Universities perform a function in our societies that they alone can perform. And, given the transcendental value of that role, they should be allowed to perform it without interference, oversight or criticism.

Cloaked in Enlightenment principles, such an uncompromising and self-regarding belief has, in recent decades, seen the higher education sector drift – detached, cautious and often unwilling to reimagine its purpose in the face of changing societal expectations, even amid the steady creep of marketisation.

Despite a growing acceptance that the current model is unsustainable, few institutions have demonstrated an inclination to challenge antiquated and conflicted governance structures, risk-averse bureaucracies, opaque decision-making cultures, dated curricula and pedagogies, questionable contributions and standards of quality, and ever-mounting costs.

This isn’t a call for greater market orientation – although it will no doubt be perceived as such. It is instead a call for reinvention and a fundamental rethink of the university’s role, purpose and methods. Had the higher education sector proven itself more capable of adopting technologies to more effectively deliver its present logics, this need might be less obvious.

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As it is, 30 years into an internet-fuelled EdTech era, we are still talking about a lack of skilled IT expertise in universities, the unique challenges of the sector, the ever-present financial constraints, the obstacles faced by faculty and students when adopting new technologies, and the very desirability of technology solutions to institutional objectives.

Universities will no doubt point to innovation in areas such as online content, flipped classrooms, learning platforms, curriculum design and assessment, but such advances represent only incremental adjustments. With the exception of relatively few institutions, it’s difficult to think of another sector that has so dismally failed to strategically engage with the transformative potential of information technology.

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Banks, among the most conservative of institutions, have automated entire administrative layers while using AI to personalise services and manage risk. The health sector, despite regulatory challenges and many of the same integrity-based considerations as higher education, has been among the first to deploy AI tools – especially predictive analytics. Even sectors seemingly immune to automation, such as legal and consulting services – characterised by deep client relationships based on trust and professional judgment – are deploying AI to enhance precision, efficiency and insights. And while it’s true that general-purpose AI tools account for much of the present usage, an awareness of the disruptive potential of AI is driving rapid adoption, innovation and fundamental business model redesign.

There is acceptance in higher education that AI will reshape teaching, research and administration, but it is largely viewed as a technology issue – with considerable practical and ethical considerations – rather than a question of underlying institutional design. Faculty conversations revolve around plagiarism or academic integrity, the societal implications of automation, or – in some rare cases – the possibility of teaching avatars and co-pilots. Responsibility for AI is delegated (where managed at all), and solutions are being bolted on – often in an ad hoc and amateurish manner.

The possibilities? Well, leaving more transformative agendas to one side, we see already, in administration, that AI can overhaul planning and operations procedures. It can drive efficiencies in areas such as marketing, admissions, finance, student services and programme management – not to mention governance and compliance.

In research, AI has begun to revolutionise the literature review process, potentially unlocking huge amounts of time for new discovery. AI’s capacity is obvious for handling massive, complex datasets and identifying emergent patterns in qualitative data. The opportunity of natural language interaction also brings advanced analytics and simulations within the practical reach of all researchers.

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In teaching, meanwhile, AI can enable long-touted adaptive learning environments able to respond to individual student progress rather than relying on an archaic one-size-fits-all design. Early-stage use-cases include dynamic content generation, assistant-based learning support and assessment designs that provide individual-level feedback at scale.

In short, near-term potential for complementing existing routines, driving efficiencies in core processes, and improving the quality of services and outputs is considerable. But this requires strategic engagement in the process of use-case ideation, assessment and prioritisation – and the participation of all key stakeholders. At present, few universities are exploring AI from a strategic perspective and even fewer are showing an inclination for engagement with more transformational AI agendas.

There is no denying a combination of unique structural, cultural and psychological factors present enormous challenges when considering institutional resistance to change in the sector. Talking to those working hard to drive technology agendas within their institutions, however, it’s clear where much resistance lies. Time and again, it comes down to those who have little or no desire to see change – often the most powerful stakeholders. Here, a disregard for the realities of technological change is almost always obfuscated by appeals to academic integrity and practice.

It’s ironic, of course, that much of the research driving our understanding of AI is produced by universities themselves. The sector is involved in frontier discovery and engaged in critical societal questions – often in collaboration with industry, government and civil society (the “quadruple helix”). And while structural incentives have played an important role and there remains considerable caution around such arrangements, it’s interesting to note the level of sectoral engagement. Change seems possible when there is alignment of missions and interests.

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The biggest threat to higher education isn’t AI – it’s institutional inertia and a chronic failure of imagination. If universities, especially those in the second and third tiers, fail to respond to the strategic challenge it poses, they risk being displaced. A raft of emerging technology solutions, corporations, consultancies, learning providers, authorities, and agencies are currently converging to fill the void and deliver certified, AI-enhanced education aligned with societal needs.

Social capital and a historical monopoly over advanced-level teaching and educational certification will not protect the sector from the technological reckoning it faces. Its monopoly on legitimacy is eroding – and so are excuses for not responding.

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is a faculty member and director of executive education at Stockholm Business School, Stockholm University. With a background in technology media, he is co-founder of the national Swedish programme AI for Executives, which seeks to drive board-level understanding and organisational adoption of AI across industries and sectors.