Strategic Context
Energy operators face increasing pressure to balance reliability, regulatory compliance, sustainability, and cost control. Ageing infrastructure, rising demand, and the growth of intermittent resources such as solar and wind make asset failures costly — both financially and reputationally.
Utilities, TSOs, DSOs, and nuclear operators are increasingly adopting artificial intelligence (AI) to shift from reactive maintenance to predictive, data-driven strategies, enabling smarter capital allocation, enhanced safety, and improved operational efficiency.
AI in Utilities, Transmission, and Distribution
AI in European utilities is being applied across predictive maintenance, inspection, congestion management, and renewable integration. Machine learning algorithms analyse sensor data, smart meters, and weather patterns to predict asset failures and optimise maintenance schedules.
Notable examples include Enel (Italy), using predictive maintenance on feeders via the C3 IoT platform; Enedis (France), applying supervised learning to anticipate outages; and ASM Terni (Italy), piloting predictive analytics for LV networks under the EU Horizon 2020 I-NERGY project.
Drone and LiDAR inspections are also widely deployed: Westnetz (Germany) and ED Netze (Germany) inspect high-voltage lines with autonomous drones, EDP Distribuição (Portugal) monitors HV and MV lines, and Sadales tīkls (Latvia) uses UAVs to accelerate data collection and improve defect detection.
Multi-stakeholder projects such as BRIDGE and SmartNet focus on TSO–DSO cooperation, sharing data, coordinating ancillary services, and employing predictive analytics to avoid congestion and optimise system-wide reliability. AI also supports renewable integration, enabling better forecasting and locational risk analysis, as demonstrated by AEMO (Australia), reducing curtailment risk and improving capital efficiency.
Notable Projects
CoordiNet Project (Europe-wide): The CoordiNet project, funded by the EU’s Horizon 2020 programme, focuses on demonstrating how Transmission System Operators (TSOs) and Distribution System Operators (DSOs) can coordinate to procure grid services efficiently. This initiative involves various stakeholders across Europe, aiming to enhance the integration of renewable energy sources and improve grid stability through advanced coordination mechanisms.
SINTEG Program (Germany): Germany’s SINTEG (Smart Energy Showcases – Digital Agenda for the Energy Transition) programme funds several projects that explore innovative solutions for energy system transformation. Within this framework, projects like enera involve collaboration between German DSOs such as EWE Netz AG and Avacon Netz, and the TSO TenneT. These projects focus on developing local flexibility markets and integrating distributed energy resources to enhance grid reliability and efficiency.
sthlmFlex Project (Sweden): The sthlmFlex project in Sweden explores TSO-DSO coordination in a multi-layered distribution system. The project involves Swedish DSOs and the TSO Svenska Kraftnät, aiming to develop and test local flexibility markets that enable efficient integration of renewable energy and demand-side management. By leveraging AI and digital platforms, the project seeks to optimise grid operations and enhance system resilience.
Italy is a leader in integrating AI into its energy sector, with Enel spearheading several initiatives. The company has implemented an AI-based system called Smart Line Monitoring, which uses line sensors and vibration analysis to detect anomalies on power lines. This system has led to a 15% reduction in outages on monitored lines. The company has partnered with C3.ai to deploy a Predictive Maintenance program across 16,000 substations in Italy, accurately predicting faults on medium voltage electric distribution feeders. These efforts are part of Enel’s broader strategy to digitise and innovate its distribution grid, with gross investments of €26 billion in Italy.
In Spain, Iberdrola has collaborated with the Basque Center for Applied Mathematics (BCAM) on the AI Innovation Data Space project to optimise grid operations. is also investing in the development of data centres to support the growing demand for AI and cloud computing. In July 2025, Iberdrola and Ireland-based Echelon formed a JV to develop and operate data centres in Spain, with the first project expected to be operational by 2030.
Energi Fyn, one of Denmark‘s largest utility providers, has implemented a comprehensive energy and utilities customer platform that unifies and personalises customer data in a single master file, providing tailored services. Denmark’s collaboration with NVIDIA to establish a national AI innovation centre aims to accelerate research and innovation in many fields, including energy transition.
Lithuania‘s electricity transmission system operator, Litgrid, has actively integrated artificial intelligence (AI) and sensor technologies to enhance grid capacity, stability, and the integration of renewable energy. In early 2024, Litgrid completed trials of its variable line capacity technology, achieving an average 52% increase in line capacity compared with design specifications, enabled by AI and real-time sensor data. The company has also tested AI applications on solar and wind farms to optimise renewable energy integration and partnered with GE Vernova to implement the GridOS® Advanced Energy Management System (AEMS), which intelligently balances the grid, increases reliability, and supports Lithuania’s synchronization with the Continental European Synchronous Area. Through these initiatives, Litgrid is modernising its operations, improving system resilience, and contributing to the decarbonisation of the energy sector.
