AI at the Local Level: What Municipal Leaders Told Us

A recent Tyler survey shows that city governments are using artificial intelligence (AI) as a capacity strategy, not a sweeping transformation effort.

Tyler heard from 109 public sector leaders nationwide, including 54 municipal officials representing 26 states and the District of Columbia. Among city respondents, the pattern is consistent: leaders are exploring AI where it can relieve operational pressure, streamline workflows, and support overextended teams.

Sixty-seven percent identified improving internal efficiency or automation as a top priority in the next 12-18 months. Forty-four percent are experimenting with generative AI tools such as chatbots and content drafting, while 35% point to resident service delivery and another 35% to data analysis and decision support.

The data reinforces a clear theme: cities are prioritizing AI where it can extend capacity.

Internal Capacity Is the Primary Constraint

For cities, the primary barrier is not skepticism about the technology’s relevance. It is internal capacity.

Sixty-three percent of municipal respondents cite limited internal expertise or staffing as the biggest barrier to adopting or scaling AI. Funding limitations (41%) and legacy systems challenges (35%) also shape the pace of progress. These constraints are amplified in lean municipal environments, where small teams are responsible for visible, high-touch services.

As a result, prioritization is deliberate. In the near term, cities are selecting use cases that can be implemented without overextending staff or introducing operational instability.

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Governance and Ethics Are Developing in Parallel

Alongside staffing constraints, 52% of municipal respondents cite uncertainty around ethical or legal implications as a significant barrier. Rather than delay adoption, many cities are responding by formalizing governance alongside experimentation.

Among city respondents, 33% report having a defined AI policy or governance framework in place, and 30% are actively developing one. Nineteen percent rely on informal practices, while 15% report having no guidance in place today. This indicates that AI experimentation and policy formation are advancing together.

Municipal leaders are not separating innovation from oversight. They are building guardrails as they test practical applications. The pattern reflects responsible risk awareness.

What Sustainable Adoption of AI Means

Taken together, these findings point to a disciplined adoption path. Municipal AI deployment is unfolding as a focused operational strategy — constrained by bandwidth, shaped by governance development, and tied to service outcomes.

For technology providers, the implication is clear. Cities are unlikely to prioritize broad, experimental AI initiatives. They will favor capabilities that are bounded in scope, embedded within existing workflows, and align with defined policy frameworks. Solutions that fit operational realities — and integrate governance guardrails rather than treating them as an afterthought — are more consistent with how municipal leaders are choosing to move forward.

Most city leaders are not debating whether AI belongs in local government. They are determining how to apply it responsibly without overextending the teams residents rely on every day.

About the Author

Burgandi Grace

Burgandi Grace is principal project manager for artificial intelligence at Tyler Technologies. Her work centers on helping government teams move from early adoption to consistent, repeatable implementation of AI-enabled products, with an emphasis on trust, readiness, and scale.