AI in Energy at a Glance

Gartner’s January 2025 outlook projects that 40% of power and utilities will deploy AI-driven operators in control rooms by 2027. It reports that 94% of power and utility CIOs plan to increase AI investments in 2025, with an average spending increase of 38.3%.

This combination signals that the near-term market is being pulled by dispatch, anomaly detection, and real-time decision support.

E.ON’s research suggests that predictive maintenance could reduce grid outages by up to 30% compared to scheduled maintenance, while Enel’s sensor and machine learning approach reduces power outages on monitored cables by 15%.

Moreover, T&D utilities are able to get 10-20% savings with advanced analytics in asset management, and a case study describes a North American T&D utility that achieved 20-25% operating expense savings in 2021. It also achieved 40-60% capital expenditure savings by targeting risky assets as well as optimizing maintenance and replacement.

At the same time, the IEA’s 2025 Energy and AI report notes that a typical AI-focused data center consumes as much electricity as 100 000 households, and that some of the largest facilities under construction are expected to consume around 20x that amount.

Why This Market Is “Now”

Forecasting is one of the highest-ROI AI wedges in power systems because it improves dispatch decisions and reduces balancing costs without long asset cycles. In the UK, AI improved National Grid ESO solar forecasting for up to 8 hours ahead. Korea is also using AI for wind speed prediction and real-time weather impact simulation.

On wind integration, NREL quantifies how ML-style short-term forecasting can reduce error at operational horizons. A 2020 study on very short-term wind forecasting reports that using regional wind flow features delivered 26% better performance than persistence at the 5-minute time horizon.

For grids with rising inverter-based resources, these minutes-ahead improvements are directly relevant to frequency response, ramp management, and curtailment reduction.

The IEA also quantifies the system-level decarbonization lever created by AI-enabled renewable integration. Reducing global average curtailment by one percentage point in 2035 could cut energy demand by about 28 million tonnes of coal equivalent (Mtce) of coal and 14 billion cubic metres (bcm) of natural gas. This will avoid approximately 120 Mt of CO₂.

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.

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

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.

 

 

Examples Designed for the New Load Curve

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.

Where the IP Is Going

 

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.

Utility Capex Becomes the Funding Engine

Energy AI is increasingly showing up in strategic acquisitions that integrate AI into utility-grade platforms. For instance, GE Vernova is acquiring France’s Alteia to enhance AI tools for utilities. GE Vernova already uses Alteia’s software within its GridOS Visual Intelligence system for utilities to monitor and inspect grid infrastructure.

Even without disclosed deal value, this is a strong signal that AI for grid inspection and situational awareness is being productized by major OEM/software incumbents.

Separately, CenterPoint’s USD 65 billion capital spending plan for 2026-2035 and load growth expectations are tied to data centers and AI. A DOE-backed LBNL estimate reports that US data center electricity demand could reach 6.7-12% of US electricity by 2028. This highlights how AI-driven demand growth is pulling forward grid capex, which indirectly expands the addressable market for AI solutions.

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.

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.

 

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.

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.

Data Inputs and Filtering

This AI in Energy industry outlook draws on the StartUs Insights Discovery Platform to map the market across 9M+ companies, 25K+ technologies and trends, and 190M+ patents, news articles, and market reports. Rather than treating AI as a generic software layer, this analysis stays stack-aware, focusing on the data and control primitives that determine scale. This includes AMI and sensor penetration, SCADA telemetry quality, digital twins, model governance, cybersecurity in OT environments, and integration into regulated processes.