Document architecture: Maintain detailed documentation covering overall system architecture, components, data flows, dependencies, and touch points with other systems. Keep updated.

Catalog assets: Build a catalog of all AI assets like models, prompts, and personas with relevant metadata like owners, access rules, and intended uses. A catalog enables discovery and governance.

Classify risk levels: Assign risk ratings to AI systems based on factors like use case sensitivity, data accessed, and real-world impact. Higher-risk systems warrant closer governance.

Formalize lifecycles: Institute formal processes governing development, testing, validation, deployment, monitoring, and retirement of AI systems. Well-defined lifecycles embed governance.

Enable oversight: Appoint oversight teams with authority to review projects for risks, audit systems, and enforce policies. Independent oversight ensures accountability.

Automate controls: Use tools to automatically monitor metrics and validate assets across environments for qualities like bias, data use, and model drift. Automation scales governance.

Centralize funding: Manage AI budgets and prioritization centrally based on validated use cases and ROI projections. Central funding oversight prevents fragmented spending.

Coordinate resources: Develop shared AI platforms, tools, and infrastructure used across the organization. Resource pooling improves scaling and consistency.

Foster collaboration: Facilitate coordination and knowledge sharing between teams via activities like training, working groups, and internal conferences. Collaboration multiplies governance efficiencies.

Continual improvement: Encourage identifying governance gaps post-deployment and refining policies through retrospective reviews. Embed continuous enhancement into processes.