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# How Municipal Governments Can Get What They Need from AI
- URL: https://www.theraceai.org/how-municipal-governments-can-get-what-they-need-from-ai/
- Published: 2026-08-24T22:41:40.000Z
- Updated: 2026-08-24T22:43:04.000Z
- Description: Municipal governments do not need to match AI vendors alone. By connecting existing professional networks, sharing lessons faster, and building a stronger vendor feedback loop, they can gain scale without giving up local authority.
- Author: Mark Howard
- Tags: Signal, Lane 6 — Civic Infrastructure and Public Capacity, Lane 7 — Implementation Capacity, Posts

There are roughly [19,500 municipal governments and 3,000 county governments](https://www.census.gov/library/publications/2026/econ/govtorg2225.html?ref=theraceai.org) in the United States. Most are nothing like New York, Chicago, or Los Angeles County. [Three-quarters of incorporated places have fewer than 5,000 residents](https://www.census.gov/newsroom/press-releases/2025/vintage-2024-popest.html?ref=theraceai.org), and their IT capabilities range from very good to just getting by. When working with AI vendors—both product providers and implementers—most local governments face a significant disadvantage in size and information.

Fortunately, they also have access to a well-established and remarkably dense support network. There is the International City/County Management Association (ICMA), the Government Finance Officers Association (GFOA), the National League of Cities (NLC), the National Association of Counties (NACo), and similar associations for attorneys, procurement directors, IT directors, HR directors, police chiefs, fire chiefs, parks directors, and many others.

In addition to these national organizations, [nearly every state has a municipal league](https://www.nlc.org/membership/state-municipal-leagues/?ref=theraceai.org), such as the Colorado Municipal League, and many metropolitan areas have regional networks, such as the [Association of Bay Area Governments](https://abag.ca.gov/about-abag?ref=theraceai.org) around San Francisco. These organizations regularly compare notes, share what is working and what is not, and establish common positions, particularly on state and federal legislation.

This network has long been very good at sharing information, spreading new practices, and establishing common positions. I have personally participated in and benefited from it. Historically, this “downward channel”—moving knowledge and expertise through the network—is what it does best.

AI creates a new challenge. The network must move information much faster, but it also needs a stronger channel in the other direction: collecting what local governments are learning and turning that experience into a more organized relationship with AI vendors.

Municipal governments do not need to consolidate authority or give up autonomy to gain scale. They can create scale through coordination, using capabilities and relationships they already have.

## A New AI Support Layer Is Already Emerging

A new support layer focused specifically on AI is already forming. The City of San José started what became the [GovAI Coalition](https://www.sanjoseca.gov/your-government/departments-offices/information-technology/ai-reviews-algorithm-register/govai-coalition?ref=theraceai.org) after it struggled to get satisfactory answers from AI vendors about privacy and data use. When San José staff spoke with neighboring cities, they discovered others were having the same experience. The city called governments together to compare notes and approach vendors with a joint message. That first meeting in 2023 attracted about 50 agencies. Three years later, GovAI includes [more than 900 agencies](https://www.sanjoseca.gov/your-government/departments-offices/information-technology/artificial-intelligence-inventory/govai-coalition/become-a-member/become-a-member?ref=theraceai.org).

GovAI has since developed vendor fact sheets, disclosure requirements, procurement practices that translate general AI principles into operational questions, risk-review models, and internal guidance. It also provides [shared resources that governments can reuse](https://www.sanjoseca.gov/your-government/departments-offices/information-technology/artificial-intelligence-inventory/govai-coalition/templates-resources?ref=theraceai.org) rather than develop independently.

That is particularly important for smaller governments. GovAI can provide pieces of common AI infrastructure—policies and governance tools, procurement materials, model contract language, shared registries, and an AI Contract Hub—that individual governments can adapt to their own circumstances.

GovAI itself is also becoming more institutionalized. In April 2026, the San José City Council [approved transitioning the coalition toward an independent nonprofit organization](https://sanjose.legistar.com/View.ashx?G=920296E4-80BE-4CA2-A78F-32C5EFCF78AF&GUID=7119A3C1-9CEE-4455-A511-0A91F1271D10&ID=15340312&M=F&ref=theraceai.org), giving it access to a formal governance structure, philanthropic funding, dedicated leadership, and greater capacity to support its membership.

