AI in Energy & Utilities Market (2026-2036)

The growth of this market is mainly driven by rapid growth in electricity demand from data centers, electrification, and industry, the integration of large volumes of variable renewable energy, distributed energy resources, and battery storage into power systems, the need to operate aging grid infrastructure more efficiently and reliably, rising exposure to extreme weather and wildfire risks, and the digitalization of utility operations with smart meters, sensors, and cloud platforms. However, factors such as fragmented and poor-quality utility data, cybersecurity risks to critical infrastructure, shortages of AI and data skills in the energy sector, and the cautious, regulated nature of utility investment are expected to restrain the growth of this market to some extent.

AI in energy & utilities refers to the use of artificial intelligence technologies, including machine learning and predictive analytics, computer vision, generative AI and large language models, and optimization and reinforcement learning, to plan, operate, maintain, and manage energy and utility systems and businesses. It includes AI for grid operations and optimization, predictive maintenance and asset management, demand and renewable generation forecasting, distributed energy resources and energy storage optimization, energy trading and market optimization, customer engagement and billing, and safety, security, and other applications. This report covers AI software and platforms, related services, and AI-enabled hardware such as edge computing devices and intelligent sensors, sold to power transmission and distribution utilities, power generation and renewable energy companies, oil and gas companies, and gas and water utilities and energy retailers. General utility software and grid equipment without AI capabilities are analyzed in separate reports. The market is measured on a revenue basis.

The energy sector is undergoing a major transformation that is changing how power systems are planned and operated. Electricity demand is rising after years of slow growth, driven by data centers, electric vehicles, heat pumps, industrial electrification, and manufacturing, and the IEA estimates that data center electricity consumption alone will roughly double from about 485 TWh in 2025 to about 950 TWh by 2030. At the same time, power systems are integrating large volumes of variable solar and wind generation, battery storage, and distributed energy resources, while much grid infrastructure in developed countries is aging and must be replaced or upgraded. These changes make power systems far more complex to plan and operate, which creates a strong need for AI.

Grid operations and optimization accounts for the largest share of the market. Utilities use AI to forecast and manage grid loads, detect and locate faults, optimize voltage and power flows, manage congestion, support restoration after outages, and maximize the use of existing transmission and distribution capacity. The IEA estimates that AI-based grid management could unlock up to 175 GW of additional transmission capacity without new lines, and that AI-based fault detection can reduce outage durations by 30% to 50%.

Predictive maintenance and asset management is the second-largest application, used across utilities, generators, and oil and gas companies. Utilities, generators, and oil and gas companies use AI to analyze sensor data, inspection images, maintenance records, and weather data to predict equipment failures, prioritize maintenance, and extend asset life for transformers, lines, turbines, pipelines, and other critical assets. AI-based analysis of drone, satellite, and LiDAR imagery is also widely used to inspect lines and manage vegetation, which is particularly important for reducing wildfire risks.

Demand and renewable generation forecasting, distributed energy resources and energy storage optimization, energy trading and market optimization, and customer engagement and billing are other important applications. Distributed energy resources and energy storage optimization is the fastest-growing application, supported by the rapid growth of battery storage, rooftop solar, electric vehicles, and virtual power plants, which require AI to forecast, aggregate, and dispatch large numbers of small resources.

The market is served by several groups of companies with different strengths in energy and technology. Grid and automation technology companies, such as GE Vernova, Siemens, Schneider Electric, Hitachi Energy, and ABB, offer AI-enabled grid software, asset performance management, and automation. Metering and grid edge companies, such as Itron, Landis+Gyr, and Utilidata, embed AI in smart meters and edge devices. Enterprise software and cloud companies, such as C3 AI, Oracle, IBM, Microsoft, and Google, offer AI platforms and applications for utilities. Specialized companies, such as Uplight, AiDash, and Envision Digital, focus on customer engagement, distributed energy management, vegetation and asset intelligence, and renewable energy optimization.

Power transmission and distribution utilities are the largest end users, followed by power generation and renewables, oil and gas, and gas and water utilities and energy retailers. North America is the largest regional market, supported by large utility investment, data center growth, wildfire and extreme weather risks, and the presence of leading technology companies, while Asia-Pacific is the fastest-growing region.

Industry collaboration on AI in the power sector has also increased significantly since 2025. EPRI launched the Open Power AI Consortium in March 2025 with technology companies and utilities to develop open AI models and tools for the power sector, and the consortium grew to more than 80 members by mid-2025. EPRI has also highlighted data readiness as the main barrier to scaling AI in utilities, which shows that data management and integration will be important parts of the market.

Over the forecast period, the AI in energy & utilities market is expected to grow strongly, supported by electricity demand growth, renewable and distributed energy integration, grid modernization, extreme weather risks, and digitalization. At the same time, data quality, cybersecurity, skills shortages, and regulatory processes will influence how quickly utilities move from pilot projects to large-scale AI deployments.

AI forecasting and battery optimization are now essential to how we run our business. They directly affect our market revenues and how well we can balance our solar and wind portfolio.

This comment explains why DER and energy storage optimization is the fastest-growing application and why power generation and renewables is the fastest-growing end user.

Our smart meter rollout is generating large volumes of data, and we want to use AI to reduce losses, improve billing, and manage rooftop solar, but data quality and security come first.

This comment explains why Asia-Pacific is the fastest-growing region and why data readiness and cybersecurity are key purchase criteria.

North America is expected to account for the largest share of 40.0% of the market in 2026, mainly due to rapid data center-driven demand growth, large grid modernization and resilience investments, wildfire and extreme weather risks, and the presence of leading technology and energy companies.

Asia-Pacific is projected to register the highest CAGR of 20.7% during the forecast period, driven by rapid electricity demand growth, large renewable energy additions, grid investment programs, smart meter deployments, and national AI strategies.

The report profiles 16 companies, including GE Vernova, Siemens, Schneider Electric, Hitachi Energy, ABB, C3 AI, Itron, Landis+Gyr, Uplight, Oracle, IBM, Microsoft, Google, AiDash, Utilidata, and Envision Digital.

The IEA estimates that AI-based grid management tools could unlock up to 175 GW of additional transmission capacity without building new lines, and that AI-based fault detection can reduce outage durations by 30% to 50%.