Leading AI in colleges and universities: a strategic framework for senior leaders

Why this matters

AI is now moving too fast, and touching too much, to leave to chance. Where it is left to chance, staff and students adopt tools in fragmented ways, risks to data and to academic integrity grow, and opportunities are missed. Where it is led well, institutions build trust, protect their reputation, save time, enrich teaching and learning, and equip learners for the workplace. The institutions that we see steering the challenges with most confidence have one thing in common — engagement and direction from a senior level.

Leading AI does not require you, as an institutional leader, to have a deep understanding of the technology, and it doesn’t require you to become embedded in the detail of the implementation. It requires you to make a small number of decisions and to delegate the rest with confidence.

This guide sets out the framework and those decisions. The detail of delivery belongs with your team.

The current context

The framework in this guide is intended to be stable, and to hold over time. The environment around it is not, though. There are key developments that are now shaping the decisions that follow, and are worth holding in mind.

AI has become contested. Public sentiment has moved markedly. Concerns once at the margins of public debate are now mainstream: the environmental cost of AI, its effect on jobs and opportunity, the ethics of how models were trained, the damage to the arts and creative work, and questions of cultural and national sovereignty when so much capacity sits with a handful of global platforms. There are real safeguarding concerns too — AI can generate harmful content on demand, and is used against people through grooming at scale, impersonation, deepfakes and scams. Your staff and students increasingly hold strong and divergent views. Staff across your institutions are now expected to offer evidence-informed positions rather than enthusiasm, and to know when to engage and when to hold back.

The cost model is shifting to usage-based charging. The familiar flat licence fee is giving way to token-based pricing, where cost depends on how much AI is used — and much of that use happens invisibly, in the background of tools you already own. Costs are harder to predict and are rising. This makes the decision to fund AI more consequential than a one-off budget line: open-ended consumption needs forecasting, monitoring and governance.

Adoption increasingly has to be justified, not just achieved. As scrutiny grows, it is no longer enough to show that a tool works. You need to be able to show that its use is intentional, impactful and well governed — and defensible against the concerns above. That raises the premium on questions this framework already asks: is adoption judged by real impact, and is there meaningful human oversight where it matters?

None of this changes the framework, first shared in 2025. It raises the stakes for getting the decisions right, and for basing them on evidence rather than on either hype or backlash.

An eye to the future

We are likely at the start of another shift. The last few decades have been the age of the website being the major interface to our systems, first on desktop computers, then laptops, and then phones.

Our view is that we are almost certainly at the start of a major change, where AI becomes the interface, and websites become infrastructure. The core skills needed will be delegation, the core technology about making systems available to AI agents, and the core governance questions, around how and when to delegate to AI tools. By putting the foundations in place now, your institutions will be in a good position to navigate this next wave.

The framework at a glance

Our framework sets out three pillars of responsible adoption:

• Skills, knowledge and culture
• Technology
• Governance

These are underpinned by two foundations — strong data maturity and security — which AI does not replace but makes more important, and framed by the core leadership questions that follow.

Each pillar spans teaching and professional services — and, in universities, research. The framework is deliberately common to colleges and universities: while the detail of delivery differs, the strategic decisions leaders must make are the same.

It is a focused, decision-oriented view of one technology; it sits within, and draws on, our wider framework for digital transformation.

What a mature AI-enabled institution looks like

When all three pillars are in place, your college or university can:

• provide students and staff — and, in universities, researchers — with trusted, consistent toolkits
• build confidence across the institution through structured training
• govern AI responsibly, balancing innovation with assurance
• preserve academic integrity while enabling innovation
• benefit from faster workflows, stronger teaching and learning, and learners ready for an AI-infused workplace

The decisions only institutional leaders can make

Most AI decisions can and should be delegated to the appropriate technology or area leads. A few cannot, because they set the boundaries, commit the resource, or carry the accountability that everyone else works within. These are for the senior team. For each, there is a decision to make now — and, once made, a more ambitious position to grow into. These indicators are not where you need to start; they are what leading institutions look like.

