- Before AI, Repair Your Information By Muddassir Siddiqi
- 06/25/26
Walk into almost any cabinet meeting, professors senate, or innovation committee at a college or university today, and you’ll hear the exact same discussion: 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 concerns. But there’s a more essential one that frequently gets avoided– and it may be the most important question of all.
Is our data all set?
It sounds simple. It isn’t. And for many organizations, the truthful answer is: Not yet.
The Tool Isn’t the Issue
Generative AI tools– ChatGPT, Gemini, Copilot, Claude– have actually moved from interest to institutional technique with impressive speed. Administrators are using them to draft communications and sum up reports. Faculty are try out them in the class. Student services teams are checking out AI-powered chatbots for recommending and financial assistance support.
The excitement is easy to understand. These tools are genuinely remarkable. But here’s what tends to get lost in interest: The quality of what generative AI produces depends nearly entirely on the quality of the details it draws from. Advanced AI sitting on top of fragmented, outdated, or badly governed institutional data will create sophisticated-sounding incorrect answers.
That’s not hypothetical. It’s already happening at institutions that released AI assistants before they had their information house in order– tools with confidence directing students to financial assistance policies that had been upgraded 2 years ago or recommending resources that existed just on a SharePoint folder no one kept.
AI can just be as efficient as the information it can gain access to. If institutional data is fragmented, outdated, or badly governed, AI will merely generate mistakes much faster and with greater self-confidence.
The Hidden Issue: Institutional Knowledge Is Scattered
A lot of institution of higher learnings have more data than they understand what to do with. Student information systems, finding out management platforms, CRM tools, financial assistance systems, and dozens of departmental applications have actually been accumulating records for years.
However data volume isn’t the like information preparedness. The real challenge isn’t having insufficient info– it is that critical institutional knowledge resides in a lot of locations, in too many formats, with insufficient governance.
Think about what it takes for an AI system to reliably address a question like: What are the transfer pathways for a nursing student who began at a neighborhood college and wishes to finish a bachelor’s degree at a state university?
The answer involves curriculum requirements, expression contracts, financial aid eligibility rules, encouraging workflows, accreditation standards, and move credit policies. That info might live across 5 different systems, three various websites, a shared drive no one has actually touched in 18 months, and a PDF that was accurate as of the last catalog cycle.
A public AI design can not distinguish between a present institutional policy and an obsoleted document buried in a departmental repository– unless the institution has actually purposefully curated and governed what the AI can gain access to. Many have not.