Preparing Data and Governance Before Piloting AI Features
Practical readiness steps aligned with institutional policies and consent.
Key takeaways
- AI output quality depends on the data underneath.
- Agree on consent and access rules before any pilot.
- Pick one narrow, measurable use case to start.
01Data quality comes first
Inconsistent records, duplicate students and missing fields lead to unreliable insights. A short data clean-up before a pilot is usually the best investment an institution can make.
AI output quality depends on the data underneath.
02Consent, access and policy
Institutions should confirm who can see which data, how consent is recorded and how long information is retained. These rules should be written down and reflected in system permissions.
- Role-based access reviewed by department heads
- Consent recorded per purpose
- Retention periods agreed with leadership
03Choose a narrow first pilot
A focused pilot, such as summarising attendance trends for one campus, is easier to evaluate and safer to run than a broad rollout.
CareerBook Editorial Team
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