American colleges and universities have spent more than a decade investing heavily in data infrastructure, yet a growing chorus of technology experts and higher education analysts warn that the industry’s obsession with accumulating information has paradoxically left institutions less equipped to help the students they serve.
The disconnect, experts say, is not a matter of resources or intention, it is structural. And it is playing out against a backdrop of accelerating workforce disruption that is raising the stakes for every student who walks through a campus door.
“AI-driven layoffs should be a wake-up call for higher education,” said Ian Gibson, dean of San Diego State University Global Campus. “The question is no longer whether AI will reshape the workforce. It already is. The real question is whether colleges and universities will move fast enough to prepare students for that reality.”
The warning comes at a critical inflection point for colleges and universities, many of which are racing to deploy artificial intelligence tools in advising, enrollment management, and student success operations. Proponents argue that AI holds the potential to transform how institutions identify and respond to at-risk students. Skeptics counter that without a coherent data foundation beneath those tools, the technology will simply replicate existing blind spots at scale.
Fred Creugers, co-founder of Intellicampus, a higher education technology firm, argues the industry has confused volume with vision.
“Higher education has spent the last decade buying more tools to manage more data and somehow ended up knowing less,” Creugers said. “The problem was never a shortage of data. It was that the data never talked to each other. Students fall through the cracks not because institutions don’t care, but because the systems that are supposed to help them were never designed to work together.”
When advising systems, financial aid platforms, course management tools, and early-alert software operate in silos, no single stakeholder has a complete picture of a student’s trajectory. An advisor may be unaware that a student who missed three classes last week also visited the financial aid office twice and dropped a course the prior semester — information that, taken together, might flag a crisis in the making.
According to the National Student Clearinghouse, more than 40 million Americans have some college credit but no degree — a figure that researchers increasingly attribute not only to financial pressures but to institutional failures in early identification and intervention.
Creugers frames the challenge as one of infrastructure before intelligence.
“There’s a dangerous assumption that more dashboards equals more clarity,” he said. “Institutions can be drowning in reports and still completely blind to what a student actually needs. That’s not a technology failure — it’s an architecture failure. You can’t build meaningful AI on top of fragmented data and expect it to work.”
The data problem is compounded, Gibson argues, by a curriculum problem. Even as institutions struggle to get their systems to communicate, many are still preparing students for a labor market that no longer exists.
“Higher education should not be training students to compete with machines at routine work,” Gibson said. “That is a losing proposition. We should be preparing them for the work AI cannot do well: exercising judgment, asking better questions, solving ambiguous problems, acting ethically, novel thinking, and continuing to learn when the tools, jobs, and industries around them change.”
Gibson, who oversees one of the country’s more prominent online and extended learning operations, said the response cannot be cosmetic. Embedding an AI policy in a syllabus or scheduling a handful of workshops, he argues, falls well short of what the moment demands.
“AI needs to be embedded across the curriculum and treated as a core professional competency,” he said. “Students should learn how to use these tools responsibly, evaluate their outputs critically, understand their limitations, and apply them in real-world settings. Avoiding AI in the classroom does not protect students. It leaves them underprepared.”
The push toward data unification is gaining traction across the sector, with a number of institutions quietly moving away from point solutions — standalone products designed to address discrete functions — toward integrated platforms capable of synthesizing information across the student lifecycle. The shift represents a significant strategic and financial commitment, and some administrators caution that implementation timelines and change management challenges remain formidable barriers.
Gibson argues that structural flexibility must accompany any curricular overhaul. Flexible programs, stackable credentials, workforce-aligned certificates, and robust online and hybrid pathways, he said, are no longer peripheral experiments.
“They are central to the future of higher education,” he said. “The future will not belong to graduates who simply know how to use one tool. It will belong to graduates who can think critically, learn continuously, adapt intelligently, and use technology to create value. Higher education has to be redesigned around that reality.”
For Creugers, the path toward that redesign runs through the data architecture that makes personalized, timely support possible in the first place.
“The breakthrough isn’t collecting more data — it’s finally connecting what you already have,” he said. “When student records, course history, advising notes, and institutional knowledge are unified into a coherent structure, that’s when AI can actually do something useful. That’s when an advisor walks into a conversation already knowing what a student needs, not finding out 20 minutes in.”
