AI’s Green Revolution: Cutting Costs and Carbon in Energy

The global energy landscape is under immense pressure. According to a recent forecast by the International Energy Agency (IEA), global electricity demand is expected to grow by 3.4% annually through 2026, driven by electrification and digitalisation.

Meanwhile, many of the world’s electricity distribution networks are aging and is struggling to keep up, necessitating significant upgrades to meet current and future energy demands. A separate recent study by IEA found that the world must add or replace 80 million km of grids by 2040, equal to all grids globally today, to meet national climate targets and support energy security

Michael Ding, Global Executive Director, Univers

As demand rises and grids strain under the weight of electrification and climate-related disruptions, the question isn’t whether change is needed, but how fast we can transition.

AI and IoT are emerging as powerful accelerators of this energy transition. No longer experimental, they’re already helping companies cut energy costs, increase resilience, and reduce carbon emissions — all while supporting business continuity.

Here are three ways AI is already transforming how we generate, manage, and consume energy:

1. Smarter Renewables: From Intermittent to Intelligent

AI is removing one of the biggest barriers to renewable adoption — unpredictability. By analysing weather data, forecasting output, and orchestrating storage, AI helps maximise the use of solar and wind energy.

According to the International Renewable Energy Agency (IRENA), integrating AI into renewable forecasting can increase forecast accuracy by up to 30%, reducing balancing costs and making renewables more competitive and reliable

This intelligence also helps grid operators and energy traders better manage supply-demand balance, reduce curtailment, and lower reliance on fossil fuel backup. For instance, a Fortune Global 500 energy operator used AI to manage a vast portfolio of renewable energy assets spread across multiple geographies. By connecting disparate systems and applying predictive analytics, they improved energy output, reduced downtime, and streamlined participation in energy markets. The result: an estimated 8–10x return on investment, driven by improved asset performance, operational efficiency, and smarter trading strategies.

AI is not just enabling more renewables — it’s making them smarter, cheaper, and easier to scale.

2. AI for Energy Efficiency at Scale

Beyond generation, AI is transforming how energy is consumed. From homes to commercial buildings, AI-driven controls help optimise HVAC, lighting, and equipment usage in real time, — reducing energy waste and cutting costs without compromising comfort or operations.

Retailers, logistics companies, and manufacturers are already seeing results. One leading European insurer reduced energy consumption by 36% within a month by using AI to manage energy across its property portfolio.

Meanwhile, a global commercial property group—managing mixed-use buildings—saw a 16% reduction in energy use, achieving full payback in under four months.

3. AI-Orchestrated Microgrids for Energy Independence

Central grids can no longer guarantee consistent power — especially during extreme weather or peak demand periods. Microgrid give organisations the ability to integrate distributed energy sources (like solar and battery storage), operate autonomously, and reduce dependence on the central grid.

In Europe, microgrids have helped with alleviating grid congestion and improving energy reliability. For businesses, this means fewer disruptions, lower energy costs, and a stronger foundation for sustainability.

A European supermarket chain, facing growing grid congestion is leveraging Microgrid for energy orchestration. By intelligently orchestrating solar, storage, and flexible loads, they reduced energy costs, increased on-site renewables, and ensured operational continuity—even during local power disturbances.

A Smarter Energy Future, Powered by AI and IoT

As climate goals tighten, infrastructure ages, and power demands surge, AI offers something rare: scalability, speed, and tangible results. It is already proving itself across the value chain, from stabilising renewables and boosting efficiency to enabling grid independence through intelligent microgrids.

But technology alone won’t drive the energy transition. What matters now is execution and urgency. Leaders in energy-intensive sectors must move beyond pilots and actively embed AI-driven solutions into core operations. Governments and regulators also have a role to play in accelerating digital grid upgrades and incentivising smart infrastructure.

The energy landscape is being redrawn in real time. Those who embrace AI as a foundational enabler will be best positioned to deliver on climate goals, maintain resilience, and create long-term value.