Mitigating the environmental risks of AI through better monitoring
Capgemini ·
While most companies have rushed to deploy AI at scale in their operations, few can claim to know AI’s impact on their sustainability numbers. The post Mitigating the environmental risks of AI through better monitoring appeared first on Capgemini .
Companies have long been struggling to accurately assess and successfully minimize their environmental impact due to a lack of standardization. Even the most commonly used reporting frameworks – including the Global Reporting Initiative, the Corporate Sustainability Reporting Directive, and the Science Based Targets initiative – use different scopes and requirements.
AI makes it harder still, particularly with the complexity of the value chain. Most companies frequently lack visibility on the real environmental impact of their AI solutions, resulting in inconsistent, uncertain, or missing metrics. As AI adoption is skyrocketing and AI models are evolving very quickly, businesses face the long-term challenge of forecasting AI’s future environmental impact. This means that companies are not fully prepared to comply with potential new regulations requiring sustainability indicators for AI use.
Building an AI-forward measurement plan
Today, AI’s share of most companies’ emissions is small, sometimes as little as 0.01% of total greenhouse gas emissions. That will not last with the currently ever-increasing usage trend. Roadmap assessments show why : AI could account for 60% of emissions within three years for some industries . The current numbers understate the risk.
An AI measurement plan builds on the same components as any environmental impact framework: set targets, track them, and build a strategy to hit them. The difference is speed. Real-time visibility into AI-linked emissions lets companies act before the footprint grows, not after.
Building a measurement framework from scratch, or overhauling an existing sustainability strategy, is complex and slow. Few organizations have the in-house expertise to do it alone.
A partner with the right expertise can close that gap: portfolio-level AI workload analysis, scenario-based projections, structured methodologies for evaluating impact use case by use case, actionable remediation measures, and employee training.
At Capgemini, we help clients see how their AI use is affecting their environmental goals — and how to manage and reduce that impact. We look at AI’s full value chain: component development, energy generation, and operational use.
Capgemini has been named a Leader in the IDC MarketScape: Worldwide Sustainability Strategy Services 2026 Vendor Assessment.
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We see this as validation of our approach: helping clients improve operational efficiency, reduce emissions, and turn sustainability into a driver of growth and competitiveness, while using AI responsibly and delivering measurable impact.
To learn more about the challenges of scaling sustainable AI, read the scientific papers (contributions by the blog authors):
Capgemini Research Institute A world in balance 2026: The resilience reset Point of view Rightsizing LLMs: the pathway to more sustainable AI Capgemini Research Institute Sustainable Gen AI Authors Philippe Cordier Global Chief AI Scientist, Sustainable AI, Capgemini Invent Philippe brings over 20 years of experience driving innovation and transformation programs across the energy sector. His expertise spans the whole energy value chain — oil and gas, renewables, hydrogen and electricity retail. Obsessed with value creation, he leverages his experience in industrial operations and large-scale AI transformation programs to help organizations navigate the frantic technology landscape and accelerate their transformation. His global industry and technological perspective gives him a pragmatic yet strategic approach to problem solving to ensure global scaling. Martin Chauvin Manager Consultant Data Scientist, Capgemini Invent Recognized for his expertise in the environmental impact of AI, Martin has delivered more than a dozen data science projects spanning AI footprint assessment, satellite imagery, and carbon measurement, and has co-authored scientific publications on evaluating the environmental footprint of AI — both at corporate scale and for individual models. Combining deep data science expertise with a focus on sustainability, Martin helps organizations understand and reduce the environmental cost of their AI systems as they scale
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