Colleges and universities are in the midst of what feels like the wild, wild west of AI. New vendors are riding into town every month, while existing vendors are rapidly repositioning their products around artificial intelligence. Boards are asking presidents how their schools are incorporating AI strategy. Enrollment leaders are being pressured to “do something” with AI … and the list goes on.
The result is a quickly evolving frontier for institutions to navigate. Yet amid all the excitement, many colleges are focusing on the wrong part of the AI equation. And much like the broader business world, higher ed is learning an uncomfortable lesson: AI alone isn’t delivering the returns many expected. While organizations report productivity gains, far fewer report meaningful improvements in revenue, outcomes, or strategic performance. The difference isn’t the model they chose. It’s the foundation underneath it.
Why Most AI Conversations Miss the Point
The challenge isn’t that AI tools are incapable. It’s that most institutions are being sold tools before they’re being given context, or the “true underpinnings” required to achieve their enrollment and revenue goals.
In reality, there are three layers to successful AI adoption:
- Architecture (the systems, integrations, and data infrastructure that support AI)
- Context (the institutional and market intelligence that informs decisions)
- Tools (the AI applications users interact with)
Unfortunately, many conversations begin at layer three. A vendor demonstrates a chatbot. An AI assistant. An agent. A dashboard.
Institutions see the mechanism, but nobody asks, “what data will power it?”
What information will ground its recommendations?
What market intelligence will shape its decisions?
What context will prevent hallucinations and poor conclusions?
Without those answers, even the most sophisticated AI tools are limited by the quality and completeness of the information they’re given.
The Data Problem No One Talks About
Most enrollment teams already possess valuable institutional data:
- Student records
- CRM activity
- Financial aid information
- Website content
- Program information
- Historical enrollment outcomes
Those assets matter, but they’re only part of the picture. Enrollment leaders don’t compete against their own data. They compete in a market, and market context is often what’s missing. Here are a few examples:
Knowing your application volume matters … but knowing how that volume stacks up to peer institutions is transformative.
Knowing your FAFSA completion rate is key … but knowing whether your target populations are outperforming or underperforming the broader market changes how you respond.
Knowing your conversion rates is important … but understanding how those rates compare across geography, student type, and market conditions creates new strategic possibilities.
Simply put, AI cannot manufacture that context. It can only work with the information it’s given.
At MARKETview, we take a different approach. Learn more in Part 2 of this blog – AI Is the Tractor. Data Is the Soil.
It explores why the future of AI in higher education won’t be determined by which institution adopts the newest tool first. It will be determined by which institutions provide AI with the richest, most relevant market context.