Artificial Intelligence has become one of the most discussed topics in higher education. New tools appear almost weekly. Universities are developing AI policies, experimenting with AI-assisted teaching, exploring its use in research and considering how it might improve operational efficiency.
Most university leaders do not need to know how to build an AI model. They do not need to understand neural networks, machine learning algorithms or the mathematics behind large language models. But they do need to understand enough about AI to make informed decisions about strategy, risk, governance, investment and organisational change.
The question is not whether leaders need to become AI experts, the question is whether they know enough to lead effectively in a world where AI is becoming impossible to ignore.
It has been said that “AI Won’t Replace Leaders. But It Will Expose The Ones Who Outsource Their Thinking”[1]. Don’t let yourself become one of those leaders who can no longer lead as they have not get kept up with the AI revolution.
1. AI is moving faster than institutional planning cycles
Universities are accustomed to operating within long planning horizons. Strategic plans often cover five years or more. New programmes can take years to develop and approve. Major institutional change frequently moves at a deliberate pace.
AI works on a very different timescale.
Capabilities that seemed impossible two years ago are now widely available. Tools continue to improve at a pace that few institutions have experienced before. What used to take hours, days, weeks or months can now, for some tasks, be done in minutes. This creates a challenge for leaders because traditional planning approaches may struggle to keep up with such rapid change.
Universities need to be flexible to adapt as technologies evolve, rather than assuming today’s landscape will still exist in five years’ time, as that will almost certainly not be the case.
2. The greatest risk may not be adoption
Much of the discussion around AI focuses on risk, raising concerns about privacy, bias, copyright, academic integrity and misinformation. These are all legitimate areas for debate.
However, there is another risk that receives less attention. What happens if competitors adopt AI effectively while your institution does not?
Universities that successfully deploy AI may improve student services, reduce administrative burdens, accelerate research activities and operate more efficiently. Institutions that delay engagement may find themselves at a competitive disadvantage.
Leadership requires balancing both forms of risk: the risks of adopting AI and the risks of failing to do so.
If you are not adopting AI due to the risk, then you must also consider the risk of not adopting AI.
3. AI will change jobs, not just processes
Many discussions about AI focus on automation. This often leads to concerns about job losses.
The reality is more complex.
Historically, technology has tended to change the nature of work rather than simply eliminate it. AI is likely to affect how academics teach, how researchers conduct literature reviews, how professional services staff process information and how leaders access decision support.
The challenge for university leaders is not simply managing technology. It is preparing their workforce for roles that may look significantly different in the future.
“Each industrial revolution has brought the fear of job losses, history has proven that this is untrue”[2].
4. Data has become a strategic asset
AI systems depend on data.
The quality, accessibility and governance of institutional data will increasingly influence how effectively universities can use AI technologies. Unfortunately, many institutions still operate with fragmented systems, inconsistent data standards and limited integration between platforms.
Universities often invest considerable attention in technology procurement but much less attention in data readiness. As AI becomes more widely embedded, the value of high-quality institutional data is likely to increase substantially.
As a university leader one of your key roles could be to ensure that your data is effectively managed.
5. Governance matters more than technology
The most successful AI initiatives may not be those with the most advanced technology. It may be those with the strongest governance.
Questions about accountability, oversight and acceptable use are becoming increasingly important. Who is responsible when AI-generated information is incorrect? Who approves the use of AI in critical processes? How should institutions monitor compliance with AI policies?
These are governance questions rather than technical questions and somebody has to take responsibility for them.
As with finance, risk management and academic quality, effective AI governance is likely to become a defining characteristic of successful institutions.
6. AI literacy is becoming a leadership skill
University leaders do not need to be AI specialists. However, they do need enough understanding to ask informed questions and challenge assumptions. Those that are working within the leader’s team must have the confidence that those leading them have a working knowledge of AI.
Leaders who lack even a basic understanding of AI may struggle to evaluate proposals, assess risks or recognise opportunities. They may become overly dependent on technical advisers or unable to distinguish realistic expectations from exaggerated claims.
A reasonable level of AI literacy is increasingly becoming part of the broader skill set required for effective leadership.
7. AI will create new ethical challenges
Higher education has always dealt with ethical questions, but AI introduces new complexities.
Issues surrounding bias, transparency, privacy, intellectual property, surveillance and accountability are already emerging across the sector. Many of these issues do not have straightforward solutions.
Universities have traditionally played an important role in helping society navigate ethical challenges arising from new technologies. Leaders must therefore think not only about how their institutions use AI, but also about the values that guide those decisions.
8. Culture may be more important than technology
Many technology projects fail not because the technology is inadequate, but because people do not embrace the change. AI is unlikely to be different.
Some staff will be enthusiastic adopters. Others may be sceptical, cautious or concerned about potential consequences. Others will be scared about their jobs, their lack of understanding or their ability to adapt. Leaders need to create environments where experimentation can occur responsibly while maintaining trust and transparency.
Technology can often be purchased. Organisational culture is much harder to change.
9. AI strategy should not be separate from institutional strategy
Some universities are creating standalone AI strategies. While this may be a useful starting point, there is a danger that AI becomes treated as a separate initiative rather than an integral part of institutional planning.
AI has implications for teaching, research, student experience, operations, governance and workforce development. It therefore touches every aspect of the university.
AI considerations should be embedded within broader institutional strategy rather than being treated in isolation.
10. Doing nothing is also a strategic decision
Leaders sometimes assume that delaying a decision preserves flexibility. In practice, choosing not to engage with AI is a strategic choice, as long as it is an informed decision.
However, the external environment will continue to evolve regardless of whether individual institutions participate. Students, researchers, competitors and employers are already adapting to a world where AI is increasingly commonplace.
The question facing university leaders is not whether AI will influence higher education. The question is how they intend to respond.
Final Thoughts
Artificial Intelligence will not solve every challenge facing higher education. It is not a magic wand, nor is it a threat that will render universities obsolete.
What it does represent is a significant shift in the environment in which universities operate.
Leaders do not need to become technical experts and they do not need to understand every new model, tool or acronym that appears.
But they do need sufficient understanding to make informed decisions, ask difficult questions and guide their institutions through a period of rapid change.
In the years ahead, AI may prove to be one of the most important leadership issues facing higher education. That alone is reason enough for every university leader to pay attention.
About the Author
Professor Graham Kendall is Acting Vice-Chancellor of GlobalNxt University, Malaysia, and an Emeritus Professor of the University of Nottingham. An active researcher in Artificial Intelligence for more than 25 years, his work has explored how AI and optimisation techniques can be applied to solve complex real-world problems. He writes regularly on higher education leadership, governance
[1] https://www.forbes.com/sites/alainhunkins/2026/02/17/ai-wont-replace-leaders-but-it-will-expose-the-ones-who-outsource-their-thinking/, accessed 30 May 2026
[2] https://www.accountancysa.org.za/the-stages-of-industrial-revolution-and-its-impact-on-jobs/, accessed 30 May 2026