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Introduction: Bridging an AI Divide

As governments and communities across the United States struggle to make sense of artificial intelligence, one of the most capable—and underutilized—partners is often hiding in plain sight: local colleges and universities. Much of the public conversation about AI focuses on big tech companies or federal regulation. Meanwhile, far less attention has been paid to how higher education institutions can help cities and nonprofits deploy AI to serve residents and strengthen public trust.

Across the United States, higher education institutions are already governing AI internally, experimenting with operational use cases, and absorbing unprecedented investment to build technical capacity. And as the appetite for an AI-trained workforce blossoms, local colleges are now a prime pipeline for talent. At the same time, local governments and nonprofits are just beginning to respond to and translate AI’s promise into public value.

This asymmetry presents a clear gap: Colleges and universities are increasingly adept at deploying AI, but the connection between local communities and higher ed remains underdeveloped.

This brief argues that AI has created a rare institutional opening to bridge the divide. Colleges are seeking clearer public relevance, governments require technical capacity, and communities are demanding institutions that are more responsive and trustworthy. Local leaders from governors to nonprofit executives who recognize this alignment—and act on it—can shape how AI strengthens democratic infrastructure rather than allowing it to evolve according to purely academic or commercial priorities.

Ground Truth: What’s Actually Happening

Most cities have not historically developed or maintained deep or strategic relationships with nearby colleges and universities. This “” persists today: Local governments focus on service delivery while universities concentrate on teaching and research. To many civic leaders aiming to collaborate with a local college, campuses appear decentralized, opaque, and difficult to navigate.

It’s this distance that makes the current moment more significant. AI is forcing institutions across sectors to reassess their roles, capabilities, and responsibilities. For the first time in decades, incentives are converging around collaboration as both local governments and colleges are feeling increased pressure to prove their relevance quickly.

The reality is that most colleges already have a civic designation. The majority of local higher ed institutions are 501(c)(3) nonprofit organizations with tax-exempt status. This fact is often overlooked because many universities exhibit private-sector tendencies, but legally and structurally, they share more DNA with public-serving institutions than is commonly assumed. That status is receiving renewed scrutiny under the Trump administration, prompting many university leaders to demonstrate more measurable public value.

To be clear, this brief does not advocate “making a deal” between cities and local campuses. Rather, it emphasizes a strategic posture: Many universities are actively searching for ways to elevate their civic mission. Local leaders should treat this not as rhetoric, but as an invitation—one that may not remain open indefinitely. If cities do not work with higher ed now to build AI capacity, workforce pipelines, and governance norms, these areas may default to private-sector priorities instead of public ones.

Higher Ed: The Local AI Power User

In many communities, higher education has quietly become the most active AI enterprise. Within weeks of the public release of ChatGPT in late 2022, colleges and universities were with implications for teaching, research, and administration, often before clear regulatory signals emerged. While other sectors hesitated, the academy began building governance structures, issuing guidance, and testing use cases.

Higher education, in many respects, had little choice but to engage with AI. Students became immediate power users, placing unprecedented pressure on the core academic enterprise. Administrators are heavy users as well: A recent reported 94 percent of higher ed staff and faculty use AI on a regular basis, with these tools deployed to streamline admissions review, assist with research administration, and improve internal workflows. Unlike sectors that could afford to observe from the sidelines, universities faced immediate operational disruption that demanded governance, policy formation, and experimentation.

At most four-year institutions, and virtually every research university, provosts and senior academic leaders quickly emerged as AI policymakers. Task forces, faculty senates, and cross-campus working groups were mobilized to define appropriate uses and establish institution-wide guidance. A of academic leadership shows that roughly two-thirds of UNESCO-affiliated institutions across 90 countries now have formal AI policies or are actively developing them. This early institutional adaptation has produced something civic leaders should recognize immediately: practical governance experience.

Universities are also erecting extensive new curricular pipelines. As of 2025, more than exist across U.S. higher education, spanning undergraduate majors, graduate degrees, minors, and concentrations. Additionally, universities shape regional labor markets. They credential talent, build cross-disciplinary governance expertise, and operate on time horizons longer than electoral cycles. That institutional permanence gives them influence over how AI capability diffuses into local economies.

Billions for AI Experimentation

The scale of investment flowing into university-based AI activity further reinforces this trajectory. In addition to new courses and credentials, there is a gold rush of external capital that is accelerating institutional AI capacity. As one professor told us, “These days, all you have to do is put AI in front of your proposal, and there is a good chance you will receive support.”

While there is not yet a comprehensive list of these corporate, philanthropic, and government investments, we tallied a number of the major awards supporting this experimentation, including: for a new research center at Harvard; to support AI cancer research at Stanford; for the University of Virginia’s Darden Business School; for a collaboration between State University of New York (SUNY) university centers and state schools; and from Google for AI training across U.S. higher education institutions and nonprofits. The University of Michigan is also advancing a roughly high-performance computing and AI research facility in partnership with Los Alamos National Laboratory, supported by a mix of university, state, and federal resources.

Much of this funding targets large scientific challenges such as precision medicine and genomics. Yet a smaller—and strategically consequential—share is oriented toward civic applications. Universities are not merely studying AI—they are operationalizing it.

As documented in our report, Making AI Work for the Public, some of the most ambitious civic AI efforts to date have often involved college partners. At Tulane University, faculty and students working through a community-engaged AI center with local nonprofits to advance criminal justice reform, transforming a traditional service-learning requirement into a vehicle for policy-relevant AI work. At Northeastern University, the has produced more than a dozen production-ready tools in six-month sprints, co-designed with public partners including the Commonwealth of Massachusetts. Georgia Tech’s has applied AI to regional challenges ranging from rural agriculture to economic development. And at the University of Michigan, researchers funded by the National Science Foundation the City of Detroit to apply AI to urban planning and climate resilience.

