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Executive Summary: AI in Energy Market Outlook 2026

  • Industry Growth Overview: The AI in Energy market is expanding at a 16.86% annual growth rate. This is driven by rising AI workloads, grid digitalization, and data-center-linked energy demand.
  • Manpower & Employment Growth: The industry employs 86 200 professionals globally, with 45% workforce growth in the last year.
  • Patents & Grants: The sector holds 471 patents filed by 444 applicants, with 29% yearly patent growth. China leads patent issuance (346 patents), followed by the USA (49 patents).
  • Global Footprint: The top country hubs are the USA, UK, India, Germany, and Canada, while leading city hubs include London, San Francisco, New York City, Houston, and Bengaluru.
  • Investment Landscape: The market recorded 1400+ funding rounds with an average deal size of USD 61.5 million. More than 1800 investors have funded 406+ companies.
  • Top Investors: Leading investors have deployed a combined USD 7.8+ billion into the sector. Key contributors include the European Investment Bank, COX Enterprises, ING, and Brookfield Asset Management.
  • Startup Ecosystem: The ecosystem includes 1011 startups. Five innovative startups – Bolo.ai (energy asset intelligence), Local Energy Solutions (infrastructure modeling & simulation software), BITA Energy (AI-powered energy management), Elastic Energy (DER orchestration engine), and WINDROVER (predictive maintenance) – showcase the sector’s global reach and entrepreneurial momentum.

 

 

Methodology: How we created this AI in Energy Report

This report is based on proprietary data from our AI-powered StartUs Insights Discovery Platform, which tracks 9 million global companies, 20K+ technologies and trends, as well as 150M patents, news articles, and market reports.

This data includes detailed firmographic insights into approximately 9 million startups, scaleups, and tech companies. Leveraging this exhaustive database, we provide actionable insights for startup scouting, trend discovery, and technology landscaping.

For this report, we focused on the evolution of AI in the energy sector over the past 5 years, utilizing our platform’s trend intelligence feature. Key data points analyzed include:

  • Total Number of Companies working in the sector
  • News Coverage and Annual Growth
  • Market Maturity and Patents
  • Global Search Volume & Growth
  • Funding Activity and Top Countries
  • Subtrends within energy AI

Our data is refreshed regularly, enabling trend comparisons for deeper insights into their relative impact and importance.

Additionally, we reviewed trusted external resources to supplement our findings with broader market data and predictions, ensuring a reliable and comprehensive overview of the energy AI market.

What Data is used to create this AI in Energy Market Report?

Based on data provided by the StartUs Insights Discovery Platform, we observe that the energy AI market stands out in the following categories relative to the 20K+ technologies and trends we track.

These categories provide a comprehensive overview of the market’s key metrics and inform the future direction of the market.

  • News Coverage & Publications: The AI in Energy domain recorded more than 5607 publications in the last year. This indicates sustained media, research, and policy attention.
  • Funding Rounds: Our database tracks 1400+ funding rounds and highlights consistent investor activity across startups and growth-stage companies.
  • Manpower: The industry employs over 86 200 workers globally and added more than 45 employees in the last year.
  • Patents: The sector holds 471 patents, underscoring active innovation across AI-driven energy applications.
  • Global News Coverage Growth: Global interest grew by 428% year over year. This signals rising market awareness and demand.

Explore the Data-driven AI in the Energy Industry Report for 2026

Adoption of AI is changing how energy systems function, grow, and use power. With the digitization of power generation, grid management, energy trading, and demand optimization, the AI in the energy sector is growing at an annual rate of 16.86%.

Our database tracks 1900 businesses, including 1011 startups. These numbers indicate widespread market engagement as opposed to solitary experimentation.

 

 

AI infrastructure’s energy needs are currently the main factor driving the market. IEA’s World Energy Outlook Special Report states that global data centers used 415 TWh of electricity in 2024. By 2030, they are expected to utilize 945 TWh, which is equal to Japan’s total electricity consumption.

AI workloads currently make up 5-15% of data center energy usage. Energy systems will be directly impacted by AI’s predicted 35-50% data center power consumption by 2030.

 

Global data centre electricity consumption in the Base Case, 2020-2030

 

This change accelerates infrastructure investment. Over USD 1.6 trillion will be spent on AI data centers worldwide by 2030. Additionally, Goldman Sachs Research estimates that approximately USD 720 billion worth of grid upgrades will be required by 2030 to accommodate new load centers.

The IEA estimates that AI-based fault detection can reduce outage durations by 30-50% by rapidly identifying and pinpointing grid faults. It also estimates that remote sensors and AI-based management could unlock up to 175 GW of transmission capacity on existing lines.

Growth in the workforce reflects this expansion. With 86 200 employees worldwide, the industry has grown by 45% in the past year due to increased demand for experts in AI, grid engineering, and energy systems.

