In the past decade, CIOs in retail acted with urgency to plug-in technology that transformed their organizations. E-commerce initiatives years in the making suddenly became essential, with special attention to AI-enabled personalization.
Expect the enablement of AI in retail to stick around, said Arun Sundararajan, Harold Price Professor of Entrepreneurship and Technology at New York University's Stern School of Business, speaking on a panel at the Virtual AI Summit Silicon Valley on Oct. 1.
"Often when we think about AI transformation we think of ... robots interacting with people in stores," he said. "I see a lot of the automation of the retail workforce as already having taken place prior to the pandemic in ways that are not as direct as one might think," such as the focus on recommendation engines as part of the online shopping experience.
The shift to online shopping, as well as the use of associated AI technologies that support personalization, will persist in the post-pandemic era, according to Sundararajan.
AI was already spreading its reach in the technology stack that powers retail, by way of personalization engines and predictive analytics that suggest to shoppers what they may need next. The pandemic will solidify these tools as the default as the retail industry relearns operations.
"The entire business model has changed in a way that requires humans less and is reliant more on simple AI technologies like recommendation systems," said Sundararajan. "I think what the pandemic has done is dramatically accelerate the shift online."
Accelerating adoption
The sharper focus on e-commerce as shoppers stayed home forced H&M to adopt digitalization "much faster than expected," given how critical the online revenue stream became, said Errol Koolmeister, product area lead engineer for AI foundation at H&M, speaking on the panel.
This acceleration pushes businesses to turn their attention to AI investments that sit closer to where customers are, a best practice when seeking to extract the value from AI investments. With shoppers now reaching brands more often online than offline, brands explore "new ways of reaching the customer and staying atop their mind," Koolmeister said.
The next phase in the Swedish retailer's AI strategy means building reusable machine learning components that can be leveraged throughout the organization. The company plans to enable broader adoption of machine learning and zero in on the value delivery component of projects.
Recommendation systems that leverage machine learning and user data became the "killer app" in the retail technology stack.
"We still see people plowing resources into [recommendation systems] 20 years after they emerge," said Sundararajan. "I don't think there has been any other AI technology that has been as powerful or as impactful."
Aside from the focus on recommendation engines, Sundararajan is closely watching the potential for augmented reality applications and how they could unlock value for retail by enriching the shopping experience.
"Those technologies are sort of in their infancy," he said. "You might actually see a new generation of augmented reality technologies that are based on the idea that we're gonna be doing a lot more shopping online."
But pinpointing one specific AI use case that will transform industry proves difficult, Koolmeister said. The focus should be on the strategies companies follow to leverage AI, how fast companies are able to roll out use cases and reach the value realization of each application.
"I know that the greatest idea won't come from me," Koolmeister siad. "They will come from the people actually very close to the business side working on a daily basis with their problem.
