How AI Could Transform Fast Fashion for the Better—and Worse

Since Shein became the world’s most popular online shopping destination—with seemingly unbeatable prices, and influencers posting “haul” videos to show off their purchases on social media—the Chinese fast-fashion giant has raised questions over how it produces its plethora of merchandise at dizzying speeds. The answer: AI-powered algorithms that allow the company to pick up changes in customer demand and interest, allowing it to adjust its supply chain in real time. As a result, Shein reportedly lists as many as 600,000 items on its online platform at any given moment, selling to customers in over 220 countries and regions globally.

But the company has also long been under scrutiny for its poor record on environmental sustainability, becoming fashion’s biggest polluter in 2023. Investigations into Shein’s supply chains have found severe labor rights violations, with factory workers in Southern Chinese manufacturing plants reporting grueling 75-hour work weeks to keep up with demand. 

Shein claims AI is the answer to solving these problems, too. During a retail conference in Berlin in January, Peter Pernot-Day, Shein’s head of global strategy and corporate affairs, explained that more than 5,000 Shein suppliers recently gained access to an AI software platform to analyze customer preferences—information that the company then uses to produce small batches of merchandise to match supply in real-time. “We are using machine-learning technologies to accurately predict demand in a way we think is cutting-edge,” Peter Pernot-Day said. “The net effect of this is reducing inventory waste.”

Shein isn’t the only fast-fashion company to tout the benefits of AI in transforming the fast fashion business. Many of its competitors, including H&M and Zara, have also turned to machine-learning technology to analyze sales data and understand customer demand by predicting trends, tracking inventory levels, and cutting down on operational costs. Retail experts are equally optimistic about the power of generative AI: a recent report by McKinsey suggests that AI could add up to $275 billion to the operating profits of apparel, fashion, and luxury sectors in the next three to five years.

“We are already seeing significant shifts in fast fashion with the use of GenAI,” says Holger Harreis, a senior partner at McKinsey who co-authored the report. Harries adds that in the long term, this could result in more personalized processes in fashion, “even to levels of quasi-bespoke tailoring with colors, styles, and sizes—all delivered by a heavily genAI-led process with human interventions focused on where humans add the most value.”

As Shein uses AI to optimize its supply chain, however, environmental experts question whether these claimed efficiencies are truly improving outcomes. “Without strong ethical, social, and environmental standards in place, AI could just as easily be driving faster production and overconsumption,” says Lewis Perkins, the president of the Apparel Impact Institute, a global nonprofit that measures the fashion industry’s climate impact. 

Companies promise waste reduction as consumption soars

As the world’s second-largest industrial polluter, fast fashion releases 1.2 billion tonnes of carbon emissions every year, accounting for 10% of global emissions, according to research from the European Environment Agency. But no company has been as prolific in generating emissions in recent years as Shein. The company’s 2023 sustainability report recorded a carbon footprint of 16.7 million tonnes last year—nearly triple the number of emissions it produced in the previous three years. Shein’s record has also soared past Zara, previously fashion’s biggest emitter, and is roughly double that of companies like Nike, H&M, and LVMH. 

Founded by Chinese billion Sky Xu in 2008, Shein became a go-to destination for online shopping during the pandemic after listing nearly 600,000 items on its marketplace. By November 2022, it was accounting for 50% of fast-fashion sales in the U.S. One in four Gen Z consumers now shop at Shein, while 44% make at least one Shein purchase monthly, according to research from EMARKETER. Shein reports that 61% of its carbon footprint came from its supply chain, while 38% came from transporting goods from its facilities to customers. In July alone, Shein sent about 900,000 packages to customers by air. 

The sustainability report also highlighted how the company plans to reduce emissions. That includes moving production hubs closer to the customers, launching a $222 million circularity fund to promote textile-to-textile recycling, and setting a 25% reduction target for emissions by 2030. While Shein did not respond to TIME’s request for comment, a spokesperson for the company recently told Grist that the company is increasing inventory in U.S. warehouses and using cargo ships to deliver to customers. The company also reiterated that AI would further help to reduce waste, asserting that “we do not see growth as antithetical to sustainability.”

Read More: Shein Is the World’s Most Popular Fashion Brand—at a Huge Cost to Us All

There is new research that could back these claims. A study by the UNSW Institute for Climate Risk & Response found that companies can harness AI-driven technologies for climate action to analyze their carbon footprint, as well as devise strategies to make these improvements. 

“In short, AI will improve the firms’ entire value chain in ways that help them avoid, mitigate, or offset the environmental impacts of their products, services, or processes,” says David Grant, who co-authored this study with colleague Shahriar Akter. Grant adds that much of this work can be done far more quickly and more accurately with AI, as opposed to humans. “The benefits to the environment, specifically in respect of climate change, are thus far greater than would otherwise be achieved,” he says. 

But still, the authors of the study warn against the risks posed by AI in the fast fashion supply chain, specifically through a “vicious circle of overconsumption, pollution, and exploitation,” says Akter, pointing to Shein’s ability to predict demand and manufacture garments at “lightning-fast speed,” which puts added strain on factory workers to churn out garments even faster.

Algorithms feed on copyrighted work

Generative AI’s risks don’t stop at the supply chain. Akter from UNSW adds that the technology is also susceptible to breaching copyrights and compromising the artistic quality of human creativity. 

In April, Connecticut-based artist and designer Alan Giana filed a lawsuit in New York’s Southern District against Shein, alleging that the company’s use of AI, machine learning and algorithms were systematically infringing on his copyrighted work. Citing “Coastal Escape,” artwork that appeared on Shein’s website without permission or attribution, the complaint alleges that “widespread copyright infringement is baked into the business” by using sophisticated electronic systems that “algorithmically scour the internet for popular works by artists.” It went further by stating that the infringement likely extends to “thousands or tens of thousands of other persons” in the U.S. 

Shein has been faced with dozens of similar lawsuits alleging design theft in the past. In July 2023, three graphic designers in China sued Shein for using “secretive algorithms” to identify trends and copy their designs. The complaint went so far as to say that the company’s copyright infringement was so aggressive that it amounted to “racketeering.” In response, Shein told NBC that it took all claims of infringement “seriously:” “We take swift action when complaints are raised by valid IP rights holders,” it stated. 

Akter from UNSW says that generative AI-based designs “might result in breaching copyrights and put a company in a questionable situation,” adding that it could also result in “algorithmic monoculture,” pushing fashion companies to rely on similar algorithms and causing them to lose the necessary creativity in fashion retailing. Moreover, he says that AI-based marketing models could also result in algorithmic bias extending to race, gender, sexual orientation, social class, religion, and ethnicity. 

But despite these risks, more brands are investing significant amounts of their budget in AI. McKinsey’s Harreis is optimistic about its ability to optimize production and reduce waste, but he adds that companies still face a big challenge. “In order for tech to add value, companies need to realize that it is never just about tech, it takes rewiring the entire organization,” he says. 

AI can help bring a systemic shift in design, production, and consumption, says Perkins at the Apparel Impact Institute, but only if it is “paired with responsible business practices, transparent supply chains, and a commitment to reducing overall impact.” It’s not impossible to imagine what this might look like. Perkins points to innovators like Made2Flow, which uses AI-driven data analytics to measure and optimize environmental impact across the fashion supply chain. Similarly, Smartex.Ai leverages AI to detect and reduce fabric defects, leading to lower material waste.

But if AI is used solely to speed up production and push more products to market, it could “fuel overconsumption,” Perkins warns. “Until there’s clear evidence that AI is being used to genuinely reduce the fashion industry’s environmental footprint, I remain cautious about how much positive impact this model is actually having,” he says.