About This Book

The Knowledge Barrier examines the state of AI adoption in American local government through empirical analysis of the ICMA 2024 Survey on Artificial Intelligence in Local Government (n=635), supplemented by peer-reviewed research, institutional reports, and documented case studies. It establishes the empirical baseline, analyzes the knowledge barrier, documents structural disparities, examines the risk record, assesses the opportunity evidence, and analyzes institutional responses.

Where the evidence is clear, this book states conclusions directly. Where it is ambiguous or insufficient, it says so. The reader will find both confident findings and frank acknowledgments of what remains unknown.

• Part I: The Empirical Landscape
• Chapter 1: What the Data Shows — ICMA 2024 survey results: adoption rates, service areas, regional variation
• Chapter 2: The Corroborating Evidence — Independent research confirming the knowledge-barrier pattern
• Chapter 3: The Concern Hierarchy — Disinformation, trust, security—how leaders conceptualize AI risk
• Part II: The Knowledge Barrier
• Chapter 4: Anatomy of a Barrier — What “77 percent lack awareness” actually means
• Chapter 5: The Knowing-Doing Gap — Why knowledge alone may be insufficient for adoption
• Chapter 6: The Organizational Disconnect — The priority gap between IT leaders and executive leadership
• Part III: Structural Disparities
• Chapter 7: The Small Community Challenge — AI readiness disparities by community size
• Chapter 8: Structural Variation — Region, government form, and jurisdiction type
• Part IV: The Risk Record
• Chapter 9: What Goes Wrong — Eight documented government AI failures and institutional patterns
• Chapter 10: The Ethics of Deployment — Obligations when AI affects people’s lives
• Chapter 11: The Worker Question — What AI does to the people inside the organization
• Part V: The Opportunity and Its Limits
• Chapter 12: Where AI Could Help — Opportunity gaps between current use and perceived potential
• Chapter 13: What the Evidence Actually Shows — Evaluating productivity claims with rigor
• Chapter 14: The Honest Assessment — Where the opportunity evidence holds up and where it doesn’t
• Part VI: Institutional Responses
• Chapter 15: The Case for Shared Infrastructure — Cooperative approaches to AI governance and capacity-building
• Chapter 16: What Remains Unknown — A research agenda for scholars and policymakers

Plus three appendices: Methodology, Key Statistics, and Documented AI Failures.

Quarterly Updates

The AI landscape moves fast. These notes track shifts that affect the book’s analysis.

Errata

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