Key topics covered in this article:

• AI adoption is accelerating across state and local government as agencies respond to workforce shortages, rising costs, and growing citizen expectations for faster, more convenient digital services. As generative AI becomes more accessible, many organizations are moving beyond pilot programs and exploring practical operational use cases.
• Early momentum is strongest in citizen services, workforce productivity, and data-driven decision-making. Governments are using AI to support chatbots, streamline administrative work, and analyze large datasets to improve service delivery, resource allocation, and operational planning.
• Governance, data security, accuracy, and oversight remain major concerns. State and local leaders are increasingly focused on developing AI policies, training staff, and establishing guardrails that support innovation without compromising compliance or public trust.

Artificial intelligence is rapidly moving from pilot programs to operational deployment across state and local governments. A recent survey found that 82% of state CIOs report employees are using generative AI in their daily work, up from 53% just one year earlier. That kind of growth signals that many are using the technology while leadership is actively evaluating where AI can deliver real value. At the same time, some state and local governments are starting to develop policies and governance standards around AI use, driven largely by concerns about security and privacy. For leaders working through those decisions, it helps to start with what’s driving adoption, where it is showing real potential, and what needs to be considered before launching a new AI initiative. 

Why AI Adoption Is Accelerating

Several factors are driving government organizations to take a closer look at AI. Workforce shortages continue to challenge many agencies, which tends to limit service capacity and create bottlenecks that affect day-to-day operations. Additionally, rising costs are increasing pressure to improve efficiency and do more with existing resources. 

Citizens, meanwhile, expect the same level of responsiveness and digital service they experience in the private sector. Whether they are paying a bill, requesting a permit, or reporting an issue, the expectation is speed and convenience. Generative AI tools have also become more accessible in recent years, allowing organizations to experiment with new applications at relatively low cost. Together, this is pushing many state and local officials to move from pilot projects and proofs of concept toward implementation. 

Where Governments Are Seeing the Greatest Potential

AI is being explored across nearly every government function, but early adoption is focused where operational pressure is highest and practical use cases are easiest to define. Three areas in particular are getting the most traction. 

Citizen Services — How residents experience their interactions with the government has a direct effect on public trust, which is part of why citizen services have become such an active area for AI adoption. 

 Denver and Montgomery County, for example, have both implemented AI tools that provide 24/7, multilingual answers to common questions. Toronto uses a chatbot to handle simple citizen requests like helping citizens fill out a request-for-service form. These use cases free up human staff for more involved questions. 

New York City’s chatbot, however, is a cautionary tale. It used an open-ended design, which led to inaccurate and sometimes unlawful responses. This became a public trust issue and highlights the need for strict guardrails when deploying any type of AI tool. 

 Workforce Productivity — Much of the AI activity inside government right now is happening behind the scenes, and it may be where near-term impact is greatest. Staff are using AI to draft communications, summarize reports, and handle repetitive administrative work. Municipalities are also exploring AI tools that can perform higher level tasks like assist with permit reviews and support 911 non-emergency calls. 

These use cases may be particularly valuable for organizations facing workforce challenges and growing service demands. When staff time is limited, tools that reduce the friction of routine tasks can improve what a team is able to accomplish on any given day. 

That being said, employees need clear direction on which AI applications are approved for use, how to use them effectively, and what information should never go into them. Without that guidance, staff often turn to whatever free or consumer-grade AI tools are available, sometimes entering sensitive resident or organizational data into platforms that offer no data security guarantees. Developing AI policies and training programs is a critical part of successful AI adoption. 

Data-Driven Decision-Making — State and local governments collect huge amounts of information every day. The challenge is often turning that information into something useful. AI is helping close that gap by analyzing large datasets faster than any team could manually and identifying patterns that inform decisions. 

Several examples are already emerging. Some governments are using predictive maintenance tools to identify roads that may need repairs before conditions worsen. Traffic management systems can adjust signal timing based on real-time conditions. Following severe weather events, AI can help emergency crews review satellite and drone imagery to plan and prioritize their response. While these applications are very different, they all use existing data to help leadership prioritize and allocate funds more effectively. 

Challenges and Considerations

There is momentum behind AI, but there are also obstacles. Governance and ethics top the list for many government leaders.  

AI is already built into tools most governments use every day, including email, ERP systems, Microsoft Teams, and vendor platforms, but most organizations haven’t established who owns oversight of how those tools collect and share data. Unmonitored AI use can trigger compliance violations, disrupt operations, and erode public trust. Many existing policies and vendor contracts predate AI entirely, and agencies on federally funded platforms may face additional restrictions on top of that. 

Resourcing makes the governance problem harder to solve. Only 25% of state CIOs report having dedicated funding for AI, which means most governments are experimenting without a real budget behind it. Agencies still running legacy systems face an additional layer of difficulty, since older infrastructure often wasn’t built to integrate with newer technology in the first place. 

Most government leaders seem clear-eyed about these challenges. Many are building governance policies alongside their other AI work rather than waiting until there are issues. Questions about data quality, bias, and accuracy are being taken seriously, and many entities appear focused on balancing innovation with accountability and public trust. 

Looking Ahead

AI adoption across state and local government is accelerating, and the next phase will likely move beyond pilots into broader implementation. Organizations that address governance, policy, and staff readiness now will be better positioned to deploy AI responsibly and effectively. For more information, contact Betsy Hedrick or Michael Garber, Partners on PBMares’ State and Local Government team.