This article was compiled with the help of generative AI based on data and analysis that is original to EMARKETER.
Retailers are experimenting with generative AI (genAI) across various use cases, from personalized marketing to conversational search. But the strategic risks of being an early adopter of genAI versus waiting for established players to set standards remain unclear.
“We don't really have a great sense of how effective it really is at this point, in terms of consumers using it,” our analyst Blake Droesch said on the Behind the Numbers: Reimagining Retail podcast. “We've got some clips from what retailers are self-reporting on how it's performing, but there's really not a lot out there in terms of how it's changing the shopping experience and how it's actually impacting conversion.”
Here are four things brand and retailers should consider when implementing genAI strategies:
1. Adoption may be gradual. There’s often a lag between retailers rolling out new technology and consumers adopting it. If you look at augmented and virtual reality technology, the actual adoption rates are still fairly low, especially among non-Gen Z consumers, said our analyst Carina Perkins.
2. Poor experiences risk backlash. Implementing prominent AI tools like conversational search carries reputational risk if the experience frustrates users. “If you stumble out of the gate, then not only are people going to be upset with you as a brand, but they’re going to be turned off in the future,” Droesch said.
3. Consider lower-risk use cases. If you haven’t gotten your virtual feet wet yet, initial genAI use cases like internal employee assistance chatbots or additive features like fashion retailer Zalando’s outfit recommender pose less risk than replacing core functionality. “There are safer use cases where retailers can test the water,” Perkins said.
4. Be aware of the limitations. GenAI still has issues with accuracy and bias, which means it isn’t a good tool for high-level decision making. “There are other forms of AI which are better suited for that. Predictive AI, which uses algorithms and statistical models to predict future trends, is much better at demand forecasting, [for example],” said Perkins. “What I think is quite interesting is we’re seeing software solution providers offering products that combine the two technologies.”
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