Administrative staff drowning in manual inquiries
A use case in higher education: routine student inquiries automated, and scattered campus AI use brought under one framework.
A university's student-services team spends thousands of hours each semester manually answering, categorizing, and routing routine inquiries — deadlines, requirements, forms, account questions — while students with complex, personal situations wait in the same queue. The work is necessary but mechanical, and it consumes the people best equipped to help students who truly need a human.
At the same time, AI is spreading across campus with no institutional oversight. Faculty use generative AI in course design, researchers adopt AI tools independently, administrative offices experiment individually, and existing vendors quietly add AI features to systems the institution already runs. No one can answer a basic question: what AI is this institution actually using?
A discovery engagement builds the institution's first complete AI inventory: dozens of tools across academic, research, and administrative departments, mapped by data access, owner, and risk. The same work identifies student-services intake as the highest-value adoption opportunity — high-volume, repetitive, and ideal for governed automation.
The institution establishes an approved-tool registry, assigns accountable owners for each AI system, maps governance activities to the NIST AI Risk Management Framework, and sets human review standards for any AI touching student information. Policies, approvals, and evidence live in one place instead of scattered across departments.
With governance in place, student services can deploy an approved AI assistant for routine inquiries. Common questions resolve instantly; complex and sensitive cases route to staff with full context. Faculty and researchers gain a clear, fast path to approval for their own tools — so adoption moves into the open.
What this makes possibleRoutine inquiry handling largely automated, staff hours redirected to students who need personal attention, and leadership holding — for the first time — a defensible, institution-wide picture of AI use, risk, and accountability.