At a time when most universities are still digitising processes in silos, Manipal Academy of Higher Education (MAHE) is attempting something far more ambitious: a full-scale shift to an AI-driven, “agentic” ecosystem that could fundamentally change how institutions function, teach, and engage with students.
For Vice Chancellor Lt Gen (Dr) M D Venkatesh, the move is less about technology adoption and more about survival in a rapidly shifting higher education landscape.
“Either you accept and adapt or you will be left behind,” he said. “What is current today may not be valid tomorrow.”
What sets MAHE’s approach apart is not just the introduction of AI, but the scale and integration it is attempting. With over 40,000 students, 4,000 faculty, multiple campuses, and hundreds of programmes across disciplines, the university is trying to build what it describes as a 360-degree digital ecosystem—connecting everything from admissions and academics to research, finance, and alumni engagement.
“We were running on multiple legacy systems. The challenge was to be responsive to every stakeholder—students, faculty, parents, regulators. That speed and data-driven decision-making is only possible if you transform digitally,” Venkatesh said.
Beyond ERP: From systems to ‘agentic’ decision-making
Universities have long relied on ERP systems to manage operations. But according to Ashwin Ballal of Deloitte, these systems are inherently limited.
“ERP systems work within a defined set of rules. Anything outside those rules requires human intervention,” he said. “What these next-generation platforms are doing is taking even that layer and ‘agentising’ it.”
In effect, AI agents are being deployed not just to automate routine tasks, but to handle exceptions, generate insights, and enable decision-making—marking a shift from digitisation to intelligence.
MAHE has begun with five such agents within its student lifecycle management system, with plans to scale to over 50 in the next two years across academic and administrative functions.
The rise of the ‘student lifecycle stack’
A key shift emerging from MAHE’s approach is the move towards a unified, lifecycle-based model—similar to how companies manage customer journeys. Instead of students navigating disconnected systems, the university is building a single, continuous digital identity spanning pre-admission to alumni engagement.
“Having information scattered across systems is not a great experience,” said Mankiran Chowhan, Managing Director, Sales and Distribution at Salesforce. “The idea is to create a connected journey where the right information is available at the right time.”
This model could enable a more personalised approach to education.
“With the scale we have, it’s difficult to customise learning,” Ballal said. “But platforms like this allow you to evaluate each student—what they are good at and where they need help and act on that.”
For students, this means real-time visibility into their academic journey. For faculty, it enables more targeted interventions rather than uniform teaching approaches.
Freeing up faculty: The hidden efficiency play
One of the biggest inefficiencies MAHE is targeting is the administrative burden on faculty. “Today, 50–60% of faculty time goes into administrative work,” Ballal said. “If you can take that away, that time goes back to students.”
The university estimates that AI-led systems could reduce overall workload by 25–30%, improving both efficiency and academic engagement. “We are not just delivering education. We are building global citizens,” Venkatesh said. “For that, faculty must have time to engage and mentor.”
A global outlier—for now
MAHE believes its approach is ahead of the curve, not just in India but globally. “Not many universities have attempted this kind of complete, 360-degree transformation,” Venkatesh said, noting that most institutions digitise in silos rather than integrating systems end-to-end.
If successful, the model could be replicated across large universities, including state institutions. However, scalability comes with its own caveats. “Scaling is not the challenge. Adoption is,” Ballal said.
The real barriers: Cost, mindset, and trust
Despite the promise, the transition to AI-driven universities faces multiple hurdles. The first is cost. “The most precious commodity today is the GPU,” Ballal noted, highlighting the infrastructure demands of AI systems. “You need to clearly evaluate the return on investment.”
But the bigger challenge may be cultural. “Most digital transformations fail not because of technology, but because of the organisation’s ability to adopt new ways of working,” he said, pointing to resistance, fear, and change management issues.
Venkatesh echoed this, stressing that mindset will be critical. “The willingness to change is the biggest factor,” he said.
Data privacy is another key concern, particularly with large volumes of student and institutional data being integrated into a single ecosystem. “We are dealing with vast amounts of sensitive data. There is no compromise on data protection,” Venkatesh said, adding that systems are being designed with strict access controls and regulatory compliance.
Chowhan emphasised the importance of trust in scaling such platforms. “Data is not the product—the control remains with the institution,” she said. “Without trust, you cannot innovate.”
What this means for higher education
MAHE’s initiative reflects a broader shift where AI is becoming an operating layer for universities, rather than just a support tool. From personalised learning and real-time insights to automated processes and lifecycle management, institutions may be moving towards deeply integrated, AI-native ecosystems.
For India, where scale has long limited personalisation, this could be a significant inflection point.
But whether this model becomes mainstream will depend on how universities address the harder questions around cost, capability, and change. As Venkatesh put it, the direction is clear—what remains uncertain is how quickly others will follow.
