- Before AI, Fix Your Data By Muddassir Siddiqi
- 06/25/26
Stroll into nearly any cabinet meeting, professors 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 initially? How do we compose an acceptable-use policy? How do we train faculty and personnel?
These are sensible concerns. But there’s a more basic one that typically gets skipped– and it might be the most crucial question of all.
Is our data all set?
It sounds simple. It isn’t. And for a lot of institutions, the sincere response is: Not yet.
The Tool Isn’t the Issue
Generative AI tools– ChatGPT, Gemini, Copilot, Claude– have actually moved from interest to institutional strategy with exceptional speed. Administrators are utilizing them to prepare communications and sum up reports. Professors are experimenting with them in the classroom. Trainee services teams are checking out AI-powered chatbots for encouraging and financial assistance support.
The excitement is understandable. These tools are really outstanding. 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 info it draws from. Advanced AI sitting on top of fragmented, outdated, or improperly governed institutional information will generate sophisticated-sounding wrong responses.
That’s not theoretical. It’s currently occurring at institutions that released AI assistants before they had their information home in order– tools confidently directing students to financial aid policies that had been updated 2 years ago or advising resources that existed just on a SharePoint folder no one preserved.
AI can only be as effective as the info it can gain access to. If institutional information is fragmented, outdated, or poorly governed, AI will merely produce errors quicker and with greater confidence.
The Hidden Problem: Institutional Knowledge Is Spread
The majority of colleges and universities have more data than they understand what to do with. Trainee information systems, discovering management platforms, CRM tools, financial assistance systems, and lots of departmental applications have been accumulating records for decades.
However data volume isn’t the same as data readiness. The real obstacle isn’t having too little details– it is that vital institutional knowledge resides in a lot of locations, in a lot of formats, with too little governance.
Think about what it takes for an AI system to reliably address a concern like: What are the transfer paths for a nursing trainee who began at a community college and wants to finish a bachelor’s degree at a state university?
The response involves curriculum requirements, expression agreements, financial aid eligibility guidelines, encouraging workflows, accreditation requirements, and transfer credit policies. That information may live across five various systems, three various websites, a shared drive no one has touched in 18 months, and a PDF that was accurate since the last catalog cycle.
A public AI design can not compare a present institutional policy and a dated file buried in a departmental repository– unless the organization has actually purposefully curated and governed what the AI can access. A lot of have not.