Words by Alison Cairns, education lead at EY Oceania
AI is starting to look like a lifeline for university leaders seeking an operating model that confers both financial sustainability and significant competitive advantage. Embedded in operating models, AI can support institutions to make evidence-based decisions with less effort and at lower cost while also improving the staff and student experience.
As just a tiny example of what’s possible, one university that EY works with is using an AI-infused, automated process to reduce admin tasks for admissions staff while cutting the time taken to respond to students with offers from weeks to days. Imagine the impact on staff and students if this approach was mirrored in every process underpinning the undergraduate experience.
The key word here is impact. This is not just about getting a return on investment by making the organisation more efficient. Successful AI-enabled transformations have a profound human impact, improving the experience of students, teaching faculty, researchers and administrative staff.
In 2023, EY and THE surveyed 3,030 undergraduate and postgraduate students across eight geographies and conducted focus groups and interviews with staff leaders at universities in these countries. The study highlighted the fact that digital and AI are key to meeting people’s expectations of the university experience. Students are demanding exceptional teaching enhanced by digital technology, and slick, convenient services. Meanwhile, academic and administrative staff are crying out to be freed from busywork and given intelligent tools and real-time data to help them do make their jobs more impactful.
In response, many universities are investing in multiple AI projects. But few of these projects have yielded the expected dividends yet. This almost always comes down to the ability to scale up. To deliver real impact, AI needs to be an enterprise-wide proposition, focusing on areas of greatest impact.
Higher education is struggling because either AI implementations are happening in departmental silos or universities lack the digital backbone and ecosystem required to truly scale. Those few institutions that are getting AI right are following these principles:
Focus on the fundamentals
Before investing in AI, universities need to consider the processes, systems and data maturity that AI will take advantage of. This will likely include:
- Process re-engineering, automating simple tasks and streamlining common workflows
- Building a solid data infrastructure that is governed to avoid bias and can be trusted to support accuracy, consistency, privacy and security
- Supporting interoperability among the university’s various systems and applications
Be selective about AI investment
Rather than having hundreds of uncoordinated, small AI experiments across the university, plan at scale and invest for a cycle – not for a quick hit. It’s important to think about value up front. How will this help improve staff and student experience, allow researchers to focus on research and academics on quality teaching? What are you trying to improve? Can you do it at scale? Even if scattered proofs of concept are successful, they rarely scale beyond the faculty or department.
To get the biggest return on impact from AI, universities must be set up for success. This requires a whole-of-institution approach that identifies priority use cases, whether that’s improving productivity, revolutionising teaching or using AI to maximise research grants. Before committing to AI investment, leaders must first understand where this promising technology will yield the greatest value in their institution. What will the return on impact be?
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