How AI is transforming the future of energy

The ongoing energy transition has moved beyond simple narratives to enter a more mature and challenging phase. What started as renewables replacing fossil fuels now spans multiple directions and technologies. Supply chains stretch globally, yet remain vulnerable to disruption. Geopolitical tensions increasingly dictate energy priorities across regions. 

At the same time, artificial intelligence (AI) is reshaping the industrial landscape. The rise of AI is not only driving electricity demand through the rapid expansion of data centres, but also offering the tools needed to manage the complexity this demand creates. Essentially, AI is emerging as both a challenge and the connective tissue that will hold the energy system together. 

Our estimates show that limiting global temperature rise to 2°C remains plausible if the world  

reaches net zero emissions by around 2060. However, this scenario would require annual investment levels across power, grids, upstream, critical minerals and new technologies to increase by 30% to an average of US$4.3 trillion between now and 2060. But investment alone won’t solve the challenges ahead. The real differentiator will be intelligence. AI provides the ability to see across systems, anticipate ripple effects, and act in real time.  

Digging into the complexity issue  

Our new technologies outlook provides an annual assessment of the evolving new-energy landscape, tracking more than 260 emerging technologies, from solar and wind to hydrogen, carbon capture, and critical minerals. These technologies don’t operate in isolation. They compete for resources, infrastructure, and policy attention. However, AI is opening new opportunities to map these interdependencies in a faster, more efficient way, revealing how decisions in one sector affect outcomes in another. 

This complexity is playing out in real time. The rise of AI itself is contributing to the challenge. Data centres – essential to powering AI workloads – are driving a surge in electricity demand. This boom is already straining grid infrastructure and forcing utilities to rethink how they plan for capacity. The traditional predictability of power systems is being replaced by volatility, with fluctuating loads and new consumption patterns that are harder to forecast. 

This tension between rising demand and limited flexibility is not just theoretical – it’s already manifesting. In Scotland, for example, wind turbines were curtailed 37% of the time in the first half of 2025 due to grid bottlenecks. Despite record renewable capacity, the system lacked the flexibility to absorb it. This illustrates how infrastructure and intelligence must evolve in tandem. Without the ability to anticipate and adapt, clean energy can go unused. 

Decisions driven by data 

Companies positioning themselves for success recognise that traditional sector-focused analysis cannot navigate today’s complexity. When every decision carries significant implications for investment returns and business performance, the ability to see and respond to the full picture becomes critical for survival. When supply chains span continents and regulations shift rapidly, integrated intelligence becomes essential. 

Success demands a comprehensive view of the entire energy landscape and the ability to analyse a full spectrum of real-world scenarios in real-time. Traditional scenario planning consumes months of valuable time. Market conditions shift before insights reach decision-makers. This timing gap undermines strategic planning. 

Wind and solar power introduce fresh grid stability challenges. Batteries and demand responses provide partial solutions to these issues. However, the energy system evolves faster than management tools can adapt. This mismatch creates operational risks and missed opportunities. 

AI compresses this timeline by transforming fragmented datasets into actionable intelligence in hours, not weeks. This speed is essential. Energy markets are shaped by policy and regulatory shifts, supply shocks, weather disruptions, and technological breakthroughs. 

AI’s expanding role in energy optimisation 

AI’s transformative power lies in its ability to process vast, real-time datasets – from sensor networks and IoT devices to satellite imaging – capturing minute-by-minute fluctuations in energy production and consumption. This data explosion enables dynamic forecasting and rapid decision-making that was previously impossible. 

During a recent heatwave a hyperscale data centre rerouted its computing load to avoid grid congestion – an AI-driven move that prevented price spikes and stabilised the local market. These kinds of invisible shifts are now visible, quantifiable, and actionable. 

At the same time, generative AI is revolutionising how organisations handle unstructured data. Large language models (LLMs) synthesise information from diverse sources, dramatically reducing the time from data ingestion to scenario simulation. This shift empowers non-technical executives to engage directly with models, fostering a more agile, data-driven culture. 

Agentic AI takes this further, enabling autonomous systems to reason, plan, and execute multi-step workflows. These systems can orchestrate complex decisions, such as assessing trade disruptions or forecasting price volatility, while adapting to new information in real time. 

The interconnectedness imperative  

The interconnectedness of the energy sector demands a shift in how decisions are made. AI enables cross-sectoral analysis, helping companies understand how developments in one part of the system affect others. It supports more agile, responsive planning – critical in a world where energy demand is increasingly shaped by digital infrastructure, electrification, and climate volatility. 

The energy world has evolved from predictable, linear patterns to complex, interconnected systems. This transformation requires new analytical approaches that can handle unprecedented complexity. Traditional forecasting methods struggle with today’s dynamic environment where multiple variables interact simultaneously. 

A trifactor approach combining trusted, real-world data and AI-augmented decision-making capabilities enables decision makers to respond instantly – whether reallocating investment, adjusting procurement, or rebalancing portfolios. The approach allows continuous scenario testing, helping companies prepare for multiple futures rather than betting on single trajectories. 

This methodology enables energy leaders to shift from reactive to proactive strategies. Companies can anticipate change rather than face unexpected disruptions that threaten operations. The ability to see emerging patterns early provides competitive advantages in volatile markets. 

As the energy system grows more complex, three capabilities will define success: seeing the complete picture, responding instantly, and adapting continuously. Those who master this interconnected journey will help shape the future of energy. Those who don’t, risk being left behind.