The retail sector is turning to agentic artificial intelligence for deployment in returns management and supply chain operations. The reason is clear: retailers faced $796 billion in total losses from shrink, returns abuse, and fraud in 2025, with $706 billion of that figure attributable to customer returns alone.
$100 billion in preventable loss stemmed from fraud and abuse within returns. Data from consulting firm BCG shows that over one-third of enterprise businesses are already using agentic AI inside their operations. While traditional machine learning models evaluate return transactions in real time, agentic AI goes further to support internal workflows—helping analysts investigate fraud patterns, identify incidents across locations, and enabling retailers to act faster.
Today's dashboards flag costly return rates, suspicious refund activity, or locations with abnormal behaviors, but these insights typically arrive after the fact. The agentic AI promise, however, is that companies can move from analysis to real-time decision-making: dashboards flag a suspicious return as it happens and update inventory and logistics data in the process. Of course, for AI to truly improve operations in returns management and to power decisions that trickle throughout the supply chain, clean and enriched returns data must be at the core. Retailers need data flowing to one centralized place for AI layers to access, and that data must be founded on decades of retail transaction history, unique omnichannel consumer identifiers, behaviors stitched across credit cards, emails and addresses, and a pool of cross-retailer consortium data.
Retailers need to be transparent about the use of AI within returns, as consumers predominantly trust humans more than AI to process returns. AI agents don't replace human judgment but act on clearly defined rules and structures put in place by loss prevention and supply chain teams. Gartner predicts that by 2030, 60% of enterprises using supply chain management (SCM) software will have adopted agentic AI features, up from 5% in 2025, with spend on this segment growing from under $2 billion in 2025 to $53 billion by 2030.
Note: This summary draws on SupplyChainBrain's publicly visible headline + subhead + opening paragraph and on sector background on agentic AI and retail returns management.
Key Takeaways:
1. Retail sector recorded $796 billion in total losses in 2025; $706 billion stemmed from returns
2. $100 billion in preventable loss from returns fraud and abuse
3. Over 33% of enterprise businesses already use agentic AI in operations (BCG)
4. Agentic AI enables shift from analysis to real-time action via flagging and inventory updates
5. Gartner forecasts agentic AI spend in supply chain software will reach $53 billion by 2030
