Artificial intelligence is becoming an important part of online shopping, helping customers discover products and receive personalised recommendations. However, new consumer survey findings show that these recommendations do not always result in successful purchases. Nearly half of US shoppers who shop with artificial intelligence have returned a product recommended by AI, according to a Blue Yonder survey.
The finding highlights a challenge for online retailers as they expand their use of AI-powered shopping tools. While the technology can make product discovery faster and more convenient, businesses must ensure that recommendations match customer expectations.
AI Recommendations Do Not Always Meet Customer Expectations
AI shopping assistants analyse information such as customer preferences, browsing activity, and product details to suggest relevant items. These tools can help shoppers compare options and make purchasing decisions more quickly.
However, customers may still receive products that do not meet their expectations. Clothing may not fit properly, colours may look different in person, or a product may not match its online description. In these situations, shoppers may decide to return their purchases.
For retailers, these returns can create additional costs involving shipping, handling, restocking, and customer service. They can also affect customer confidence in online shopping recommendations.
Retailers Explore AI to Reduce Product Returns
Retailers are increasingly exploring ways to use AI to improve the shopping experience and reduce unnecessary returns. In the clothing sector, some businesses are adopting virtual try-on technology, intelligent sizing tools, and personalised product suggestions.
These systems can help shoppers understand how a product may look or which size may be suitable before placing an order. More accurate product descriptions, detailed images, and reliable customer reviews can further support purchasing decisions.
Major retailers are also exploring AI-based solutions to address clothing returns associated with incorrect sizing and unsuitable purchases. However, the effectiveness of these systems depends on the quality of the information they use and how accurately they reflect individual customer needs.
Measuring the Value of AI Investment
For retailers, the expected return on AI investment depends on whether the technology produces measurable business improvements. These may include fewer product returns, better conversion rates, lower operating costs, and stronger customer loyalty.
Businesses should evaluate AI tools using actual performance data instead of relying only on claims about their capabilities. Regular testing and customer feedback can help retailers identify weaknesses and improve recommendation accuracy.
The findings do not necessarily mean that AI shopping tools are ineffective. Instead, they indicate that recommending a product is only one part of the customer journey. The product must also satisfy the shopper after delivery.
Building More Reliable AI Shopping Experiences
As AI-powered shopping becomes more common, retailers will need to balance personalisation with accuracy and transparency. Customers should receive recommendations supported by clear product information, dependable sizing guidance, and realistic descriptions.
Retailers that focus on customer needs rather than simply increasing sales may be better positioned to build trust in AI-assisted shopping.
Ultimately, the success of AI in e-commerce will depend on how well the technology helps customers choose products they want to keep. Improving recommendation quality could benefit both shoppers and retailers by creating a more reliable and efficient online shopping experience.
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