The future of energy starts now
- By Frédéric Godemel
- 31 Mar 2026
- 5 min read
- 3.7%Rise of global electricity demand in 2026
- 45%Share of global electricity generation by renewables by 2030
- 9 billionWorld’s population forecast by 2037
The energy sector is transforming at an unprecedented scale, driven by structural forces that are reshaping how we produce and consume power.
Global electricity demand is expected to rise by around 3.3% in 2025 and 3.7% in 2026, more than twice as fast as total energy demand growth, reflecting accelerating electrification and digitalization. At the same time, the transition to low-carbon energy sources making progress renewables are expected to provide nearly 45% of global electricity generation by 2030, up from 28% in 2022.
Underlying all of this is fundamental demographic change, with the world's population forecast to increase from 8 billion today to more than 9 billion by 2037, driving higher demand for electricity services worldwide. But it’s not just population growth that matters. All those evolutions are changing the way we produce and consume energy.
As electricity becomes increasingly integral to everyday life and industries worldwide, from heating and transport to data center and digital services, more advanced energy management systems are needed.
Electricity generation is becoming more decentralized. Demand is rising due to the electrification of heating, cooling, electric vehicle charging, and data center operations. This shift calls for a fundamental rethinking of how energy is managed, supplied, and consumed. AI plays an key role in this energy transition, unlocking the potential to manage the energy supply and demand more efficiently than ever before.
AI excels at processing large datasets, enabling predictions about energy consumption and optimizing energy use in real-time. Renewable energy sources such as solar and wind, which are inherently variable, introduce challenges in balancing energy supply and demand.
As output varies from these sources depending on the weather, their operations can change rapidly. AI tackles these complexities by analyzing data, forecasting usage patterns, and scheduling energy usage during periods of renewable energy abundance.
The push toward decarbonization highlights the critical role of buildings, which account for roughly 37% of global CO2 emissions. Efficiency improvements in this sector should be looked at with keen interest. Electrification and digitalization are capable of delivering 30-40% energy savings. AI will be a crucial tool to supporting this decarbonization too.
The need for change extends beyond buildings. Nearly 94% of energy-related CO2 emissions originate from just three sectors: buildings, industry, and transport. By deploying AI to manage energy consumption, each of these sectors can significantly reduce their carbon footprints.
But those sectors can’t make those changes on their own. Modernizing energy grids is also critical. Many legacy systems are ill-equipped to handle the increasing demand brought by electrification and digitalization.
While infrastructure upgrades are necessary, optimizing energy demand through AI solutions often provides greater and more immediate savings. This dual approach of grid reinforcement and demand optimization is essential to building a resilient, future-ready energy system.
Traditionally, energy management has been the domain of engineers, requiring advanced technical expertise and time-intensive calculations. Today, AI simplifies this process and intuitive AI-based tools enable operators and homeowners to monitor energy usage, detect inefficiencies, and identify potential issues without extensive technical knowledge.
This ability to reduce complexity and turn data into actionable insight is not limited to homes and buildings, it is equally critical at the grid level.
This is achieved through processing vast volumes of operational data, filtering alarm overload, ensuring that urgent events receive immediate attention while less critical issues are addressed efficiently. This not only enhances operational performance and response times, but also helps operators manage increasingly complex, decentralized energy systems more effectively.
In addition, AI provides actionable insights and user-friendly tools that help the next generation of operators manage increasingly complex systems with confidence and ease.
One of the most promising applications of AI lies in microgrid management. Microgrids are localized energy networks that integrate renewable energy sources like solar or wind with battery storage and grid connections. These systems offer enhanced resilience, efficiency, and sustainability by dynamically managing energy generation and consumption.
We’ve successfully implemented AI-powered microgrids at our facilities, including our Boston R&D hub. Using our EcoStruxure Microgrid Advisor, this system balances solar generation, battery storage, and grid supply to optimize energy use. The platform can autonomously manage flexible loads like electric vehicle chargers and HVAC systems, while ensuring uninterrupted power. By layering AI algorithms onto operational data, the EcoStruxure Microgrid Advisor provides actionable insights that help facility managers identify savings opportunities and improve efficiency.
These systems typically deliver 20-30% savings on energy bills, with payback periods of about seven years, however this can be even faster in regions with high electricity prices. Over time, the recurring cost and environmental benefits create lasting value, enhancing both competitiveness and sustainability.
Additionally, AI-driven microgrid solutions are scalable, capable of managing multiple facilities and adapting to diverse industries. Rather than replacing human decision-making, these systems provide recommendations, leaving ultimate control in the hands of users.
This approach ensures that AI serves as a trusted advisor, empowering businesses to achieve their energy and sustainability goals.
While AI brings transformative efficiencies to energy management, it also comes with its own energy demands, particularly in data centers. AI-driven operations in these facilities currently account for 1.5-3% of global electricity use, a figure that is expected to grow as AI adoption expands.
To address this, we work collaboratively with technology leaders like NVIDIA to design energy-efficient data centers and infrastructure systems. By prioritizing performance-per-watt efficiency, these partnerships aim to mitigate the rising energy consumption of AI while maintaining reliability and scalability.
Despite its energy footprint, the net impact of AI on energy management remains highly favorable. By enabling more efficient energy use across sectors, AI offsets its own consumption many times over.
However, achieving this balance requires a comprehensive approach that includes grid reinforcement, increased renewable adoption, and advanced AI deployment for demand-side optimization.
Looking to the future, we see immense potential in deepening AI integration to drive efficiency and resilience across data centers, buildings, and other critical sectors.
Advances in AI-driven cooling and heat extraction technologies will play a vital role, particularly as data centers face increasing computational loads. As energy management grows more complex, the ability to simplify operations while enhancing performance becomes even more critical.
Achieving this vision demands a unified, intelligent ecosystem that eliminates data silos, ensuring seamless collaboration between energy systems, digital tools, and human decision-makers.
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