Retailers continue to experiment with generative AI and are seeing that the technology holds great promise for reigniting growth—if they move quickly to seize the opportunity. Once generative AI (gen AI) hit the mainstream, in late 2022, it took little time for retail executives to realize the potential in front of them. Mentions of artificial intelligence (AI) in retailers’ earnings calls soared last year—which was no surprise, given that gen AI is poised to unlock between $240 billion to $390 billion in economic value for retailers, equivalent to a margin increase across the industry of 1.2 to 1.9 percentage points. This, combined with the value of nongenerative AI and analytics, could turn billions of dollars in value into trillions.

Over the past year, most retailers have started testing different gen AI use cases across the retail value chain. Even with all this experimentation, however, few companies have managed to realize the technology’s full potential at scale. We surveyed more than 50 retail executives, and although most say they are piloting and scaling large language models (LLMs) and gen AI broadly, only two executives say they have successfully implemented gen AI across their organizations (see sidebar, “Our survey findings”).

Some retailers have found it difficult to implement gen AI widely because it requires rewiring parts of the retail organization, such as technical capabilities and talent. Data quality and privacy concerns, insufficient resources and expertise, and implementation expenses have also challenged the speed at which retailers can scale their gen AI experiments.

Our survey findings

Retail companies that have succeeded in harnessing gen AI’s power typically excel in two key areas. First, they consider how gen AI use cases can help transform specific domains rather than spreading their resources too thin across a range of scenarios. Second, they effectively transition from pilot and proof-of-concept to deployment at scale. This requires not just data prioritization and technological integration but also significant organizational changes to support widespread AI adoption.

In this article, we explore which use cases can offer the most value and what organizational transformations are necessary to scale these technologies successfully.