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By Sanjay Laul
When most people hear ‘AI on campus,’ they picture a chatbot answering FAQs at 2 a.m. or a plagiarism detector flagging a submitted essay. That version of AI visible, conversational, contained is already old news. The more consequential transformation is happening in the background, embedded inside the systems that run admissions pipelines, manage academic records, track student engagement, and predict who will drop out before the semester ends.
The modern campus is being quietly re-engineered by AI. And the data is beginning to tell that story with uncomfortable clarity.
From 49% to 66%: The Tipping Point Has Passed
According to Ellucian’s 2025 AI in Higher Education Survey one of the most comprehensive annual benchmarks in the sector institution-wide AI adoption surged from 49% in 2024 to 66% in 2025, a 17-percentage-point jump in a single year. That is not incremental adoption. That is a tipping point. And 88% of respondents in the same survey expect institutional AI use to keep rising over the next two years.
The departments leading this shift are instructive. Information Technology (81%), Data & Analytics (75%), and Executive Leadership (73%) are already deep into AI-driven operations. Even the historically cautious verticals Financial Aid (43%) and Admissions & Enrollment (47%) are accelerating fast. These are not exploratory pilots. These are operational commitments with budget lines behind them.
Students Moved First. Institutions Are Catching Up Late.
Students did not wait for policy. HEPI’s 2025 annual survey of UK undergraduates found that 94% now use AI in some form up from 66% the year before. Globally, the Digital Education Council puts the figure at 86%, with 54% using AI tools weekly and 25% daily. The same HEPI data found that generative AI use for assessed work jumped from 53% to 88% in a single academic year.
The Coursera AI in Higher Education Report, published in February 2026, found that four in five students say AI has improved their academic performance. A 2025 randomised controlled trial in Scientific Reports found that an AI tutor outperformed traditional in-class active learning, with an effect size between 0.73 and 1.3 standard deviations with students completing tasks in 49 minutes versus 60 for in-class peers.
Yet a 2025 Gallup-Lumina survey found that more than half of students say their institution either discourages or outright prohibits AI even as they use it routinely. The governance gap is real, and it is widening. Students expect their institutions to prepare them for a workforce where AI fluency is a baseline requirement. Institutions that fail to close this gap will produce graduates who are already behind.
Where AI Is Actually Running Campuses: Operations, Not Just Outcomes
The most consequential AI deployments in 2025–26 are not about learning. They are about operations. EDUCAUSE data shows that 52% of institutions now use AI to automate administrative workflows, and 54% apply it to curriculum design support. Northern Virginia Community College, highlighted in WCET’s 2025 Higher Education AI Survey, deployed AI to evaluate academic transcripts compressing processing times from weeks to days.
The deeper shift is in predictive intelligence. When CRM, SIS, and LMS platforms share data academic records from the student information system, engagement signals from the learning management system, and outreach history from the admissions CRM AI models can identify at-risk students before their grades slip. Research published in the 2025 Journal of Learning Analytics, involving over 8,000 data points across multiple UK institutions, found that AI-driven early intervention produced measurable gains in student attainment with lower-performing students benefiting most. In higher education more broadly, AI-enhanced tutoring has been linked to a 25% drop in course failure rates (SQ Magazine, 2025).
This integration logic CRM, SIS, and LMS functioning as a unified data layer is precisely what separates institutions with real AI capability from those running disconnected point solutions. Without it, even sophisticated models fail. The data cannot be trusted, the interventions cannot be timed, and the insight cannot be acted on.
The Infrastructure Problem No One Wants to Talk About
The global AI in education market was valued at approximately $7.05 billion in 2025 and is projected to reach $136.79 billion by 2035, growing at a compound annual rate that reflects serious institutional intent (Engageli, 2026; market research consensus). But the majority of that capital is at risk of underdelivering because the underlying infrastructure remains fragmented.
WCET’s 2025 survey of 224 higher education institutions confirmed that most are still in the early stages of AI integration with systems deployed in silos, without the unified data architecture needed to power reliable prediction, personalisation, or automation. NCES data from the same year puts average first-year retention at four-year institutions at 81%, falling to 68% at open-access schools and nearly 40% of students will not earn a degree within six years. For a mid-sized university, a 10% retention improvement can translate to over $15 million in preserved tuition revenue across a four-year cohort.
AI can move those numbers. But only when it is operating on clean, integrated data across the full student lifecycle. Fragmented systems do not just slow AI down. They make it unreliable. And unreliable AI in high-stakes academic decisions erodes institutional trust faster than no AI at all.
What ‘Quietly Running’ Actually Means
The AI story in higher education is not about visible, dramatic transformation. It is about structural change the kind that happens inside platforms and data pipelines, inside the decision rules that determine which student gets a proactive advisor outreach and which one doesn’t. That invisibility is not a failure of ambition. It is the nature of infrastructure working correctly.
For institutions building their next-generation technology stack, the strategic question is no longer whether to adopt AI. It is whether the underlying systems are designed to let AI do what it is actually capable of. That means moving away from a world where CRM, SIS, and LMS sit in separate vendor ecosystems, maintained by separate teams, producing data that never meaningfully converges.
Institutions that have consolidated their core systems into a unified platform are not just operationally leaner. They are building the only foundation on which meaningful, scalable, trustworthy AI can run. The campuses that figured this out early are already operating differently.
And they are doing it quietly.
(The author is Sanjay Laul, Founder, MSM Aventra, and the views expressed in this article are his own)
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