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Most people encounter generative artificial intelligence through an application. They upload a document, ask a question, and receive an answer without having to think much about where the model is running or who controls the infrastructure behind it. But for a university, those questions matter. They shape what happens to institutional data and which models students, staff, faculty, administrators, and researchers can use. They also determine how much control the institution keeps over the systems it depends on. 

Artificial intelligence is changing how universities search, summarize, analyze, and generate information. AI is powerful, which is perhaps why less experienced users are sometimes surprised by this limitation:

his piece tackles a problem that’s becoming more relevant as AI tools spread into everyday work: how do you check the quality of something an AI produced, when there’s no answer key to grade against?

Research administration is risk-averse on purpose. The work navigates federal regulations, sponsor-specific terms, institutional policies, and audit trails that survive personnel changes by years. RAs have been trained well, and the training has stuck: when in doubt, slow down; when the rule is unclear, ask; when the answer is unverifiable, do not submit. That posture is not a deficit to be retrained. It is an asset the institution already has, and it should be respected as such.