First Glance
AI In Utilities Statistics: Artificial intelligence is becoming a core part of utility operations as energy providers manage rising demand, ageing infrastructure, renewable generation, extreme weather, and increasingly complex power networks. Utilities are applying AI to demand forecasting, grid optimisation, predictive maintenance, outage management, asset inspection, customer support, and energy trading. These systems help operators analyse real-time data, detect risks earlier, allocate resources efficiently, and improve service reliability.
AI also supports the integration of distributed energy resources, battery storage, electric vehicles, and virtual power plants. However, legacy systems, fragmented data, cybersecurity risks, regulatory requirements, workforce gaps, and model reliability continue to limit adoption.
AI in utilities statistics provide a practical view of market growth, deployment priorities, operational outcomes, investment patterns, and the industry’s progress toward intelligent, resilient, and sustainable infrastructure. In this article, we explore the key statistics and trends shaping AI adoption across utilities.
Recommended Reading by the Editors
- The AI in energy and utilities market could grow from USD 14.78 billion in 2025 to USD 147.62 billion by 2035.
- Around 65% of utilities use AI in at least one operational area.
- About 52% of utilities use AI for load and demand forecasting.
- Nearly 49% of operators use AI for predictive maintenance.
- AI can improve grid reliability by up to 25% and reduce maintenance costs by 43%–56%.
- AI-powered scheduling can raise frontline workforce productivity by 20%.
- Around 38% of utilities report measurable reductions in power outages.
- Octopus Energy’s AI customer service achieved 80% satisfaction, compared with 65% for trained human teams.
- Oracle’s Opower programs saved 44.23 TWh of energy and reduced customer bills by nearly USD 4.3 billion.
General AI In Utilities Facts
- 94% of utility executives expect AI to contribute significantly to revenue growth within the next three years.
- 88% of utility executives say AI will deliver measurable competitive advantage.
- More than two in five utility companies (40%+) use AI in field workforce optimization, predictive maintenance, outage management, and energy demand management.
- AI adoption for complex utility applications is expected to approach near-total deployment by 2028.
- AI adoption has delivered a 10% improvement in service reliability, an 11% boost in grid uptime, a 10% increase in energy efficiency, and a 10% improvement in customer satisfaction.
- Over half (50%+) of utility executives expect AI to unlock new technology capabilities that transform their business models.
- 70% of utility executives agree AI will enable expansion into entirely new service areas, especially in customer interaction.
Global AI In Energy and Utilities Market Share
- The AI in Energy and Utilities market was valued at USD 15.23 billion in 2025 and is expected to grow significantly, reaching USD 93.29 billion by 2035.
- This market is projected to grow at a strong yearly rate of 20%, known as CAGR, during the forecast period from 2026 to 2035.
- The market is divided into different categories, including Type, Application, End-User, and other segments.
- North America currently leads the global AI in Energy and Utilities market, ahead of other regions.
(Source: insightaceanalytic.com)
AI Adoption in Utilities
- A large majority of utility companies, around 65%, are now using AI in at least one part of their operations.
- More than half of utilities, about 52%, use AI specifically for load and demand forecasting to better predict energy needs.
- Nearly 44% of utilities use AI to optimize their power grids, helping improve efficiency and performance.
- Almost half of all operators, around 49%, use AI for predictive maintenance, allowing them to fix problems before they cause bigger issues.
- However, 46% of utilities say outdated legacy infrastructure remains their biggest challenge in adopting AI technology.
- Despite these challenges, 38% of utilities report seeing measurable improvements in reducing power outages after using AI tools.
| Metric | Value |
| Utilities using AI in at least one operational area | 65% |
| Utilities using AI for load and demand forecasting | 52% |
| Utilities using AI grid optimization | 44% |
| Operators using AI predictive maintenance | 49% |
| Utilities citing legacy infrastructure as top barrier | 46% |
| Utilities reporting measurable outage reduction | 38% |
Real-World AI in Utilities Case Studies
| Utility Company | AI Application Area | Detailed Outcomes & Success Metrics |
| Duke Energy | AI Cybersecurity & Emissions | Deployed a Microsoft Azure AI platform to transition from estimated methane calculations to precise, real-time leak monitoring. Additionally utilizes AI to monitor grid control systems, detecting malware faster than manual methods to protect national infrastructure. |
| Octopus Energy | GenAI Customer Service | Implemented Generative AI to manage and automate customer email responses. Achieved an 80% customer satisfaction rate, outperforming the 65% satisfaction rate achieved by highly trained human staff. |
| PG&E | Grid Optimization & Wildfires | Deployed visual AI across 630+ high-definition cameras monitoring 90% of high-fire-risk zones. The AI autonomously detects smoke plumes, triggering rapid-response alerts to protect infrastructure and communities. |
| Utility Beta (Anon) | Computer Vision for Drones | Utilized AI to automatically filter blurry images during wind turbine drone inspections. This quality-control AI reduced the need for drone operators to return to sites by 80%, saving significant labor hours. |
AI in Utilities Use Cases
- Predictive Maintenance and Asset Failure Prevention: Detects early failure signals in transformers, switchgear, pumps, and pipes, and prioritizes maintenance by risk. Boosts grid reliability by up to 25% (McKinsey) and cuts maintenance costs by 43-56% (Argonne).
