AI Governance: Best Practices and Guide

Summary

Mirantis delivers a pragmatic roadmap for organizations ready to move beyond AI governance theory into actual implementation. This 2024 guide cuts through the complexity by focusing on two critical success factors: building the right organizational capacity and selecting frameworks that actually work in practice. Rather than offering another high-level overview of AI ethics, this resource provides concrete guidance on transparency mechanisms, fairness assessments, and accountability structures that organizations can operationalize immediately.

The implementation-first approach

What sets this guide apart is its emphasis on organizational readiness before framework selection. Most AI governance resources start with regulatory requirements or ethical principles. Mirantis flips this approach by first addressing the human, process, and technology foundations that determine whether governance efforts succeed or fail.

The guide recognizes that AI governance isn't a compliance checkbox—it's an organizational capability that requires dedicated resources, clear roles, and sustained leadership commitment. This practical perspective makes it particularly valuable for teams who've struggled to translate governance concepts into day-to-day operations.

Core implementation pillars

Organizational capacity building

• Cross-functional governance teams with defined roles and responsibilities
• AI literacy programs that extend beyond technical teams
• Resource allocation models for ongoing governance activities
• Change management strategies for governance adoption Framework selection criteria
• Alignment with existing organizational risk management processes
• Scalability across different AI use cases and deployment scenarios
• Integration capabilities with current technology stacks and workflows
• Measurability of governance outcomes and effectiveness

Operational transparency

• Documentation standards for AI system development and deployment
• Stakeholder communication protocols for AI-related decisions
• Audit trails that support both internal oversight and external compliance
• Clear escalation paths for governance concerns and violations

Who this resource is for

• AI program managers and governance leads who need to translate high-level governance requirements into operational processes and team structures.
• Technology executives evaluating governance frameworks and seeking guidance on organizational capacity requirements before making strategic investments.
• Cross-functional teams including legal, compliance, product, and engineering professionals who need a shared understanding of AI governance implementation challenges and solutions.
• Organizations in early governance stages that have identified AI governance as a priority but lack clear implementation pathways or organizational models.

Quick implementation checklist

Based on Mirantis's framework selection criteria, assess potential governance approaches against these practical filters:

• Integration effort: How well does this framework connect with existing risk management, security, and compliance processes?
• Resource requirements: Do we have the organizational capacity to implement and sustain this approach?
• Measurement capability: Can we track governance effectiveness and demonstrate value to stakeholders?
• Scalability: Will this framework work across our diverse AI use cases and future growth plans?
• Stakeholder alignment: Does this approach address the concerns of all key stakeholders, from technical teams to executive leadership?

The guide's emphasis on organizational capacity as a prerequisite for successful governance implementation offers a refreshing reality check for organizations rushing to adopt complex frameworks without building the foundational capabilities needed to sustain them.