Data, automation and AI in Higher Education

Universities in the UK are entering the final quarter of the financial year, a year that’s been filled with challenges for many working in the Higher Education sector. The Guardian’s 15 April article paints a stark picture, warning of a “bonfire of jobs” and anticipating that up to three quarters of universities could be in financial deficit by July 2025. 

It comes as no surprise then, that recent conversations with our Higher Education customers have focused on how to unlock cost savings via process efficiencies, particularly in automation, data management, integrations and the potential for Artificial Intelligence (AI). 

Efficiency over hype 

At the UCISA Leadership Conference in Brighton recently, and the Times Higher Education Digital Universities 2025 event at Lancaster University, the conversation was dominated with how to do more with less, how partnerships need to fill employee gaps and how systems can work together better, instead of wholesale replacements, which take too long to deliver real savings, if ever. 

Cyber security is also high on the agenda. With limited IT teams often stretched thin, the need for 24/7/365 service is becoming non-negotiable. Universities are trying to lower their operational, financial and reputational risks, but it’s like tackling a pyramid of perpetual problems. 

The AI temptation – and the risk 

Talk of AI is everywhere, from keynote speeches to government blueprints, but the push towards efficiency via driving data, automation and AI solutions can be distracting if you’re looking for quick answers. Keir Starmer and Rachel Reeves’ ‘Blueprint to turbocharge AI,’ published in January, has added political heavyweight fuel to the existing fire of chatter. However, this media-fuelled enthusiasm is starting to trickle down in problematic ways. Under the pressure of protecting the future of their institutions as best they can, some top ‘C’ level leaders are now at risk of pushing digital teams with demands like, “just get us AI,” expecting immediate and transformative results. But that’s simply not how it works. 

At Softcat we spend a lot of time working with key technical partners. During a recent visit to IBM’s head office in Winchester, we were reminded of what’s really needed to achieve the savings and improvement universities are craving. While automation and AI hold genuine potential, they’re not magic switches to save millions of pounds. We discussed how the best outcomes come from realistic, structured approaches grounded in proper foundations. 

Small gains, long-term benefits 

While better data, automation and AI solutions can bring efficiency, some of the benefits are often built up in fractions of job roles and time, making them difficult to quantify. They don’t always translate neatly into statutory financial statements, but they’re still essential in reducing internal pressure. 

To enable a university to move closer to unlocking such benefits and improved integrations within key processes, there are some important foundational challenges that should be considered. Some institutions are further along this journey than others, but five key steps stand out: 

1. Address strategic vision and leadership 

Establish a clear vision for AI integration, supported by strong leadership. This involves setting goals, defining the scope of AI projects, and ensuring alignment with the university's mission and values. 

2. Consider an AI Centre of Excellence (CoE) 

Creating an AI Centre of Excellence to lead AI initiatives, set best practices and provide guidance is a great way of bringing the right ideas together in one place. It facilitates collaboration across departments, promotes AI literacy and ensures ethical use of AI technologies, especially when it reaches into research disciplines too. 

3. Supporting infrastructure and technology 

Many universities still have large legacy technology estates, making it harder to modernise. A strong foundation is crucial, however, including high-performance computing resources, data storage solutions and AI development tools. Softcat’s expertise and partnerships in networking and connectivity, security and hybrid platforms can help with these significant transformation challenges and unlock future potential. 

4. The need for data management and governance 

Data is often fragmented across different systems and departments. Developing robust data management practices and policies, including data ownership, collection, storage and security is vital. Ensuring data quality, privacy and compliance with regulations is a big step towards ensuring AI tools deliver value. 

5. Policy to support the ethical and responsible use of AI 

It’s easy to rush towards AI solutions without thinking through the implications - think about the themes in the film Jurassic Park here, perhaps! Suppliers of software products and systems are already busy sewing AI enhancements into their products and selling those onto interested customers. With this in mind, universities must put in place guardrails: clear policies, ethical frameworks and thoughtful procurement that considers their supply chain too. 

Taking the next step with Softcat 

There is good news in all of this! At Softcat, we’ve recently taken a major step to enhance our ability to support universities with data, automation and AI challenges and projects. Our acquisition of Oakland, a data and AI consultancy business adds to our existing capabilities and expertise in this space.  

This will allow us to help universities with their key challenges, settling some of the anxieties across Higher Education by building realistic roadmaps and shaping maturity steps to unlock better data and automation, reaching the efficiencies everyone in the sector really wants. Our goal is to bring clarity and structure to a space that often feels overwhelming. 

If you’d like to find out more, please get in touch with the team: HigherEd@softcat.com.