Google’s approach to in-store product tracking is more scaled-down. Referring to large retail chains implementing an Amazon Go level of real-time data gathering and analysis, Tepfenhart said, “there is no way that is ever going to pay out.”
Instead, Google is trying to deploy “an affordable augmentation” that relies on having scans performed on each aisle at some set interval — say, once a day — rather than constantly. This could be done by an array of fixed cameras or a drone that flies around the store. The software would then analyze the images or video and automatically update the system/site data and the text reflecting that data.
“You don’t really need continuous monitoring,” Tepfenhart said.
Vise sees some budgetary wisdom in Google’s in-between approach. “Computer vision is extremely expensive,” he said.
A system that leverages once-a-day scans would mean a more modest outlay for stores that aren’t ready to go all in on computer-vision AI.
Applications beyond retail
It seems likely that a combination of these approaches, all based on different deployments of AI, will improve product data reliability and comprehensiveness — and ultimately the customer experience, both online and in physical stores.
From a data analysis perspective, what fixes retail’s problems could theoretically work for any other vertical. It’s a matter of IT leaders looking at their vertical’s data problems, then reexamining the root cause in an AI context to see if video analysis, a set of agents, or other AI tools could cost-effectively fix the headache.
In healthcare, for instance, genAI tools might help overworked ER residents pull key data from patients’ massive medical records. In finance, an agentic AI system might verify transactions without introducing a delay, thus improving fraud prevention. The list goes on.
Just about every vertical can learn from retail. What problems might automation and AI agents fix in yours?
This story originally appeared on Computerworld
