Generative AI Policy Guidance Document for Local Governments

Summary

The Centralina Regional Council has created what may be the first comprehensive policy guidance specifically designed for local governments grappling with generative AI adoption. This document cuts through the complexity of AI governance to provide practical, actionable frameworks that mayors, city managers, and municipal IT directors can actually implement. Unlike broad federal guidance or academic frameworks, this resource addresses the unique constraints and opportunities facing local governments—from small towns to major cities—including limited budgets, public records requirements, and the need for transparent decision-making in public service delivery.

Who this resource is for

Primary audience:

• Municipal leadership (mayors, city managers, department heads)
• Local government IT directors and CISOs
• City attorneys and compliance officers
• Regional councils and county administrators

Also valuable for:

• Government technology vendors serving local markets
• Public administration consultants
• State-level officials developing AI oversight programs
• Academic researchers studying AI governance implementation

What makes this different from other AI guidance

Most AI governance resources target federal agencies or large enterprises, leaving local governments to adapt guidance that doesn't fit their reality. This document acknowledges that a city of 50,000 people can't implement the same governance structure as a federal department. It provides scalable approaches that work whether you're a small town considering your first AI pilot or a major city building comprehensive AI oversight.

The guidance specifically addresses municipal pain points like public records laws, budget approval processes, and the political dynamics of implementing new technology in highly visible public services. Rather than generic "establish an AI committee" advice, it provides concrete templates and decision trees tailored to local government structures.

Core implementation framework

The document structures AI governance around four practical pillars that align with how local governments actually operate:

Risk-based deployment approach: Start with lower-risk internal operations (document summarization, meeting transcripts) before moving to public-facing applications. This mirrors how most municipalities pilot new technologies and builds institutional confidence.

Data governance integration: Connects AI oversight to existing public records, privacy, and information security frameworks rather than creating parallel governance structures. This prevents policy conflicts and leverages existing staff expertise.

Vendor management protocols: Addresses the reality that most local governments will purchase AI capabilities rather than build them, with specific guidance on contract terms, liability allocation, and ongoing oversight of third-party AI tools.

Transparency and accountability mechanisms: Balances the need for public oversight with practical operational requirements, including guidance on when and how to notify the public about AI use in government services.

Getting started with minimal resources

The document recognizes that many local governments lack dedicated AI expertise or large IT budgets. It provides a phased implementation approach:

Phase 1 (Immediate): Establish basic AI use policies, inventory existing AI tools already in use, and create approval processes for new AI acquisitions. This phase requires no additional budget or specialized staff.

Phase 2 (6-12 months): Develop department-specific use cases, pilot low-risk applications, and establish monitoring procedures. Can typically be managed within existing IT and legal resources.

Phase 3 (Long-term): Scale successful pilots, implement more sophisticated governance structures, and potentially explore public-facing AI applications with appropriate oversight.

Watch out for common pitfalls

Public records complications: The document warns against assuming standard public records laws automatically apply to AI-generated content. It recommends proactive legal review of how AI outputs should be classified and retained.

Vendor lock-in risks: Many AI vendors target local governments with attractive pilot pricing but limited long-term flexibility. The guidance emphasizes contract terms that preserve government control over data and decision-making processes.

Political sensitivity underestimation: AI implementations that seem purely technical can become political issues quickly. The document stresses early stakeholder engagement and clear communication strategies before launching any AI initiatives.

Quick reference for immediate action

• First step: Inventory AI tools already in use across all departments (many governments discover they're already using AI through existing software)
• Essential policy: Establish clear approval requirements for any new AI tool acquisition
• Legal review: Have city attorney review public records implications before any AI pilot
• Budget planning: Factor AI governance costs into annual budget cycles, not just tool acquisition costs
• Staff training: Plan for ongoing education, not just initial implementation training