How AI is trying to solve retail’s returns problem

It pinches here; drags there; the draping is wrong. These are some of the examples of the feedback a new crop of artificial intelligence apps might give a prospective customer trying on clothing ahead of a purchase, and in the process reduce the chances of a product being returned to a store.

Fashion retailers are increasingly turning to AI to solve the issue of rising product returns, a persistent drag on profitability and something many in the industry refer to as the industry’s “silent killer”.

A growing number of AI start-ups have emerged to provide virtual try-on technology, allowing potential customers to visualize fit and style before they buy.

While tech companies have attempted to solve online fit issues since the 2010’s, the rapid development of generative AI has finally made these applications good enough to meaningfully impact retailers’ bottom lines. 

The U.S. National Retail Federation late last year estimated that 15.8% of annual retail sales were returned in 2025, totaling $849.9 billion. For online sales, that number jumped to 19.3%. Gen Z is driving this trend, with shoppers aged 18 to 30 averaging nearly eight online returns per person last year, the NRF found.

Most returned items never make it back to the shelves and often cost the retailer more to process than the value of the refund itself. It’s a multibillion-dollar problem for the industry that’s eating directly into companies’ margins.

“Figuring out how to proactively use returns and then how to minimize them can be a meaningful driver of business and profitability,” Guggenheim Senior Managing Director Simeon Siegel told CNBC.

While fit technology will never be as good as trying something on in person, it’s a great way to bridge the gap, Siegel said. “It’s going to continue to get better, I think that’s going to continue to reduce returns.”

The primary reason for returns and abandoned shopping carts is uncertainty over fit, Ed Voyce, founder and CEO of AI startup Catches, told CNBC in an interview.

Catches has developed a platform that allows users to create a “digital twin” to try on clothes virtually with what it calls “mirror-like realism.” The application went live last month on luxury brand Amiri’s website for a select range of clothes.

Unlike other models that Voyce says “just look pretty,” the Catches platform incorporates the physics of fabric texture and how material interacts with a moving body.

“The reason we built Catches was to take advantage of a kind of confluence of technologies that is taking place right now to solve this issue effectively,” says Voyce, who founded the startup backed by LVMH’s Antoine Arnault and built on Nvidia’s CUDA platform.

“The reason it’s solvable now in terms of timing is that you have to be able to run visuals for end users on bare metal in the cloud, cheaply enough to make a [return on investment] for brands,” Voyce says.

“This technology has the potential to impact the whole industry and really usher in the new wave of what end users expect.” 

These AI tools aren’t only meant to reduce returns, but also to help enhance purchases.

While e-commerce has grown rapidly in recent years, with online shopping driving retail sales growth, the current U.S. trade policy under President Donald Trump has put a dampener on the sector which relies heavily on manufacturing in Southeast Asia. Across the price spectrum, retailers are struggling to maintain margins as costs rise and consumers become increasingly price sensitive amid inflationary pressures.

While returns are a meaningful drag on profit margins, they are also a critical factor in consumers’ purchasing decisions. NRF data shows that 82% of consumers consider free returns essential, yet the cost of providing them is becoming unsustainable for many brands.

Retailers are now testing a mix of tech and policy to protect margins.

Strategies to reduce returns range from charging for return shipping to providing more granular sizing information and incentivizing exchanges over refunds.

Zara, owned by Inditex, was one of the first to implement return fees for online orders, and while it was a contentious change for some customers, it helped the Spanish retailer protect its gross margin and discourage “bracketing” – the practice of buying multiple sizes to try on at home. 

The retailer also rolled out a virtual try-on tool, “Zara try-on,” in December. 

Meanwhile, ASOS recently highlighted a stark improvement in profitability, partly driven by a 160 basis point reduction in its returns rate.

The online fast fashion player has been experimenting with virtual try-ons in partnership with deep-tech startup AIUTA, allowing prospective customers to see a piece of clothing on a range of body types, heights, and skin tones. ASOS, however, cautions that the tool is designed to give general guidance and that customers must still check size guides before purchasing. 

Shopify, meanwhile, has integrated startup Genlook’s AI virtual try-on app into its commerce platform, which it says “removes sizing doubts, boosts buyer confidence and drives higher conversion rates while reducing costly returns.” 

Tech giants like Amazon, Adobe, and Google have also created virtual try-ons in various shapes and forms, partnering with major brands to roll out the technology. 

From April 30, Google’s virtual try-on tech can be accessed directly within product search results across Google platforms, according to Google Labs’ website. 

As for Catches, it projects that its app can drive a 10% increase in conversions and a 20- to 30-times return on investment for brand partners. It focuses on luxury brands because of their higher price point. The startup hasn’t yet put a number on how much returns might decline with the use of its platform, but targets “massive reductions.”

“There are certainly companies that have absolutely seen benefits – figuring out how to quantify them is more difficult,” said Siegel. 

While the benefits are clear, the analyst cautions that AI is not a magic wand. Beyond fit, retailers are looking at AI for inventory management, customer targeting, and fraud prevention.

“All of those are really interesting use cases, as long as companies don’t abandon who they are,” Siegel says.

“What you sell is always going to be more important than how you sell, and so I just think remembering that will help dictate who wins and benefits and amplifies from AI versus who gets consumed by it.”