AI is the terrain: Why universities must rethink strategy in an age of artificial intelligence

Embracing AI requires university leaders to ask themselves: “How does our mission need to evolve in a world shaped by AI?” not “How can we use AI to deliver better on our mission?”

As artificial intelligence rapidly reshapes the world, universities across the world are only beginning to reckon with its strategic implications across teaching and learning, research and operations. 

Nous analysis of 45 recent institutional strategic plans in Australia, Canada, and the UK reveals that, while references to AI are becoming more common, particularly in the context of research and innovation, most universities have yet to reflect the scale of the AI shift in their core strategic thinking. 

In a few cases, universities have developed separate AI frameworks, policies, strategies or roadmaps. These articulate how the institution plans to invest, implement and drive adoption of AI in its operations. These standalone frameworks and strategies are helpful in that they record institutional decisions on AI adoption, investment, development and ethical use. They also no doubt provide a degree of comfort to Councils, senior leadership and staff that institutions are doing something about AI.

This comfort, however, should be a little fleeting. Standalone plans or strategies risk treating AI as a tool to be managed, rather than as a driver that demands meaningful adaptation of institutional purpose, capability, and direction.

Artificial intelligence represents a strategic inflection point for higher education, one that calls, not for upgrades, but for substantial evolution. Some have likened AI to the MOOC wave of the early 2000s, when platforms such as Coursera and edX were heralded as transformative. Yet MOOCs ultimately became a cautionary tale, widely hyped but with limited lasting impact on universities’ core models. AI is different: it is a Kodak moment, signalling, not temporary noise, but a structural shift with the potential to displace established practices if institutions fail to adapt.

AI is more than the next chapter in a long story of digital transformation. It is a new book that accelerates questions about the role universities play in society. Universities have and do serve as institutions for the creation, validation, and dissemination of knowledge. But the rise of AI, capable of producing original text, research summaries, and code, at scale, starts to call into question the university’s monopoly on expertise. As the pioneer of open innovation Henry Chesbrough observed: “No one has a monopoly on knowledge.” They certainly don't anymore.

In this context, institutions must ask themselves what their distinctive contribution is. What does it mean to educate, when knowledge is not scarce but commodified? How do we deliver public value commensurate with the amount of public and student money invested? 

The shifts accelerated by AI extend well beyond pedagogy and administrative service delivery. They interact with pressures from policy, demographics, and funding to influence the university’s economic and social purpose. AI is accelerating changes in the labour market that were already underway, displacing routine knowledge work and elevating demand for skills in problem-solving, adaptability, and human-centered judgment (including creativity and design). Firms like Google, IBM, and RBC are shifting their focus from degrees to demonstrable capabilities.

For universities, this moment forces a strategic choice: continue delivering long-form degrees designed for an earlier era, or rethink the purpose, duration, and delivery of their offerings to align with a transformed economic context.

Students, too, are entering this new environment with different expectations and pressures. With AI tutors, writing assistants, and personalised learning interfaces increasingly accessible, students are questioning the value of a traditional, one-size-fits-all education. 

Why pay thousands in tuition when intelligent systems can deliver content, feedback, and coaching more flexibly and affordably? Institutions are also grappling with the erosion of legacy assessment models, as generative AI undermines the reliability of essays, exams, and participation as indicators of learning. These pressures reveal that AI is not merely a tool to be embedded in current operations, it is a strategic context that redefines what students need, how they learn, and what universities must provide.

Responding to this new context does not mean immediate reinvention on every front. A delivery-first mindset offers a pragmatic path forward. Rather than waiting for a perfect institutional strategy to materialise, institutions can begin solving their problems in real time. 

This means identifying practical, low-barrier steps, using tools already at hand, such as Microsoft Copilot, to experiment, learn, and build confidence. Crucially this approach demystifies AI. It shifts the conversation from abstract transformation to tangible action. In this spirit, early wins are not just operationally valuable, they help drive a culture of change and shift institutions from hesitation to innovation.

During a pivotal period of transition, the cost of standing still is not just inefficiency, but a gradual loss of relevance. 

