--- title: "The Cities Getting AI Right Are Investing in Workforce Upskilling" summary: |- Cities that are successfully scaling AI are investing in workforce upskilling alongside governance and technology deployment. Case studies from Washington, DC, San Jose, Seattle, and Cleveland show that employee training and AI literacy are critical to turning pilot projects into lasting improvements in public service delivery. date: "2026-06-18" issues: ["Artificial Intelligence", "Data Innovation", "State and Local", "Skills and Future of Work"] authors: ["David Kertai"] content_type: "Blogs" canonical_url: "https://datainnovation.org/2026/06/the-cities-getting-ai-right-are-investing-in-workforce-upskilling/" --- # The Cities Getting AI Right Are Investing in Workforce Upskilling Across the United States, city governments are deploying AI systems to improve service delivery and streamline internal processes, such as through AI-driven [curb management platform](https://statescoop.com/houston-ai-pilot-curb-management-loading-zones/)s and [residential permitting tools](https://www.nlc.org/article/2025/07/31/use-ai-to-transform-city-operations/). These efforts increasingly show that successful adoption depends not only on the technology itself but also on whether public employees are [prepared](https://www.aon.com/en/insights/articles/ai-isnt-the-differentiator-workforce-readiness-is) to use it effectively and consistently. As a result, leading cities now treat workforce development and AI upskilling as core components of their AI strategies—key factors in whether experimentation becomes sustained operational change. To ensure AI delivers durable performance gains rather than isolated pilots, other cities should integrate structured workforce upskilling directly into their deployment strategies. Washington, D.C. emerged as an early leader in workforce upskilling in 2024 by requiring AI training for all government employees and contractors with the goal of ensuring AI literacy reaches the entire public workforce. Under Mayor Muriel Bowser’s [Order 2024-028](https://techplan.dc.gov/page/mayors-order-articulating-dc%E2%80%99s-artificial-intelligence-values-and-establishing-artificial), district employees must complete training on responsible AI use, including prompt engineering, misinformation risks, and ethical use of generative tools. The city reinforces these expectations through its [AI Taskforce](https://cities-today.com/dc-becomes-first-major-us-city-to-require-ai-training/), which evaluates proposed AI deployments against principles of transparency and public benefit. Agencies must demonstrate compliance with these standards before gaining access to approved AI systems, linking training to governance and procurement. By tying training to formal approval processes, D.C. government ensures AI literacy shapes operational behavior. Employees apply responsible‑use principles when requesting, deploying, and using AI systems, embedding accountability into everyday workflow decisions. This governance framework now guides city AI initiatives such as [Talent Capital](https://mayor.dc.gov/release/mayor-bowser-launches-talent-capital-first-its-kind-regional-workforce-and-economic), an AI-powered workforce platform designed to match residents with jobs and training opportunities. As a result, D.C.’s approach reduces [uneven adoption](https://thedecisionlab.com/biases/ai-literacy-gap) across agencies and [standardizes](https://thedecisionlab.com/biases/ai-literacy-gap) how AI is used throughout city government, ensuring governance frameworks influence implementation rather than remaining abstract policy guidance. In California, the City of San Jose has become an early leader by building AI skills through hands‑on workforce development. Since 2024, the city’s [AI Upskilling Program](https://www.sanjoseca.gov/your-government/departments-offices/information-technology/itd-generative-ai-guideline), developed with San Jose State University, has combined self‑paced coursework with a 10‑week cohort model in which employees design AI tools tailored to their job functions. More than 1,000 employees, roughly [15 percent](https://statescoop.com/how-san-jose-trained-1000-city-employees-to-build-their-own-ai-tools/) of the municipal workforce, have completed the program. Participants have created practical [applications](https://statescoop.com/how-san-jose-trained-1000-city-employees-to-build-their-own-ai-tools/), such as tools that verify emergency‑vehicle readiness, review contractor submissions for missing documentation, and support the city’s carbon‑neutrality goals. Instead of treating AI training as a compliance exercise, San Jose embeds it in department‑level problem‑solving. Employees identify operational bottlenecks and design AI‑enabled solutions within a controlled environment. This approach 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](https://statescoop.com/ai-training-fuels-an-especially-lean-staff-in-san-jose-calif/) with priority needs. By integrating training into real-world and real-time operational workflows, the city strengthens internal capacity and improves consistency in how AI is applied across local government. Seattle, Washington, similarly stands out in AI adoption. Through a policy‑driven approach, it embeds AI upskilling within a broader governance and implementation framework. The city’s [2025–2026 AI Plan](https://www.seattle.gov/documents/Departments/Tech/Privacy/AI/City%20of%20Seattle%202025-2026%20AI%20Plan%20%281%29.pdf) establishes a structured approach to responsible AI adoption by launching a multi‑phase employee training initiative. The initiative begins with introductory AI‑literacy courses, progresses to applied workshops on data science and integration, and culminates in advanced partnerships with universities and industry to further [build technical capacity](https://www.govtech.com/artificial-intelligence/seattles-new-ai-plan-builds-on-past-work-updates-policy) across departments. Rather than treating training as a standalone initiative, Seattle integrates workforce development into procurement and governance processes so that [employees can engage](https://www.designdata.com/2026/04/15/whats-the-risk-of-letting-staff-figure-out-ai-on-their-own/) with AI tools through approved systems and established safeguards that create a consistent foundation for responsible use while enabling gradual scaling across city operations. Cleveland, Ohio, also reflects a foundational approach to building AI readiness by prioritizing governance and workforce preparation before large‑scale deployment. The city’s Urban Analytics and Innovation team has begun developing an AI adoption strategy using a phased [“Crawl, Walk, Run”](https://www.acceleratorforamerica.org/wp-content/uploads/2026/02/Cities-Leading-the-AI-Transition.pdf) approach that moves from small pilots to broader testing and full deployment. In its initial stage, Cleveland has focused on establishing governance structures, identifying early use cases, and building internal capacity through targeted training for designated data leads within departments. These staff members serve as early adopters who translate AI tools into [practical applications](https://www.acceleratorforamerica.org/wp-content/uploads/2026/02/Cities-Leading-the-AI-Transition.pdf) for their teams, helping build familiarity with the technology while maintaining oversight. By starting with limited deployments and structured internal training, Cleveland shows how cities can build institutional readiness [incrementally](https://markets.financialcontent.com/pennwell.oilgasjournal/article/tokenring-2025-11-20-cleveland-forges-future-with-city-wide-ai-upskilling-initiative) rather than waiting for fully mature systems before investing in workforce capacity. This phased model allows governance, data infrastructure, and employee skills to develop in parallel, ensuring future expansion is grounded in [organizational preparedness](https://www.aon.com/en/insights/articles/ai-isnt-the-differentiator-workforce-readiness-is) rather than rapid, uneven adoption. Across these cities, a consistent pattern emerges: AI adoption becomes operationally meaningful when municipalities invest in the workforce responsible for using it. Cities that embed training and hands-on experience into their AI strategies are beginning to turn experimentation into durable improvements in public service delivery. Taken together, these cases show that the cities with the most effective implementation are those that identify core challenges early and ensure their workforce keeps pace with technological change. *Image credit for social preview: Canva* --- *Source: Information Technology & Innovation Foundation (ITIF)* *URL: https://datainnovation.org/2026/06/the-cities-getting-ai-right-are-investing-in-workforce-upskilling/*