Where could AI add value at your university?
A university process can look straightforward until a request arrives without the right information or an unusual case needs attention. Those moments are a useful place to start a discussion about where AI could help.
Our webinar, Inside the AI-Ready University: A Live Walkthrough with Bournemouth University, includes two demonstrations that make these questions concrete. Dr Cris Bloomfield walks through procurement and records review in a fictional university setting.
These examples illustrate possible workflows and are separate from Bournemouth’s account of its own AI journey.
Does the request contain what the next person needs?
In the procurement example, a researcher requests laptops for a funded project. The assistant reviews the information, flags a funding-code issue and asks for missing details before the request moves forward.
The useful question for another university is where its own requests become incomplete. Which details are essential? How often do colleagues have to return a request or chase an answer? A demonstration should help the team see how those gaps would be handled, including when a person needs to resolve the uncertainty.
Who makes the decision?
The procurement walkthrough keeps the requester and approver separate. The approver can review the information before making a decision. Cris also explains that a later ordering step could still involve a person, depending on the institution’s arrangements.
The records-review example explores a different exception. An assistant highlights material that may relate to an active student appeal. A member of staff examines the connection, requests a hold on relevant records and confirms the justification. The assistant does not decide the appeal.
Together, the examples help a team examine where responsibility sits, what a reviewer needs to see and how a decision is recorded.
How will you measure the impact?
In the leadership discussion, Bournemouth University COO Gerry Greyling describes the importance of understanding current data and practices before making investment decisions.
For your own team, start with one process. Record the volume of requests, staff effort, waiting time and recurring corrections where that information is available. Make estimates visible and identify the gaps that need checking.
Then describe the intended improvement. A reduction in waiting time, better information for staff or released capacity each needs a suitable measure. Time released does not automatically become a cash saving, so explain how any capacity would be used.
See what AI could do for your university
Explore Bournemouth University’s AI journey and see practical demonstrations of how AI could support university teams, from handling requests to reviewing records.
Watch the webinar recording: https://info.ics.ai/Inside-the-AI-Ready-University---Bournemouth-University/registration-form.html
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