Europe can no longer afford to treat energy transition as purely a sustainability challenge. The geopolitical upheaval triggered by Russia's invasion of Ukraine fundamentally altered the continent's energy equation, forcing policymakers and industry leaders to balance three competing demands simultaneously: decarbonization, affordability, and security. This shift has elevated energy efficiency from a secondary concern to a strategic priority alongside renewable energy expansion.

How Has the Ukraine War Changed Europe's Energy Strategy?

Before Russia's invasion, Europe could rely on cheap, relatively secure gas supplies, making energy security a low-priority concern. That assumption collapsed overnight. Gas availability became uncertain, and prices skyrocketed when supplies remained available. This forced a fundamental rethinking of how Europe approaches its energy future. The challenge now is building an energy system that is simultaneously cleaner, affordable, and secure, without undermining the competitiveness of European industry.

"Before the Russia-Ukraine war, the security of gas coming in and the cost of using it was so low; the risk was just not there. Now, it may not be available, and if it is available, it's at a high cost," said Anant Maheshwari, president of strategy and global regions at Honeywell Technologies.

Anant Maheshwari, President of Strategy and Global Regions, Honeywell Technologies

This new reality has forced Europe to ask a different question: rather than simply asking how much additional power the continent can generate, policymakers and business leaders are now asking how to make more intelligent use of existing infrastructure. The answer increasingly points to artificial intelligence and advanced automation as tools for squeezing more efficiency out of current systems.

What Can Europe Learn from China and Brazil's Energy Approaches?

While Europe grapples with its energy security challenge, other regions have already begun adapting. China, facing its own energy security constraints and lacking control over many traditional energy sources beyond coal, has pursued a diversified strategy. The country has invested rapidly across a broad mix of technologies, from solar and wind to biofuels, sustainable aviation fuel, and carbon capture, while simultaneously prioritizing energy efficiency improvements.

What distinguishes China's approach, according to Maheshwari, is not simply the scale of investment but the breadth and speed of deployment. The country maintains the widest range of technology applications at pace and scale, treating energy security and economics as inseparable from sustainability goals.

Brazil offers a different model worth studying. The country has emerged as a frontrunner in biofuels, leveraging its agricultural resources and experience with crops including sugarcane and macaúba, a native palm tree specifically grown for fuel production. This approach creates renewable fuels for sectors where electrification remains difficult, such as aviation. The opportunity lies in developing fully renewable fuel sources that can be grown and converted into jet fuel without relying on fossil fuels.

How Can AI Make Buildings and Factories More Efficient?

Europe possesses significant advantages in addressing its energy challenge: world-class companies capable of building and deploying new technologies at scale, strong research and development capabilities, deep engineering expertise, and a large, wealthy market for commercialization. The continent also has an established industrial base and sophisticated energy infrastructure that could accelerate adoption of new technologies. The critical question is whether Europe can deploy these capabilities quickly enough and at an economic cost that keeps European businesses competitive.

Artificial intelligence is emerging as a key tool for unlocking efficiency gains across existing infrastructure. Conventional building control systems operate according to programmed rules: if temperature changes, adjust heating or cooling accordingly. AI transforms this into a system that learns from changing conditions, including occupancy patterns, and adjusts energy use dynamically, cooling or heating only the parts of a building that actually need it.

A practical example illustrates the potential. A global fast food restaurant chain initially had no central visibility of energy usage across its 600 United Kingdom stores. Once the stores were connected to a central monitoring system, significant variations in consumption became visible, with some locations using 30 to 40 percent more energy than others. This visibility created immediate opportunities to reduce energy waste.

Steps to Implement AI-Driven Energy Efficiency in Industrial Settings

• Establish Central Monitoring Systems: Connect dispersed facilities to a unified platform that provides real-time visibility into energy consumption patterns across all locations, enabling identification of inefficiencies and anomalies.
• Integrate Legacy Equipment Carefully: Work with vendors to connect new AI systems to existing industrial equipment that may remain operational for decades, ensuring compatibility without requiring complete infrastructure replacement.
• Automate Routine Maintenance Tasks: Use AI to remotely test and monitor equipment like fire detectors and safety systems, identifying which devices require physical inspection and reducing manual labor while maintaining human oversight.
• Deploy Occupancy-Aware Climate Control: Implement AI systems that learn building usage patterns and adjust heating, cooling, and lighting in real time based on actual occupancy rather than fixed schedules.

However, applying AI to the physical economy is more complicated than applying it to information technology systems. Industrial systems can remain in operation for decades, requiring companies to integrate new technology with legacy equipment. The data generated by those systems is often difficult to access because it sits within proprietary systems designed to control physical equipment rather than share information.

"Operating technology has to connect data to physical processes. That creates a particular challenge for AI because companies have to introduce new technology while maintaining and integrating legacy equipment," explained Maheshwari.

Anant Maheshwari, President of Strategy and Global Regions, Honeywell Technologies

Maheshwari describes this broader opportunity as "physical AI," though he acknowledges the term lacks a settled definition. In some parts of the world, it is synonymous with robotics and drones. For Honeywell, the concept is broader: using AI to make buildings, factories, assets, and supply chains more efficient and increasingly autonomous. One application is safety automation, where AI systems can remotely test multiple fire detectors and identify those requiring physical inspection, turning days of manual work into a much faster process while maintaining human oversight.

Why Is Energy Efficiency Now as Important as Renewable Energy Expansion?

Europe currently produces 48 percent of its energy from renewable sources and 28 percent from nuclear power, according to official European Union data. Despite this substantial clean energy base, Maheshwari argues that Europe needs to make better use of all parts of its energy mix by expanding renewables and nuclear capacity while making fossil fuel generation cleaner through carbon capture and emissions reduction technologies.

But efficiency improvements may offer a faster and cheaper route to meeting energy demand than building new generation capacity. Rather than asking how much additional power Europe can generate, the more practical question becomes how to extract more value from existing infrastructure. This shift in thinking reflects a maturation of energy strategy, moving beyond the assumption that sustainability and efficiency are separate concerns.

"Europe needs to make sure that regulation helps industry to accelerate, rather than decelerate," noted Maheshwari.

Anant Maheshwari, President of Strategy and Global Regions, Honeywell Technologies

The implications extend beyond individual buildings or factories. As AI makes it possible to use existing infrastructure more efficiently, the conversation shifts from how much more power the world can generate to how to make more intelligent use of what already exists. Maheshwari returns to this core argument repeatedly: the world will need more energy than it currently uses and produces, but the critical question is whether that energy will be consumed as inefficiently as it is today.

For Europe, this represents both a challenge and an opportunity. The continent's energy security crisis, triggered by geopolitical upheaval, has forced a reckoning with the true cost of inefficiency. By embracing AI-driven efficiency improvements alongside renewable energy expansion and nuclear investment, Europe may yet build an energy system that is simultaneously cleaner, more affordable, and more secure than the one it relied upon before Russia's invasion of Ukraine.