Before AI, Fix Your Data
Walk into almost any cabinet meeting, faculty senate, or technology committee at a college or university today, and you’ll hear the same conversation: How do we use AI? Which tools do we pilot first? How do we write an acceptable-use policy? How do we train faculty and staff?
These are reasonable questions. But there’s a more fundamental one that often gets skipped — and it may be the most important question of all.
Is our data ready?
It sounds simple. It isn’t. And for most institutions, the honest answer is: Not yet.
The Tool Isn’t the Problem
Generative AI tools — ChatGPT, Gemini, Copilot, Claude — have moved from curiosity to institutional strategy with remarkable speed. Administrators are using them to draft communications and summarize reports. Faculty are experimenting with them in the classroom. Student services teams are exploring AI-powered chatbots for advising and financial aid support.
The excitement is understandable. These tools are genuinely impressive. But here’s what tends to get lost in enthusiasm: The quality of what generative AI produces depends almost entirely on the quality of the information it draws from. Sophisticated AI sitting on top of fragmented, outdated, or poorly governed institutional data will generate sophisticated-sounding wrong answers.
That’s not hypothetical. It’s already happening at institutions that deployed AI assistants before they had their information house in order — tools confidently directing students to financial aid policies that had been updated two years ago or advising resources that existed only on a SharePoint folder nobody maintained.
AI can only be as effective as the information it can access. If institutional data is fragmented, outdated, or poorly governed, AI will simply generate errors faster and with greater confidence.
The Hidden Problem: Institutional Knowledge Is Scattered
Most colleges and universities have more data than they know what to do with. Student information systems, learning management platforms, CRM tools, financial aid systems, and dozens of departmental applications have been accumulating records for decades.
But data volume isn’t the same as data readiness. The real challenge isn’t having too little information — it is that critical institutional knowledge lives in too many places, in too many formats, with too little governance.
Think about what it takes for an AI system to reliably answer a question like: What are the transfer pathways for a nursing student who started at a community college and wants to complete a bachelor’s degree at a state university?
The answer involves curriculum requirements, articulation agreements, financial aid eligibility rules, advising workflows, accreditation standards, and transfer credit policies. That information might live across five different systems, three different websites, a shared drive nobody has touched in 18 months, and a PDF that was accurate as of the last catalog cycle.
A public AI model cannot distinguish between a current institutional policy and an outdated document buried in a departmental repository — unless the institution has intentionally curated and governed what the AI can access. Most haven’t.
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