Before AI, Fix Your Data

• By Muddassir Siddiqi
• 06/25/26

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 Sophisticated AI sitting on top of fragmented, outdated, or poorly governed institutional data will generate sophisticated-sounding wrong answers.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 If institutional data is fragmented, outdated, or poorly governed, AI will simply generate errors faster and with greater confidence.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.

Institutions that have invested in curated knowledge repositories and governed data environments are finding that AI assistants can deliver far more accurate and reliable support for students, employees, and institutional decision-making.

4 Things Leaders Should Do Now

The good news is that data readiness isn't an all-or-nothing proposition. Institutions don't have to solve every data problem before they can begin using AI responsibly. But they do need to treat information as a strategic asset — not a byproduct of operations — and start building toward it now. Four priorities stand out.

1) Map where your institutional knowledge actually lives. Before deploying any AI assistant to support students, faculty, or staff, institutions should conduct an honest audit of where critical knowledge resides. Curriculum maps, advising resources, financial aid policies, accreditation documents, and operational procedures are often scattered across fragmented repositories with varying levels of accuracy and currency.

The goal isn't to build a perfect knowledge base overnight. It's to identify authoritative sources, eliminate outdated content, and make critical information findable — by both humans and AI systems. Institutions that invest in this work first will find that their AI tools perform significantly better than those that skip it.

2) Build governance that actually keeps up. Reliable AI requires reliable information — and information changes constantly. A governance framework that isn't continuously maintained will decay quickly, and AI tools trained on decayed information will erode trust just as quickly.

Institutions should assign clear ownership for critical data assets and establish accountability for keeping the information AI systems use current and accurate. That means designating subject-matter experts responsible for validating content, establishing version control protocols, and creating feedback loops that surface data quality problems before they affect student-facing decisions.

In the AI era, information governance is rapidly becoming as important as financial governance and cybersecurity governance.

3) Connect your fragmented systems. Most institutions run a collection of enterprise systems that were built at different times, for different purposes, and that don't talk to each other particularly well. Student information systems, CRM platforms, LMS tools, financial systems, and departmental applications frequently function as separate data islands.

This fragmentation directly limits what AI can do. When information is siloed, AI applications can only access a partial view of institutional knowledge — and partial knowledge produces partial, sometimes dangerous, answers.

Improving interoperability and adopting shared data standards won't happen overnight. But institutions should prioritize connecting their most critical information sources and look for integration opportunities as they evaluate new AI investments.

4) Protect privacy and earn trust before you need it. Trust is the currency that makes institutional AI work — and it's far easier to spend than to rebuild. Higher education institutions manage some of the most sensitive personal information that exists: academic records, financial data, behavioral patterns, health information, and demographic data.

When AI systems rely on poorly governed information, generate inaccurate outputs, or operate without transparency, trust erodes — and it erodes fast. Institutions must implement strong privacy protections, clearly defined access controls, and transparent policies about how AI is being used and what data it can access. Stakeholders — students, faculty, staff, and governing boards — need to know that AI is being deployed responsibly, not just efficiently.

Data Readiness Is Institutional, Not IT

Many leaders still view data readiness as an information technology issue. It is not. Data readiness is an institutional capability that requires collaboration among academic affairs, student services, enrollment management, institutional research, finance, and information technology. AI draws from the collective knowledge of the institution, and preparing that knowledge for responsible use requires institution-wide ownership.

The Competitive Advantage Is Preparation, Not Speed

There's a temptation in higher education — as in most sectors — to treat AI adoption as a race. Which institution deploys first? Which vendor gets the deal? Which campus has the most impressive demo?

But the institutions that will derive the greatest long-term value from AI are not necessarily the ones moving fastest. They're the ones preparing most thoughtfully. The institution that invests in curating trusted knowledge assets before deploying AI will often outperform the institution that prioritizes speed over preparation. This is a leadership issue as much as a technology issue. Chief information officers, chief academic officers, and institutional research leaders need to make the case to their presidents and boards that data readiness is a core institutional capability — as essential to long-term competitiveness as financial stewardship or accreditation compliance.

The institutions that invest in trusted, accessible, and well-governed information assets today are the ones that will be positioned to innovate, adapt, and serve their students effectively in an increasingly AI-enabled environment.

Before asking what AI can do for your institution, ask the more important question first: Is our data ready?