Cities Need a New Model for Incentivizing Responsible AI

For the past seven years, I have led a team at the City of San Antonio responsible for evaluating emerging technologies, with a particular focus on artificial intelligence for public sector use. Our structured pilot program partnered with vendors on time-bound initiatives, enabling us to rigorously test outcomes and deliver evidence-based recommendations to city leadership. Nearly 30 pilots later—spanning AI-powered cameras, resident-facing chatbots, and Generative AI tools for procurement—we had a clear view of both the promises and pitfalls of AI in government.

What we discovered was sobering. Many AI systems simply were not designed for the realities of municipal environments. Classification algorithms trained on closed campuses in distant midwestern cities often failed to perform when deployed in the field in San Antonio. APIs were poorly documented and insecure, explainability was limited, and accountability risks were high. Worse still, procurement practices favored large vendors, leaving little room for competition and offering few incentives for responsible AI development.

Most American cities do not build AI systems—they buy them. The market for AI applications in government and public services is expected to grow 17% annually to over $50B by 2030, with companies like Anthropic and OpenAI already introducing their first GenAI models tailored for government use in the last year. In a world where AI is used to shape decision-making that has material impacts on communities, like determining eligibility for benefits, evaluating roadway improvements, or identifying criminal behavior, cities cannot rely on vendor promises or fragmented oversight.