AI is driving a fundamental overhaul of business models in education, altering everything from student acquisition to alumni engagement. Without a deliberate strategy to unify fragmented data systems and establish governance, institutions risk automating dysfunction rather than improving outcomes, experts warn.
Fragmented data silos undermine AI promises
Current business models in education rarely operate in a consolidated state. Many institutions rely on disjointed systems-administrative data in an ERP, engagement data in a CRM-creating silos that weaken predictive analytics. "If the AI cannot detect a student's academic struggle because that data is trapped in an isolated platform, it cannot execute timely interventions," said Kuldeep Kundal, Founder & CEO of CISIN.
The competitive ground has also shifted. "The foundation of competition between schools was reputation," said Mark Friend, Company Director of Classroom365. "But schools now find themselves competing based upon their ability to provide personalized learning, faster feedback loops, and data-driven outcomes." Disconnected processes and unclear responsibilities make these expectations hard to meet, and layering AI on top risks producing misleading conclusions.
AI reaches every stage of the student lifecycle
"AI touches every stage of the student lifecycle, from identifying prospective students and personalizing outreach, to adaptive learning delivery, to spotting retention risk weeks before a human advisor would," said Michael Fauscette, CEO and Chief Analyst of Arion Research. The AI-driven changes in education, a topic explored under AI for Education resources, are pushing institutions to treat these touchpoints as one connected data problem rather than four separate point solutions. Fauscette added that organizations that do so see compounding returns.
Kenneth Gonzalez, Lead Advisor at Pretty Simple Group, Inc., cautioned that viewing AI simply as a new distribution channel misses the point. "It will require a decent amount of thought up front to assess the learner's needs, potential engagement models, and the mutual value that this represents," he said. That analysis must cover how agentic processes work with existing transactional systems and how data integrity holds across process boundaries.
What it takes to build AI-ready foundations
"Executives need governance, privacy, and ethical guardrails in place before AI touches admissions, grading, or student support, not after the first incident forces the conversation," Fauscette said. Cameron Woodford, CEO and Founder of Appello Software, underscored the need for foundational readiness: "To achieve a successful transition to AI-driven educational institutions, education leaders must have clean data, organize their back-end ERP and CRM systems, develop strong privacy policies/protocols, establish governance, and provide technology that allows for a secure connection of student, financial, learning, and engagement data." Woodford warned that AI will not resolve fragmentation; it will highlight it.
Vasilii Kiselev, CEO & Co-Founder of Legacy Online School, said the organizations that gain the most from AI development are those laying the best foundations now-reliable data, efficient information systems, clear governance, and a willingness to innovate with control. "AI implementation in education will change it into a proactive area instead of a reactive one, as educators would be capable of predicting problems and providing students with necessary assistance prior to difficulties," he said.
The risk of automating without purpose
Jasmine Ahluwalia, Founder of Asian School of Design & Applied Vastu, cautioned against racing to automate teaching itself. "My worry is that everyone races to automate the teaching itself and ends up selling a pile of AI lessons nobody finishes," she said. "The schools that last will use AI to cut costs and keep their best teachers on the work nobody can automate, the critique session where someone tells you your plan does not work, and exactly why."
Kiselev also noted that frontline AI deployments did not always deliver major benefits. "We noticed that there were no major benefits of using AI on the frontline level," he said. "In particular, such innovations could facilitate the admissions process, optimize interactions with families, and detect students requiring additional assistance and other important aspects without increasing the workload of educators." He cautioned that organizations must avoid chasing AI without appropriate rewiring.
Why this matters for education professionals
AI will change how institutions operate, whether they choose to adopt it or not. Shifting data flows, stakeholder expectations, and credentialing technologies will force change. Education leaders must map their operating model-processes, data, systems, and skills-before deploying AI tools. Those that unify their data and establish governance early will be positioned to compete on personalization and predictive support, while others will struggle with amplified inefficiencies. The question is not whether to use AI, but whether the organizational backbone is strong enough to support it.
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