Organizations today must deal with the dual pressure of digital acceleration and climate responsibility to remain competitive.
But this poses a dilemma: Generative artificial intelligenceone of the emerging technologies with the greatest potential to drive digital transformation, has a significant impact on the environment.
The perception that generation AI can produce a multitude of business benefits has rapidly expanded its use. But this technological optimism is tempered by the realization that the current trajectory could exacerbate the climate crisis and further strain our natural resources.
AI generation, which can produce new content based on patterns in existing data, requires considerable energy and water consumption. This technology cannot work without the production of graphics processing units – fueling both the extraction of rare earth metals and greenhouse gas (GHG) emissions.
Capgemini believes that embracing generation AI and protecting the environment are not mutually exclusive. However, we recognize this dilemma and aspire to face it head on – and we encourage others to do the same.
Deploying AI-powered solutions in a way that optimizes an organization’s energy consumption while mitigating its own impact will require careful planning and adoption of ethical frameworks.
Approached intentionallyAI generation integration can boost eco-friendly business practices – what we call “sustainability at the speed of AI”.
According to an analysis of the software engineering website Baeldungtraining
ChatGPT-5 is expected to consume 3,500 megawatt hours, enough energy to power approximately 320 average American households one year. Simultaneously, the global AI market is expected to reach $4.8 trillion by 2033
– compared to 189 billion dollars in 2023.
The relentless demand for large-scale deployment of LLMs poses a significant challenge for organizations with sustainability commitments.
THE Capgemini Research Institute report,
Developing sustainable generation AIexamined the environmental impact of technology and the need to develop sustainable usage practices. The team conducted a survey of 2,000 senior executives from organizations with annual revenues exceeding $1 billion and who have already launched Gen AI initiatives.
Only 12 percent of executives
interviewed
said their organizations measure Gen AI’s environmental footprint, and only 20% rank that footprint among the top five factors when selecting or creating Gen AI models. Nonetheless, 48% say their use of AI generation has increased GHG emissions.
This highlights the urgent need for more responsible approaches to AI development.
Businesses
And
country
not all have met many climate commitments, which often come second to financial priorities. Should we view AI as an opportunity to finally achieve our goals or as another challenge to force us to go even further backwards?
Developing environmentally friendly AI for business requires a two-pronged approach: AI must improve the sustainability of operations, and AI itself must be managed sustainably. This is what it looks like.
Organizations can use Gen AI tools to integrate sustainability across countless functions – for example, scenario modeling
for environmentally friendly decision-making, ESG reporting for compliance and transparency, process optimization for efficient use of energy, supplier analysis for Reduction of scope 3 emissions,
help with ecodesign
for the development of cleaner products, etc.
Many of Capgemini’s clients using AI and data for sustainability purposes have reported improved employee retention, reduced emissions, supply chain acceleration, water conservation and cost savings.
Agentic AIwho makes decisions and acts autonomously, can evolve these new capabilities with precision and speed. Integrated into daily operations, AI agents can monitor pollution and optimize purchases while humans provide ethical judgment, strategic direction and creativity.
More and more studies predict that AI will create or increase half of all business decisions
by 2027 – but human monitoring will remain crucial.
But the problem lies in The consequences of AI on our natural resources.
A lack of transparency The information provided by leading LLM providers regarding the impacts of using AI makes it difficult for organizations to integrate this information into their net zero strategies.
Cornell University recently published a peer-reviewed article by our colleagues at Capgemini, who proposed a comprehensive methodology for estimating the environmental impact of a company’s AI portfolio. Their methodology breaks down the impacts of AI solutions at the enterprise level into interconnected models, so that initiatives can be aligned with sustainability goals.
“To minimize environmental risks, all actors in the AI value chain – including hardware manufacturers – must actively contribute to responsible deployment and use,” the authors write. “Success requires greater transparency through information sharing among stakeholders, including environmental impact data and optimization methods. Without a coordinated effort from model providers to end users, environmental impacts will increase significantly.”
To operationalize these strategies, Capgemini has developed a framework for integrating sustainability into every phase of AI development and deployment. This includes diagnostics to assess environmental impact, design principles for sustainable AI, training for data science teams, and governance models to ensure ethical and transparent use.
This approach, which supports rapid prototyping, pilot implementation, and enterprise-wide scaling, is designed to help organizations move from isolated use cases to holistic transformation strategies. These strategies contribute to the broader goal of reducing the impact of business information technologywhich contributes significantly to the global carbon footprint.
Capgemini’s Responsible AI Framework is already helping clients identify areas where AI tools can drive greater efficiency and sustainability:
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A biopharmaceutical company has identified more than 80 use cases to improve digital operations using AI.
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A pet food company used Gen AI to cut product development time in half.
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A mining company used drones and a geographic information system – both powered by AI – to automate land monitoring, saving hundreds of thousands of euros per year.
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In healthcare, one company improved patient support with AI assistants, reducing response times by more than 80%.
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And an aerospace company used AI to optimize manufacturing and forecasting, improving its competitiveness and sustainability.
These examples demonstrate that AI can improve environmental and financial performance when applied responsibly. But it remains essential to monitor the environmental impact of AI-based solutions in each case.
By integrating sustainability into AI and AI into sustainability, businesses can unlock new values, reduce their environmental footprint and take the lead in environmental management.
A strategic business transformation partner can help companies develop a structured approach to identifying opportunities, prioritizing initiatives, developing business cases and evolving solutions that, with diagnostic, design, implementation and governance tools, can help organizations quickly move from concept to value realization.
The convergence of AI and sustainability is reshaping the business landscape in ways that are rife with challenges and opportunities.
Learn more about Capgemini’s vision for develop sustainable Gen AI practicesand please feel free to contact us to continue exploring solutions.
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Christopher Scheefer
Vice President, Global Head of Data and AI for Energy, Utilities and Sustainability
Capgemini
Christopher Scheefer is Global Head of Data and AI for Sustainability at Capgemini, where he helps companies harness data, generative AI and automation to accelerate their sustainability transformation. With decades of experience in energy, utilities and manufacturing, he specializes in designing targeted, AI-driven solutions that reduce carbon impact, improve resilience and unlock new business value.
Published September 4, 2025 at 8:00 a.m. EDT / 5:00 a.m. PDT / 1:00 p.m. BST / 2:00 p.m. CEST