Capgemini: How Gen AI Drives Rise in Corporate Emissions
The adoption of Gen AI across global enterprises has created an unexpected environmental challenge: rising energy consumption in corporate data centres.
As organisations rush to implement AI technologies that can generate text, images and code, the environmental cost of these systems has begun to become a significant concern for sustainability targets.
This tension between technological advancement and environmental responsibility comes as businesses face increasing pressure to reduce their carbon footprint while remaining competitive in the AI game.
The technology's energy demands stem from the massive computational power required to train and operate AI models, with implications for both electricity consumption and water usage.
These environmental implications have prompted new scrutiny of AI implementation strategies, according to research from Capgemini’s 2025 report: “Developing Sustainable Gen AI,” that reveals that 48% of executives believe their use of Gen AI has increased greenhouse gas emissions.
Data centres strain power infrastructure as AI adoption grows
The computational demands of Gen AI systems require substantial energy resources. For instance, industry forecasts suggest AI development will increase data centre power consumption by 15% to 20% annually, reaching between 100GWh to 130 GWh hours by 2030 - equivalent to powering two-thirds of US households.
Additionally, graphics processing units (GPUs) require rare earth metals for manufacturing and the mining process for these materials generates additional emissions and depletes natural resources.
As a result, current projections indicate Gen AI could generate between 1.2 to five million tonnes of electronic waste by 2030.
Training LLMs increases environmental impact
The environmental cost of developing AI models continues to grow.
Training GPT-3, an AI model with 175 billion parameters, consumes electricity equivalent to 130 US households annually and its successor, GPT-4, requires power comparable to 5,000 US homes per year.
Meanwhile, water consumption presents an additional environmental challenge and each set of 20-50 queries to a LLMs requires approximately 500ml of water for cooling systems.
Steven Webb, UK Chief Technology and Innovation Officer at Capgemini, says: "Organisations need to make the footprint of Gen AI visible within their business analysis.
Its vital businesses fully track the impact of Gen AI as this enables strategies to measure and mitigate appropriately, as well as unlocking opportunities to implement sustainable practices throughout the Gen AI lifecycle."
Industry examples highlight environmental applications
Companies have begun implementing AI solutions focused on environmental objectives.
For instance, in Germany, Compliance Solutions has developed an ESG AI-Agent that automates environmental, social and governance research, evaluation and reporting processes.
Steven identifies broader opportunities for positive impact: "Gen AI is already being used to support sustainability in enterprise. With its ability to transform the workforce by automating tasks and complex processes, Gen AI in the form of AI agents can play a pivotal role in optimising use of resources and improving efficiency at all levels, which is key to accelerating sustainability."
Need for industry standards emerges
The increasing environmental impact of AI deployment has prompted calls for standardised measurement and reporting frameworks.
Steven Webb, UK Chief Technology and Innovation Officer at Capgemini, says: "Organisations need to make the footprint of Gen AI visible within their business analysis.
"Its vital businesses fully track the impact of Gen AI as this enables strategies to measure and mitigate appropriately, as well as unlocking opportunities to implement sustainable practices throughout the Gen AI lifecycle."
Industry examples highlight environmental applications
Companies have begun implementing AI solutions focused on environmental objectives.
For instance, in Germany, Compliance Solutions has developed an ESG AI-Agent that automates environmental, social and governance research, evaluation and reporting processes.
Steven identifies broader opportunities for positive impact: "Gen AI is already being used to support sustainability in enterprise. With its ability to transform the workforce by automating tasks and complex processes, Gen AI in the form of AI agents can play a pivotal role in optimising use of resources and improving efficiency at all levels, which is key to accelerating sustainability."
Need for industry standards emerges
The increasing environmental impact of AI deployment has prompted calls for standardised measurement and reporting frameworks.
Cyril Garcia, Capgemini's Head of Global Sustainability Services and Corporate Responsibility and Group Executive Board Member, says: "If we want Gen AI to be a force for sustainable business value, there needs to be a market discussion around data collaboration, drawing up industry-wide standards around how we account for the environmental footprint of AI, so business leaders are equipped to make more informed, responsible business decisions and mitigate these impacts."
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