AI Governance Checklist

Summary

Thomson Reuters Practical Law delivers what many organizations desperately need: a no-nonsense, actionable checklist that transforms AI governance from abstract concepts into concrete implementation steps. This isn't theoretical guidance—it's a practical roadmap that walks you through establishing governance frameworks across your entire AI ecosystem, from traditional machine learning to cutting-edge generative AI and autonomous agents. Published in 2024, it reflects the current regulatory landscape while providing flexibility for the rapidly evolving AI governance space.

The bottom line

Most AI governance resources tell you what to do. This checklist shows you how to do it, step by step. It tackles the messy reality of implementing governance in organizations that may be using AI in dozens of different ways across multiple departments, often without central oversight.

What you'll actually get

Risk-based lifecycle approach: The checklist structures governance around how AI systems actually develop and evolve, from initial design concepts through deployment and ongoing monitoring. Each phase has specific checkpoints and decision criteria.

Multi-AI system coverage: Recognizing that modern organizations aren't just dealing with one type of AI, the resource addresses governance for traditional AI systems, generative AI tools, and emerging agentic AI that can act autonomously.

Cross-functional integration: Rather than treating AI governance as purely a technical or legal issue, the checklist incorporates perspectives from IT, legal, compliance, risk management, and business units.

US regulatory alignment: Built with US legal and regulatory requirements in mind, including considerations for sector-specific regulations in healthcare, finance, and other heavily regulated industries.

Who this resource is for

Chief Risk Officers and Compliance Teams looking for structured approaches to AI risk assessment and ongoing monitoring programs.

Legal Departments tasked with developing AI policies but lacking technical background on AI system lifecycles and implementation challenges.

IT and Data Science Leaders who understand the technology but need frameworks for communicating governance requirements to business stakeholders and ensuring cross-organizational buy-in.

Smaller to mid-size organizations that need enterprise-grade governance but lack the resources to build comprehensive frameworks from scratch.

Organizations in regulated industries that must demonstrate systematic approaches to AI oversight for audit and regulatory purposes.

Getting started strategically

Before diving into the checklist, assess your organization's current AI footprint—you likely have more AI systems in use than you realize. Start by conducting an AI inventory across departments, then use the checklist's risk categorization framework to prioritize which systems need immediate governance attention versus longer-term oversight development.

The resource works best when approached as a phased implementation rather than a one-time compliance exercise. Consider beginning with high-risk or customer-facing AI applications before expanding governance to lower-risk internal tools.

Watch out for

This is a comprehensive checklist, which means it can feel overwhelming if you try to implement everything simultaneously. The resource doesn't provide much guidance on prioritization or phased rollouts—you'll need to adapt the recommendations to your organization's risk tolerance and resources.

While the checklist covers generative and agentic AI, the rapidly evolving nature of these technologies means some specific technical considerations may require supplemental guidance as new capabilities and risks emerge.