Correct Your Data Before AI
If you attend almost any college or university cabinet meeting, faculty senate, or technology committee today, you will hear the same discussion: How do we use AI? Which tools do we test first? How do we write a usage policy? How do we train teachers and staff?
These are legitimate questions. But there’s a more fundamental question that often gets skipped – and it may be the most important question of all.
Is our data ready?
It sounds easy. That’s not it. And for most institutions, the honest answer is: not yet.
The Tool is Not the Problem
Generative AI tools – ChatGPT, Gemini, Copilot, Claude – have moved from curiosity to institutional strategy with remarkable speed. Administrators use them to write messages and summarize reports. Teachers experiment with these tools in the classroom. Student services teams are exploring AI-powered chatbots for advice and financial assistance.
The excitement is understandable. These tools are really impressive. But what gets lost in the enthusiasm is that the quality of what generative AI produces depends almost entirely on the quality of the information it draws from. Sophisticated AI sitting on fragmented, outdated, or poorly managed institutional data will generate sophisticated-sounding false answers.
This is not hypothetical. This is already happening at institutions that were deploying AI assistants before they had their information center in order – tools that safely pointed students to financial aid policies that were updated two years ago or recommended resources that only existed in a SharePoint folder that no one was maintaining.
AI can only be as effective as the information it can access. When institutional data is fragmented, outdated, or poorly managed, AI will simply generate errors faster and with greater certainty.
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 is not the same as data readiness. The real challenge is not that there is too little information, but that critical institutional knowledge exists in too many places, in too many formats, and with too little governance.
Consider what it takes for an AI system to reliably answer a question like this: What transfer paths are available for a nursing student who started at a community college and wants to complete a bachelor’s degree at a state university?
The response includes curriculum requirements, articulation agreements, financial aid eligibility rules, advising processes, accreditation standards, and transfer credit policies. This information could exist on five different systems, three different websites, a shared drive that no one has touched in 18 months, and a PDF file that was correct at the time of the last catalog cycle.
A public AI model cannot distinguish between a current institutional policy and an outdated document buried in a department archive—unless the institution has intentionally curated and regulated what the AI can access. Most don’t.
Ensuring that your institution is ready for AI begins with a commitment to data governance and quality. Institutions must take a proactive approach by consolidating data sources, updating information regularly, and implementing strong governance policies to ensure data accuracy and accessibility. Only then can AI tools be leveraged effectively and responsibly.
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