97% of Companies Fail to Measure AI’s Energy Impact, New Global Analysis Reveals

New analysis by the Thomson Reuters Foundation shows 97% of companies fail to measure AI’s environmental impact. Studying 1,000 firms across 13 sectors, the AI Company Data Initiative finds governance lags adoption, with energy use largely ignored. Experts say optimisation-focused AI, especially in building systems, can cut consumption and costs if measured properly and embedded within day-to-day operational oversight frameworks.

A new global analysis shows that while artificial intelligence is rapidly being adopted across industries, most companies still fail to measure how their AI systems affect energy use and the environment. Experts warn that this gap is not only a governance issue but also a missed opportunity to reduce operational costs and emissions, particularly in sectors with large physical assets such as commercial real estate.

On 12 December 2025, the Thomson Reuters Foundation publishes the first findings from its AI Company Data Initiative (AICDI). Developed in collaboration with UNESCO, the initiative analyses AI governance disclosures from 1,000 companies across 13 sectors, making it the largest dataset of its kind to date.

The findings highlight a clear imbalance between AI adoption and oversight. Companies are deploying AI systems at speed, but governance frameworks are lagging behind, particularly when it comes to understanding operational risks and real-world impacts.

Environmental impact remains largely unmeasured

One of the most striking conclusions of the analysis is the lack of attention paid to environmental factors. According to AICDI, 97% of companies do not assess the environmental impact of their AI systems, including energy consumption and carbon emissions.

While many firms describe their AI initiatives as “ethical”, “trustworthy”, or “secure”, the data shows that very few link AI deployment to changes in electricity demand, emissions, or climate commitments. As a result, AI governance often remains focused on policy documents rather than measurable operational outcomes.

Energy efficiency as a missed opportunity

Energy efficiency experts argue that this oversight reflects a narrow view of AI’s role. According to Exergio, organisations should evaluate AI tools not only for governance and compliance risks, but also for how they affect energy performance in daily operations.

“Many people assume AI will always waste energy, so they never stop to ask about its environmental impact. But that’s not true. There are tools where AI does the opposite – it cuts consumption. Advanced building management systems, for example, use AI to lower heating and cooling demand instead of raising it,” explained Donatas Karčiauskas, CEO of Exergio.

He notes that the governance gap is especially visible in sectors managing large physical infrastructure. In commercial buildings, poorly governed AI can result not only in compliance risks but also in significant, unmeasured energy waste.

Governance gaps inside organisations

Beyond environmental issues, the report also identifies weaknesses in internal AI governance. While 76% of companies with an AI strategy state that AI oversight exists at management level, only 41% make their AI policies accessible to employees or require staff to formally acknowledge them.

According to Karčiauskas, this reflects a deeper problem in how AI impact is assessed.

“The study exposes a governance gap around measurement. If you don’t watch what AI is doing in real time, you’re guessing whether it helps or harms your goals. In buildings, that means knowing when systems switch on, how much power they pull, and what actually changes once AI starts running them. Without that operational data, AI governance is just paperwork,” added Karčiauskas.

Operational visibility in building systems

The impact of measurement becomes clearer when AI is embedded into building management systems. In these environments, AI can smooth demand peaks, prevent boilers and chillers from running longer than needed, and avoid simultaneous system activation that drives unnecessary power use.

Such operational transparency allows organisations to see whether AI reduces or increases energy demand, turning governance from a reporting exercise into a tool for optimisation.

Europe leads on strategy, not on energy transparency

Regionally, the AICDI findings show that companies in EMEA lead in publishing AI strategies, with 53% reporting one. This trend is largely driven by regulatory pressure, including the EU AI Act. However, even in Europe, disclosures rarely include information on AI-related energy use or emissions.

“Europe is ahead on regulation, but even here the energy footprint of AI is mostly absent from the discussion. As the EU AI Act matures, operational transparency – including how much power AI uses and whether it saves any – needs to be part of governance. Otherwise, it’s too easy to sell ‘responsible AI’ on paper while ignoring what happens to real-world energy use,” noted Karčiauskas.

AICDI concludes that most companies are still at an early stage of understanding where AI is used across their operations and how it affects both performance and environmental outcomes. Without clearer measurement and disclosure, organisations risk overlooking both rising energy costs and opportunities for efficiency.

About Exergio

Exergio is a company built on extensive experience in building maintenance. Addressing the limits of conventional approaches, it develops an AI-driven system that analyses live building data and makes strategic decisions to reduce energy waste by up to 29%. The system works with static infrastructure while enabling adaptive responses to environmental conditions, optimising energy consumption without compromising comfort or productivity. Exergio currently operates projects in Poland, the United Kingdom, Ireland, the Czech Republic, Hungary, Oman, Sweden, and Lithuania, with planned expansion into Germany and France.