Cities Prioritize AI for Efficiency — But Say They Lack the Staff to Implement It

Cities want AI. Their teams are already running at capacity. A March 2026 Tyler Technologies survey of 109 public sector leaders — including 54 municipal officials across 26 states — puts numbers on what anyone working in municipal technology already suspects.

The findings are striking:

• 67% of city respondents identified improving internal efficiency or automation as a top priority in the next 12–18 months
• 44% are experimenting with generative AI tools like chatbots and content drafting
• 35% point to resident service delivery as a priority use case
• 35% cite data analysis and decision support

But 63% say limited internal expertise or staffing is the biggest barrier to adopting or scaling AI. Funding limitations (41%) and legacy systems (35%) compound the problem.

This is not a confidence problem. Municipal leaders are not debating whether AI belongs in local government. The survey found that 33% already have a defined AI policy or governance framework, and another 30% are actively developing one.

The real constraint is capacity. Small teams responsible for visible, high-touch services cannot absorb a major technology rollout on top of their existing workload. As the Tyler report puts it, cities are “selecting use cases that can be implemented without overextending staff or introducing operational instability.”

This framing matters. It explains why municipal AI adoption looks different from private-sector adoption. Cities are not making sweeping transformation bets. They are choosing bounded, practical applications that solve specific operational pain — and doing so carefully.

For cities evaluating AI tools, the Tyler findings point to clear buying criteria: solutions must fit within existing workflows, require minimal implementation burden, and deliver measurable capacity relief from day one.

That is precisely the design philosophy behind Muni. Rather than asking municipalities to adopt a new system and train staff on a new platform, Muni integrates AI into the interaction layer — the conversations residents are already having with their local government.

When a resident messages about a utility bill, Muni handles it. When someone reports an issue, Muni routes it. When staff would otherwise field a routine call, the AI answers instead. No new system to learn. No additional burden on teams already stretched thin.

The 67% of cities looking for efficiency gains through AI are not looking for moonshots. They are looking for relief. That is a problem worth solving well.