AI’s rise in industry: Enhancing efficiency and addressing challenges

One of the most notable technology trends in recent years is the widespread adoption of artificial intelligence (AI) across various sectors. With the help of AI, businesses are able to continuously tailor customer needs to predict preferences and provide product recommendations based on user experiences. From self-ordering kiosks to chatbots, these innovations are pivotal in delivering essential services, enhancing both convenience and efficiency.¹ As these advancements improve certain aspects of our daily lives, industries can leverage these resources to address operational and customer challenges.

A major issue that retailers continue to grapple with is managing returned items and integrating used goods back into the supply chain through reverse logistics. As online shopping grows, understanding the role of AI in optimizing reverse logistics for unwanted items is becoming increasingly crucial. This will help pave the way for a more sustainable future by ensuring efficient processing and promoting responsible product life cycles through recycling, reuse, and refurbishment.²

How high return volumes impact retail operations and customers

Many companies today struggle with high return volumes leading to operational inefficiencies, customer dissatisfaction, and lost revenue. In 2023 alone, the average retail return rate was 14.5%, with online purchase returns reaching 17.6% compared to 10.02% for brick-and-mortar stores.³

While these rates can vary significantly based on product type, this has resulted in more than $351 billion in lost sales annually due to merchandise returns, with 5 billion pounds of returned goods ending up in landfills.⁴ The processing delays, issuance of refunds, and inconvenience of driving to the nearest retailer or post office to return an item highlight the significant hurdles customers and businesses face in managing returns effectively.

Retailers are now increasingly leveraging AI to streamline their return processes. For example, advanced image recognition systems can automatically assess the condition of returned items, reducing manual inspection time and improving accuracy. By capturing the item’s condition through high-resolution images and comparing them against a database of acceptable standards, the systems can quickly determine if a product is eligible for resale or needs further processing, thus increasing efficiency and reducing costs associated with a returned product.⁵

Additionally, predictive analytics tools can help identify potential fraud by analyzing customer behavior patterns and detecting anomalies. Relevant data points such as transaction history, customer demographics, and device information can play a key role in determining customer tendencies and purchases.

AI-driven chatbots and virtual assistants are also being deployed to handle customer inquiries and provide instant support for return-related questions, enhancing the overall customer experience. These AI technologies can streamline the return process by immediately generating return labels and documenting any customer feedback, which is critical for retailers to gather to improve product quality and reduce future return rates.

By leveraging AI with key elements such as workload and processing center capacity, businesses can properly determine the optimal route to take when deciding which processing facility each return should be routed to. Determining this distance will help drastically reduce transportation costs and reduce the environmental impact on where the item should go upon being returned.

Sample scenario of AI in reverse logistics:⁶

Innovation in returns management

A clothing and accessories retailer has been a recent pioneer in adopting returns management technology with a reverse logistics company.⁷ Its return experience focuses on an intuitive online returns portal for quick and easy initiation of returns—with instant exchanges, fraud protection, and label-free drop-off locations. This is allowing customers to have easier access to an application so items in turn can get back in stock and on shelves much faster without any major delays. By using the company’s mobile app:

• Shoppers can initiate a return online and generate a quick-response (QR) code.
• Shoppers can bring a return to any store without a receipt or label for associates to process them.
• Associates can then either place the item back in stock, route it to the closest distribution center with other returned items, or recycle it if damaged.
  

The value that can be gained from this process is that:

• Retailers can save labor and transportation costs with an efficient process for associates in terms of what to do with returns.
• In-store associates can spend more time assisting customers and less time processing returns.
• Retailers can capture more data on reasons for an item being returned and make smarter inventory planning decisions on goods routed to distribution centers for resale online.
• Retailers can utilize AI to identify the location for a product to be sent for return, whether that be a distribution center, store, or landfill.
• Easier in-store returns can drive additional foot traffic and likely 
repeat engagement from customers since their return process was so frictionless.

Conclusion
Integrating AI into reverse logistics processes can provide a tremendous impact to boost customer satisfaction and overall efficiency. By leveraging AI in processing returns, companies can provide clearer, verifiable records for received items, ensuring the transparency and reliability customers expect. This technology not only can help streamline operations, but it has the potential to enhance the accuracy and speed of return assessments, reducing costs and minimizing waste. It is critical for businesses to proactively adopt these advanced technologies to maintain a competitive edge and stay at the forefront of innovation and efficiency. This will be key in addressing customer needs and driving sustainable growth 
going forward.

Endnotes

¹ Erhan Musaoglu, “The role of artificial intelligence (AI) in returns management,” Logiwa, January 12, 2023.
² Scott Fletcher, “Return to learn: Four ways to use AI and machine learning to improve reverse logistics,” Forbes, June 21, 2022.
³ Appriss Retail and National Retail Federation (NRF), 2023 consumer returns in the retail industry, 2023.
⁴ Daphne Howland, “Study: 5B pounds of retail returns end up in landfills,” Retail Dive, December 27, 2017.
⁵ NVIDIA, “AI-powered supply chain management,” accessed May 2025.
⁶ Erhan Musaoglu, “Using artificial intelligence for returns management,” SupplyChainBrain, September 26, 2023.
⁷ Lori Ann LaRocco, “How reverse logistics can help retailers avoid ring around the collar,” Freight Waves, July 26, 2024.