Preparing for digital transformation in the age of AI

Strive Higher thanks Melody Askari for co-hosting this webinar with Emma Bayne and Laura Jackson. Watch the full webinar for insight, challenge and optimism about the future of higher education in the age of AI.

This webinar took place on 16 June 2026 and followed on from the publication of a related series of insights by Melody which are available via the following links:

• Strategy and foundations for digital transformation

• User journeys and operational readiness

Watch the webinar:

https://www.youtube.com/watch?v=UThon3_r2Zc

The following reflection on the webinar was authored by Emma Bayne, Principal Consultant:

We had a really engaging discussion about how higher education institutions can get ready for digital transformation in a rapidly evolving world. We explored everything from digital foundations and data readiness to culture, governance and experimentation - as well as the practical challenge of turning ambition into real, meaningful change.

There was a strong sense of both excitement and realism throughout. While the potential of AI and automation is clear, the conversation kept returning to the importance of getting the fundamentals right first.

 

Key takeaways

One thing came through clearly: AI on its own won’t transform institutions. Real, lasting value comes when it’s built on solid foundations - clear processes, reliable data, sustainable platforms, good governance, and a culture where people feel confident to learn, test and adapt.

Some of the themes we kept coming back to were:

• Start with the basics

  Digital transformation isn’t just about new technology. It starts with understanding and simplifying processes - figuring out what each step is really for and removing duplication or unnecessary complexity.

• Adopt, don’t over-customise

  It’s tempting to tailor systems heavily, but that often creates technical debt and makes future upgrades harder. Customisation should be the exception, not the default.

  
  

• Get your data in shape

  AI relies on good data. That means clear ownership, consistency and quality. Without that, it’s hard to trust the outputs or use them to make confident decisions.

  
  

• Keep people at the centre

  AI can highlight patterns and support decisions, but it can’t replace human judgement. People still need to ask the right questions, interpret context and take responsibility for outcomes.

  
  

• Focus on purpose, not the technology

  The most effective use of AI starts with a clear problem and a defined outcome. It’s about what you’re trying to achieve - not just adopting AI for the sake of it.

  
  

• Use governance to enable, not block

  Clear guardrails and accountability help people experiment safely and openly, rather than pushing innovation into the shadows.

  
  

• Make progress step by step

  You don’t need perfect conditions to begin. Starting small, with focussed use cases, can build confidence while foundations continue to improve.

 

Where discussion took us

The discussion naturally evolved through some thoughtful questions:

• What if our data isn’t perfect?

  The consensus was to be pragmatic. Data rarely is perfect, but that shouldn’t stop progress. Focus on specific use cases, involve people who understand the data, validate outputs and be transparent about any limitations.

  
  

• How do we help people see the potential of AI? 

  Real examples matter. Pilots, champions and communities of practice help make AI tangible and relevant to people’s day-to-day work - much more than abstract strategies or fear-driven narratives.

  
  

• How do we move from isolated experimentation to something more joined-up? 

  Innovation often starts with individuals, but scaling it safely needs structure. Things like clear ownership, documentation, support models and strong champions are key to bridging that gap.

Where to start?

If you are taking this forward, some practical first steps could be:

• Identify a few high-value use cases where AI or automation could make a visible difference - start with the problem, not the tool

• Define what success looks like, including impact on staff and student experience

• Simplify processes before trying to digitise or automate them

• Assess your readiness across data, processes and governance

• Run small, focused experiments to build confidence and evidence

• Create safe spaces for people to explore within clear guardrails

• Build strong cases that show both organisational value and individual impact

• Keep human judgement front and centre, especially where decisions affect people

 

Want to learn more?

We’d really love to hear how this resonates and what you plan to do next. This conversation on 16 June 2026 highlighted both the opportunities AI and automation offer, and the foundations needed to make the most of them.

We’re keen to keep the momentum going, so we’ll be continuing this as a series of conversations exploring AI and automation across the sector.

If you would like to join us, please contact us.