How AI is solving retail returns problems

I’ll stop here. You will be dragged there. The drapes are wrong. These are some of the examples of feedback that new artificial intelligence apps can give to prospective customers who try on clothing before purchasing, making them less likely to have the item returned to the store in the process.

Fashion retailers are increasingly turning to AI to solve the problem of increased product returns, a persistent drag on profitability, and what many in the industry are calling the industry’s “silent killer.”

A growing number of AI startups are offering virtual try-on technology that allows potential customers to visualize fit and style before making a purchase.

Technology companies have been trying to solve the online fit problem since the 2010s, and the rapid development of generative AI has finally made these applications powerful enough to have a significant impact on retailers’ bottom lines.

Late last year, the National Retail Federation estimated that 15.8% of annual retail sales would be returned to consumers in 2025, amounting to a total of $849.9 billion. For online sales, that number jumped to 19.3%. Gen Z is driving this trend, with shoppers ages 18 to 30 making an average of nearly eight returns per person online last year, according to NRF research.

Most returned items never make it back to the store, and the retailer’s processing costs are often higher than the refund itself. This is a multibillion-dollar problem for the industry, eating directly into corporate profits.

Simeon Siegel, senior managing director at Guggenheim, told CNBC that “finding ways to leverage revenue proactively and minimize revenue can be a meaningful driver of business and profitability.”

While fit technology can’t replace trying something on in person, Siegel says it’s a great way to bridge the gap. “I think we’ll continue to see improvements, but I think revenues will continue to decline.”

Ed Boyce, founder and CEO of AI startup Catches, said in an interview with CNBC that uncertainty about suitability is the main reason for returns and shopping cart abandonment.

Catches has developed a platform that allows users to create “digital twins” that allow users to virtually try on clothes with what it calls “mirror-like realism.” The application was launched last month on luxury brand Amiri’s website for select clothing items.

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

“The reason we built Catches was to take advantage of the kind of convergence of technologies that are happening right now to effectively solve this problem,” said Voyce, who founded the startup. LVMH Antoine Arnault and his foundations Nvidia’s CUDA platform.

“The reason it’s solvable now in terms of timing is that you have to be able to run end-user visuals on bare metal in the cloud at a low enough cost to provide a (return on investment) for the brand,” Boyce said.

“This technology has the potential to impact the entire industry and really usher in a new wave of expectations from end users.”

These AI tools aim not only to reduce returns, but also to enhance purchases.

While e-commerce has grown rapidly in recent years, with online shopping driving retail sales growth, current U.S. trade policy under President Donald Trump is hurting an industry that relies heavily on Southeast Asian manufacturing. Retailers are struggling to maintain profit margins across price points as costs rise amid inflationary pressures and consumers become increasingly price-sensitive.

Returns have a huge impact on profit margins, but they are also an important factor in consumer purchasing decisions. According to NRF data, 82% of consumers believe free returns are essential, but the cost of offering returns is becoming unsustainable for many brands.

Retailers are now testing a combination of technology and policy to protect margins.

Strategies to reduce returns range from charging return shipping costs to providing more detailed sizing information to encouraging exchanges over refunds.

owned by zara Inditexwas one of the first companies to introduce restocking fees for online orders, and while it was a controversial change for some customers, it helped the Spanish retailer protect its gross margins and discourage “bracketing” (the act of buying multiple sizes to try on at home).

The company also introduced Zara Try-on, a virtual try-on tool, in December.

meanwhile, asos recently highlighted a significant improvement in profitability, partially driven by a 160 basis point decline in profitability.

The online fast fashion player has partnered with deep tech startup AIUTA to experiment with virtual try-on, allowing potential customers to see clothes on different body types, heights, and skin tones. However, ASOS warns that the tool is designed to provide general guidance and customers should check the size guide before purchasing.

ShopifyMeanwhile, the company is integrating startup Genlook’s AI virtual try-on app into its commerce platform to “eliminate sizing questions, increase buyer confidence, and increase conversion rates while reducing expensive returns.”

The tech giants are Amazon, adobeand google We have also partnered with major brands to deploy technology to create virtual try-ons in various shapes and formats.

Starting April 30th, Google’s virtual try-on technology will be accessible directly from product search results on Google Platforms, according to the Google Labs website.

For Catches, we predict that our app will increase conversions by 10% and deliver a 20-30x return on investment for our brand partners. We focus on luxury brands because of their high prices. The company has not yet released numbers on how much profit will be reduced by using the platform, but it is targeting a “significant reduction.”

“There are certainly companies that are definitely seeing benefits, but it’s more difficult to understand how to quantify that,” Siegel said.

While the benefits are clear, analysts warn that AI is not a magic wand. Beyond fit, retailers are turning to AI for inventory management, customer targeting, and fraud prevention.

“These are all very interesting use cases, as long as companies don’t abandon themselves,” Siegel says.

“What you sell is always more important than how you sell it, so I think keeping this in mind will help you decide who benefits from and scales AI and who gets consumed by it.”