The University as City: Building AI Governance Before Buying AI Tools

Universities are racing to plug AI into everything from advising to admissions, but the institutions that will actually win the future are quietly doing something far less glamorous: rewiring their operating system. Borrowing from the best digital government reforms, they are building cross-functional AI councils, setting USDS-style service standards, and investing in workforce fluency before they sign another software contract. In a world of endless pilots and flashy tools, this article argues that the real competitive advantage for higher education is not the next platform, but the governance scaffolding, data infrastructure, and human capacity that make responsible AI adoption possible at scale.

Modernization efforts in digital government have followed a specific trajectory: governance first, technology second. Cities that have successfully implemented AI and digital tools did not begin with software selection. Instead, they developed cross-functional governance models, codified workforce development plans, and enforced operational standards for service delivery. This structure-first approach has allowed municipalities to scale innovation across departments and sustain it beyond leadership changes or budget shifts. Higher education institutions are now facing similar demands but lack the structural alignment needed to respond effectively.

Academic institutions often operate with decentralized decision-making, siloed departments, and inconsistent digital policies. These characteristics hinder their ability to implement AI at scale. In contrast, digital government initiatives rely on centralized governance bodies, such as chief digital officers or digital service teams, that coordinate efforts across agencies and set consistent standards. Higher education can adapt this model by establishing AI task forces that include academic leadership, IT professionals, faculty, accessibility experts, and student services staff. This cross-functional alignment creates the foundation for responsible and scalable AI adoption in education.

Identifying Structural Barriers in Higher Education

Universities face several structural challenges that impede modernization: fragmented data systems, uneven AI experimentation, limited digital capacity, and a lack of unified governance. For example, one campus department might use AI-supported chatbots for advising, while another relies on legacy systems for course enrollment. These inconsistencies are not just technical issues - they reflect a lack of institutional strategy and coordination. Without clear governance structures, AI pilots remain isolated and fail to integrate into long-term academic planning.

Additionally, many institutions lack workforce development strategies that account for AI literacy. Faculty and staff often receive minimal training on emerging technologies, leaving them unprepared to adopt and manage new tools. According to the EDUCAUSE 2023 Horizon Report, digital fluency among academic staff remains a top concern, yet few institutions have developed comprehensive training pathways to address it¹. This gap mirrors early-stage digital government projects that faltered due to underinvestment in workforce readiness. The lesson from civic modernization is clear: without equipping people to use technology, structural change stalls.

Translating Government Models into Academic Transformation

There are direct lessons from the civic sector that higher education can adopt. Digital service standards, such as those established by the United States Digital Service (USDS), provide clear, actionable criteria for designing and deploying digital tools. These guidelines emphasize accessibility, user-centered design, iterative development, and performance measurement². Universities can use similar frameworks when rolling out AI tools in advising, admissions, or learning management systems. Rather than piloting tools in isolation, institutions can embed them within structured guidelines that ensure consistency and accountability.

Governance policies are equally important. Cities like Boston and San Francisco have implemented AI transparency registers and ethics boards to oversee AI deployment in public services³. Higher education institutions can replicate this by forming AI governance councils that review proposed projects, evaluate data privacy implications, and ensure alignment with institutional values. These structures do not delay innovation - they enable it to scale responsibly. When governance is in place, universities can move from reactive experimentation to proactive planning.

Where Structure Matters More Than Tools: Micro-Credentials, Accessibility, and Career Pipelines

Emerging educational models such as micro-credentials, AI-supported accessibility design, and employer-aligned career pipelines illustrate the importance of structure over tools. Micro-credentials, for instance, require coordinated curriculum development, verification systems, and employer recognition frameworks. Without these, even the most advanced credentialing platforms cannot deliver value. The City University of New York (CUNY) has demonstrated this through its CUNY 2X Tech initiative, which aligns micro-credentials with employer needs and tracks outcomes through shared data protocols⁴.

In accessibility design, AI tools can generate captions, transcribe lectures, or personalize learning materials. However, their effectiveness depends on structured accessibility policies and clear institutional responsibilities. The University of Washington’s IT Accessibility Policy, which mandates compliance across all digital platforms, is a model of how structure drives impact⁵. Similarly, career pipelines that connect students to emerging job markets require aligned partnerships, data-sharing agreements, and outcome tracking. These are not software challenges - they are governance and strategy challenges.

Evaluating Institutional Readiness and Building Shared Governance

Institutions should begin by evaluating whether they have an AI governance structure or merely a collection of disconnected pilots. A structured readiness assessment can help. Questions to consider include: Is there a central body coordinating AI initiatives? Are there established guidelines for ethical use? Do faculty and staff have access to AI literacy training? Without affirmative answers, campuses risk falling into a cycle of fragmented innovation that cannot scale or sustain itself over time.

Building shared governance requires intentional collaboration. Institutions should convene working groups that include academic leadership, IT, accessibility offices, and student services. These groups should co-develop AI policies, approve pilot projects, and monitor outcomes. Governance models should also include student representation to ensure transparency and trust. Structured, inclusive governance is what turns experimentation into institutional capacity. It is the difference between a temporary pilot and a durable system.

Engaging in Workforce Modernization and AI Literacy

To support long-term transformation, universities must invest in workforce modernization and AI literacy. This involves more than technical training - it requires helping faculty and staff understand how AI fits into their roles, pedagogy, and campus services. Cities that have made progress in digital transformation, such as Louisville, Kentucky, have launched AI literacy programs for public employees to demystify technology and build confidence in using it⁶. Higher education can replicate these models by offering workshops, peer learning sessions, and digital skills certificates tailored to campus roles.

Participation in working sessions should be viewed as a strategic investment, not a one-off training. Institutions can partner with civic organizations, professional associations, and technology providers to co-create curricula that align with organizational goals. These sessions also serve as a feedback loop, helping governance bodies understand the challenges and opportunities faculty and staff face when engaging with AI. The goal is to build a digitally confident workforce that is prepared to support structured innovation.

Structure as the Engine of Sustainable Innovation

As the original argument outlines, AI adoption in higher education will be defined by structure, not tools. This insight is not theoretical - it is grounded in the practical experience of civic modernization efforts. Governance frameworks, workforce readiness plans, and operational standards are what allow technology to scale and serve the public good. The civic sector has demonstrated that these structures are not barriers to innovation but its precondition.

Higher education now stands at a similar crossroads. Institutions that invest in governance, training, and cross-functional coordination will be AI-ready, not just AI-equipped. Those that do not will find themselves managing a patchwork of pilots with no clear path to integration. The future of AI in education will not be determined by who adopts the flashiest tools, but by who builds the strongest scaffolding around them. Structure is not the afterthought - it is the strategy.

Bibliography

1. EDUCAUSE. 2023 Horizon Report: Teaching and Learning Edition. Boulder, CO: EDUCAUSE, 2023.

2. United States Digital Service. “Digital Services Playbook.” Accessed April 20, 2024. https://playbook.cio.gov.

3. City and County of San Francisco. “AI Use Guidelines.” San Francisco Office of Emerging Technology, 2023. https://sf.gov/resource/2023/ai-use-guidelines.

4. City University of New York. “CUNY 2X Tech.” Accessed April 20, 2024. https://www.cuny.edu/about/administration/offices/workforce/cuny-2x-tech/.

5. University of Washington. “IT Accessibility Policy.” Accessed April 20, 2024. https://www.washington.edu/accessibility/policy/.

6. City of Louisville. “AI Literacy and Workforce Training.” Louisville Office of Civic Innovation, 2023. https://louisvilleky.gov/government/civic-innovation.