Enacting StatGPT: Experimenting with AI-enabled Performance Management

Municipal governments stand at a critical inflection point where traditional performance management approaches are fundamentally inadequate for addressing current governance challenges. While the promise of artificial intelligence (AI) dominates discussions about the future of government, many municipal leaders remain uncertain about how to move from abstract potential to tangible impact. This article cuts through the noise. It challenges conventional thinking about data-driven city management by proposing StatGPT, an AI-enabled transformation framework that fundamentally reimagines how cities measure, understand, and optimize their performance. It offers a strategic opinion derived from a series of intensive, expert-led workshops with senior city managers, data and innovation officers, and service delivery leads from across the United States. Instead of a theoretical exploration, this piece presents a practical vision for AI adoption, arguing that the most promising opportunities lie within three strategic pillars: moving from retrospective reporting to predictive performance intelligence, compliance-focused and optimization-oriented processes, and enhancing strategic communication and external engagement. We present pathways, including visions for unlocking visual data and modernizing service delivery, not as finalized blueprints, but as examples of the kind of focused, high impact thinking that can drive real-world transformation. This is a view from the field, intended as a call to action for leaders ready to harness AI as a tool for building a more responsive, efficient, and trusted government.