Ai's sustainability promise: a footprint to watch?
Sustainability teams are drowning in data, juggling reporting, compliance, and the thorny task of responsible sourcing. Even at brands boasting multi-million dollar budgets, these teams are often stretched thin, spending more time wrestling with spreadsheets than crafting impactful sustainability strategies. Enter AI – a seemingly miraculous solution promising to alleviate the burden and streamline operations. But is the technology itself adding to the environmental challenge it aims to solve?
The allure of automated sustainability
The potential is undeniable. Industry insiders suggest AI can automate environmental reporting, drastically improve data quality, and even verify supply chain traceability. Annie Agle, VP of Impact and Sustainability at Cotopaxi, notes the shift, stating, “It’s certainly having a lot of positive outcomes around helping sustainability teams [carry out] reporting, allowing them to focus much more on programming than compliance.” This newfound efficiency extends to the supply chain itself, with AI optimizing material use and demand planning.
The unseen digital footprint
But there's a significant catch. The true environmental impact of organizational AI usage remains largely undefined. Brands risk inadvertently increasing their footprint while relying on AI to reduce it. Megan Doyle’s recent piece in Generative AI highlighted the energy-intensive nature of generative AI, warning against uncritical adoption. As Agle cautions, “The upsides are very apparent, but we also know there are negative impacts that are not understood yet. We don’t know the implications of that digital footprint on our GHG [greenhouse gas] measurement.”
How brands are embracing – and wrestling with – ai
Vogue Business recently spoke with brands across various sizes and market segments to understand their AI integration strategies. H&M, for instance, has woven AI into its supply chain, logistics, marketing, sales, and customer experience, aiming to produce only what it sells through optimized production and sales forecasting. Luxury group Kering, leveraging AI to forecast demand, optimize inventory, and automate reporting, has appointed Pierre Houlès as its chief digital, AI, and IT officer. Everlane, while still in the exploratory phase, focuses on using AI to support internal processes and lighten administrative loads.
Beyond efficiency: creativity and customer service
Spanish retailer Mango has taken a more expansive approach, developing 15 internal machine learning platforms since 2018. Their AI-powered assistant, Iris, handles over 7.5 million customer inquiries annually, and their Gaudi tool generates personalized product recommendations. Jordi Alex, Mango’s chief information technology officer, highlights the use of generative AI for campaign and collection imagery. This broader adoption underscores the urgency for brands to analyze the underlying environmental impacts.
The energy cost of innovation
The growth of AI demands significant energy and water resources, particularly for cooling data centers. A 2025 Nature paper estimates that AI servers in the US alone could generate 24-44 million metric tons of CO2 annually. The International Energy Agency predicts AI workloads will account for half of all data center capacity by 2030 – a stark illustration of its growing energy needs. Anthropic, a US AI company, has even acknowledged the potential for increased energy prices due to its technology’s demands.
Towards measurement and mitigation
Kering, uniquely among those contacted, provides an outline of its approach: prioritizing resource-efficient models and partnering with technology providers to support decarbonization. However, until standardized metrics are established, brands are left to their own devices. As Agle puts it, Cotopaxi will be undertaking “back of napkin math” to understand its impact, starting with tracking AI usage and quantifying associated emissions.
A path forward: efficiency and responsibility
Literal Labs is pioneering a different approach, developing energy-efficient AI models that don’t require specialized hardware. DeepGate is similarly focused on targeted AI applications that minimize energy consumption. The challenge is clear: brands must move beyond simply adopting AI and actively assess and mitigate its environmental consequences. As Lectra’s deputy CEO, Maximilien Abadie, aptly states, “It’s going to be very complicated to understand the impacts of AI. If it creates more negative impact, then it’s not worth investing in it.”