These efforts share several characteristics: They are problem‑driven, low‑fanfare, and grounded in real public needs. They also demonstrate AI’s unique ability to surface value quickly, rebalance power between institutions and communities, and change the nature of civic collaboration. Such partnerships can be more systematic rather than exceptional.

Where Hype Diverges from Reality: The Implementation Challenges

It is important not to overstate higher education’s progress on AI implementation and stay clear-eyed about the challenges that remain. Most activity remains concentrated at well-resourced research universities with strong engineering and data science capacity. Community colleges have engaged less, reflecting longstanding resource constraints.

And faculty adoption also varies widely by discipline. Many scholars, in fact, remain in an exploratory phase, uncertain about how deeply the technology will reshape their work. Structural barriers, including differing timelines, incentives, and definitions of success, do not disappear simply because a new technology arrives.

The fundamental divides between higher ed institutions and local governments that still must be overcome to translate AI into public value include:

• Culture Clash: Colleges and local government operate very differently, with almost diametrically opposing cultures and incentives. The public sector tends to work reactively—in crisis mode—with a goal of getting something done immediately. Meanwhile, university scholars plot out their work far in advance, and it can take years before results come back.
• Organization-Wide Partnership Is Rare: While most communities benefit from targeted programs and relationships with individual faculty, very few have university-wide or enduring collaborations. These types of partnerships, in which the college presidents and provosts engage directly with city managers and nonprofit executives, are needed to advance and sustain ambitious AI efforts.
• Opaque Operations: Often, it is hard to know who to talk to or where to even begin when initiating a university partnership. Higher ed is a notoriously decentralized environment in which the dean, research center director, or any number of other staff or faculty may be the best contact. Unfortunately, there are few org charts or stakeholder maps to find the right contact.

Recognizing these realities means approaching partnership with intentional design. Durable partnerships rarely emerge organically—they are built through leadership, clarity of purpose, and sustained institutional commitment.

Recommendations: Translating AI Assets to Public Value

AI provides a timely excuse to do what local leaders should have done long ago: establish intentional, problem-driven partnerships with nearby colleges and universities. Because AI is new, unsettled, and high stakes for both academic and public sectors, it creates a mutual incentive to collaborate early and thoughtfully.

As discussed throughout this brief, higher education brings assets that are difficult for other partners to replicate: deep technical expertise; experience governing sensitive data; institutional capacity to test emerging tools; a core focus on training a future, AI-ready labor force; and a civic mission that is aligned, at least in principle, with public service. The opportunity now is to translate those assets into public value.

• Get the Issues Right—Conduct an AI Scan: Effective partnerships require clarity about needs. Governments and nonprofits should conduct an internal scan to identify where AI could meaningfully advance public priorities—including data protection, permitting, benefits access, transportation planning, and service delivery. This diagnostic step is not administrative housekeeping; it is strategic groundwork that ensures partnerships remain problem-led rather than technology-driven.
• Focus on Economic and Workforce Development: As part of the inventory, we highly recommend focusing on economic and workforce development. Employers increasingly seek AI-ready workers, yet few regions have articulated coherent labor force strategies. Community colleges, in particular, are nimble but underresourced actors that could anchor local talent pipelines if properly supported. Aligning higher ed capacity with regional economic strategy is smart policy in the short term and can lead to long-term competitiveness.
• Get the Incentive Right—Articulate Mutual Benefit: With more clarity about goals, the next step is to spend time thinking through shared incentives. Often, higher ed seeks funding, networks, or research avenues from cities. If you simply approach anchor institutions with your hand out, the response may not be encouraging. But there are often multiple areas of overlapping benefit that can be leveraged. For example, universities are often looking for student placements or research opportunities that, in many instances, intersect with a municipal challenge. And as this brief has argued from the start, both universities and local governments share an incentive to more visibly demonstrate civic impact and provide real value to local residents.
• Build in Accountability and Structure: Short-term projects alone rarely produce a durable impact. Local leaders should work with higher education partners to articulate shared goals for AI collaboration and document them in a civic AI compact. A “compact” is a public memorandum of understanding (MOU) that binds colleges and universities to community priorities, and, in many instances, is a useful mechanism to ensure focus on civic goals. Recently, the Trump administration has —traditionally drafted jointly by colleges and city representatives around shared goals—as a federally driven policy framework that ties institutional behavior to eligibility for funding. This move marks a significant shift in how such agreements have previously been deployed. We still recommend using compacts, but in a way that is drafted and maintained by local leaders. Such compacts clarify priorities, roles, and expectations, and create a foundation for sustained collaboration rather than episodic engagement. For additional guidance on forming successful civic compacts, see the report .

AI is arriving faster than most public sector institutions can comfortably absorb. Higher education, by contrast, has already spent several years governing its use, testing applications, and confronting risks—creating a reservoir of experience that remains largely untapped. For local governments and nonprofits, the opportunity is real, but time-limited. Once institutional practices solidify and capital investments lock in, shaping their trajectory becomes significantly harder.

By engaging higher ed now—before pathways harden—cities can help ensure that AI strengthens public capacity, surfaces resident priorities, and builds trust rather than eroding it.

The challenge is not discovery, but intentional engagement. The leaders who recognize this moment—and act with urgency—will not simply adopt new technology, they will help define how democratic institutions function in an AI era.