Failure-Proofing a Legacy Institution

The Challenge of Institutional Inertia

In early 2020, like much of the world, Princeton University was forced into an unexpected experiment in transformation. The pandemic swept across campuses, rendering centuries-old traditions temporarily obsolete. The challenge wasn’t just about going remote; it was about reimagining a legacy institution with 8,000 students, over 7,000 employees, and deeply entrenched systems, without losing the rhythm of research, instruction, or trust.

The sudden shift to online learning and operations exposed the limitations of existing systems and processes. Jay Dominick, Vice President for Information Technology and CIO, recognized the urgency of the situation and spearheaded the upleveling of university systems. What followed was not just crisis response; it was foundational reengineering. Princeton rapidly infused its academic and operational systems with AI-driven intelligence, agile workflows, and startup-grade adaptability, resulting in a resilient, data-centric digital core that survived disruption and now thrives despite it.

Shortcomings of Legacy Systems

Before the pandemic, Princeton’s infrastructure reflected the prestige and process of a historic university. IT systems supported routine operations, but weren’t designed for the elasticity or speed that pandemic learning required: “We moved from our most sophisticated technology being chalk to being completely dependent on some cloud technologies that people hadn’t really invested in,” says Dominick.

The university's processes were rigid and prevented rapid decision-making and implementation. When the shutdown hit, every inefficiency was magnified. The systems were primarily designed for on-campus learning and lacked the flexibility required for a remote learning and working environment. Jay recalled, “We had to change a lot of rules, including our policies and practices around work at home or what students could do with respect to technology in their studies.”

Students scattered across time zones needed immediate, synchronous learning. Faculty required digital whiteboards, lab simulations, and secure file-sharing. Administrative offices had to process grants, payroll, and admissions from home.

Implementing Agile and AI-Driven Solutions

Faced with a systemwide stress test, Princeton launched a sweeping digital modernization effort, including a comprehensive transformation of its IT department. At its core was a shift toward agile operations and an intelligence-driven architecture emphasizing integration, speed, and experimentation.

To bypass slow procurement cycles and infuse fresh thinking, Princeton engaged directly with early-stage AI startups. “One of the reasons we work with startups is to give our organization the opportunity to see when people are just thinking differently. It gives us a sense of the innovator's mind but also challenges us to think, ‘Okay, if those guys are doing that, where does that fit with what we're doing?” These partnerships accelerated pilot testing in scheduling optimization, proctoring, and virtual labs by adopting cutting-edge technologies.

The university also invested in AI-driven tools and data analytics to enhance decision-making, streamline operations, and personalize learning experiences. Upgrades to the university's IT infrastructure to a more modern landscape facilitated remote access, improved cybersecurity, and supported the scalability of digital platforms. Initiatives like the Princeton Language and Intelligence (PLI) initiative fostered cross-departmental research in AI and machine learning.

Measurable Impact and Results

The shift to an adaptive, intelligence-enabled environment delivered measurable gains across Princeton’s core missions:

• Remote continuity: Within weeks, the university deployed scalable learning platforms and collaboration tools that supported synchronous and asynchronous instruction.
• Operational automation: AI tools were embedded in administrative workflows, reducing manual effort in IT ticketing, document routing, and grant processing.
• Increased cybersecurity: With expanded attack surfaces, Princeton upgraded identity and access controls, enhanced endpoint detection, and applied machine learning to threat detection.
• Academic innovation: Faculty piloted AI-driven tools for content generation, personalized tutoring, and data visualization, particularly in STEM and humanities fields.

The impact was just as profound. Teams accustomed to semester-long planning cycles began working in two-week sprints. Campus leaders embraced agile ceremonies like stand-ups and retrospectives to unblock issues and accelerate decisions.

How Princeton Made Change Continuous

Rather than treat technology as a stopgap, Princeton embedded it as a permanent component of university operations. Jay continues to evolve its ecosystem with an eye toward the future: “I think the thing that I've learned most from this is really to be flexible, to be nimble and to think about the possibilities the technology is bringing us.”

These upgrades position Princeton to lead during the next systemic disruption. Dominick and his team proved that even the most tradition-bound institutions can move with agility when values, leadership, and systems align.

As universities confront AI ethics, global access, and digital equity in the decade ahead, Princeton’s model is a blueprint for modernizing without losing mission. In higher education, the future won’t be defined by who has the most data, but by who makes it actionable. As Princeton continues to evolve, the university adapts its approach to future challenges, ensuring it remains at the forefront of innovation and academic excellence.