Sponsored content powered by ICMA partner, Tyler Technologies.
 

A recent national survey conducted by Tyler Technologies highlights how local governments are approaching artificial intelligence not as a sweeping transformation, but as a practical strategy to extend limited capacity.

The survey gathered perspectives from 109 public sector leaders across the United States, including 54 municipal officials representing 26 states and the District of Columbia. Among respondents, a consistent pattern emerged: leaders are exploring AI where it can ease operational pressure, streamline everyday workflows, and support teams that are already stretched thin.

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. Full findings and real-world examples behind these trends can be found in the e-book, AI for Impact: Proven Results for Government.

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

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

Internal Capacity Is the Primary Constraint

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.

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 local governments are responding by formalizing governance alongside experimentation.

Among 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. Local governments are unlikely to prioritize broad, experimental AI initiatives. They will favor capabilities that are bounded in scope, embedded within existing workflows, and aligned 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 local government 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.