The dust has barely settled on the biggest online Black Friday in history, but for retailers, the real operational test is just beginning. While the front-end sales numbers break records, a quiet crisis is building on the back end: the “ripple effect” of return volume.
According to the 2025 State of Returns and Refunds Report by Seel, retailers are bracing for roughly $1 trillion of returns in 2025. With 20–25% of annual sales expected to come back through the door (and online return rates hovering near 19.3%), the post-purchase experience has historically been viewed as a necessary evil, a massive cost center managed through damage control.
However, a new paradigm is emerging. By leveraging AI systems that don’t just analyze data but actively manage risk and workflows, forward-thinking brands are flipping the script. They are taking returns from a margin-killer to a competitive moat that drives conversion and customer lifetime value.
Here is how AI is rewriting the economics of returns in 2025.
The AI Shift: Moving Beyond Damage Control
For decades, return policies were static instruments, blunt tools applied across millions of customers. The Seel report, analyzing nearly 10 million transactions, suggests this approach is obsolete.
The new model relies on AI that can underwrite risk in real-time. Instead of a merchant absorbing the volatility of returns on their P&L, AI platforms can now handle return requests, underwrite the risk, and manage the workflow. This allows retailers to treat returns as a fixed, predictable cost rather than a variable disaster.
Industry data supports the need for this intelligence:
- 75% of returns are now driven by delivery failures.
- 81% of shoppers read return policies before buying.
- 71% are less likely to return to a retailer after a single poor experience.
When AI manages this process, it spots issues before they snowball, detecting spikes in defects or identifying shipping anomalies, allowing brands to move from reactive support tickets to proactive resolution.
The Resale Paradox: Why Secondhand Needs AI
One of the most fascinating insights from Seel’s 2025 data involves the booming resale economy. While the secondhand market is growing rapidly, it faces a massive trust barrier.
Seel’s data reveals a stark reality: return rates on secondhand items are 140% higher than new products. Yet, over 70% of secondhand shoppers say they wouldn’t complete a purchase without a return option
This creates a paradox. Merchants want to sell used inventory to capture revenue, but the risk of returns makes it operationally painful. The pre-owned market is fraught with uncertainty regarding condition, fit, and authenticity.
This is where AI-driven “Worry-Free Purchase” protection becomes a conversion engine. By using AI to assess the risk of specific items and offering shoppers the ability to add return assurance for a nominal fee, merchants eliminate the friction. The report notes a 50% increase in shoppers adding purchase policies on items over $50.
For the resale market, AI doesn’t just manage the return; it facilitates the sale in the first place, turning a high-risk experiment into a scalable revenue stream.
The Asymmetry of Returns: Fashion vs. Electronics
A generic return policy no longer serves because it ignores category-specific nuance. Seel’s analysis of 2025 trends highlights a divergence in why things come back, creating an asymmetry that only AI can effectively navigate.
- In Fashion & Accessories: The problem is logistics. “Delivered too late” returns jumped 124% year-over-year, and missing package claims rose 42%.
- In Electronics: The problem is product integrity. Defective item returns climbed 33%, while late delivery claims actually dropped.
A traditional support team struggles to adapt policy to these shifting sands in real-time. An AI system, however, recognizes that an electronics return requires a warranty-focused workflow, while a fashion return requires a logistics-focused workflow. This segmentation preserves margins by applying the right solution to the right problem instantly.
The Holiday Signal: Speed is the New Currency
We are currently in the thick of the “peak season signal.” The report indicates that return activity surges 16% during November and December. But the qualitative shift is more important than the quantitative one: shoppers are buying earlier, but they are also returning sooner and demanding faster refunds.
Three out of four shoppers now say they won’t make a purchase if an item isn’t returnable. In an era of rising customer acquisition costs, the post-purchase experience is the final frontier of differentiation.
AI allows brands to offer “instant refunds” or “guaranteed returns” without exposing themselves to massive fraud risk. By analyzing patterns across millions of orders, the system can distinguish between low-risk shoppers and those with higher-risk behavior, allowing brands to move faster while flagging potential abuse behind the scenes.
The Bottom Line
As we look toward 2026, the retailers who win will not be the ones with the strictest policies, nor the ones who ignore the problem. They will be the ones who deploy AI to decouple “returns” from “financial loss.”
By using agentic platforms to underwrite risk and smooth out friction, brands can stop viewing returns as a $1 trillion problem and start viewing them as a mechanism for trust. In a market where 82% of decisions are influenced by return flexibility, the smartest investment a retailer can make is in the technology that happens after the buy button is pressed.
