Yount emphasized the need for technology to address common challenges such as slow category growth, inefficient trade spending, and inconsistent and expensive in-store labor. The focus shifted to the critical nature of shelf execution and the challenges of ensuring product availability and display.
According to Storesight, the enablement of data-driven AI has “freed up” visibility for retailers. This enables real-time shelf monitoring while machine learning models predict changes in demand.
“The AI layer is the new part,” Yount said. “There were some false starts. There were some things started in this space to try to capture data at scale from what was actually happening in the store, but I think what was missing was not necessarily the ability to take the collection or the data back to the home office, but the ability to actually analyze the photos and aggregate them to create new metrics.
“AI has unlocked all of that, and frankly, open source modeling with AI has allowed us to change that regularly and keep up with it. … I think it’s really the right time, right place.”
“I think we’ve been able to get some information from stores. Many of us have data feeds from stores. But what AI can do with that information, what it can extract from photos and videos, is rapidly changing and allowing AI to be deployed at scale.”
According to co-speaker Henry Ho, co-founder and chief strategy officer at Storesight, the future of AI-powered retail intelligence is bright. From optimized planogram compliance to real-time shelf monitoring and demand insights and forecasting, automated AI tools ensure the right inventory levels that ensure product placement and increased availability.
Ho says AI gives CPG teams the speed, scale, and easy-to-use tools they need to win in retail.
This was originally published in CGT’s sister publication P2PI..
