Universities cannot scale AI in HR and payroll on fragmented foundations

Here's a practical way to strengthen your HR, payroll and finance foundations to promote successful AI adoption and digital transformation.

Higher Education Institutions are asking how to embrace AI

Artificial intelligence has moved quickly from an interesting possibility to a strategic question for every UK university. Higher education leaders can see the potential: more responsive services, better use of data, less repetitive administration and new ways to support students and staff. 

Although the adoption of AI in education, broadly, is seen as a positive, it doesn’t come without potential difficulties. AI exposes the quality of the foundations beneath it, and many institutions are trying to prepare for the future while managing financial pressure, legacy technology, cyber risk and limited capacity.

Where is the higher education sector on AI adoption?

Jisc’s new report, Beyond digital foundations, provides a useful industry-wide reality check. Based on responses from 83 higher education providers, it finds that digital capability, including AI capability, has the lowest maturity ratings in the assessment. 

Reliable infrastructure may be relatively well established, but that does not mean the digital estate is finished. Respondents describe cyber threats, cloud adoption, AI and growing digital dependency as changing sources of risk that need active leadership.

The report’s most important message is that AI cannot be separated from the wider operating environment. One participant described a university at a “pivotal point” in developing its digital estate but acknowledged that there had not always been the appetite or capacity to build enterprise architecture, information governance and data quality. The same participant also noted a growing appreciation that these enablers are essential to delivering future strategy. That shift in understanding matters.

For HR, payroll and finance leaders in universities, this is not an abstract technology debate. These functions hold some of the most sensitive and operationally important information in the institution: who works here, what skills are available, what people cost, whether they are paid correctly, where risk is building and how capacity is changing.

Yet they are only one part of the footprint. AI-ready transformation also depends on student systems, registry, learning, research, estates, finance, security and governance working towards a connected view.

Solving the issue means starting with what is real

The answer is not to wait for a perfect, institution-wide platform. Nor is it to add an AI tool to a process that is already fragmented. Jisc’s research points towards a more practical starting point: establish a baseline, understand the gaps, align digital priorities with institutional strategy and build capability across the organisation. In a challenging environment, that discipline helps leaders decide what to protect, what to simplify and where investment can produce a measurable return.

Our MHR guide, "AI Readiness in Higher Education: A guide for HR, payroll and finance leaders", applies that principle to the people and cost layer of the university. It sets out five connected priorities.

1. Map the systems, manual workarounds and data owners involved in high-value processes such as onboarding, payroll change, workforce planning and absence management. 
2. Agree common definitions for employee, worker, headcount, full-time equivalent, vacancy, absence and cost. Without that shared language, even a sophisticated model will produce arguments rather than insight.
3. Prioritise cases where the benefit is clear and the risk is proportionate. AI-assisted payroll checks, workforce scenario modelling, faster answers to routine HR questions and improved onboarding are practical places to learn.
4. Build controls alongside capability: human review, audit trails, security, supplier assurance, model testing and routes for challenge.
5. Invest in adoption: People need to understand what is changing, what is not, and how AI will support judgement rather than quietly replace it.

The guide also makes a simple but important case for cross-functional ownership. HR, payroll and finance leaders should have a seat at the data governance table, alongside IT, registry, data protection, procurement and academic leadership.

A university does not become AI-ready by making one department more efficient in isolation. It becomes more ready when the information and decisions that cross departmental boundaries become more trustworthy.

Building new foundations to enable AI in HR and payroll in universities

MHR will be attending “HE Transformation 2026” at the QEII Centre in London on 22nd October, where Jisc’s report will be presented in a session focused on digital maturity. I am also looking forward to hearing discussion across the wider programme with contributions from the Department for Education, as well as a range of university and student leaders.

The range of topics - from AI adoption, data and cyber security to operating models, workforce capability, financial planning and institutional collaboration - reflects the reality universities are facing. The environment is challenging, but it is also a moment to learn from one another and to find the right partners.

The best partnerships will start with honest diagnosis, practical delivery and a shared understanding that transformation is about the right people, transparent processes and innovative platforms.

I look forward to seeing how universities across the UK encompass AI into their organisations.