Smarter Systems, Deeper Costs: AI’s Impact on Fashion’s Sustainability Journey
Artificial intelligence is reshaping fashion. Algorithms now predict trends, design collections, and can even determine what ends up in stores. Chatbots advise consumers, while data models optimise global supply chains. What once sounded futuristic has quietly become business as usual. AI is being hailed as a tool for sustainability, promising to reduce waste and make supply chains more efficient. Yet beneath the optimism lies a more complex truth. Can technology truly make fashion more responsible, or will it amplify the same patterns of overproduction it claims to solve?
AI’s most transformative potential lies in its ability to make fashion systems leaner and smarter. Across design, manufacturing, and logistics, data-driven decision-making can minimise errors, improve forecasting, and ultimately reduce waste. Studies show that AI can help brands anticipate demand, optimise raw material use, and refine logistics in ways that reduce both costs and emissions.
In practice, many companies have already adopted such tools. Some use advanced analytics to monitor inventory and cut overproduction, while others apply predictive models to improve material sourcing and production efficiency, helping reduce surplus fabric and transport emissions. Machine learning systems are also being deployed to forecast demand and reduce stock imbalances, and experimental solutions are testing how AI can repurpose textile waste into new fabrics and circular design applications.
Other innovators are introducing AI-led fit platforms that enable on-demand manufacturing and reduce the waste caused by returns and unsold stock. At the same time, predictive AI tools are enabling brands to merge consumer, production and logistics data into more responsive, resource-efficient systems, boosting business performance and supporting sustainability goals. While efficiency can accelerate sustainability, it can also accelerate consumption. Without deliberate oversight, AI may end up optimising systems still driven by speed and volume. If sustainability goals are not clearly defined, the same tools that reduce waste can also deepen overproduction.
AI’s environmental benefits are often celebrated, but its own footprint must be considered. Training and operating large-scale machine learning models require immense computational power and energy. Research found that training a single generative model can emit as much carbon dioxide as five cars over their entire lifetime. Data centres can consume tens of millions of litres of water annually for cooling their servers, emphasising that AI-driven infrastructure carries significant hidden resource costs.
This reveals a paradox at the heart of fashion’s digital transformation. AI can help the industry cut material waste and emissions in one area, yet the infrastructure enabling it can quietly increase energy and water consumption elsewhere. As innovation advances, the environmental cost of digital systems must be measured alongside their material impact. True sustainability cannot depend on invisible energy use.
Beyond emissions and efficiency, AI’s most immediate impact is social. As algorithms assume a growing number of operational and creative tasks, the question is no longer whether fashion will automate, but what that means for its workforce. Analyses suggest that AI is already reshaping roles across marketing, merchandising, and product development, leading to restructuring and job displacement.
Creative professionals, from designers to stylists and copywriters, are also seeing their skills replicated by generative tools, prompting questions about the future of originality and authorship. At the same time, discussions around consent and data rights are gaining momentum, with industry voices calling for stronger protections against the unauthorised use of human likenesses and digital replicas. These issues underscore that sustainability must extend beyond environmental performance to include fair, transparent, and dignified digital labour practices.
AI is neither inherently sustainable nor unsustainable. It reflects the intentions of those who deploy it. Used to forecast demand, design out inefficiency, and enable circular systems, it can be a genuine driver of progress. Yet when directed toward accelerating production or fuelling hyper-personalised marketing, it risks reinforcing the same cycles of overproduction and waste that sustainability efforts seek to overcome.
Responsible integration therefore depends on intention and governance. Transparent data management, accountability for energy and resource use, and the inclusion of ethical standards in innovation strategies must become standard practice. Emerging frameworks, from the EU’s AI Act to evolving corporate responsibility standards, point toward greater oversight, but voluntary leadership remains essential.
AI’s power lies in optimisation, while fashion’s responsibility lies in intention. Aligning the two will define whether digital innovation becomes a catalyst for transformation or another layer of systemic imbalance. As fashion embraces artificial intelligence, it must also nurture human intelligence, the empathy, creativity, and foresight that no algorithm can replicate. If the past decade was about digitising fashion, the next must be about humanising it. The challenge is not whether the industry will use AI, but how consciously it will choose to do so.
COP30 concluded in Belém with the resounding message that the global community is grappling with the widening gap between climate promises and meaningful implementation.
In a year defined by growing scrutiny of climate commitments and the urgency to decarbonise, transparency has emerged as one of fashion’s most powerful accelerators for change.
On 5 November 2025, at Rio de Janeiro's Museum of Tomorrow, The Earthshot Prize announced its five 2025 Winners, each receiving £1 million to advance transformative climate solutions.
