Agentic AI is transitioning from experimental phases to integral roles in energy operations, enhancing automation in critical safety and mission systems. Despite this progress, it is anticipated that by 2030, less than half of these systems will have fully developed agent architectures and lifecycle management. The primary obstacle to the widespread adoption of agentic AI in energy and utility sectors is not the technology itself but the fragmented and siloed data environments. Many organizations lack the governance frameworks necessary to support autonomous systems in essential workflows. Initial applications of agentic AI are expected in production forecasting and bid optimization rather than core grid operations or decision-making processes.
Energy and utility companies are advancing the integration of IT and OT systems to modernize for AI-driven, low-carbon operations. This involves standardizing cloud-edge architectures that enable real-time decision-making and leverage centralized cloud platforms for analytics. Combining IoT sensors with edge computing enhances real-time decision-making, improving asset management and operational efficiency while reducing costs for consumers. By 2027, AI-powered asset intelligence is projected to decrease outages by 40%, resulting in significant operational savings.
Additionally, digital twins in the energy sector are evolving into advanced simulation twins, which not only monitor assets but also aid in planning, safety, and operations. These twins integrate real-time data, AI models, and scenario simulations to evaluate conditions and suggest optimal actions. The true value lies in using these simulations to guide operational decisions, thereby enhancing efficiency and reducing costs.
