AI Is Already in the Local Government Workplace

Artificial intelligence is no longer a future concept in public sector workplaces. According to new research from MissionSquare Research Institute, many state and local government employees are already using AI tools in their daily work, often without formal direction.

In a recent interview, Gerald Young, senior researcher at MissionSquare Research Institute, shared findings from a nationwide survey of approximately 2,000 state and local government employees. The study examines how AI is actually being used, where concerns are emerging, and what local government leaders should focus on next.

One clear takeaway from the data is that AI adoption is largely decentralized.

Young explained that AI use is often driven by individual teams rather than leadership mandates. “About a third said that their employers were telling them that they needed to use it,” he said, while “more than a half were saying that it was their team’s own preference.”

How Employees Are Using AI in Local Government Today

The survey found that AI in local government generally falls into three categories:

1. Customized applications built specifically for a government.
2. Tools developed by another government, association, or regional entity.
3. Commercial, off-the-shelf AI embedded in widely used platforms.

“Overall, it works out to be about a third, a third, a third in terms of how people are using it,” Young said.

Most use cases center on productivity tasks such as document processing, report writing, and meeting coordination. These applications often improve efficiency without removing human oversight from decision-making.

Training and Policy Gaps Create Risk

Despite widespread experimentation, many employees report little organizational preparation for AI adoption.

“About 60% were saying that they hadn’t received any training from their employers,” Young noted. While some organizations have discussed issues like cybersecurity or AI hallucinations, those conversations do not always translate into formal policy.

One particular concern involved employee awareness of AI internal policy and guidelines. When asked whether AI use was regulated or prohibited by their organization, “there were about 15 to 20% who said, ‘I don’t know,’” Young said. “If you have a policy whatsoever, it should at least be clear to people that a policy exists.”

Efficiency Gains Come with Oversight Challenges

Employees who reported using AI were far more likely to see efficiency gains than implementation problems. Confidence dropped, however, when it came to relying on AI outputs for decisions.

One issue is the review process. “Twenty-eight percent said that the AI results were rarely or never reviewed,” Young said.

From a governance standpoint, review and fact-checking of AI output is an area for improvement. “If I’m taking a report to the city council and the results have rarely or never been reviewed by the city manager or the department head, that’s concerning,” he added.

Job Loss Isn’t the Dominant Fear, but Anxiety Exists

While AI-related job loss is often cited as a major concern, the survey suggests that most local government employees do not expect AI to eliminate their positions. Still, a sizable minority is worried.

“Twenty percent of the respondents said that they were very or extremely concerned” about retraining or job elimination. Young emphasized that uncertainty alone could affect morale and retention if left unaddressed.

At the same time, many employees anticipate growth rather than contraction. “The majority felt like three years from now… they see their department having more staff than it does today,” Young said, with only about 6% expecting reductions.

Why Human Expertise Still Matters

Young stressed that AI does not replace institutional knowledge or policy expertise, especially in the public sector.

“The AI models are not going to be experts in your government,” he said. “You are going to be the expert in your government.”

While AI can speed up background research, it lacks familiarity with local codes, policy environments, and community context. That makes expert review and judgment essential.

What Leaders Should Focus on Next

Based on the research, Young recommends a deliberate approach to implementation. Organizations should start small, pilot AI in lower-risk areas, protect sensitive data, and provide clear training on appropriate use and review expectations.

Transparency is critical. “You don’t want to be in a position where you’re asked afterward what your methodology was, and you just kind of shrug your shoulders and say, ‘I don’t know. AI did it for me,’” Young said.

Clear communication and training also appear to improve workplace outcomes. Young noted that employees who use AI routinely also report higher morale, suggesting that engagement, professional development, and clarity play a key role in successful adoption.

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