Planning your university’s next step with AI: a practical guide
After a AI discussion, university leaders need to decide what happens next. What evidence is needed before committing resources? Who should help assess the opportunity?
In our Inside the AI-Ready University webinar, Gerry Greyling, Chief Operating Officer at Bournemouth University discussed the university’s consideration of its own capability and capacity. He explained the value of working witha partner and taking a phased approach, with agreed points to review progress before moving forward.
His account offers a useful starting point for other institutions: define a next step that leadership, finance and service colleagues can assess together.
The guide below sets out ICS.AI’s suggestions for preparing for that discussion.Start with the decision you need to make
An initial conversation might focus on whether a particular service problem warrants further assessment.
Choose one process and involve the colleagues who understand how it works. Bring a recent example and consider:
What is the problem, and who does it affect?
Where does work get delayed or repeated?
Which decisions require human judgement?
What would a better outcome look like?
What do you already know, and what still needs investigating?
This gives colleagues something specific to discuss. It also helps them test the scope and assumptions before considering a wider investment.
Ask for a demonstration that reflects the work
A relevant demonstration should reflect the requests, unusual cases and approvals the service handles.
Agree beforehand what you need to understand. That could include which information AI system can access, what happens when a request is incomplete, which checks take place before an action, and when a member of staff needs to review or approve a decision.
Record any questions the demonstration leaves unanswered. These can inform further assessment and planning.
A demonstration can help you understand an approach, but it does not establish a financial return or prove how it would perform in your university’s environment.
Connect readiness with the business case
The SMART: AI Assessment introduced in the webinar brings together strategy and leadership, data and infrastructure, people and skills, process readiness, and ethics and governance. It also sets out prioritised use cases, a deployment roadmap and a costed business case.
For a leadership team, the value lies in understanding how those elements affect each other. If a promising use case depends on improving data quality or changing a process first, those dependencies should be visible in the plan, timings and costs.
That makes it easier to assess what is feasible now, what needs preparation and where to focus effort.
Agree what would justify moving forward
Before progressing to another phase agree what evidence is needed, who will review it and what findings would lead you to change the plan.
Distinguish between potential cash savings and staff time released for other work. For service improvements, choose measures the team can track, such as turnaround times, repeat enquiries or time spent on manual checks.
These measures give colleagues a shared basis for reviewing progress and deciding whether to continue, adjust the approach or pause.
Bring one process to the conversation
You do not need a finished plan to start. Bring one process you would like to improve, the outcome you want and the questions you need answered.
Our Education team can help you explore whether a focused demonstration or a SMART: AI Assessment would be a useful next step.
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