The Real Reason Your City Isn’t Using AI (And It’s Not What You Think)
by Alton Henley
The story politicians and pundits tell about government and technology usually involves one of two villains: bureaucratic inertia, or political opposition. Neither one explains what’s actually happening with AI in American local government.
The ICMA 2024 Survey on Artificial Intelligence in Local Government asked 635 local government practitioners a direct question: what barriers prevent AI adoption in your organization? The answer challenged almost everything I expected to find.
Seventy-seven percent cited lack of AI awareness and understanding.
Not budget. Not politics. Not fear of change. Knowledge.
The 8:1 Ratio
Here’s the number that stopped me: only 10 percent of respondents cited lack of support from elected officials as a barrier. That’s the political resistance measure. Set it against the 77 percent knowledge figure and you get a ratio of nearly 8:1.
The secondary barriers round out the picture. Fifty-three percent cite insufficient trained personnel. Forty-one percent say they have no organizational policies or procedures for AI use. Forty percent cite funding. These are real constraints. But they all cluster around knowledge and capacity, not politics.
Local government isn’t resisting AI. It’s uncertain about it. And that’s an important distinction, because uncertainty and opposition require completely different responses.
What “Awareness” Actually Means
When practitioners check the box for “lack of AI awareness,” they’re not saying they’ve never heard of ChatGPT. The open-ended survey responses tell a more specific story.
“We don’t have anyone who can tell us what’s safe to use and what isn’t.”
“How do we know if a vendor’s AI claims are real or marketing?”
“We would like to explore AI options but don’t know where to begin.”
“It is hard as a senior manager to have the time to learn how to use this new technology. I find it intimidating.”
This is a cluster of interconnected uncertainties: not knowing what AI can do, not knowing how to evaluate it, not knowing what’s safe, not knowing where to start. It’s not ignorance—it’s the entirely rational recognition that the professional discourse around AI has raced ahead of the guidance practitioners actually need.
Independent research confirms the pattern isn’t limited to the ICMA sample. A 2025 survey of 1,028 local elected officials, published in PLOS ONE, found that 54 percent feel “inadequately informed” to make AI-related decisions. Fifty-seven percent say they’re unlikely to make such decisions in the next few years. Deloitte identified AI skills gaps as the most commonly cited scaling challenge across state and local government.
This Pattern Has Happened Before
If you’ve studied the history of government technology adoption, this finding is familiar.
In 1996, researchers studying GIS adoption in local government found that the primary determinants of adoption were awareness, exposure, and professional networking—not political resistance or budget constraints. Everett Rogers’ theory of innovation diffusion, first published in 1962, places knowledge as the very first stage of adoption. You cannot skip it.
Local government has traveled this road before, with e-government, with body-worn cameras, with enterprise resource planning systems. Every major technology transition has produced the same pattern: a small group of innovating communities, a larger group watching and waiting, and a substantial segment that hasn’t engaged at all. The specific technology changes. The pattern does not.
Why This Is Actually Good News
Political opposition is hard to overcome. Knowledge gaps can be closed.
Long Beach, California ran AI literacy workshops for city staff and tracked the outcomes. The proportion of participants who felt “not at all informed” about city AI use dropped from 61.5 percent to 3.9 percent after training. That’s not marginal improvement—that’s near-elimination of a declared barrier.
Only 4.5 percent of responding governments in the ICMA survey have implemented organization-wide AI training. That figure looks like a gap. It also looks like an opportunity: most communities haven’t tried systematic education yet. The latent demand is there. Leaders believe in AI’s potential—more than half see it in resident engagement alone—but 69 percent have taken no governance actions at all. They’re waiting for help understanding it.
The Harder Question
There’s a complication worth naming. Jeffrey Pfeffer and Robert Sutton, in The Knowing-Doing Gap, argued that the distance between knowing what to do and actually doing it is often more consequential than the distance between ignorance and knowledge. Organizations routinely fail to act on what they know, not because of information deficits, but because of institutional inertia and fear of failure.
The 77 percent figure might also, in part, reflect what practitioners are comfortable saying. “We don’t know enough” is a safe response that doesn’t implicate leadership failures or budget mismanagement. Bergen and Labonte documented how survey respondents systematically gravitate toward socially acceptable answers.
These complications don’t invalidate the knowledge-gap finding. Knowledge is almost certainly necessary for adoption—you can’t skip Rogers’ first stage. But education programs that only deliver information may underperform. Programs that combine information with peer support, low-stakes experimentation, and institutional permission to try things—and fail—are more likely to translate knowledge into action.
The Practical Upshot
If the dominant barrier is knowledge, then the path forward involves education, peer learning, and accessible guidance—not budget advocacy, not political strategy, not waiting for the technology to mature.
Communities aren’t opposed to AI. They’re uncertain about it. That’s a problem professional associations, state governments, and regional networks are actually positioned to solve.
Alton Henley is the author of The Knowledge Barrier: AI Adoption in American Local Government and Ready or Not: A City Manager’s Guide to AI in Local Government.