Journal / Kirill Naumov
A practical framework for using AI in retail returns and service recovery: spot patterns, make routine remedies easier and keep consequential exceptions reviewable.
By Kirill Naumov ·
A return is not automatically a bad outcome. It may reveal a product-description gap, a delivery problem, a sizing issue or simply a purchase that did not work for one customer. AI can help a retailer see patterns and route cases faster. The dangerous shortcut is to turn a risk score into a silent “no”.
One job is service recovery: understand what happened, explain the policy and get the customer to a fair next step. The other is loss prevention: check whether the facts suggest abuse or a process weakness. They can use some of the same transaction signals, but they should not be treated as the same decision. A frustrated customer with a damaged item is not a fraud label waiting to happen.
A bounded assistant can summarise the order, item, delivery status, prior support exchanges and the customer’s stated issue—only where the retailer is authorised to use that information. It can point out missing evidence or route the case to the right team. Keep links to the original record, mark uncertain details and do not let a generated summary replace the receipt, carrier scan or product inspection.
For a routine, low-value case covered by a clear policy, automation may help explain the options or prepare a remedy for approval. Before giving a system authority to issue a refund, replacement or credit, define the limits, eligible cases, audit trail and escalation route. The point is not to make the bot sound generous; it is to let it complete a remedy the business is willing to stand behind.
A 2026 retail service-recovery study in the [Journal of Retailing](https://www.sciencedirect.com/science/article/pii/S0969698926003231) separates the identity of the agent from its authority to act. Across experiments and UK retail field studies, the authors report that generative AI was not inherently better than a human; the advantage depended on whether the system could actually complete an appropriate remedy. The result is context-specific, not a blanket case for automated refunds. It is a useful design question: if the assistant cannot fix the problem, what is it asking the customer to do next?
A risk signal can help prioritise a check; it is not proof. Missing scans, a new account, a shared address or a cluster of unusual returns can have innocent explanations. Show the reviewer which facts triggered a flag and which information is missing. Give staff a way to correct the record, record the final reason and refer a disputed or high-impact decision to a person with authority.
The National Retail Federation’s [discussion of return-fraud mitigation](https://nrf.com/blog/rethinking-return-fraud-in-retail) describes a layered approach involving item verification and AI-plus-human review of suspicious cases. Treat it as an industry perspective, not an independent evaluation of a particular system. The practical lesson is to combine evidence and review rather than make an opaque profile the entire case.
Track time to resolution, repeat contacts, remedy accuracy, appeal or override rates, confirmed losses, false alerts and the staff time spent reviewing cases. Break results out by channel, product type and customer segment where lawful and appropriate. A reduction in refunds is not success if valid customers are delayed, support queues grow or staff spend their day explaining a machine’s decision.
Run the new workflow beside the existing one on a limited case type. Have reviewers check a sample of both flagged and unflagged returns, agree what counts as a justified intervention, and set a pause rule for unexplained disparities or a rise in unresolved complaints. Keep the comparison period and policy definition stable.
Choose one repetitive, low-risk failure—such as a missing delivery scan or a product arriving visibly damaged. Let AI assemble the timeline and recommend a next step; keep a colleague responsible for confirming the facts and approving anything outside a narrow policy. Compare resolution time and customer effort with the current process, while checking accuracy and exceptions. Expand only if the process gets easier without making fair treatment harder.
AI in returns should make a good decision easier to reach, not make a refusal harder to challenge. For more practical applications across retail, sales, learning and loyalty, see the [AI for Retail workshop](https://www.kirillnaumovai.com/workshops/ai-retail-november).
Explore the AI for Retail workshop → · Start the AI and Human Experience Diagnostic