The energy transition exposes the complexity of our energy system. This transition requires a complete and very complex change in the energy system. The fossil, central energy system is changing into a decentralized system based on renewable energy. To achieve a sustainable future, the system must become more 'green', but electricity production and consumption must also be in balance at all times. The digitalization and available data of energy systems have increased enormously. This makes it possible to significantly accelerate the energy transition through the use of AI. AI is already being used on a small scale, for example in the smart thermostat or charging an electric car, especially at a time when energy demand and price are low. However, the energy system as a whole is still far from smart and can become a lot more efficient through the use of AI.

Decisions surrounding the energy transition, such as investments in infrastructure or the installation of renewable energy sources, rely heavily on predictions, often based on limited data, and involving a wide range of interests. New AI technology can support these kinds of investment and design decisions by providing a more fact-based foundation, better considering multiple interests, and better explaining the choices made and the associated uncertainties. Developing the necessary technology offers a unique opportunity to lead the way internationally.

In-depth knowledge of AI provides energy and sustainability experts with important tools. Consider, for example, algorithms specially developed for controlling energy distribution systems, setting up and improving 'control' in energy infrastructure (as required for underground heat storage) and smart support for complex decision-making processes, for example when it comes to investments that have an impact on the entire energy system. Because testing solutions in practice is often not possible and is very laborious and expensive, detailed models and simulators must be developed that can accommodate some of the practical tests.

Decisions regarding the energy transition, such as investments in infrastructure or the installation of renewable sources, depend a lot on predictions, often based on limited data, and there are many different interests. New AI technology can support these types of investment and design questions by basing them better on facts, by taking multiple interests better into account, and by explaining the choices made and related uncertainties better. Development of the required technology offers Western countries a unique opportunity to take an international lead and export this technology.

In addition, there are opportunities for AI in automating energy system operations, supporting energy services and predicting and automating maintenance. The use of AI also offers opportunities for other social issues related to sustainability to make a difference. For example, around health and care, education, agriculture and nutrition, the technical industry, the built environment, safety, peace and justice and the climate.

Challenges in the energy sector and around sustainability can therefore be met by using new AI solutions, which would require major investments.

The European energy transition is accelerating rapidly, with solar, wind, battery storage, and green hydrogen energy production playing a critical role in reshaping the energy landscape. Operating these diverse technologies across multiple countries presents both challenges and opportunities. To capture the full value a centralized asset management organization is needed. It enables us to integrate data, processes, and expertise across our portfolio, creating efficiencies and unlocking long-term value. Artificial Intelligence (AI) will be a key driver in this model. AI-agents as co-workers ensure efficient, consistent and transparent reporting. Applying forecasting models to optimize revenue generation and maintenance cost over the full asset portfolio. And applying digital twin models to predict and prescribe maintenance and operations towards maximum availability of the production assets.

Centralization offers clear benefits in a fragmented landscape. Assets are distributed across different countries and markets, each with unique weather conditions, regulations, and grid structures. By managing them through one asset management platform, we can standardize and share best practices, reduce duplication, and ensure insights gained in one country are rapidly shared across the portfolio. This diversification—across technologies and geography also strengthens sustainability- and risk management, smoothing out volatility in production and revenues. An AM platform will act as AI Agent to assist asset management experts. Tasks are performed independently (or semi-independently) based on data, rules, and predefined goals. It observes by retrieving data from various sources, analyzes this data using AI models and automatically generates actions or output (such as reports, alerts, or optimization proposals) according to predefined frameworks.
Digital assistants therefore take over repetitive and data-intensive processes, allowing asset experts to focus on strategy and decision-making.

Performance optimization is another area where AI creates tangible value. By analysing weather forecasts, demand patterns, and technical parameters, AI can recommend strategies to maximise production—whether that involves adjusting wind turbine settings, optimizing solar panel cleaning schedules, or orchestrating battery charge and discharge cycles. AI also helps automate routine tasks such as work order planning, spare parts logistics, and compliance reporting, freeing up human expertise for more strategic decision-making.

Asset Ambition and Strategy will be to facilitate remote controlled operations and unmanned maintenance. AI further unlocks value by maximizing asset availability and reliability. Predictive maintenance, powered by real-time sensor data and historical performance, allows us to detect and address potential issues before they result in downtime. Digital twins, or virtual replicas of assets, enable us to run simulations and optimise output without interfering with real-world operations. This translates into higher availability, lower maintenance costs and improved safety.

Greater asset availability leads to more predictable cash flows, while reduced operational costs improve margins. Advanced data analytics provide transparency and allow us to present investors with reliable performance forecasts, scenario modelling, and risk assessments. This data-driven approach not only strengthens trust but also ensures that opportunities—such as repowering projects or green hydrogen integration—are identified early and executed with confidence.

Centralized asset management enhanced with AI-enabled tools, builds a scalable, resilient, and future-proof operating model. This approach ensures a renewable energy portfolio delivers consistent, optimised returns while actively contributing to Europe’s green transition. For shareholders and investors, it represents a compelling opportunity to align financial growth with sustainable impact.