Faculty, department leaders, and chairs are redesigning courses and workflows, clarifying policies, and building new skills in a moment already marked by financial pressure and rapid change. The institutions most likely to get AI adoption right are those that build the right conditions first, not those that move fastest.

Institutional readiness is a powerful advantage. When institutions prioritize leadership alignment, tiered training, and peer collaboration, they create an environment where people feel included, confident, and equipped. This approach closes the gap between ambitious ideas and their execution, turning AI adoption into a capability that institutions can sustain and grow.

Three areas of leadership practice define this work. Institutions must understand where faculty are before designing solutions, build AI understanding from the top down, and create structures for shared learning across departments.

AI Adoption Is About People, Not Tools

When institutions launch AI initiatives without understanding the unique needs of the people using AI in classrooms or across departments, the result is predictable. Low adoption, widening knowledge gaps, and a growing trust problem between leadership and the people who are expected to make AI adoption work follow close behind.^Footnote1

Resistance across academic institutions isn't simply reluctance or technophobia. Research shows that it runs deeper.^Footnote2 Some hold principled objections about professional identity, concerns about the impact of AI on human intelligence, and broader questions about the societal consequences of AI. For others, the resistance comes down to practical questions.

The questions they're asking deserve honest answers.

• What does meaningful AI integration look like across different academic disciplines?
• How do faculty redesign courses without compromising outcomes they've spent years building?
• When will anyone find time to learn any of this?^Footnote3
• How do department leaders address policy questions without the right vocabulary?

These questions are opportunities to discover and close gaps. Leaders who listen can address them early, strengthening the alignment institutions need to make AI adoption work.

Deliberate conversations with faculty and department leaders help institutions start building trust. Small-group conversations by department work better than general surveys. These discussions uncover teaching and workload concerns that a broad survey misses, giving people an opportunity to shape what comes next.

The question worth asking isn't "How do we get faculty on board?" It's "What conditions do faculty need to engage thoughtfully?" The first treats adoption as a communication problem. The second treats it as a change management challenge. This reframing creates the conditions for natural engagement.

AI Readiness Starts with Leadership

Institutional readiness begins with leaders who understand what they're asking their people to do. Across the country, administrators are advancing AI adoption through initiatives, workshops, and strategic partnerships that are building momentum on their campuses.

When department chairs develop a working vocabulary around AI, they facilitate more productive conversations with faculty about AI policy. When deans understand what these tools can and cannot do, they shape curriculum direction with greater clarity. Ultimately, leaders at every level who invest in this understanding make faster, better decisions.

Effective AI training requires a model that is tailored to each level of the institution, giving each group the knowledge it needs to engage with AI in ways that match its role.

A tiered training approach should equip groups with the following competencies:

• Administrative and senior academic leadership: A working understanding of AI capabilities, limitations, student learning impacts, and governance decisions.
• IT leadership: Technical fluency with enough instructional context to bridge faculty concerns and institutional infrastructure.
• Department chairs: Facilitation and change leadership skills that equip chairs to hold space for skepticism, uncover barriers, and support meaningful faculty participation.
• Faculty: Practical, field-specific training built around hands-on experimentation with prompting strategies in their actual courses and assignments.

The format matters as much as the content.

Successful institutions run half-day leadership workshops that focus on change management strategy rather than tool use. "Promptathons" give faculty time to experiment with prompting alongside peers on challenges they face every day. Department chairs benefit from change facilitation training that frames AI adoption as the change management effort it is. Together, these approaches build the shared understanding institutions need to move forward.

The connecting principle is simple. When leaders invest in understanding AI at every level, they build trust faster, uncover barriers sooner, and create a more collaborative path to adoption. Faculty and department leaders are more likely to engage when they have a genuine voice in the process, and what they learn becomes a shared asset that benefits the whole institution.

From Isolated Pilots to Institutional Learning

A strong foundation matters, but it doesn't move knowledge across an institution on its own. Faculty often experiment with AI in isolation. For example, one professor figures out a prompting approach that meaningfully improves how students develop analytical skills and mentions it to a colleague in passing, but nobody documents it. The colleague tries something different, gets frustrated, and moves on. Six months later, a faculty member in another department invents the same approach from scratch. The knowledge of what worked and what didn't was there, but because nobody documented it, the knowledge wasn't passed on.

Isolation costs time, momentum, and knowledge. Building structures that make sharing easy and worthwhile and letting people drive experimentation solves the problem without adding complexity.

Peer-driven structures are the most effective way to drive change because people trust their colleagues. For example, a biology instructor may be more persuaded by a peer's account of using AI in lab report feedback than by a broad institutional success story.

Cross-departmental sharing also matters because some approaches that work in one academic field carry over to others in unexpected ways. A department chair may be more convinced by how a peer navigated faculty skepticism than by an institutional mandate.

There are several practical sharing structures worth considering:

• Showcase events: Structured forums where faculty present honest accounts of what worked and what didn't, treating experimentation as a process.
• Peer-led forums: Informal, small-scale sessions where faculty work through problems together and co-create knowledge rather than presenting finished approaches.
• Cohort models: Small groups of faculty in early experimentation who learn and develop together rather than in isolation.
• Shared documentation: Repositories where faculty record approaches in enough detail for colleagues in other departments to build from.
• Departmental office hours: Low-barrier touchpoints where faculty can talk through specific challenges with colleagues who understand them firsthand.

Shared workshops and peer exchanges build collective understanding and accelerate practical applications across departments. None of these efforts require significant resources; they require only an institutional commitment to capturing and sharing what people learn. Dedicated time and recognition turn occasional knowledge exchange into something the institution can rely on.

The Work Worth Doing

The higher education institutions that are getting AI adoption right are the ones that invest in people first, not the ones with the biggest budgets or the boldest initiatives. Listening before designing, building understanding across every level, and creating structures for shared learning aren't quick fixes. This groundwork turns AI adoption into something people trust and institutions can build on.

Meaningful change takes time, and a thoughtful approach to building it isn't a delay. It's how lasting progress is made. Institutions that are willing to invest in continuous improvement, partnership, and the conditions people need to succeed are the ones that build sustainable AI adoption.^Footnote4 When institutions get those things right, AI stops being something imposed on people and starts being something they embrace as a natural part of how they work and learn.

Notes

1. WCET, "Insights into AI's Transformative Role in Higher Education," Frontiers, August 21, 2025.Jump back to footnote 1 in the text.↩
2. Aya Shata,"Opting Out of AI: Exploring Perceptions, Reasons, and Concerns Behind Faculty Resistance to Generative AI," Frontiers in Communication 10 (June 2025).Jump back to footnote 2 in the text.↩
3. Artificial Intelligence and Academic Professions (American Association of University Professors, May 2025).Jump back to footnote 3 in the text.↩
4. Claire Baytas and Dylan Ruedigerm, Making AI Generative for Higher Education (Ithaka S+R, May 2025).Jump back to footnote 4 in the text.↩

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Tynan Fischer is Senior Vice President of Learning Operations at Educate 360.