According to our data, leading country hubs are the United States, the United Kingdom, India, Germany, and Canada. City-level activity is anchored by London, San Francisco, New York City, Houston, and Bengaluru, which combine skilled labor pools, cloud infrastructure, and energy markets.

A Snapshot of the Global AI in Energy Market

StartUs Insights’ Discovery Platform reports the industry’s 471 patents, which were submitted by 444 applicants, show that ownership is spread rather than dominated by a select few.

With 346 patents, China is the top issuer of energy AI patents. With 49 patents, the US comes in second, powered by hyperscale infrastructure and commercial AI deployment.

The AI in energy market is expected to grow at a 36.9% CAGR, with the Asia-Pacific industry exhibiting the greatest regional momentum.

Capital activity and market visibility are still high. In the past year, the industry was covered in more than 5607 articles, and our database shows 1400 funding rounds. Global news coverage for the industry increased by 428% annually.

Structural issues are also brought about by rising energy intensity. Contemporary GPUs draw high power, like Nvidia’s GB200, and are bringing rack densities to around 132 kW. Especially in dense data center clusters, these pressures lead to greater grid congestion and higher regional electricity rates.

As a result, clean energy purchases and efficiency improvements are accelerating. In 2024, data centers accounted for 17 GW of corporate clean energy purchases, and advancements in AI hardware increased energy efficiency by 40% yearly.

These trends collectively place artificial intelligence in the energy sector as a system-level optimization layer and a demand accelerator. It also directly connects digital intelligence to the stability, affordability, and sustainability of the world’s energy infrastructure.

Explore the Funding Landscape of the AI in Energy Market

Both scale and concentration are reflected in the funding activities in the AI in Energy market. Over 1400 funding rounds with an average deal value of USD 61.5 million were recorded in this area. This level corresponds to the capital needs of data center-linked energy systems, grid intelligence platforms, and AI-driven energy infrastructure.

Over 1800 investors have contributed to the funding of over 406 companies, according to our data. This demonstrates the ongoing interest of corporates, financial institutions, public lenders, and venture capital organizations. Simultaneously, capital is increasingly concentrated in infrastructure-heavy, later-stage rounds, reflecting larger trends in AI investment.

This trend corresponds with the global increase in funding for AI. According to EY, investment in AI companies drove over 71% of all US-based venture capital activity in Q1 2025.

Trillions of dollars are expected to be spent globally by 2030 on grid upgrades, electricity generation, and AI data centers. Consequently, scalable platforms, demonstrated performance, and long-term energy availability are becoming more important to investors than early-stage testing.

Who is Investing in the Energy AI Market?

 

The top investors alone have deployed more than USD 7.78 billion into the AI in Energy ecosystem. Their participation highlights the role of development banks, global financial institutions, and infrastructure-focused investors in enabling large-scale deployment.

  • The European Investment Bank invested USD 1.3 billion across three companies.
  • COX Enterprises committed USD 965 million to at least one company.
  • ING deployed USD 760.6 million across two companies.
  • Brookfield Asset Management invested USD 750 million in at least one company.
  • BNP Paribas invested USD 683.5 million in at least one company.
  • KfW IPEX-Bank committed USD 683.5 million to at least one company.
  • Societe Generale invested USD 683.5 million in at least one company.
  • Svensk Exportkredit deployed USD 683.5 million into at least one company.
  • UniCredit invested USD 683.5 million in at least one company.
  • Ant Group committed USD 594.3 million to at least one company.

Beyond these investors, private equity and hyperscalers increasingly shape capital flows. Large funds are backing data center platforms, grid-scale power assets, and clean energy generation tied to AI demand.

Corporate investors also play a larger role, as energy access becomes a strategic constraint for AI growth rather than a secondary cost factor.

M&A activity reinforces this investment shift. Data center and energy acquisitions surged in 2025, driven by competition for power-secure assets and grid-connected infrastructure. Grid modernization, clean energy procurement, and nuclear partnerships also attract capital alongside traditional AI software investments.

Top Energy AI Innovations & Trends

 

 

1. Energy Management & Control

This is a large and mature domain in AI-powered energy management. It includes 11 800 companies and 1 million employees, with 223 new employees added in the last year. The 2.02% annual growth rate indicates steady expansion, driven by rollout and integration work.

This domain also shows measurable operational impact in buildings and facilities. A 2024 peer-reviewed research reports that AI models for HVAC control can deliver energy savings of up to 37% in offices, and up to 23% in residential and 21% in educational buildings, depending on baseline system maturity.

2. Predictive Maintenance

Predictive Maintenance has the largest footprint among the three trends. It includes 73 600 companies and 5.2M employees, with 1200 new employees added in the last year. The -0.12% annual growth rate suggests a mature market where adoption is widespread and growth shifts from “new pilots” to “execution at scale.”

