Artificial intelligence governance refers to the policies and oversight structures organizations use to guide how AI systems are selected, deployed, and monitored. New findings from the Thomson Reuters Foundation’s AI Company Data Initiative show that while AI governance disclosures are becoming more common, measurement of AI environmental impact remains rare. For laboratory leaders, this gap has direct implications for compliance planning, facility operations, and sustainability reporting as AI adoption expands across research, analytics, and building systems.
The initiative analyzed AI governance disclosures from 1,000 companies across 13 sectors worldwide. Developed with UNESCO, the dataset examines how organizations describe and oversee their AI use. The analysis found that 97 percent of companies did not assess the environmental impact of their AI systems, including energy use or carbon footprint, despite growing regulatory attention to responsible AI governance.
AI environmental impact remains absent from governance disclosures
The findings highlight a disconnect between AI adoption and operational oversight. Many organizations emphasize ethical principles, security, and accountability in AI governance disclosures, yet few connect AI deployment to electricity demand, emissions, or climate commitments. This omission limits an organization’s ability to evaluate how AI affects day-to-day operations, including compute loads, cooling requirements, and long-term energy costs.
For laboratories, where AI may support image analysis, data processing, predictive maintenance, or automation, failing to document AI environmental impact can complicate procurement decisions and internal reviews. Without measurement, it becomes difficult to validate efficiency claims from vendors or assess how new AI tools change facility energy profiles.
Governance access gaps raise operational concerns
The analysis also identified gaps in how AI governance policies reach employees. While 76 percent of companies with an AI strategy report management-level oversight, only 41 percent make AI policies accessible to employees or require acknowledgment. In lab environments, limited visibility into AI governance disclosures can increase operational risk, particularly when AI tools influence workflows, instrument scheduling, or safety-related systems.
Clear documentation and staff awareness help ensure that AI use aligns with internal controls, data integrity requirements, and compliance expectations. When policies exist only at the management level, laboratories may struggle to demonstrate consistent implementation.
Optimization-focused AI may reduce energy use
The press release also included expert commentary from Exergio, a company developing energy-efficiency tools for real estate, which argued that assumptions that AI will always increase energy use are incomplete. According to Donatas Karčiauskas, CEO of Exergio, some AI applications are designed specifically to reduce consumption.
“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,” Karčiauskas said.
He added, “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.”
What the findings mean for lab managers
Companies in Europe, the Middle East, and Africa lead in publishing AI strategies, with 53 percent reporting one, a trend linked to the EU AI Act. Even so, AI environmental impact remains largely absent from governance disclosures. For lab managers, the findings suggest a need to treat energy use as a measurable operational parameter within responsible AI governance.
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Integrating AI environmental impact checks into procurement reviews, facility change control, and management review processes can strengthen documentation and decision-making. As AI governance disclosures evolve, laboratories that track how AI affects energy use and operations may be better positioned to meet compliance expectations and sustainability goals.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