Most important for the argument here, GovAI has established an [Industry Relations Committee](https://www.sanjoseca.gov/your-government/departments-offices/information-technology/artificial-intelligence-inventory/govai-coalition/committees-working-groups?ref=theraceai.org). Its work includes supporting engagement with vendors and helping public agencies identify AI solutions that meet their needs and values.

Those are important beginnings. The opportunity now is to connect this emerging AI-specific capacity much more deliberately to the broader network of organizations that already supports local governments across the country.

## The Existing Network Needs to Move Faster

The traditional means of spreading knowledge through the municipal support network include regular meetings, conferences, reports, professional publications, and informal peer exchange. All will remain useful. But AI vendors can change their models, interfaces, terms, and capabilities far faster than this network traditionally moves.

For AI, rapid diffusion therefore needs to become a core operating function. Useful practices, warnings, procurement lessons, and implementation experience cannot take a year to make their way through conferences, publications, and individual associations. They need to move quickly across professional associations, state leagues, regional organizations, and specialized AI networks such as GovAI.

## The More Important Opportunity Runs in the Other Direction

The more significant change is in the other direction. Local governments across the country are conducting many real-world experiments with AI. They are learning where systems fail, what vendors will disclose, which contractual provisions matter, and what implementation problems recur.

Municipal support networks should systematically collect that experience. Their role should be to identify recurring problems and distinguish isolated incidents from patterns appearing across jurisdictions.

Those patterns can then become common requirements presented to AI vendors. Governments and vendors could work toward clearer approaches to auditability, data use, portability, continuity, provider replacement, and other recurring concerns. A vendor hearing the same requirement from hundreds of governments is more likely to take it seriously than one hearing isolated requests one customer at a time.

The goal should therefore be regular and sustained engagement with AI vendors rather than episodic contact. Leaders from organizations such as NLC, NACo, ICMA, GFOA, associations representing municipal technology officials, and GovAI could establish a working group that aggregates concerns and priorities across their networks.

Such a group would not eliminate the scale advantage of major AI companies, but it could materially reduce the disadvantage facing individual governments. It could aggregate the experience and purchasing concerns of thousands of municipalities and counties while leaving each government free to decide how AI should be used in its own organization and community.

Vendors could benefit as well. Instead of separately gathering feedback from hundreds of governments, they could receive clearer signals about recurring market needs, identify developing problems earlier, and reduce the burden of hundreds of one-off negotiations. More standardized expectations could make the public-sector market easier for both sides to navigate.

The working group could meet regularly with major AI product companies and implementers. Those meetings should surface recurring problems, requested capabilities, contract concerns, and emerging expectations. Later meetings should report what vendors have changed, what they have chosen not to change, and why. That would turn industry relations from occasional consultation into an actual feedback loop.

This broader model is not yet proven. GovAI has established pieces of it, and [municipal organizations have long used collective action effectively in legislative settings](https://www.nlc.org/advocacy/federal-advocacy-committees/?ref=theraceai.org). The next step is to determine whether those capabilities can also give local governments greater influence over the AI products and services they increasingly rely upon. Without such a mechanism, smaller governments risk becoming users of systems they have little ability to shape.

## Matching Scale to Scale

The easiest outcome to imagine is continued fragmentation: AI vendors deal with local governments one at a time, effectively saying, “Here is what we provide; you need to adapt to it.”

Local governments have another option. They can use the networks they already possess to reduce that scale disadvantage without centralizing authority. The necessary institutions largely exist. What needs to change is how deliberately they connect, how quickly information moves through them, and whether they can turn dispersed local experience into an organized voice in the market.

I am suggesting three steps:

1. *Connect the support network* — NLC, NACo, ICMA, GFOA, state leagues, regional councils, functional associations, GovAI, and others should deliberately exchange AI knowledge across organizational boundaries rather than principally through their own membership channels.
2. *Build the return channel* — Create routine mechanisms for governments to report what they are learning—vendor problems, implementation failures, useful practices, contract issues, and missing capabilities—and aggregate those experiences into recognizable patterns.
3. *Institutionalize the vendor loop* — Carry those patterns into regular engagement with providers and implementers, seek responses, and report back what changed.

The most consequential municipal AI institution may therefore not be another AI office. It may be the connective tissue that allows hundreds of governments to learn, and increasingly to bargain, as though they were much larger than any of them is alone.