15
6.2 Roles and Accountabilities
Clear roles and responsibilities must be defined across the AI lifecycle to ensure effective
oversight and accountability. These include:
• AI Governance Lead /Chief AI Officer. Overall AI strategy, governance oversight, and
policy development. Reports to CRO or Chief Technology Officer.
• AI Developer. Builds and configures AI tools, implements models, conducts initial
testing. Reports to Technology or Business function.
• AI Validator. Independent validation and testing of AI tools prior to production
deployment. Reports to Risk function must not report to Developer.
• AI Risk Manager. Ongoing risk monitoring, revalidation scheduling, performance
tracking, and issue escalation. Reports to Risk function.
• Business Owner. Accountable for AI tool performance, business outcomes, and
adherence to approved use cases. Reports to Business function leadership.
• Compliance Officer (AI). Ensures regulatory compliance for AI systems, conducts
compliance testing, liaisons with regulators. Reports to Compliance function.
• Board /Senior Management: Strategic oversight, risk appetite setting, resource
allocation for AI governance, escalation resolution.
Clear accountability prevents conflicts of interest, supports independent challenge, and
ensures transparent governance.
The CCRO recognizes the importance of segregation of duties, particularly between model
development, validation, and deployment functions. Establishing clear ownership helps
prevent conflicts of interest, supports independent challenge, and ensures that AI Systems are
governed in a transparent and responsible manner.
Best Practices for Organizational Readiness
• Balanced capabilities and skills between front, middle, and back offices
• Onboarding programs with AI governance principles and usage training
• Certification programs for AI Tool development and usage
• Ongoing development of quantitative and technical skills amongst risk professionals
engaged in AI-facing or -supporting activities
7. Conclusion
Artificial Intelligence presents transformative opportunities for commodity market participants,
offering enhanced decision-making, operational efficiency, and risk management capabilities.
However, these benefits come with complex and evolving risks that demand structured
oversight and proactive governance.
This white paper outlines a comprehensive framework for AI Risk governance, grounded in
industry best practices and adapted to the unique challenges of the commodity markets. By
addressing ethical, operational, regulatory, and systemic risks, firms can build resilient
governance structures that support safe and effective AI deployment.
6.2 Roles and Accountabilities
Clear roles and responsibilities must be defined across the AI lifecycle to ensure effective
oversight and accountability. These include:
• AI Governance Lead /Chief AI Officer. Overall AI strategy, governance oversight, and
policy development. Reports to CRO or Chief Technology Officer.
• AI Developer. Builds and configures AI tools, implements models, conducts initial
testing. Reports to Technology or Business function.
• AI Validator. Independent validation and testing of AI tools prior to production
deployment. Reports to Risk function must not report to Developer.
• AI Risk Manager. Ongoing risk monitoring, revalidation scheduling, performance
tracking, and issue escalation. Reports to Risk function.
• Business Owner. Accountable for AI tool performance, business outcomes, and
adherence to approved use cases. Reports to Business function leadership.
• Compliance Officer (AI). Ensures regulatory compliance for AI systems, conducts
compliance testing, liaisons with regulators. Reports to Compliance function.
• Board /Senior Management: Strategic oversight, risk appetite setting, resource
allocation for AI governance, escalation resolution.
Clear accountability prevents conflicts of interest, supports independent challenge, and
ensures transparent governance.
The CCRO recognizes the importance of segregation of duties, particularly between model
development, validation, and deployment functions. Establishing clear ownership helps
prevent conflicts of interest, supports independent challenge, and ensures that AI Systems are
governed in a transparent and responsible manner.
Best Practices for Organizational Readiness
• Balanced capabilities and skills between front, middle, and back offices
• Onboarding programs with AI governance principles and usage training
• Certification programs for AI Tool development and usage
• Ongoing development of quantitative and technical skills amongst risk professionals
engaged in AI-facing or -supporting activities
7. Conclusion
Artificial Intelligence presents transformative opportunities for commodity market participants,
offering enhanced decision-making, operational efficiency, and risk management capabilities.
However, these benefits come with complex and evolving risks that demand structured
oversight and proactive governance.
This white paper outlines a comprehensive framework for AI Risk governance, grounded in
industry best practices and adapted to the unique challenges of the commodity markets. By
addressing ethical, operational, regulatory, and systemic risks, firms can build resilient
governance structures that support safe and effective AI deployment.
