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The growing challenge of e-commerce returns is putting pressure on retailers, with costs soaring and logistics becoming increasingly complex. According to industry estimates, around 10% of all online purchases—equivalent to 4 billion parcels annually—are returned, creating a costly burden on retailers that still rely on inefficient, manual processes to manage reverse logistics. However, AI-driven automation is beginning to transform this space, offering solutions that streamline returns, reduce handling costs, and improve overall efficiency.
Most retailers process returns by moving products back to warehouses for inspection, reconditioning, and resale—a labor-intensive and expensive approach. Due to limited upfront data and fragmented logistics, handling a return costs about $20 on average, further straining retailers' margins.
In an article for Forbes, Dennis Mitzner gives an overview of how AI is changing the game. For example, AI is introducing automation at multiple stages of the returns process. AI can make instant decisions by collecting detailed data—including images, videos, and purchase history—reducing reliance on customer service agents. Additionally, AI-powered return routing helps minimize unnecessary transportation and processing steps, significantly cutting costs.
“In cases where physical inspections are still necessary, AI assists warehouse operators by sorting and assessing items, dramatically reducing processing time,” said Kalle Koutajoki, CEO and co-founder of Renow, an AI-optimized recommerce platform. “With these efficiencies, inventory turnaround times can shrink from months to days, and handling costs can drop by 20% or more.”
Improving accuracy with AI
Looking ahead, AI-driven solutions may eventually eliminate the need for unnecessary handling. They would allow returned products to be resold and shipped directly to the next customer without warehouse intervention.
AI is making returns more efficient and helping retailers prevent them. One primary reason for returns is a mismatch between customer expectations and reality, often caused by inaccurate product descriptions, misleading photos, or incomplete details.
Karl Paadam, founder of Gain and co-founder of Yummy, highlighted the power of AI in improving product accuracy: “At Yummy, we combined AI-driven customer insights with recipe image generation, leading to exceptional customer satisfaction and retention.”
Retailers can leverage AI to analyze SKU-level data, identifying high-risk products and optimizing product listings to reduce return rates. This shift from reactive returns processing to predictive returns prevention is key to improving efficiency and the customer experience.
Beyond logistics
Beyond optimizing logistics, AI is helping retailers convert returns into revenue rather than losses. Many returned products are traditionally discarded or liquidated at deep discounts due to damaged packaging, minor defects, or seasonality. However, AI allows retailers to analyze product conditions, customer return patterns, and market demand in real time, ensuring that each returned item is routed to the most profitable outcome.
“AI-powered image recognition is another game-changer,” said Aviad Raz, CEO and co-founder of ReturnGO. “Instead of relying on manual inspections, AI can instantly analyze customer-uploaded images or videos to assess product condition, determine resale eligibility, and even dynamically price returned items based on demand.”
By implementing AI-driven resale strategies, retailers can minimize waste, improve inventory allocation, and recover more value from returns.
Increasing competition in the AI space
As AI reshapes the returns landscape, competition is heating up among startups and logistics providers looking to carve out a space in this growing industry. Joose Toiviainen, CEO and co-founder of Helsinki-based startup Daze, believes long-term success in returns optimization will depend on strategic business models.
“The new generation of brands understands that returns are part of the business model, not just a cost,” said Toiviainen. “Rather than making returns complicated for customers, brands that optimize return windows and logistics density will stay ahead.”
Unlike Amazon’s vertically integrated approach, startups like Daze are focusing on regional efficiency. They optimize return flows to balance cost and speed without overextending operations.
AI’s role in returns management is expanding rapidly. Retailers are adopting machine learning models to streamline logistics, prevent unnecessary returns, and maximize resale value. Experts believe that AI-driven automation will be crucial in post-holiday inventory management, reducing excess stock and improving efficiency across industries such as fashion, electronics, and home goods.
“The winning formula lies in combining flexible operations with AI-driven optimization,” said Dr. Yishai Ashlag, co-founder and CEO of Onebeat. “Retailers can no longer afford to rely on manual analysis—they must leverage AI-powered insights to make real-time inventory and merchandising decisions.”