Institutional responses to AI will not happen by accident. They will require leadership. The scale and speed of change demand strategic vision, cultural transformation, and governance that is both responsive and forward-looking. Senior leaders, presidents, provosts, deans, and boards must lead the shift from enhancement to evolution by creating the conditions for deeper, often uncomfortable, institutional self-reflection.

The first task of leadership is to frame AI as a strategic inflection point, as discussed above, and to help constituents understand that AI is not just something for IT teams or teaching and learning committees. It rather touches everything from enrolment and credentialing to staffing models and community engagement. Leaders must challenge their institutions to ask not only how AI can improve what they already do, but whether they are doing the right things in the first place.

Leaders also have a critical role in building the strategic agility required to respond to change. This includes reviewing and modernising governance processes, empowering cross-functional innovation, and creating space for experimentation with new delivery models, credentials, and student experiences. They must champion a culture that values inquiry over certainty and welcomes constructive disruption rather than protecting legacy approaches and beliefs.

True leadership in this space means narrating a future that staff, faculty, students, and external partners can believe in. The institutions that succeed will be those whose leaders can inspire confidence, build trust, and hold a long-term vision, even as they make bold near-term moves.

This is where the stakes of leadership become most clear. If institutional leaders rise to the challenge, what might a truly adaptive university look like in the age of AI? Institutions that succeed will invent, adapt, and redesign their operating models, academic offerings, and public roles around the possibilities AI unlocks. This could look like:

This is not a far-off vision. Examples of adaptive AI uses are beginning to emerge, piecemeal in some places, strategically in others. The difference lies in leadership. Institutions that proactively design for this future will lead the sector. Those that delay risk falling behind their peers as momentum builds elsewhere.

Leaders will face hurdles on the path to reforming strategies and transforming institutions to respond to an AI world in which they operate.  We have included three key hurdles below and ways that leaders can respond to them as they arise. 

Hurdle 1: "AI strategy" as a safe harbour 

• Observation: Many institutions are drafting standalone AI or digital strategies as a response to sector pressures. These strategies feel safer and more manageable than questioning the institution’s foundational mission.
• Strategic response: Align AI initiatives with the university’s mission. Safe does not mean sustainable; embedding AI in core strategic agendas ensures initiatives support long-term goals rather than creating siloed or underfunded efforts.

Hurdle 2: Decentralized governance

• Observation: Universities are often highly federated and collegial. Driving institution-wide strategic shifts is political, bureaucratic, and operationally complex.
• Strategic response: Create cross-functional AI councils or steering bodies including academic, professional, legal, and student voices. Leaders should pilot solutions with a handful of units to demonstrate impact, build credibility, and create momentum for wider adoption.

Hurdle 3: Public mandate and fiscal constraints

• Observation: Institutions are under intense scrutiny for budgets and relevance. There’s a tendency to seek efficiencies (via AI) rather than reinvent service or education models.
• Strategic response: Pursue quick wins to demonstrate value, while planning for mission-aligned transformation. Fund curriculum redesign, co-designed teaching approaches, and AI-enabled student services. Engage students in shaping AI tools, and provide shared spaces for experimentation and peer-led innovation. 

The universities that will lead in an AI age won’t be defined by how efficiently they deploy chatbots or automate grading. They’ll be defined by how boldly they respond to what AI demands of them. The true leaders will be those that recognize AI, not as a discrete initiative, but as a defining moment that challenges them to revisit their purpose, rethink their structures, and reimagine their role in society.

This is not a sprint of implementation. It is a marathon of evolution. Institutions must move beyond tactical responses and take on the more difficult, but more necessary, task of redesigning themselves for a world in which intelligence is no longer the sole domain of human beings. 

That means asking different questions, investing in new capabilities, and embracing a mindset of continuous transformation. AI should not be bolted onto the edges of a legacy mission. It should be treated as the terrain upon which a new mission must be built. For those willing to lead strategically – who are ready to rethink, redesign, and rebuild – this moment offers an extraordinary opportunity to shape the future of higher education.

Get in touch to discuss how your university can embrace AI to better position itself in a competitive market.

Connect with Dylan Houghton and Aryeh Ansel on LinkedIn.