3. Generative AI

Generative AI is the fastest-scaling trend in our database. It includes 24 000 companies and 1.3 million employees, with 753 new employees added in the last year. The 18.73% annual growth rate indicates rapid adoption and product expansion.

5 Top Examples from 1000+ Innovative Energy AI Startups

Bolo.ai offers an Asset Intelligence Copilot

Bolo.ai is a US-based company that develops an asset intelligence copilot. It allows energy companies to convert asset performance data into operational insights within engineers’ workflows.

The copilot ingests structured signals from asset performance management (APM), IoT, and enterprise resource planning (ERP), along with unstructured data like procedures and maintenance documentation. Then, it utilizes an energy-focused semantic layer and agent orchestration to map user queries to the appropriate schemas, retrieve pertinent context, and produce responses rooted in enterprise data.

The copilot also improves auditability in maintenance, dependability, and compliance use cases by returning verified and traceable outcomes. Further, it integrates security measures like role-based access, privacy safeguards to prevent model training on client data, and flexible deployment.

To enhance its AI system of action strategy for industrial operations, the firm announced a USD 8.1 million seed round headed by True Ventures in June 2025.

Local Energy Solutions builds Infrastructure Modeling & Simulation Software

Canadian startup Local Energy Solutions provides Grid Builder, energy infrastructure modeling and simulation software. It allows utilities, developers, large energy users, and municipalities to plan local energy networks and assess project viability.

Grid Builder layers multi-year lifecycle modeling with modules for storage degradation and multi-revenue use cases, demand response and dynamic pricing interactions, and waste-heat recovery thermal modeling. It also supports renewable generation sizing, hybrid configuration analysis, and emissions accounting compliant with reporting standards.

Further, the company’s other software, Grid Operator, integrates current assets into software-defined local energy markets to automate real-time energy exchanges and settlement. It also optimizes pricing while tracking emissions without additional hardware.

This workflow is applied to industrial parks and community-scale systems where residual energy streams, like waste heat and local renewables, generate chances for dispatch and trade among nearby players.

This way, the company lowers planning latency, enhances investment-grade feasibility proof, and integrates distributed energy resources.

BITA Energy offers AI-powered Energy Management

BITA Energy is a Turkish company that builds AI-powered energy management software for electricity distribution and transmission operators. It reduces outages using early fault detection and maintenance planning.

The software leverages drones to gather high-resolution images of field assets and automate inspection workflows. Image-processing models then locate problems at the component level, connect results to asset records, and provide crews with reports that are supported by evidence.

To ensure early failure risk identification, the software applies predictive analytics. At the same time, continuous learning models improve performance as new inspection cycles add labeled cases.

The company’s BITA Fusion Cloud platform further allows teams to centralize defect detection, asset management, and maintenance optimization to act on results. By integrating visual proof, real-time alerts, and security monitoring, energy companies are able to optimize maintenance operations for power poles, wind turbines, solar plants, and natural gas lines.

Elastic Energy provides a DER Orchestration Engine

Elastic Energy is a Canadian startup that develops a DER orchestration engine. It enables utilities and energy operators to coordinate distributed energy resources as a single controllable system.

Connecting batteries, EV chargers, solar assets, and flexible loads, it functions as a cloud-based control and optimization layer. The engine also analyzes real-time price and telemetry inputs to forecast demand and dispatch assets in accordance with market and grid limitations.

While preserving asset-level limitations and user preferences, the engine also converts grid requirements like peak shaving, congestion alleviation, and supplementary services into practical control actions. It utilizes APIs to interface with market operators and utility systems to support settlements, performance tracking, and program design.

WINDROVER offers Predictive Maintenance

WINDROVER is a German startup that offers predictive maintenance software for wind turbine blades. It utilizes embedded sensing and machine learning to detect structural damage before it reaches the surface.

The startup integrates MEMS-based vibration sensors into blades, uses Wi-Fi or LoRaWAN to send data to a cloud platform, and leverages a neural network pipeline to learn the vibration signature of each blade. This way, it identifies anomalies associated with holes, splits, and cracks.

The software also categorizes identified damage by type, position, and size before offering maintenance suggestions to accelerate work-order prioritizing. To distinguish between typical noise and early-stage fault patterns, the software provides automated warnings and a baseline period that compares turbines with similar blade types.

The company’s software lowers turbine downtime and blade maintenance expenses by switching from surface inspections to continuous, data-driven risk planning.

Gain Comprehensive Insights into Energy AI Trends, Startups, and Technologies

The AI in Energy market is transitioning from experimentation to system-level deployment. Growth now depends on how effectively AI integrates with grids, data centers, and clean energy infrastructure. Investment, patent activity, and workforce expansion all point to sustained long-term commitment rather than short-term adoption cycles. As AI workloads continue to reshape electricity demand, energy intelligence becomes a strategic requirement for utilities, infrastructure operators, and technology providers alike.

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