Applied Artificial Intelligence (AI) in Energy and Utilities Global Market Report 2025

The applied artificial intelligence (AI) in the energy and utilities market size has grown rapidly in recent years. It will grow from $3.17 billion in 2024 to $3.8 billion in 2025 at a compound annual growth rate (CAGR) of 19.7%. The growth in the historic period can be linked to the increasing adoption of smart grid technologies, growing demand for energy efficiency, rising integration of renewable energy sources, expanding need for predictive maintenance in utilities, and increasing investment in the digital transformation of energy infrastructure.

The applied artificial intelligence (AI) in the energy and utilities market size is expected to see rapid growth in the next few years. It will grow to $7.7 billion in 2029 at a compound annual growth rate (CAGR) of 19.3%. The growth in the forecast period can be driven by the rising adoption of AI-based analytics in utilities, a stronger focus on operational efficiency, wider deployment of smart meters and sensors, increasing integration of renewable energy sources, and greater investment in grid modernization. Major trends in the forecast period include advancements in autonomous grid management, the development of AI-powered energy forecasting tools, innovation in digital twin technology for utilities, progress in AI-enabled renewable integration systems, and the emergence of edge AI for real-time energy optimization.

The increasing demand for electricity is expected to drive the growth of the applied artificial intelligence (AI) market in the energy and utilities sector. This rise in electricity demand is largely attributed to factors such as industrialization, urbanization, and the electrification of various sectors. As industries expand, their need for power to support machinery, production processes, and overall operations grows, leading to higher energy consumption. Applied AI in energy and utilities helps manage and optimize electricity demand by forecasting consumption trends, enhancing grid efficiency, and enabling smarter energy distribution. For example, the U.S. Energy Information Administration (EIA) projects that electricity consumption in the United States will grow at an average annual rate of 1.7%, with the commercial sector increasing by 2.6% and the industrial sector rising by 2.1%, while residential demand is expected to grow more modestly at 0.7% in 2026. As a result, the growing demand for electricity is a significant driver of the expansion of AI applications in the energy and utilities market.

Companies in the applied AI energy and utilities market are focusing on developing solutions that improve grid efficiency, resilience, and scalability, allowing utilities to rapidly expand capacity and better manage distributed energy resources. By leveraging decentralized, AI-powered energy systems, utilities can optimize power distribution and ensure reliable energy delivery. For example, in February 2024, Siemens AG launched Gridscale X, a next-generation grid management suite designed to support autonomous grid operations. This AI-driven platform features real-time analytics, predictive maintenance, and digital twin integration, enabling utilities to detect faults automatically and optimize energy flow, ultimately building smarter and more sustainable energy networks.

In March 2025, Siemens AG acquired Altair Engineering Inc. to enhance its AI-driven solutions for the energy and utilities sector. This acquisition allows Siemens to integrate Altair’s advanced simulation, high-performance computing, and AI-driven data science capabilities into its Xcelerator digital platform. Altair Engineering, a U.S.-based company, specializes in applied AI solutions for the energy and utilities industries, further strengthening Siemens' leadership in industrial software and AI-powered design and simulation.

Major players in the applied artificial intelligence (AI) in energy and utilities market are Microsoft Corporation, E.ON SE, Enel S.p.A., Siemens AG, Hitachi Ltd., Accenture plc, IBM Corporation, Iberdrola S.A., Oracle Corporation, Schneider Electric SE, Honeywell International Inc., SAP SE, ABB Ltd, NVIDIA Corporation, Capgemini SE, Infosys Ltd., Wipro Ltd., Palantir Technologies Inc., Teradata Corporation, C3.ai Inc., Stem Inc., AutoGrid Systems Inc., Bidgely Inc., and Verdigris Technologies Inc.

North America was the largest region in the applied artificial intelligence (AI) in the energy and utilities market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in applied artificial intelligence (AI) in energy and utilities report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa. The countries covered in the applied artificial intelligence (AI) in energy and utilities market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

Note that the outlook for this market is being affected by rapid changes in trade relations and tariffs globally. The report will be updated prior to delivery to reflect the latest status, including revised forecasts and quantified impact analysis. The report’s Recommendations and Conclusions sections will be updated to give strategies for entities dealing with the fast-moving international environment.

The rapid escalation of U.S. tariffs and the resulting trade tensions in spring 2025 are significantly impacting the utilities sector, particularly in power generation, grid infrastructure, and renewable energy projects. Higher duties on imported equipment such as turbines, transformers, solar panels, and battery storage systems have increased capital and operational costs for utility providers, forcing them to reconsider project timelines or pass on expenses to consumers through higher energy rates. The water and waste management segments are also affected, with tariffs driving up the cost of essential machinery, piping, and treatment technologies. Additionally, retaliatory tariffs have disrupted global supply chains for critical raw materials like rare earth metals used in clean energy technologies, further complicating the transition to sustainable energy sources. The sector must now prioritize domestic sourcing, digitalization, and efficiency-driven innovations to manage escalating costs while ensuring energy security and regulatory compliance.

The applied artificial intelligence (AI) in energy and utilities market research report is one of a series of new reports that provides applied artificial intelligence (AI) in energy and utilities market statistics, including applied artificial intelligence (AI) in energy and utilities industry global market size, regional shares, competitors with a applied artificial intelligence (AI) in energy and utilities market share, detailed applied artificial intelligence (AI) in energy and utilities market segments, market trends and opportunities, and any further data you may need to thrive in the applied artificial intelligence (AI) in energy and utilities industry. This applied artificial intelligence (AI) in energy and utilities market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

Applied artificial intelligence (AI) in the energy and utilities sector refers to the implementation of AI technologies to optimize the production, distribution, and consumption of energy. Its primary goal is to boost efficiency, lower operational expenses, enhance grid reliability, and support the integration of renewable energy sources.

The main types of applied artificial intelligence (AI) in energy and utilities include predictive analytics, asset optimization, grid management, customer engagement, energy trading, and renewable integration. Applied artificial intelligence (AI) in energy and utilities predictive analytics involves the use of advanced AI algorithms and machine learning models to examine large volumes of real-time and historical energy data to forecast future events, trends, or potential failures across power systems and utility operations. It is deployed through cloud-based and on-premises modes for applications such as robotics, renewable energy management, demand forecasting, AI-driven inventory management, energy production and scheduling, asset tracking and maintenance, digital twins, AI-based cybersecurity, emission monitoring, and logistics network operations. The major end users include electric utilities, oil and gas companies, renewable energy firms, industrial energy consumers, and energy service providers.

The applied artificial intelligence (AI) in the energy and utilities market consists of revenues earned by entities by providing services such as predictive maintenance of energy infrastructure, smart grid optimization, demand response management, and energy consumption forecasting. The market value includes the value of related goods sold by the service provider or included within the service offering. The applied artificial intelligence (AI) in the energy and utilities market also consists of sales of products including AI-powered energy management systems, smart sensors, intelligent grid controllers, predictive analytics software, and automated monitoring platforms. Values in this market are ‘factory gate’ values, that is, the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors, and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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