At NERCOMP 2026 in Providence, the conference theme, “Connect Locally, Think Globally” underscored the event’s role as a forum for higher education institution leaders to share experiences and best practices on how they are navigating universal pressures. NERCOMP (Northeast Regional Computing Program) is a nonprofit consortium serving higher education library and IT professionals in the Northeastern U.S. and is composed of nearly 300 colleges and universities. This year’s conference positioned local collaboration as the practical response to global pressures shaping every campus, including AI acceleration, cybersecurity risk, budget compression, and changing expectations around teaching, learning, and service delivery.
What stood out immediately was the tone of the conversations: urgent, tactical, and grounded in institutional pressures. The challenge is no longer recognizing AI’s potential, but applying it in ways that ease strain rather than deepen faculty and staff fatigue.
That sense of urgency is exactly why I attended. I came to listen to higher education leaders and assess the pace of change in academic delivery and the systems that support it. Specifically, I was looking for three things: where AI is already delivering measurable ROI, which use cases offer the clearest near-term value, and what governance and culture shifts are required to move from pilots to meaningful operational impact.
Ultimately, I wanted to understand how higher education institutions are aligning AI initiatives with strategic priorities and measuring their impact on key institutional challenges and outcomes. One example shared at the conference was Clark University’s use of AI in admissions operations. During peak application periods, AI helped the institution manage 300 to 400 calls, save staff time, and improve the accuracy of GPA calculations.
The opening general session on Tuesday put the deeper question on the table: what does the undergraduate experience need to deliver in an AI-shaped era? Beth McMurtrie, Senior Writer at The Chronicle of Higher Education, led a keynote titled “The Future of Learning: How AI and Other Forces Are Changing the Undergraduate Experience.”
The session captured the challenges colleges and universities face: students increasingly experience college as transactional, faculty are still carrying pandemic-era burnout, and generative AI has disrupted long-held assumptions about authentic teaching and learning just as institutions are trying to rebuild student engagement, belonging, and confidence in the value of the college experience.
For me, that session brought the conference’s most consequential questions into focus. AI is no longer just a technology issue. It is now inseparable from the value proposition of higher education itself.
These concerns extend well beyond NERCOMP. In a Chronicle of Higher Education survey supported by CollegeVine, 81% of more than 800 higher education leaders and faculty members said they either strongly or somewhat agree that “AI is forcing higher education to re-examine the value it offers to students.”
Faculty support is now a front-line operational issue. In one conversation I had after lunch, a technology trainer from a fully online public university described the challenge bluntly: how do you train faculty on the latest AI tools for course design and delivery when the tools change constantly and the choices multiply by the week? That is not a niche problem. It has become a broad institutional burden. Faculty are being asked to redesign assignments, rethink assessment, respond to student AI use, and adopt new tools, compounding fatigue that has not fully eased since the pandemic.
Further, many campuses still lack a workable AI governance model and risk framework. That gap has become an execution problem. AI decisions now cut across institutional operations, student and academic affairs, advancement, communications, institutional research, IT, legal, and other areas. Without clear ownership, institutions end up with fragmented pilots, inconsistent policy, growing distrust among stakeholders, and slower adoption.
Finally, colleges and universities are facing an AI paradox that is especially acute in higher education. AI is being advanced as a way to reduce operating costs, ease workload pressure, and personalize the student experience. At the same time, it raises legitimate concerns about over-automation, academic integrity, critical thinking, and the erosion of the human relationships that make higher education valuable in the first place. Institutions that ignore this tension risk undermining trust, weakening outcomes, and drifting from core academic values. The goal is not maximum AI adoption. Institutional leaders must determine where AI can deliver the greatest value with the least risk to trust, academic integrity, and the human dimensions of teaching and learning.
Every institution is facing the same external pressures: AI acceleration, cybersecurity and privacy risk, budget constraints, demographic shifts, and rising demands for better service and faster decision-making. Regional communities like NERCOMP help institutions respond by giving them a place to compare approaches, learn from peers, and reduce the risk of making fast-moving AI decisions in isolation. In practice, “Connect Locally, Think Globally” reflected how institutions and providers are working together to establish an operating model for:
The conference’s second general session on Wednesday, “Campus AI: Navigating the Cloud and the Clout,” highlighted how higher education institutions and technology providers are working together to advance AI adoption. The panel brought together leaders from Central Connecticut State University, Dartmouth College, and Bentley University, who shared how they have created structured, well-governed environments where faculty and staff can test AI tools. They also described how continuous AI skills development is helping them support innovation while building trust and advancing ethical, inclusive, and accessible AI practices. The session made clear that the challenge is not launching AI, but integrating it in ways that preserve trust, judgment, and institutional credibility.
If the conference made anything clear, it is that institutions need to be more disciplined about where AI is governed, how its value is measured, and which use cases are worth scaling first.
Start by determining which AI decisions require institution-wide governance and which use cases can be piloted by departments or units within clear guardrails. Every institution needs both an AI strategy and a governance model that answers a core question: How much AI risk is the institution willing to accept, and who is accountable for managing it? That work should begin with data governance, decision rights, risk thresholds, and a defined model for human review.
From there, institutions need a consistent way to measure value. Define a limited set of shared metrics tied to time saved, cost avoided, service quality, adoption, and risk reduction. Without a common measurement model, leaders cannot make sound decisions about where to scale, where to invest, or where to pull back.
Just as important, institutions should prioritize two or three use cases with clear value and manageable change friction. Better starting points include admissions and advancement communications, workflow automation, knowledge support, and staff productivity. These areas are more proven in practice, easier to govern, and easier to measure than broad efforts to transform teaching and learning. Prove value first, then scale.
For solution and services providers, the message is equally clear. Institutions need more than AI features. They need governance-ready solutions that fit existing workflows, integrate with current platforms, reduce operational friction, and prove value quickly. NERCOMP 2026 revealed that the institutions seeing the strongest results are testing AI in controlled environments, applying it to well-defined priorities, and treating governance, trust, and measurable impact as core conditions for success. The providers that will win in this market are the ones that help institutions move from experimentation to operational value. They will reduce implementation friction, fit into existing systems and governance models, and demonstrate measurable results in productivity, service quality, and risk reduction.
Share Article:
© Copyright 2026, The Tambellini Group. All Rights Reserved.
Get exclusive access to higher education analysts, rich research, premium publications, and advisory services.
Weekly email featuring higher education blog articles, infographics or podcasts.