How ambitious to be

Decide whether AI sits in your institutional strategy as a driver of change, or as a risk to be managed. Either is a legitimate position — but until you take one, your institution will guess, and guess inconsistently.

Indicator: AI is a visible, funded strand of institutional strategy, with progress reported to your board, corporation or council.

Your risk appetite

Say, in plain terms, how much reputational, integrity and data risk the institution will accept in return for moving quickly. This is the boundary condition for every delegated choice. Leave it unspoken and others will set it for you, unevenly.

Indicator: A differentiated appetite — bolder in teaching and administration, more cautious with sensitive data, assessment and (in universities) research — reviewed each year as the technology changes.

Whether AI is a funded priority

Decide whether to invest in capability and tools centrally, or to leave departments and services to find the time and money themselves. Either is a choice; not deciding is the one option that reliably wastes effort. Usage-based charging makes this decision sharper, since costs are no longer fixed and predictable.

Indicator: A multi-year investment with clear measures of impact on workload, quality and the student experience — not a one-off spend.

Who owns it

Name a single, senior owner for AI at executive level. Fragmented ownership is the most common reason adoption stalls, so this small act of assignment matters more than it looks.

Indicator: An executive owner supported by a cross-institutional group spanning education and curriculum, professional services, IT and — in universities — research, reporting regularly to the governing body.

Your position on academic standards

Agree an institutional position on what AI use means for the standards and integrity of your provision, from further education qualifications to any higher education your institution delivers and research your institution undertakes. This touches your reputation directly, so it needs your sponsorship — not quiet delegation to course or module leaders.

Indicator: The institution helps shape the sector conversation on academic standards and AI, rather than only responding to it.

The three pillars, at altitude

Skills, knowledge and culture

Adoption is uneven not because the tools are hard but because people feel unsure. Where staff feel supported and trusted, AI saves time and enriches teaching and learning; where they feel excluded, tools are underused or resisted. A structured path — capability assessment, guidance, role-based training, and communities to share practice — builds the confidence on which everything else depends. In a contested landscape, that path should include critical AI literacy: the ability to weigh claims and concerns, not just operate the tools.

Are you confident there is clear senior accountability for building AI capability across teaching and professional services — and, in universities, research?

Technology

The right tools save time and improve the student and staff experience; the wrong ones waste money and create risk. The value comes from a coherent, trusted core toolkit — reused or partnered, not built — that meets educational, ethical and value-for-money standards, and that gives learners the industry-standard tools they will meet in work.

Are you confident your institution offers a consistent, trusted core toolkit rather than fragmented, unassured adoption?

Governance

Weak governance fragments AI use and exposes the institution on data, fairness, integrity and trust. Strong governance is not bureaucracy; it is the foundation that lets the institution innovate with confidence and protect its reputation. It rests on published principles, reviewed existing policies, sound procurement and vendor assurance, and clear guidance.

Are you confident AI governance and oversight has a clear senior owner and published institutional principles?

Where is your institution now?

Most colleges and universities are moving from making AI operational, through embedding it institution-wide, towards using it to improve how they work. Use the ladder below to locate yourself and to see the next step. The “optimised or transformed” column shows these indicators — where the most advanced institutions are heading, not where you must be today. These are the later stages of our AI maturity model.

If you do nothing else

Three decisions are worth resolving at your next executive meeting:

• Name a single senior owner for AI
• State, in one or two sentences, your institutional risk appetite for AI
• Commission your team to prepare the companion implementation plan against this framework

Taking this forward

The decisions above are yours. The detail of delivery is not — and should not be. The companion implementation guide sets out the practical steps for your senior team — including those responsible for IT and digital, education and curriculum, and quality: capability tools, training, toolkits, policy review, procurement questions and monitoring.

We support colleges and universities across all three pillars: identifying, testing and negotiating licences for trusted AI tools; providing AI literacy resources; and offering guidance templates and communities of practice. By partnering with us, you can be confident your institution is enabling innovation while maintaining oversight, compliance and the integrity on which its reputation depends.