AI Readiness in Higher Education: A guide for HR, payroll and finance leaders

Universities are moving from curiosity to practical experimentation with AI, but adoption is uneven. UCISA has identified the same challenges now facing many institutions: building an AI-ready culture, creating ethical governance, working within limited funding and resources, managing skills gaps, and overcoming barriers to adoption across academic and operational domains. The message is clear: successful adoption is as much an organisational and governance challenge as it is a technical one.

This is especially relevant for HR, payroll and finance leaders. MHR research found that 80% of education respondents rated AI adoption as a high priority for improving daily processes and supporting longer-term goals, compared with 64% across all sectors. Yet 28% of education respondents said their current HR, payroll or finance platform offered minimal or no AI functionality, twice the overall average of 14%. This gap between ambition and current capability is why foundation work matters.

Education respondents were less likely to identify budget constraints as their main obstacle than the cross-sector average, but ethical concerns were more prominent: 40% cited them as a challenge. For institutions handling sensitive employee, student and research data, this is a sensible signal. AI initiatives need transparent purpose, proportionate controls and human accountability from the outset.

A single university can end up with over a hundred different platforms. Student information systems hold academic records, CRM platforms support recruitment and engagement, learning platforms track participation, research systems manage grants and outputs, estates and timetabling systems coordinate physical resources, finance platforms handle budgets and reporting, and HR and payroll systems manage the workforce.

Each system may work well in isolation. The difficulty comes when information cannot move reliably between them. Leaders then make decisions from fragments: headcount without the associated cost, student demand without workforce capacity, or financial plans without a current view of vacancies, skills and contractual commitments.

HR, payroll and finance are not the whole answer, and they should not be treated as a substitute for wider enterprise architecture. They are, however, a useful starting point for transformation. Together they connect people, cost, compliance and operational capacity, surfacing the information leadership teams need when deciding where to invest, where to redesign work and where risk is building.

Workforce planning and organisational insight

Clean, connected workforce data can help HR and finance answer questions that are difficult to resolve when using spreadsheets. This includes where skills gaps are emerging, which teams are exposed to turnover, how headcount is changing with student demand, and what different resourcing scenarios would cost. AI can support forecasting and anomaly detection to help save time and achieve better compliance. However, leaders still need to define the assumptions, test the outputs and make the decisions.

More accurate and resilient payroll

Payroll is a high-volume, rules-based process with little tolerance for error. AI-assisted checks can flag unusual pay movements, duplicate records, unexpected deductions or patterns that warrant investigation before payroll is finalised. Used responsibly, this can reduce avoidable rework and give payroll professionals more time for controls, employee support and continuous improvement. However, it does not remove the need for payroll expertise or approval controls.

Better employee experience and more strategic HR

Automation can handle routine queries, workflow reminders, document checks and parts of recruitment or onboarding. That creates capacity for the human work that technology cannot replace, such as advising leaders, supporting employees through change, coaching managers and building trust. MHR’s research and customer work point to a practical distinction: AI changes tasks before it changes jobs, and the value comes from effective use of the time saved.

Operational and financial intelligence

Finance, HR and payroll data can help institutions model the operational implications of changing enrolment, funding, workforce mix or pay awards. Connecting this information to wider institutional data, such as student demand, research activity, estates and timetabling, allows leaders to test scenarios with more confidence. The wider architecture still matters; the people and cost layer simply provides an important anchor for decision-making.

Data quality comes first

AI will reproduce inconsistencies that people have learned to work around. If employee records contain duplicate identities, inconsistent job structures, manual overrides or different definitions of full-time equivalent and absence, the resulting insight will be unreliable. Establish common definitions, ownership and quality checks before scaling AI use cases.

Governance and compliance must be designed in

Employee and student data is sensitive. Institutions need clear rules for lawful use, access, retention, security, explainability and human review. Particular care is needed where AI could influence recruitment, performance, scheduling, absence or other people decisions. A useful governance model brings HR, payroll, finance, IT, data protection, procurement and academic leadership together, with a clear route for escalation.

