HBR: AI risk management needs collective team wisdom

The rise of generative AI presents organizations with both unprecedented opportunities and significant challenges in implementing this transformative technology safely and effectively.

Current risk management landscape: Most organizations have implemented basic risk mitigation strategies for generative AI through policies and critical thinking protocols.

• Companies typically rely on formal usage guidelines and individual assessment of AI outputs
• These traditional approaches, while necessary, may not be sufficient given the complex and evolving nature of AI technology
• Organizations need more robust frameworks to address AI-related challenges including accuracy issues, hallucinations, and inherited biases

The team-based judgment framework: A third layer of risk management emphasizing collective decision-making and expertise offers a more comprehensive approach to AI governance.

• Collective judgment involves team discussions to evaluate AI outputs and ensure accuracy through multiple perspectives
• Domain judgment leverages specific expertise by delegating AI-related decisions to team members with relevant knowledge or proximity to the work
• Reflective judgment requires regular team meetings to share experiences and lessons learned from AI implementation

Implementation considerations: Successfully deploying team-based judgment requires thoughtful organizational structure and commitment.

• Teams must establish regular communication channels and feedback loops
• Organizations should clearly define roles and responsibilities for AI oversight
• Regular assessment and adaptation of the framework ensures continued effectiveness as AI technology evolves

Benefits and outcomes: Organizations that successfully implement team-based judgment can expect improved AI risk management and better adaptation to technological change.

• Enhanced accuracy and reliability of AI outputs through collective verification
• Better alignment between AI applications and business objectives
• Increased organizational learning and knowledge sharing about AI implementation
• Improved ability to identify and address potential AI risks before they materialize

Looking ahead: As generative AI continues to evolve rapidly, organizations that develop robust team-based judgment capabilities will be better positioned to navigate future challenges and opportunities.

• The collective intelligence approach provides a more resilient framework for managing unknown future risks
• Organizations can build on existing team structures to create more sophisticated AI governance systems
• Regular reflection and adaptation will remain crucial as AI technology and its applications continue to advance