Returns pose a significant challenge for e-commerce, driving up costs and straining operations. Currently, around 10% of all online purchases are returned—totaling 4 billion parcels annually. Most retailers rely on costly, manual processes to handle returns, moving products back to warehouses for inspection, reconditioning, and resale. This inefficiency adds up, with handling costs averaging $20 per return due to limited upfront data and fragmented logistics. AI is transforming this landscape by enhancing efficiency at multiple stages of the returns process.
By collecting detailed data—such as images, videos, and purchase history—at the moment of return, AI can make instant decisions, reducing reliance on customer service agents. It also optimizes return routing, cutting down unnecessary transportation and processing steps. In cases where physical inspections remain necessary, AI assists warehouse operators in sorting and assessing items, dramatically reducing processing time. As a result, inventory turnaround improves from months to days, and handling costs decrease by 20% or more. Looking ahead, the goal is to eliminate unnecessary handling altogether—enabling returned products to be resold and shipped directly to the next customer, bypassing costly warehouse processes. With AI-driven automation, e-commerce returns are becoming faster, more cost-effective, and more sustainable.
Many new players have entered the space, recognizing the need for cost-efficient solutions in returns management.
“We have created a combination of software and local warehouse partners, which gives retailers a turnkey solution for handling reverse logistics in a more efficient way. We utilize different AI algorithms in all the above scenarios: automated decision-making, routing, and AI agents assisting physical inspectors. This, coupled with a sales channel for products that cannot be sold as new, results in a better and more cost-effective model for reverse logistics. A model that doesn’t use middlemen and where products are inspected at the point of return—or at least very close to it. Solving the recommerce puzzle requires more than just a single solution. The entire process needs to be revamped, which is what we’ve done,” says Kalle Koutajoki, CEO and co-founder of Renow, an AI-optimized recommerce platform for returns, trade-in, and resale.
Using AI to Reduce Returns: Prediction Over Prevention
One of the key reasons for customer dissatisfaction—and ultimately why customers return items—is a mismatch between expectations and reality. When a product does not match its description, whether due to inaccurate photos, misleading specifications, or incomplete details, customers are more likely to feel disappointed and send the item back. Ensuring clear, precise, and honest product descriptions is crucial in minimizing returns and improving overall customer satisfaction.
“At Yummy, we used AI to learn from our customer data and combined it with our recipe image generation—resulting in exceptional LTV and customer satisfaction with our product. The same AI engine allowed us to quickly dominate small markets that are unappealing to larger players while outcompeting local competitors,” says Karl Paadam, founder of Gain and co-founder of Yummy.
AI’s role in returns management extends beyond optimizing logistics—it also helps predict and prevent returns before they happen. By leveraging SKU-level data, retailers can proactively identify high-risk products, optimize product listings, and refine inventory decisions to reduce returns. This transition from reactive returns processing to predictive returns prevention is key to improving both efficiency and customer experience.
“The winning formula lies in combining flexible operations with AI-driven optimization—giving smart retailers the agility to stay ahead. In addition, there is no longer time to manually analyze insights and adjust the supply chain accordingly. Retailers must move beyond traditional BI analytics and leverage AI-driven proactive insights that translate in real time into inventory and merchandising decisions, ensuring continuous supply chain optimization. Building inventory positions for promotions or holiday seasons is a process filled with inefficiencies,” says Dr. Yishai Ashlag, co-founder and CEO of Onebeat, a toolkit for optimized inventory execution at scale.
According to Ashlag, AI will be crucial in streamlining post-holiday operations by reducing excess inventory. “This is especially critical in industries like cosmetics, liquor stores, DIY stores, and consumer electronics, where a large portion of sales comes from promotions and holiday-driven demand. By leveraging AI, retailers can better align inventory with peak demand, reducing overstock and improving overall efficiency.”
How AI is Turning Retail Returns from a Costly Problem into a Revenue Opportunity
One of retail’s biggest hidden costs isn’t just processing returns—it’s what happens to them afterward. Many returned products can’t simply go back on the shelf. Some are seasonal, others have missing packaging, and some are lightly used or damaged, forcing retailers to either liquidate or discard them—both of which lead to major revenue losses.
AI is transforming the way retailers handle excess returns, shifting them from financial burdens to revenue-generating opportunities. Rather than applying a one-size-fits-all approach, AI enables retailers to assess product condition, analyze return patterns, track customer feedback, and gauge market demand in real time. This allows them to determine the most profitable next step—whether that’s restocking, dynamically pricing resale items, routing to refurbishing facilities, donating for tax benefits, recycling materials, or even leveraging generative AI and NLP to extract insights that improve product descriptions or future designs.
“AI-powered image recognition is another game-changer. Instead of requiring manual inspections, AI can analyze customer-uploaded images or videos to assess product condition in real time, instantly determining whether the item should be restocked, refurbished, or liquidated. This speeds up processing, reduces warehouse inefficiencies, and eliminates unnecessary shipping costs. Another major application is dynamic pricing. Traditionally, retailers apply flat discounts to returned goods, often leading to unnecessary losses. AI can adjust resale pricing dynamically based on real-time demand, inventory levels, and seasonality, helping retailers recover the highest possible value for every returned product. By optimizing resale strategies, reducing waste, and improving inventory allocation, AI is turning returns from a financial burden into a revenue opportunity,” says Aviad Raz, CEO and co-founder of ReturnGO, a self-service return management portal.
Competing with AI Giants in the E-Commerce Returns Race
As the returns optimization market grows, many new players are entering the space. However, long-term defensibility remains a crucial question: Will a company’s market position remain strong 10 or 20 years into the future?
Joose Toiviainen, co-founder and CEO of Daze, a Helsinki-based startup focused on optimizing online returns, believes that success starts with the right business model.
“We don’t work with retailers that are afraid of high return rates and try to make the end customer’s life difficult with expensive and complicated return flows. The new generation of brands has incorporated returns into their business model and understands the compounding power of customer satisfaction and retention.”
Toiviainen emphasizes that sustainable differentiation comes from strategic focus. His company prioritizes efficiency by optimizing return windows and logistics density, rather than trying to replicate Amazon’s vertically integrated model.
“For example, we don’t do on-demand. We focus on the next-day window, which strikes the right balance of speed and efficiency for most retailers. The same goes for geographic focus—we need a certain density for the routes to work, so we aggressively exclude rural postcodes. We know our customer segment and their needs well. While I admire Amazon, their model doesn’t translate directly to D2C brands. In post-purchase logistics—meaning everything that happens after checkout—there’s still no dominant player. Brands currently rely on legacy carriers with low NPS, which leaves an opportunity for a better solution.”
AI is reshaping e-commerce returns, turning a costly challenge into an opportunity for efficiency, revenue generation, and sustainability. By leveraging AI for predictive analytics, automated decision-making, and optimized logistics, retailers can reduce return rates, cut processing costs, and maximize resale value. As competition in this space grows, success will depend on innovation, agility, and the ability to integrate AI-driven solutions seamlessly into the returns ecosystem.
