Cities across the United States are treating workforce upskilling as a core requirement for successful AI adoption in government, with Washington, D.C. mandating AI training for all employees and contractors and San Jose training roughly 15 percent of its municipal workforce through hands-on development programs. According to a June 18, 2026 analysis by the Information Technology and Innovation Foundation, municipalities that integrate structured training into their AI deployment strategies are turning experimental pilots into sustained operational improvements, while cities without workforce development see uneven adoption and isolated projects that fail to scale.
Washington, D.C. became an early leader in 2024 by requiring AI training for all government employees and contractors, ensuring AI literacy reaches the entire public workforce. Under Mayor Muriel Bowser's Order 2024-028, district employees must complete training on responsible AI use, including prompt engineering, misinformation risks, and ethical use of generative tools. The city's AI Taskforce evaluates proposed AI deployments against transparency and public benefit principles, requiring agencies to demonstrate compliance before accessing approved systems. San Jose's AI Upskilling Program, developed with San Jose State University since 2024, has trained more than 1,000 employees—roughly 15 percent of the municipal workforce—through a 10-week cohort model in which employees design AI tools tailored to their job functions. Participants have created practical applications that verify emergency-vehicle readiness, review contractor submissions for missing documentation, and support carbon-neutrality goals. Seattle's 2025–2026 AI Plan launched a multi-phase employee training initiative that begins with introductory AI-literacy courses, progresses to applied workshops on data science and integration, and culminates in advanced partnerships with universities and industry. Cleveland's Urban Analytics and Innovation team has developed an AI adoption strategy using a phased "Crawl, Walk, Run" approach, starting with governance structures, early use cases, and targeted training for designated data leads within departments.
The report finds that D.C.'s approach reduces uneven adoption across agencies and standardizes how AI is used throughout city government, ensuring governance frameworks influence implementation rather than remaining abstract policy guidance. According to the analysis, San Jose's model ties workforce development directly to service-delivery improvements and reduces the risk that AI tools remain isolated pilot projects that fail to scale or align with priority needs. The report states that Seattle integrates workforce development into procurement and governance processes so employees can engage with AI tools through approved systems and established safeguards, creating a consistent foundation for responsible use while enabling gradual scaling across city operations.
The analysis explains that successful AI adoption depends not only on the technology itself but also on whether public employees are prepared to use it effectively and consistently. Cities that embed training into department-level problem-solving allow employees to identify operational bottlenecks and design AI-enabled solutions within controlled environments, strengthening internal capacity and improving consistency in how AI is applied across local government. By tying training to formal approval processes, municipalities ensure AI literacy shapes operational behavior, with employees applying responsible-use principles when requesting, deploying, and using AI systems. Cleveland's phased model allows governance, data infrastructure, and employee skills to develop in parallel, ensuring future expansion is grounded in organizational preparedness rather than rapid, uneven adoption.
The report concludes that AI adoption becomes operationally meaningful when municipalities invest in the workforce responsible for using it, with cities that embed training and hands-on experience into their AI strategies beginning to turn experimentation into durable improvements in public service delivery. To ensure AI delivers sustained performance gains rather than isolated pilots, the analysis recommends that cities integrate structured workforce upskilling directly into their deployment strategies. The cities with the most effective implementation are those that identify core challenges early and ensure their workforce keeps pace with technological change.