- US retailers spent an average of $403,000 last year on AI tools focused on customer experience, according to a Storyblok survey.
- Despite the investment, 30% of executives said AI led to only slight improvements in customer experience.
- The top AI use cases include customer service (61%), marketing analysis (60%), and administrative automation (42%).
Retailers in the US are investing big in AI. A new survey shows they spent an average of $403,000 last year on AI tools aimed at boosting customer experience. But many aren’t seeing the dramatic results they expected, per Retail Dive.
AI Impact Still Modest
A report from Storyblok, which surveyed 300 executives across the US and Europe, found that 30% saw only slight improvements in customer experience after using AI. While 97% said they’ve seen a solid return on investment, most improvements were minor.
Where AI Is Being Used
Customer service leads the list of use cases, with 61% of companies using AI for that purpose. Marketing analysis follows closely at 60%, with administrative automation at 42%. Other uses include translation (41%) and content creation (40%).
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Big Brands Testing Generative AI
Major companies continue to experiment with AI. Walmart recently launched “Wally,” a generative AI tool to help merchants analyze data and flag product issues. L’Oréal is using Google’s Imagen 3 and Gemini models to create storyboards and redesign packaging.
AI at Industry Events
AI was a major topic at recent retail conferences like Shoptalk and NRF’s Big Show. Executives discussed using AI for everything from localized ads to customer support. However, most companies are still in early testing phases.
Human Touch Still Matters
Despite rising AI adoption, retailers are not ready to go all-in. Target CEO Brian Cornell said during NRF’s Big Show that retail will continue to rely on human judgment and service, especially in customer-facing roles.
Looking Ahead
While AI is becoming a standard investment, it hasn’t yet revolutionized the customer journey. Companies may need more time—and better tools—before AI delivers on its full promise.