(Source: Anl.gov)
- Field Workforce Scheduling and Dispatch Optimization: Cuts travel time, missed appointments, idle time, and repeat truck rolls via AI-driven scheduling. McKinsey reports a 20% boost in frontline crew productivity.
- Outage Prediction and Faster Restoration: Predicts fault likelihood and location using weather, load, and asset data; prioritizes restoration by customer criticality. An EY-Eversource Energy framework avoided about 40,000 customer outages in two months.
(Source: EY.com)
- Demand Forecasting and Load Management: Covers short-, medium-, and long-term forecasting as EVs, electrification, and AI data centers reshape load. IEA predicts data center electricity demand will double by 2030.
- Customer Service Automation: Summarizes customer history for agents, auto-drafts communications, routes contacts by intent/urgency, and cuts call volume via proactive notifications.
- Compliance and Audit Readiness: Automates evidence collection, flags non-compliance risks early, and speeds up reporting without weakening audit trails.
- Knowledge Copilots for Engineers and Operators: Helps field crews and engineers quickly find procedures, safety steps, asset history, and internal standards.
Benefits of AI in Utilities
- AI offers many benefits that help utility companies work more efficiently and serve their customers better.
- AI improves energy efficiency by using predictive analytics to monitor and control energy usage in real time. By studying past data, AI can recommend ways to save energy and reduce waste.
- AI enhances customer service by analyzing customer data to offer personalized recommendations for energy usage and savings. AI-powered chatbots also provide round-the-clock support, helping resolve customer queries quickly.
- AI optimizes grid management by monitoring grid data in real time, helping utilities spot and fix problems before they occur. Predictive maintenance also reduces power disruptions and helps manage renewable energy sources more effectively.
Future Insights of AI in Utilities
- AI systems will increasingly support real-time balancing of electricity supply and demand across utility grids, improving reliability and resilience during peak load conditions.
- AI models capable of processing text, sensor data, images, and video simultaneously will improve situational awareness in control centers and support faster incident response.
- Utilities will increasingly deploy virtual replicas of infrastructure systems to simulate performance scenarios and optimize maintenance planning.
- Predictive analytics will help utilities anticipate extreme weather impacts and strengthen disaster preparedness strategies.
Recent AI In Utilities Developments
- On September 18, 2026, National Grid Partners reported that 78% of surveyed U.S. utility innovation leaders were deploying at least one AI application to manage interconnection demand; it also announced investments in Terragrit and LineVision.
- On August 20, 2026, Siemens and Electric Power Group formed a global partnership to combine real-time grid data with AI-powered monitoring, supporting faster fault detection and more resilient utility networks.
- On June 30, 2026, Schneider Electric agreed to acquire industrial data and AI software company Cognite for USD 3.1 billion; Cognite generated more than USD 170 million in revenue during 2025.
- On June 30, 2026, Delta Energy launched QORTEX, an agentic AI platform that combines data from SCADA, AMI, GIS, outage management, customer, weather, and asset systems for utility operations.
- On June 20, 2026, Schneider Electric agreed to acquire around 90% of AiDASH at an enterprise value of USD 350 million, adding satellite and AI-powered grid-risk monitoring to its One Digital Grid platform.
- On June 9, 2026, GE Vernova launched GridOS for Transmission, a unified software platform for near-real-time transmission operations, and released two AI studies covering grid planning and autonomous grid-edge management.
- On May 6, 2026, National Grid partnered with Air Space Intelligence to use predictive AI for outage detection, grid-resilience planning, energy-storage deployment, and distributed-energy interconnections.
- On April 13, 2026, Oracle expanded its AI-powered Utilities Industry Suite; its Opower programs had enrolled 44.6 million households, saved 44.23 TWh of energy and reduced customer bills by nearly USD 4.3 billion since 2009.
The Bottom Line
AI is set to become an essential decision-support layer across utility operations, helping providers strengthen reliability, manage renewable energy, improve asset performance, and respond to changing demand. However, progress will depend on overcoming outdated infrastructure, fragmented data, cybersecurity risks, regulatory concerns, skill shortages, and limited trust in automated decisions.
Future investment must focus on scalable solutions that support human expertise, protect critical infrastructure, and create more flexible, resilient, and customer-focused utility networks.
FAQ
AI helps utilities with tasks like predictive maintenance, outage prediction, demand forecasting, grid optimization, and customer service automation. It’s mainly used to improve reliability while cutting operational costs.
Predictive maintenance is currently the largest AI use case in utilities, with widespread adoption across grid, water, and energy asset management.
Yes, E. ON’s research shows predictive maintenance can cut grid outages by up to 30%, while Enel’s sensor-based approach reduces outages on monitored cables by 15%.
Gartner projects 40% of power and utility companies will use AI-driven control room operators by 2027, automating real-time dispatch and anomaly detection.
PG&E deployed over 630 AI-enabled cameras for wildfire detection and built demand-side AI that capped peak growth at 10%, helping stabilize the grid.
Yes, Veolia documented 4 billion liters in annual water savings through AI-enabled leak detection, and Thames Water cut storm overflow events by 80%.
Budget constraints are the biggest barrier at 25%, followed by a lack of expertise at 24% and cybersecurity concerns at 22%.
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