How GenAI contributes to a more efficient and resilient energy sector

Earlier PwC findings reveal that improvements in how we use energy - not just produce it - can significantly reduce global energy intensity and deliver up to two trillion dollars in annual savings by 2030. Our current research shows that GenAI can be a powerful driver in this mix, provided it's strategically applied, integrated into operations, and supported by robust governance. 

The energy trilemma and the promise of GenAI 

The sector grapples with the 'energy trilemma': high energy costs, supply security uncertainty, and the urgency to reduce CO2 emissions. GenAI can address all three dimensions simultaneously. For instance, AI shows promising results in automated dispatch optimisation of heat, electricity, or storage, leading to lower fuel costs, higher margins, and reduced CO2 emissions. 

'Energy and energy-intensive industries have traditionally been early adopters of digital technologies, driven by margin pressure, operational complexity, and regulatory requirements,' says Yuliya Makyeyeva, a PwC expert and one of the report's authors. 'This positions them well to explore the benefits of GenAI, especially in the context of energy efficiency. These industries generate vast amounts of complex data, from network operations to supply chains. This data forms a solid foundation for AI-driven optimisation. GenAI can unlock this untapped efficiency potential through better data interpretation, real-time monitoring, and decision support. Successful implementation requires a clear, strategic roadmap and addressing challenges such as data quality, integration complexity, and governance.' 

By taking this approach, companies align with European and global policies. The European Union upholds the principle of 'energy efficiency first', and the IEA (World Energy Outlook 2023) notes that companies still invest too little in efficiency. The International Energy Agency calls for better use of innovative technologies. GenAI is particularly effective in this context: it can analyse large datasets, generate real-time insights, and increasingly propose or initiate concrete actions. AI systems are beginning to behave more like 'agents'. This shift towards proactive intelligence is still in its early stages but has the potential to transform complex, data-driven sectors. 

From pilots to impact: breaking the 'pilot trap' 

Despite the potential, many companies remain stuck in what experts call the 'pilot trap'. They launch isolated AI experiments—often in trading, customer analysis, or equipment optimisation—without linking them to broader digital strategies or sustainability goals.  

Makyeyeva notes: 'Such an approach often leads to poor implementation and fails to leverage the technology's capabilities. Especially in sectors reliant on complex processes and facing significant risks and challenges with new technologies. This leads to scepticism and reluctance to further integrate the technology into operations.' 

Therefore, strategy and governance are crucial. The perception of AI risks is often greater than reality: GenAI typically adds complexity to known risks rather than introducing entirely new risk categories. At the same time, AI demands caution: data centre energy consumption could double by 2026, according to IEA estimates, highlighting the importance of strategic deployment and energy-efficient architectures. 

Where GenAI already adds value: concrete applications 

Examples of GenAI use in the energy sector include: 

• Automated dispatch optimisation of heat, electricity, and storage: lower fuel costs, higher margins, reduced CO2 emissions. 
• Predictive maintenance for grid and production assets (transformers, cables, HV/MV switchgear, wind turbine components, boiler components): less downtime, longer asset lifespan, lower operational expenses. 
• Grid congestion and load forecasts: prevent costly power outages and lower redistribution costs. 
• Dynamic planning of engineers, scheduled interruptions, and contractors: fewer delays, less overtime, better availability. 
• Automatic fault location and 'self-healing networks': interpretation of security signals, PMU data, and waveform analyses for faster reconfiguration; improved SAIDI/SAIFI performance. 
• Predicting demand flexibility: modelling EV charging, temporarily shutting down heat pumps, and industrial 'demand response' to prevent overload and emergency redistribution. 

The necessity of a coherent AI strategy 

'However, the challenge doesn't lie in identifying the opportunities,' says Menno Braakenburg, PwC partner and strategy and transformation expert within Energy & Utilities. 'Those are clear. The challenge most energy and utility companies face is developing a coherent AI strategy and driving its adoption within the organisation and with partners to realise tangible value.' 

To make progress, companies must start by embedding GenAI initiatives in their broader strategic and sustainability goals. This way, employees and stakeholders see AI not as a standalone tool but as part of the long-term transformation agenda. This requires laying the right data foundation, fostering collaboration between business and technology teams, and integrating governance from day one. 

AIDE: five steps to a broader AI strategy 

At PwC, we support companies on this journey through the AIDE framework, a structured roadmap designed to help companies align GenAI initiatives with broader digital strategies. The framework outlines a five-step process encompassing strategic vision, pilot selection, risk management, technology integration, and continuous evaluation. 

Better positioned to guide the energy sector through a turning point 

Braakenburg emphasises, 'AI systems must be transparent and explainable, aligning with evolving regulations like the EU's AI Act.' Scaling from pilots to impact also means creating structures that support continuous learning and integration. Companies acting with clarity, structure, and responsibility now will be better positioned to lead the sector through this pivotal moment.'