A Practical Roadmap for AI Governance in Government

Introduction

Key steps and considerations to help government organizations build effective, responsible AI governance programs. The goal is not governance for its own sake. The goal is to make better decisions, strengthen accountability, and help AI deliver real value in the environments where it will actually be used.

1. Start with an AI inventory

Catalog current and planned AI use cases, owners, purpose, data, and intended outcomes. Visibility creates a shared starting point for governance.

2. Define decision rights

Clarify who approves use cases, who reviews risk, who owns controls, and who monitors performance once a system is live.

3. Prioritize higher-impact use cases

Focus governance effort where the consequences, sensitivity, scale, or public impact are greatest.

4. Build practical controls

Use proportionate human oversight, documentation, testing evidence, escalation paths, and monitoring.

5. Move from framework to practice

Embed governance into project intake, procurement, implementation, and ongoing operations rather than treating it as a separate paperwork exercise.