Silos are also a people and process problem

Disconnected systems often reflect disconnected ownership. A new interface will not fix unclear responsibilities, competing definitions or processes built around local workarounds. Data stewardship therefore needs senior sponsorship and practical participation from the teams that create and use the data every day.

Bring staff with you

Staff anxiety is real, particularly where automation is associated with job reduction or surveillance. HR has a central role in explaining purpose, involving employees in design, identifying new skills and showing how AI will support rather than replace judgement. Early, visible benefits such as removing repetitive administration or improving access to information help build trust.

• HR leaders see their current workforce, skills, absence and cost information without reconciling multiple spreadsheets.
• Payroll teams use automated checks to identify anomalies earlier, while retaining clear review and approval controls.
• Finance can model workforce scenarios alongside budgets and operational demand, rather than treating people cost as a static line item.
• Employees receive faster answers to routine questions and a more consistent onboarding and self-service experience.
• Senior leaders can connect people, cost and institutional priorities while understanding the limits and confidence of the data behind each insight.

These outcomes depend on the wider institution too. The aim is not to create a perfect HR, payroll or finance island, it is to make these functions reliable participants in a connected, governed transformation.

1. Establish the baseline

Map the systems, integrations, manual workarounds and data owners involved in a small number of high-value processes, such as onboarding, payroll change, workforce planning or absence management.

2. Agree common definitions

Create a shared vocabulary for employee, headcount, full-time equivalent, vacancy, absence and cost. Record who owns each definition and how quality will be monitored.

3. Prioritise use cases

Choose use cases where better data and automation can produce a measurable benefit without creating disproportionate risk. Start with assistive or analytical use cases before high-impact automated decisions.

4. Build controls alongside capability

Set expectations for human review, audit trails, security, supplier assurance, model testing and incident handling. Involve data protection and employee representatives early.

5. Invest in adoption

Train teams not just on the new tool but on the new processes. Communicate what changes, what does not, and how employees can challenge or correct an outcome. Ensure that this doesn’t just involve HR, payroll, finance and IT teams but all relevant departments across the university.
 

The London School of Economics and Political Science (LSE) provides a useful example of transformation grounded in operational need. Its previous HR and payroll systems were disconnected and depended on manual processes. As part of a wider digital transformation strategy, LSE selected MHR to support a workforce of around 5,000 people across varied roles and contract types.

The implementation focused on usability, adoption and better access to data, rather than technology for its own sake. More than 4,600 employees were using the platform within one month; 92% adoption was reported after the first month, 90% of surveyed users agreed the system was user-friendly, and 100% said they would recommend it. 

LSE’s approach also illustrates the human side of AI readiness. By using AI and automation for operational tasks, the team have more time to spend on direct contact with the users of their HR services.

“We’ll be able to use that time freed up from automation and create value-added work where staff have direct calls with people who have particularly difficult or challenging issues,” says Indi Seehra, LSE’s Director of Human Resources.

The biggest opportunity offered by AI automation is releasing capacity for more direct, value-added conversations with employees and service users. That lesson is relevant to every university planning its next phase of AI-enabled digital transformation.

AI readiness is a strategic realignment of data, systems and people. HR, payroll and finance should be central to that conversation because they connect workforce capacity, employee experience, compliance and cost. But they are one part of a much larger transformation that also includes student systems, learning, research, estates, finance, security and institutional governance.

The data needed to begin is likely already present. The work is to connect it, clean it and govern it so that AI supports sound decisions rather than amplifying old inconsistencies. Institutions that start with practical use cases, strong controls and staff engagement will be better placed to scale AI responsibly.

MHR helps higher education institutions modernise HR and payroll, improve workforce visibility and build the foundations for more connected decision-making. If you are assessing your AI readiness, start with the processes where better people and cost data could make the clearest difference.