Global Agentic Artificial Intelligence (AI) in Energy and Utilities Market to Reach US$2.8 Billion by 2030

The global market for Agentic Artificial Intelligence (AI) in Energy and Utilities estimated at US$451.5 Million in the year 2024, is expected to reach US$2.8 Billion by 2030, growing at a CAGR of 35.3% over the analysis period 2024-2030. Predictive-Maintenance AI Platforms Solution, one of the segments analyzed in the report, is expected to record a 31.9% CAGR and reach US$744.3 Million by the end of the analysis period. Growth in the Energy-Optimization AI Software Solution segment is estimated at 31.9% CAGR over the analysis period.

The U.S. Market is Estimated at US$132.1 Million While China is Forecast to Grow at 33.9% CAGR

The Agentic Artificial Intelligence (AI) in Energy and Utilities market in the U.S. is estimated at US$132.1 Million in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$469.8 Million by the year 2030 trailing a CAGR of 33.9% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 32.0% and 31.2% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 25.1% CAGR.

Global Agentic Artificial Intelligence (AI) in Energy and Utilities Market - Key Trends & Drivers Summarized

How Is Agentic AI Transforming Decision-Making Across Energy Systems?

Agentic Artificial Intelligence is redefining how energy and utility systems are designed, operated, and optimized by introducing autonomous intelligence capable of continuous planning, reasoning, and execution across complex infrastructures. Unlike conventional analytics or rule-based automation tools, agentic AI systems operate as goal-driven entities that can interpret system-level objectives such as grid stability, loss minimization, or emissions reduction and independently determine the sequence of actions required to achieve them. These systems continuously ingest data from distributed energy resources, sensors, smart meters, substations, weather models, and market platforms to maintain a real-time understanding of network conditions. By retaining contextual memory across operational cycles, agentic AI can anticipate demand fluctuations, adjust generation dispatch strategies, and coordinate assets without requiring constant human oversight. This capability is particularly critical as energy systems transition toward decentralized and bidirectional architectures driven by renewables, storage, and prosumer participation. Agentic AI is increasingly acting as the orchestration layer that connects generation, transmission, distribution, and consumption into a unified, adaptive system capable of responding dynamically to physical, economic, and regulatory signals.

Why Are Utilities Embedding Agentic Intelligence Into Grid and Asset Operations?

Utilities are embedding agentic AI into grid management and asset operations to address mounting operational complexity and reliability challenges. Aging infrastructure, rising penetration of intermittent renewable energy sources, and increasing electrification of transport and industry are placing unprecedented stress on traditional grid control mechanisms. Agentic AI systems offer a scalable solution by autonomously coordinating grid assets, optimizing power flows, and managing contingencies in near real time. These systems can independently schedule maintenance, predict asset degradation, and reconfigure network topology to prevent outages or reduce downtime. In transmission and distribution networks, agentic AI is enabling self-healing grids that detect faults, isolate affected sections, and restore service without manual intervention. For utilities managing large asset portfolios, the ability of agentic systems to continuously evaluate operational trade-offs between cost, reliability, and regulatory compliance is becoming a critical capability. As utilities adopt advanced digital twins and sensor-rich environments, agentic AI is increasingly deployed as the control intelligence that converts predictive insights into autonomous operational actions.

What Role Does Agentic AI Play in Market Operations and Energy Transition Strategies?

Agentic AI is playing a growing role in energy market operations and long-term transition strategies by enabling autonomous participation across complex, fast-moving market environments. Wholesale electricity markets, capacity auctions, ancillary services, and real-time pricing mechanisms require rapid decision-making under uncertainty, a condition well suited to agentic systems. These AI agents can independently evaluate price signals, forecast demand and supply conditions, and optimize bidding strategies for generation, storage, and demand response assets. In parallel, agentic AI supports the integration of distributed energy resources by coordinating aggregation, dispatch, and settlement processes across thousands or millions of small-scale assets. From a strategic perspective, energy providers are using agentic AI to simulate transition pathways, evaluate investment scenarios, and adapt portfolios in response to policy changes, carbon pricing mechanisms, and evolving demand patterns. The ability of agentic systems to continuously learn from market outcomes and regulatory developments allows energy companies to refine strategies dynamically rather than relying on static planning cycles.

What Is Powering Market Expansion Across Energy and Utilities?

The growth in the Agentic Artificial Intelligence in Energy and Utilities market is driven by several factors that are directly linked to structural changes in energy systems, operational requirements, and consumption behavior. Rapid growth of renewable energy capacity is increasing variability and uncertainty across power systems, creating demand for autonomous intelligence capable of real-time balancing and coordination. The expansion of distributed energy resources, including rooftop solar, battery storage, and electric vehicles, is driving adoption of agentic systems that can manage decentralized assets at scale. Increasing grid congestion and reliability risks are pushing utilities toward self-healing and predictive operational models enabled by agentic decision-making. Liberalization of electricity markets and growth of dynamic pricing models are accelerating demand for autonomous market participation and optimization tools. Rising electrification of transport, heating, and industrial processes is further amplifying system complexity and data volumes, strengthening the case for AI agents that can act continuously without manual intervention. Together, these drivers are positioning agentic AI as a foundational capability for managing next-generation energy and utility systems