Returns management used to be the retail equivalent of damage control. You processed items as quickly as possible, hoping to recover some value, and moved on. That mindset is costing retailers billions. With return rates reaching an average of 40% in apparel and returned merchandise totaling $890 billion in 2024, these inventory volumes rival your largest suppliers.
The retailers who understand this shift are already ahead. They're using artificial intelligence to transform returns from a necessary evil into a competitive advantage. Instead of letting returned inventory disappear into markdown purgatory, they leverage AI to make smarter decisions about where those products go, how they are processed, and what insights they provide for future planning. This transformation requires a fundamental change: returned inventory is not waste. It is untapped potential sitting in your reverse supply chain, ready to drive profitability.
The hidden costs of traditional returns management
Most retailers handle returns with a "process and forget" mentality that creates enormous hidden costs. When a customer returns a jacket, traditional systems route it to a distribution center where it sits, waiting to be inspected and often marked down for clearance. During those weeks, the demand for that specific style may shift to a different location where you are already out of stock. Meanwhile, you are buying new inventory to fill gaps that your returned items could have addressed.
The real cost is not the markdown you take—it is the missed chance to sell that returned jacket at full price by not routing it to the right store at the right time. AI-driven returns management systems can calculate these opportunity costs in real-time, and route returns to locations where they have the highest probability of selling at full price.
AI-powered routing makes every return count
Smart returns management starts with intelligent routing decisions. When an item is returned, AI analyzes multiple data points simultaneously: current inventory levels across all locations, local demand patterns, seasonality, and even weather forecasts that might influence demand. Instead of defaulting to a central warehouse, the system will route the return directly to a store in a region where similar products are selling well.
This is strategic inventory placement that treats returns as fresh stock. Retailers using AI-powered routing report dramatic improvements in how quickly returned items sell again, often at full price. The automation extends beyond routing. AI can determine the optimal disposition for each return based on its condition, demand forecasts, and profitability analysis. Some items may be returned directly to the sales floor, while others will be sent to online channels, and some will be sent to outlet stores. The key is making these decisions automatically and consistently based on data.
Returns data reveals hidden planning insights
Every return tells a story about customer behavior, product quality, and market demand. AI turns these individual stories into actionable insights that inform your broader planning and buying decisions. When returns data shows that customers consistently return a specific size in a particular style, that is valuable intelligence about sizing inconsistencies. AI can identify these patterns across thousands of products and help adjust future order quantities and size curves.
Geographic return patterns also reveal important insights about regional preferences. If certain styles are consistently returned from specific regions, AI can factor that into allocation decisions, sending fewer units to those areas. The most sophisticated systems provide returns insights directly into planning. Just as you forecast forward demand, you can also forecast return rates and timing, allowing you to plan inventory levels more accurately. This is especially valuable for seasonal merchandise.
