Across higher education, AI initiatives aren’t failing because of a lack of tools or access.
They’re stalling because institutions are trying to move forward without a shared understanding of how this change actually plays out across faculty, students and leadership, and without enough alignment around what meaningful adoption looks like in practice.
That was the focus of Managing Change in the Age of AI, a recent D2L webinar featuring:
- Dr. Luke Hobson, assistant director of instructional design at MITxPRO and instructor at the University of Miami’s School of Education and Human Development
- Erin Robson, change management consultant at D2L
- Robyn Hammontree, vice president of academic partnerships at D2L
Together, they unpacked what’s really holding AI initiatives back and what institutions can do to move from experimentation to meaningful progress.
Start With People, Not Platforms
One of the most consistent patterns across institutions is where AI conversations begin, and in many cases, that starting point is misaligned with what drives adoption.
Too often, discussions begin with tools rather than teaching, focusing on questions like which platform to adopt or whether to standardize tools like ChatGPT or Gemini. While those decisions matter, starting there often skips over a more important set of questions.
Hobson noted that he hears people say they need to use tools like ChatGPT or Gemini. But that framing misses the point. The issue isn’t whether AI tools are being used; it’s how they’re introduced.
It’s very common for institutions to start with technology instead of teaching. Instead, he emphasized starting with a simpler set of questions:
- What are faculty already doing today?
- Where are the friction points?
- What do they wish they could do more effectively?
From there, the role for AI becomes much clearer.
That shift moves AI from something that feels imposed into something grounded in real teaching challenges.
The Gap Across Campus Is Real
AI adoption in higher education isn’t happening at a consistent pace. That unevenness is creating complexity for institutions trying to move forward.
Students are often already experimenting with AI tools as part of their workflows, while faculty are approaching those same tools more cautiously, weighing implications for pedagogy, academic integrity and learning outcomes. At the same time, institutions are trying to define policies that account for both groups.
This misalignment introduces friction and makes it difficult to create a coherent experience.
Resistance Isn’t the Problem. It’s a Signal
Faculty resistance often shows up early in AI conversations and it’s frequently framed as something to overcome. The panel challenged that idea.
Resistance often reflects deeper concerns about autonomy, identity and the evolving role of teaching, which are central to academic work.
As Robson emphasized, “don’t underestimate the impact on people… it requires behavior change, it requires buy-in.”
When these concerns are dismissed, they don’t disappear. They tend to resurface later with greater impact on adoption efforts.
Policy Matters, But It Won’t Carry Change on Its Own
Unclear governance is one of the most cited barriers to AI adoption. Policy is essential, but it is rarely sufficient on its own.
Without shared understanding and communication, policies can create uncertainty, especially when expectations vary across courses.
As Hobson put it during the session, policy without buy-in doesn’t move people forward. “You need people to buy in. You need them to be excited… that’s the mindset piece.”
Change Management Isn’t a One-Time Effort
There’s a tendency to treat AI adoption as something that can be implemented within a defined window, but in practice, it behaves more like an ongoing transition.
Robson described this through three areas of readiness: mindset, knowledge and application.
“It’s not just a one-time event,” was a consistent theme throughout the discussion.
Institutions need sustained engagement, including workshops, peer discussions, and ongoing support systems.
Leadership Has to Make The ‘Why’ Visible
Leadership alignment plays a critical role in shaping how AI initiatives are understood and adopted.
When initiatives feel disconnected from institutional priorities, they can come across as reactive or unclear. When they are tied to goals like student success and teaching quality, they become easier to engage with.
That clarity needs to show up consistently in communication and in how leaders participate in the broader conversation.
Create Space For Safe Experimentation
One of the most practical examples shared in the webinar came from MIT, where faculty were given access to a secure sandbox environment that allowed them to experiment with multiple AI tools.
The goal was not immediate adoption, but exploration. Faculty were encouraged to test ideas, share what worked and learn from one another before anything was scaled.
This approach helps reduce hesitation while building internal expertise grounded in real experience.
Access Alone Isn’t Adoption
Many institutions have already taken an important first step by providing access to AI tools, which signals a clear intent to move forward.
At the same time, access alone does not translate into meaningful adoption, particularly when faculty and students are left without guidance or shared examples.
Without that support, individuals develop their own approaches in isolation, which can lead to inconsistent use and hesitation.
As Hammontree framed it, the goal is to connect technology, experience, and support in a way that helps people move forward with clarity and confidence.
That is what ultimately transforms access into sustained, practical use.
Moving Forward With Intention
There’s no single roadmap for AI adoption in higher education. What is becoming clearer are the patterns behind what works.
Institutions that prioritize people before platforms create stronger alignment. Those that treat resistance as insight address underlying concerns more effectively. And those that pair policy with communication and support are more likely to see progress.
AI adoption is not simply a technical shift. It’s a cultural one that requires time, trust and intentional effort to get right.
Missed the webinar? Catch the full discussion now.
Looking for more practical hands-on experience? Join us at our annual conference, Fusion, for an in-depth AI workshop led by Dr. Luke Hobson. Seats are filling up fast. Secure yours today.
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