Building an AI-ready university

Last week, I had the privilege of joining senior university leaders from across the UK, Ireland, and continental Europe for our AI Strategy Summit in London. It was a focused and engaging forum where we explored how artificial intelligence is shaping the future of higher education—from early experimentation to deeper integration and institutional maturity.

The energy in the room was palpable. What struck many of us was how UK and EU leaders are already approaching AI holistically. They’ve moved beyond experimentation with new tools and are thinking ahead to long-term, institution-wide transformation. Thought-provoking conversations unfolded around the current risk and challenges of this, yet two threads consistently rose to the surface: how to meaningfully engage academic staff with AI, and how to harness it to enhance administrative efficiency.

Before delving into these themes more, I’d like to set the stage with a bit of context. Why are these particular challenges resonating so strongly? And what do they reveal about the sector’s revolving relationship with AI?

What AI in higher education usually looks like

Generative AI is already a fixture in many universities, with students and staff significantly engaging with these tools. Recent surveys show 86% of students and 69% of staff have used AI in the past year. Even though most have already experimented, few universities have begun embedding AI deeply into workflows or academic culture.

Signals from students and employers alike point to a growing urgency for AI in their courses. Today’s students—current first-years—expect to graduate ready for an AI-infused workplace. And more than half of recent graduates say they weren’t adequately prepared. Employers are echoing this sentiment, too. Nearly two-thirds of business leaders say they’d prefer to hire a less experienced candidate with strong AI skills over one with more experience but no AI fluency.

But one major roadblock remains: academic engagement. Lecturers are still hesitant to embrace AI in their teaching. In fact, 42% currently prohibit students from using GenAI in the classroom. And while we might joke that no one—not even a vice-chancellor—can tell academics what to do, the reality is more nuanced.

Lecturers own their curriculum, but institutional leaders shape the conditions in which that curriculum evolves. At our roundtables, we talked about two ways we can bridge this gap.

Engaging academics in AI

In the short term, institutions should focus on building foundational AI literacy among lecturers and students. This means encouraging small-scale experimentation, like using AI to streamline course design, enhance assignments, or introduce new modes of student engagement. Longer-term, the goal is more ambitious: becoming an “AI university” where academic staff confidently integrate AI into teaching and research, and where students seek out the institution because they believe it will prepare them for an AI-driven future.

To get there, universities must offer foundational training, supportive policies, ready-to-use assignment resources, and celebrating academic-led innovations to help overcome any lecturer dissonance.

4 Steps Academic Leaders Must Take to Integrate AI Tools Into Pedagogy

Boosting administrative efficiency with AI

The most forward-looking institutions are preparing for the next wave, automating workflows and offloading repetitive tasks without human input. While fully autonomous tools aren’t yet standard practice, they preview a future where AI functions as a true co-worker—freeing up time for the strategic, relational, and mission-driven work that humans do best.

AI offers a tangible opportunity to improve productivity, reduce costs, and support financial sustainability. At the summit, one participant asked, “But what about the glorious failures?” What have peer institutions tried—and failed at—so others can learn without repeating the same mistakes? It was a refreshing moment of candour, and a reminder that AI progress depends not just on what’s working, but also on what we’re willing to share when things don’t.

Turning potential into progress

At last week’s summit, we heard from leaders at every stage of this journey, from early pilots to institution-wide strategies. What stood out wasn’t just the variety of approaches, but the shared recognition that real progress happens through deliberate, people-driven change.

And, although AI may feel like magic, making it work is far from effortless. EAB’s Professional and Advisory Services help universities around the world turn AI from a buzzword into capability. And it’s conversations like the one we had last week that make that possible. These moments of shared reflection and exchange don’t just spark ideas; they create the momentum needed to turn them into action. They also offer space to ask tougher questions: What hasn’t worked? What’s next? And how can we build the future of higher education together, rather than alone?

For more information on how we partner with your university to make progress on AI (and your other higher education strategy goals), please fill out the form below.