AI-Driven Business Models Are Reshaping Education: What Executives Need to Know

Sam Gupta, the Founder and CEO at ElevatIQ, with some help from a few of his peers throughout the enterprise technology marketplace, outlines a few core principles that executive decision-makers should know about how AI-driven business models can (and will) reshape education.

AI is more than fancy co-pilots and chatbots. It’s fundamentally changing the business models of educational institutes, starting from student acquisition to alumni engagement. Between shifting traffic patterns, emerging adoption of alternative credentialing technologies, and changing stakeholder behaviors, the traditional model is no longer sufficient.

The new AI-driven changes mandate a complete rethinking of the business model, requiring decision-makers to align technology and processes with the constraints of the new world. Organizations that simply chase AI without understanding its true potential and constraints are likely to struggle. Success will require an understanding of what’s reshaping, the factors driving those changes, and the necessary steps to transform the current business model into an AI-driven one.

The Current State of Business Models

Given the complexity and transactional speed requirements of educational institutions, the education sector rarely operates in a consolidated state. While the fragmented state of processes enables autonomy and meets front-office experience expectations, it also poses additional challenges for AI-driven business models. These challenges emerge because autonomous agentic processes require clearly defined process boundaries, hand-offs, and interaction models to avoid misleading conclusions and reconciliation nightmares.

In his experience, “many institutions rely on disjointed systems-with administrative data in an ERP and engagement data in a CRM-creating silos that neuter predictive analytics,” says Kuldeep Kundal, Founder & CEO, CISIN, a custom software development company serving educational institutions. “If the AI cannot detect a student’s academic struggle because that data is trapped in an isolated platform, it cannot execute timely interventions.” This is a prime example of the kind of challenges associated with the fragmented state of educational institutions.

Additional historical context and insight into evolving sectoral changes only reinforce Kundal’s point: “The foundation of competition between schools was reputation,” says Mark Friend, Company Director, Classroom365, an IT support provider for schools. “But schools now find themselves competing based upon their ability to provide personalized learning, faster feedback loops, and data-driven outcomes.” The current state of this market lacks the necessary process alignment to deliver on these changing expectations. 

Incorporating AI-driven processes in this state might yield worse outcomes due to underdefined process flows, unclear responsibilities, and the use of custom systems to perform manual tasks.

What’s Reshaped by the AI-Driven State?

“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, “ says Michael Fauscette, CEO and Chief Analyst of Arion Research, a research firm with a major focus on agentic AI. The touches he mentions require an understanding of the nuances of the different AI technologies being used, whether procured or homegrown, and how the new agentic structures align with the overarching organizational context without causing unnecessary disruptions to current outcomes. Once they fit well with existing outcomes, the next stage explores newer benefits. 

Fauscette recommends that “institutions that treat these as one connected data problem rather than four separate point solutions [would] see compounding returns.” This outcome is likely because, “in education, the governance bar is higher than almost any other sector. “ It’s also likely because, “you’re working with minors’ data, FERPA obligations, and decisions that shape life outcomes.” 

“If the only thing an education leader does is view AI as a new distribution/access channel, they’re really missing the boat. 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, ” says Kenneth Gonzalez, Lead Advisor, Pretty Simple Group, Inc, a research firm covering enterprise software with a focus on learning management technologies. 

This analysis must consider factors such as the processes being reshaped by changing traffic patterns, the agentic processes that would collaborate with existing transactional systems, and data integrity that will be maintained across process boundaries.

What’s Required to Enable AI-Driven Business Models

“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, ” recommends Fauscette. 

“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,” says Cameron Woodford, CEO and Founder, Appello Software, a digital experience provider for the education sector. “AI will not resolve the fragmentation of an organization’s operational model, but it will highlight it.”

“The organizations that will gain more from artificial intelligence development are those laying down the best foundations at the moment, ” says Vasilii Kiselev, CEO & Co-Founder, Legacy Online School.  “Therefore, they should have reliable data, an efficient information system, clear governance, and a willingness to innovate with appropriate control.” In his opinion, “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.”

What’s required to enable an AI-driven business model is a comprehensive analysis of the target operating model and a residency analysis of each included AI component, spanning processes, data, systems, and skill sets.

What to Avoid.

Most organizations overuse AI without understanding its core intent. An example of this overuse: “My worry is that everyone races to automate the teaching itself and ends up selling a pile of AI lessons nobody finishes,” says Jasmine Ahluwalia, Founder, Asian School of Design & Applied Vastu, a digital education provider. “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.” Automation for the sake of automation fails at “AI-speed.” Avoid that!

People’s experiences with AI are shaped by various factors. Not every experience would be positive, despite its potential. “We noticed that there were no major benefits of using AI on the frontline level,” cautions Kiselev. “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.” While this may have been a one-off experience, the potential of AI for front-end processes can’t be discounted.

What organizations should avoid, instead, is chasing AI without much thought and appropriate rewiring aligned with newer constraints.

Conclusion

AI is more than a technology that changes how educational institutions operate. Even organizations unwilling to change may be affected, as upstream, downstream, and external factors can drive these shifts.

Educational institutions must take a comprehensive approach to determine how they can leverage AI-driven business models. Using shinier technologies with a legacy business model is like using last year’s course materials for this year’s exams. It’s perhaps not the wisest approach to graduate with AI-driven business models.