Generative AI Risk Management for Energy & Commodities: Governance, Compliance, and Workforce Transformation

The energy and commodities sector stands at a pivotal moment. Generative AI is rapidly reshaping how organizations operate, manage risk, and compete. Yet, the sector’s unique regulatory landscape, operational complexity, and workforce dynamics demand a tailored approach to AI risk management—one that balances innovation with robust governance, compliance, and workforce transformation.

Generative AI’s ability to create contextualized content, synthesize vast datasets, and automate complex tasks is already delivering material impact across energy and commodities. From optimizing trading strategies and asset maintenance to codifying institutional knowledge and enhancing customer engagement, the technology is unlocking new value pools. For example, commodities traders are leveraging AI for real-time market monitoring, demand forecasting, and risk scenario generation, while operators use it to predict equipment failures and optimize refinery processes.

However, these opportunities come with sector-specific risks:

• Data privacy and proprietary information leakage
• Regulatory compliance in high-stakes, safety-critical environments
• Operational safety and reliability
• Workforce disruption and the need for upskilling
• Ethical concerns, including bias and misinformation

To realize the benefits of generative AI while mitigating these risks, energy and commodities organizations must adopt a comprehensive risk management strategy.

Effective governance is the cornerstone of safe and successful generative AI adoption. For energy and commodities companies, this means:

• Codifying Institutional Knowledge: Generative AI can help capture and institutionalize decades of operational expertise, especially as the sector faces a wave of retirements and workforce attrition. By structuring and digitizing best practices, maintenance logs, and safety protocols, organizations can reduce the risk of knowledge loss and accelerate onboarding for new talent.
• Establishing Data Governance and Security: Given the sensitivity of operational and trading data, robust data governance is essential. This includes anonymizing data, setting clear access controls, and ensuring that proprietary information does not leave the organization’s secure environment. Standalone, sandboxed AI tools with strict guardrails can enable innovation without risking data leakage.
• Implementing Responsible AI Frameworks: With evolving global regulations—such as the EU AI Act and sector-specific mandates—energy and commodities firms must proactively define ethical guidelines, model documentation standards, and human-in-the-loop oversight. This ensures transparency, traceability, and accountability in AI-driven decisions, especially in safety-critical operations.
• Cross-Functional Collaboration: Governance is not just an IT or compliance function. It requires collaboration across business units, risk management, legal, and technology teams to set policies, monitor usage, and respond to emerging risks.

The energy and commodities sector is subject to some of the world’s most stringent regulations, from environmental reporting to market conduct and operational safety. Generative AI introduces new compliance challenges:

• Data Privacy and Confidentiality: AI models must be trained and operated in ways that protect sensitive data, comply with privacy laws, and avoid inadvertent exposure of proprietary information.
• Auditability and Explainability: Regulatory bodies increasingly require organizations to demonstrate how AI-driven decisions are made. Maintaining detailed model documentation, version control, and audit trails is essential for both internal governance and external compliance.
• Sector-Specific Regulations: Whether it’s pipeline safety, emissions monitoring, or commodity trading, generative AI solutions must be tailored to meet the specific regulatory requirements of each domain. Automated compliance reporting, scenario simulation, and real-time monitoring can help organizations stay ahead of regulatory changes and reduce the burden of manual compliance tasks.
• Proactive Risk Assessment: By generating synthetic scenarios and stress-testing operational and trading strategies, generative AI can help organizations anticipate regulatory risks and design more resilient controls.

Generative AI is not just a technology shift—it’s a workforce transformation. In energy and commodities, where a significant portion of the workforce is nearing retirement, AI offers a powerful tool to bridge the skills gap and future-proof the organization.

• Codifying and Transferring Knowledge: AI can capture the tacit knowledge of experienced workers, making it accessible to new hires and reducing the learning curve. This is especially critical as the sector faces a demographic shift and the risk of institutional brain drain.
• Upskilling for the AI Era: As routine tasks become automated, the demand for new roles—such as AI engineers, prompt designers, and data stewards—will grow. Organizations must invest in targeted upskilling programs, blending technical training with domain expertise. For example, prompt engineering and AI oversight are emerging as essential skills for both frontline and back-office staff.
• Empowering the Connected Worker: Generative AI can augment field technicians, operators, and analysts by providing real-time insights, automated recommendations, and access to consolidated knowledge bases. This not only improves efficiency but also enhances safety and decision-making in high-risk environments.
• Change Management: Successful workforce transformation requires more than training. It demands a culture of experimentation, continuous learning, and collaboration between human and AI systems. Leaders should foster an environment where employees are encouraged to innovate, learn from failures, and adapt to new ways of working.

1. Start with a Shared Knowledge Base: Build transparency and trust by educating all stakeholders on the capabilities and limitations of generative AI. Use this foundation to identify high-value, low-risk use cases for early wins.
2. Establish Robust Governance and Guardrails: Define clear policies for data use, model oversight, and ethical AI deployment. Collaborate across business units to prevent shadow IT and duplication of effort.
3. Prioritize Data Security and Privacy: Implement sandboxed environments, anonymization protocols, and zero-trust architectures to protect sensitive information.
4. Align AI Initiatives with Regulatory Requirements: Stay ahead of evolving regulations by embedding compliance into the AI lifecycle—from model development to deployment and monitoring.
5. Invest in Workforce Upskilling: Launch targeted training programs to equip employees with the skills needed to collaborate with AI, manage risk, and drive innovation.
6. Foster a Culture of Experimentation: Encourage teams to pilot new AI solutions, learn from setbacks, and scale successful initiatives across the organization.

Publicis Sapient brings deep expertise in digital business transformation and generative AI, helping energy and commodities organizations navigate the complexities of AI risk management. Our approach combines:

• Proven frameworks for AI governance, compliance, and ethical deployment
• Sector-specific guidance on regulatory requirements and operational best practices
• Workforce transformation strategies to upskill and empower employees
• End-to-end support, from ideation and proof of concept to enterprise-scale implementation

By partnering with Publicis Sapient, energy and commodities leaders can confidently harness generative AI to drive operational efficiency, ensure compliance, and build a future-ready workforce—turning risk into a source of sustainable competitive advantage.

Ready to transform your organization with generative AI? Connect with our experts to start your journey.

Relevant Links

• Generative AI Risk Management Playbook
• Generative AI Risk Management Playbook
• Generative AI Risk Management in Retail: Overcoming Data and Integration Challenges
• Gestión de Riesgos en la Implementación de IA Generativa en el Retail Latinoamericano (LATAM)
• La gestion des risques liés à l’IA générative : Un guide pour les dirigeants européens (Europe)