Where should a university use AI in administration?
In the repetitive work around a decision. AI can triage an incoming request to the right office, draft a first version of a routine reply, and flag a missing admission document before it causes a delay. Think of a help desk that gets a thousand messages a week: AI can read each one, label it, and send the password resets one way and the enrollment questions another, so a person opens a sorted queue instead of a wall of email. These uses save staff real time and don't touch anyone's record.
Drafting is the other everyday win. An advisor answering the same enrollment questions can start from an AI draft that already pulls the right dates and the relevant policy, then edit and send, which turns a blank page into a two-minute reply. The draft is a starting point for a person, not an answer that goes out on its own, and that line is exactly what governance holds in place.
Which decisions should AI never make about students?
Anything with consequence for a student: academic standing, financial aid, admission, discipline, or any decision a student could appeal. A person decides those, and AI can prepare the information but doesn't get the final say. For example, AI can gather an aid applicant's documents and summarize them, but a financial-aid officer signs off on the award. This also governs what AI may see: under FERPA, student education records are protected, so AI should only reach the records a given task needs and nothing more.
What does AI governance mean for a university?
It means four guarantees you can point to:
- Permissions: AI sees and touches only the data a task allows, in line with FERPA.
- Audit trail: every action AI takes is logged and can be reviewed.
- Version control: you always know which version of a process is running.
- Controlled releases: a change is tested and approved before it reaches live work.
Say a university turns on an AI step that drafts admission reminders. With these guarantees, it only sees the applicants it should, every draft it writes is logged, and the new step is tested before a single student receives it. RunMyProcess deploys AI with controlled token spend and full audit trails, and operates under ISO 27001 and ISO 27701 for information security and privacy, so the governance is built in.
How do you add AI to an existing process safely?
Add AI as a step inside a process you already run and trust, not as a new system of its own. The process already has the rules, the approvals and the audit trail, so AI slots in to suggest, draft or check while a person keeps the final decision. A registrar's transcript-review process, for instance, can gain an AI step that pre-checks each request against the rules, while the registrar still approves. Start with one process and one well-scoped use, prove it, and expand from there.
None of this requires a campus-wide AI program. A university can turn on one governed step, watch the logs, and decide from evidence whether to extend it, which is a far safer path than buying a tool and hoping it behaves. Governance is what makes that incremental, reversible approach possible, because you can always see what the AI did and switch a step off without unpicking a whole system.
Pick one process you already trust and one task inside it, and bring both to a consultation. We will map where a governed AI step fits, what it would log, and how you would switch it off, so you can start small and expand only on evidence.
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