Why Higher Ed's AI Keeps Stalling

AI spend is rising faster than returns

Higher education is investing in AI at a pace that would have been hard to imagine two years ago. Ellucian's third annual AI survey, fielded in fall 2025 with about 779 higher education administrators across more than 300 U.S. and Canadian institutions, found institution-wide AI adoption rising from 49% to 66% in one year. Roughly two-thirds of executive leaders said their institution now sets aside budget specifically for AI.1

EDUCAUSE found the same strategic shift from another angle. In its 2025 AI Landscape Study, 57% of roughly 800 respondents said AI is now a strategic priority, up from 49% the year before.2

Wasabi's 2026 education cut of the Global Cloud Storage Index, based on 241 education IT decision-makers, found that 98% of education organizations expect AI infrastructure budgets to hold steady or grow, with 46% planning increases.3

The returns are not keeping pace. Wasabi found that only 37% of education AI projects already in place were delivering positive return.3

Outside education, MIT NANDA's 2025 GenAI Divide report reviewed more than 300 enterprise AI initiatives and found that roughly 95% of generative AI pilots produced no measurable profit-and-loss impact, despite an estimated $30-40 billion in spending.4

Gartner reached a similar conclusion from the agentic AI side, predicting that more than 40% of agentic AI projects will be cancelled by the end of 2027 because of cost, unclear business value, and inadequate risk controls.5

The pattern is not that the models are too weak. The recurring failure is what MIT called the learning gap: AI tools and organizations fail to integrate the technology into real workflows, data, and operating rhythms. Higher education is now arriving at that same inflection point, with more money going in than ever and a data foundation that remains fragmented on many campuses.4

Closing that gap is the whole exercise. The institutions seeing real returns are not the ones running the most advanced model. They are the ones that connected what they know about a student to what they actually do about it.

Why making AI work is not optional for colleges

The demographic math is no longer hypothetical. Economist Nathan Grawe's widely cited projections anticipated a roughly 15% decline in traditional college-age demand between 2025 and 2029.6

WICHE projects that the total number of U.S. high school graduates will decline 13% from its 2025 peak through 2041.7

Fitch has kept a deteriorating outlook on the higher education sector, while Federal Reserve research has sharpened the sector's view of closure and financial-distress risk.89

When institutions cannot recruit their way to stability, retention becomes the lever. National Student Clearinghouse data show that first-spring persistence for the fall 2023 cohort reached 86.4%, while first-spring retention at the starting institution reached 83.7%. That is an improvement, but it still means roughly one in seven first-year students did not return anywhere in higher education the next spring, and roughly one in six did not return to the institution where they started.10

Catching those students early, before they slip away, is exactly the kind of problem AI is supposed to help solve. The pressure to deploy is real. The question is why so much of that deployment stalls.

The real reason campus AI stalls

It usually is not the technology. The models available to a regional public university are broadly the same models available to a global bank. What differs is the ground they land on, and on most campuses that ground is fragmented.

The institutions themselves point straight at it. When education respondents in Wasabi's survey were asked what most challenged their AI projects, the single most-cited answer was data: the cost of storing it and the difficulty of accessing it. Two-thirds of AI infrastructure spending went to data, storage, and compute rather than to the models on top. Wasabi is a cloud-storage vendor, so the survey naturally foregrounds storage, but the direction of the finding matches what MIT and EDUCAUSE reach from outside the data-infrastructure business: the bottleneck is the foundation, not the algorithm.3

Part of that foundation problem is tool sprawl. K-12 gives the clearest public quantification: LearnPlatform by Instructure found that districts accessed an average of 2,982 distinct edtech tools annually in 2024-25. Higher education has its own version of the same dynamic: software adopted department by department, with overlapping tools, duplicate contracts, and a map of privacy obligations no one fully owns.11

That sprawl produces something worse than wasted budget: a fractured view of the student. Admissions data sits in one system, advising notes in another, financial aid in a third, and LMS activity in a fourth. The early-warning signals exist. They are just scattered across systems that do not talk to one another.

The sector's strategy data confirms the pattern. EDUCAUSE found that about 55% of institutions described AI strategy as happening in pockets around the institution, while fewer than 40% had an acceptable-use policy in place.2

This is the institutional version of MIT's learning gap: AI bolted onto a foundation that was never unified to begin with. Drop a sophisticated model on top of siloed, inconsistent data and one of two things happens. It stalls, or it produces answers no one trusts.4

The signals that would catch a wavering student already exist. They are simply scattered across systems that never hand off to one another, so the insight never becomes an action. Fixing that has less to do with a smarter model than with how the pieces are assembled.

It works when the foundation is there

The most instructive higher education proof point is also one of the oldest. Georgia State University set out a decade ago to attack summer melt, the gap between admission and enrollment. By pairing proactive conversational texting with guided enrollment tasks, the university reduced summer melt from roughly 19% to 9%, according to Georgia State's National Institute for Student Success. In the first summer, the chatbot interacted with incoming first-year students 185,000 times.12

The same work has evidence beyond a case-study headline. Brookings summarized randomized controlled trial findings from the Georgia State effort, and later Georgia State research reported course-level chatbot work associated with higher grades and stronger retention outcomes.1314

That last detail is the point. The technology did not replace the staff. It routed routine work away from them so their attention could go where it mattered. And it worked because the assistant was connected to actual enrollment tasks and embedded in a redesigned process. A bare chatbot dropped onto the same campus today, disconnected from those systems, would not reproduce the result, however capable the underlying model.

The foundation is the work

The hard part was never the technology. The campuses that pull ahead over the next few years will not be the ones chasing the newest model. They will be the ones that do the groundwork first: one connected view of the student, redesigned workflows, and a way to turn a signal into an action before a student slips away.

That is a buildable program, not a single purchase. In a companion piece, Making AI Work in Higher Education, we lay out what that operating model looks like in practice, and how the pieces fit together into one loop.