Ukraine‘s electricity TSO, Ukrenergo, has been actively integrating digital technologies and artificial intelligence (AI) to enhance grid resilience and operational efficiency, particularly in the face of ongoing conflicts and infrastructural challenges. In collaboration with Microsoft, Ukrenergo has implemented Dynamics 365 to automate operational management processes, aiming to optimise resource allocation and improve response times during emergencies. Additionally, Ukrenergo has certified a 1 MW/2.25 MWh battery energy storage system capable of providing frequency containment reserve services, demonstrating rapid response capabilities that traditional power plants cannot match. These initiatives are part of a broader strategy to modernise Ukraine’s energy infrastructure and integrate it more seamlessly with European systems, leveraging AI to support grid stability and facilitate the transition to a more resilient and sustainable energy system.
Also DTEK, Ukraine’s largest private energy company, has been actively integrating AI and digital technologies into its operations to enhance energy resilience, particularly in response to the ongoing conflict and infrastructure challenges. Its subsidiary YASNO deployed an AI assistant powered by Microsoft Azure OpenAI Service and Azure AI Search to assist customer service agents in retrieving information more efficiently, improving response times and overall service quality. The company has collaborated with the UK’s Octopus Energy Group to launch the RISE initiative, aiming to deploy rooftop solar panels and battery storage systems across Ukrainian businesses and public institutions. This project leverages Octopus Energy’s AI-powered Kraken platform to optimise energy usage, enabling surplus energy to be sold back to the grid and enhancing the resilience of local energy systems.
In Greece, the Independent Power Transmission Operator IPTO is participating in the HEDGE-IoT project, an EU-funded initiative that leverages AI and edge computing to explore local flexibility markets. This project aims to address real operational challenges by utilising distributed energy resources, such as flexible demand and distributed storage, to support grid stability through smart coordination. The project positions IPTO at the forefront of integrating AI-driven solutions into grid operations.
HEDNO, Greece’s sole DSO, is advancing grid modernisation through the deployment of smart meters and AI-enabled systems. The company has partnered with Itron to implement a grid edge portfolio, including a Meter Data Management (MDM) system to manage 7.7 million meters and the UtilityIQ platform for 1 million smart meters. These systems enable real-time data collection and analytics, facilitating improved grid efficiency and customer service. Additionally, HEDNO is investing in a €546 million project to install 3.12 million smart meters nationwide, supported by a €150 million loan from the European Investment Bank.
Both IPTO and HEDNO are also involved in the OneNet project, which focuses on TSO-DSO coordination to enhance grid flexibility and integrate distributed energy resources. The project develops platforms and methodologies for active power management, addressing challenges such as congestion and balancing in the context of high renewable energy penetration.
AI in Nuclear Energy
In the nuclear sector, AI enhances safety, efficiency, and operational reliability. Predictive maintenance and anomaly detection are applied to pumps, valves, and turbines, as seen at EDF (France) and Exelon (US), identifying early signs of equipment degradation. Robotics and computer vision facilitate inspections in hazardous environments: Sellafield Ltd (UK) and CEA (France) deploy AI-driven drones and crawlers to identify structural defects in containment and waste facilities.
Digital twins, implemented by Westinghouse Electric and Rosenergoatom (Russia), simulate reactor components in real time, optimising operations and predicting wear without interrupting service. AI also supports safety analysis, radiation mapping, and decommissioning through projects at the IAEA, Framatome, and UKAEA, while fusion research initiatives like ITER and UKAEA’s STEP programme use AI for plasma control, disruption prediction, and materials performance under neutron bombardment. Across the nuclear lifecycle, AI reduces risk, enhances reliability, and improves decision-making.
Integration, Challenges, and ROI
Successful AI adoption requires high-quality, structured data and robust data architectures, alongside workforce training and system integration. Utilities and nuclear operators often face siloed datasets, inconsistent formats, and legacy IT infrastructure, which can hinder model accuracy and operational trust.
Despite these hurdles, pilot projects show substantial returns: predictive maintenance and drone inspections can deliver 10–20% reductions in operations and maintenance costs, accelerate defect detection, improve safety, and defer capital expenditure.
Looking Ahead
For executives, the decision framework is to identify high-impact use cases: transformer health, overhead line inspections, energy generation forecasting, and congestion risk being among them. Pilot with vendors or in collaboration with universities (as in Australia) to establish baseline metrics (current failure/repair costs, downtime, inspection cost, etc.), then quantify what improvements AI can bring (e.g., 20–30% reduction in emergency failures, 30–40% reduction in inspection costs). These pilots should feed into a roadmap for integrating AI systems, investing in data architecture, embedding explainable analytics, and eventually scaling across regions or asset classes.
Projects such as Monash/Worley (generation forecasting) and Westnetz/Beagle (drone inspections) demonstrate multi-million euro savings while providing measurable operational improvements.
AI is poised to evolve fast from isolated pilots to enterprise-wide adoption, supported by digital twins, edge computing, and generative AI for decision support. Utilities, TSOs, DSOs, and energy plants operators that invest early in data quality, workforce readiness, and cross-functional integration will capture the greatest value. As energy generation and transmission become more distributed and decarbonised, AI-enabled asset management safeguards reliability and efficiency and serves as a strategic differentiator in the energy transition.
