To reach sustainable AI, businesses must speak a common language
Capgemini ·
As AI models become more complex, their environmental, resource, and economic impacts are growing. Organizations are aiming to meet the moment with standardized frameworks and coalition-building. The post To reach sustainable AI, businesses must speak a common language appeared first on Capgemini .
Generative AI has rapidly shown the ability to improve business operations, delivering greater productivity and performance. But as companies reap the benefits of these new tools, many are confronting another reality: AI’s steep environmental cost.
Powering AI at scale requires an enormous amount of electricity, with large Gen AI models consuming up to 4,600 times more energy than traditional models. And the demand for more power and AI compute is only rising. In a high-adoption scenario with many companies deploying complex AI models, such as agentic AI, electricity use is projected to rise by a factor of 24.4. 1 This would lead to higher carbon emissions, strains on resources like water, and negative community impacts. Temperatures in neighborhoods around data centers are generally higher by 2°C than equivalent neighborhoods without this infrastructure.
For businesses adopting AI, this prompts a key question: how can I deploy AI at scale, while mitigating its environmental impact?
Companies must seek to align on AI across their internal teams, as perspectives can vary widely across company functions and AI-related decisions are often made in silos. Consequently, organizations may optimize their AI use for certain objectives, while unintentionally making it harder to achieve others.
To scale AI sustainably, organizations need clear communication and a common shared framework based on standardized metrics covering environmental, resource, and economic impacts and objectives. Creating this shared language enables effective decision-making across all aspects of a business.
Take transport and logistics. AI is being increasingly used in this sector to optimize operations, but its value depends on where, when, and how it is deployed, as competing priorities must be balanced. Business teams may focus on reducing delivery times and lowering fuels costs, while sustainability teams want to cut emissions, and technology teams prioritize model performance. To optimize these different objectives, a common measurement framework can help quantify value by considering costs, resource use, environmental impact, and increased efficiency.
While action at company level is vital to managing the relationship between business value, resource consumption, cost, and environmental impact, industry-wide standards are also critical. Isolated measures that address hardware efficiency, model efficiency, or grid improvements alone cannot mitigate Gen AI’s impact. Achieving this will require coordination across the AI value chain, from power generation to operational efficiency, to the product lifecycle.
Governments and businesses are already stepping in to minimize AI impact and align AI development with net-zero goals. The International Telecommunication Union (ITU) recently published guidelines for assessing the environmental impact of AI systems (ITU-T L.1801) and other efforts are underway. One French-led initiative, the Coalition for Sustainable AI, brings together the UN Environment Programme, ITU, Capgemini, and many other companies and organizations to address AI’s impact. Coalition members are committed to reaching the UN Sustainable Development Goals and UN Agenda 30, leveraging AI tools to support climate action and environmental protection.
Capgemini Research Institute A world in balance 2026: The resilience reset Capgemini Research Institute Sustainable Gen AI Capgemini Research Institute AI meets the grid: Shaping the data center power play Point of view Rightsizing LLMs: the pathway to more sustainable AI Authors Jérôme Coignard CTO, frog France, part of Capgemini Invent Jérôme Coignard is CTO of frog France, leading a team of creative technologists that turn ideas into scalable solutions through technology, data, and AI. With over 25 years of experience, he helps organizations drive innovation, reinvent business models, and unlock growth through emerging technologies. He is also a strong advocate for Sustainable AI, helping organizations scale AI responsibly while balancing innovation with sustainability. Shalini Rajeev Director, Sustainability CoE, Capgemini Shalini leads the Sustainable Gen AI stream within the Sustainability CoE, driving innovation and sustainable business value for clients. With over 24 years of experience spanning strategic consulting, delivery leadership, pre-sales and solutioning, sustainability, and generative AI, she helps organizations accelerate transformation and maximize value from emerging technologies. Vincent de Montalivet Senior Director – Sustainability Transformation, Data & AI Portfolio, Capgemini Vincent leads the Global Sustainability Practice for Data & AI Portfolio along with North America Sustainability GTM. With a background in Sustainability Strategy, Engineering and Architecture, Vincent has been instrumental in driving sustainability digitalization efforts and transformation programs within major organizations across industries while ensuring tangible business outcomes. The post To reach sustainable AI, businesses must speak a common language appeared first on Capgemini .