AI Plans Outrun Store Realities as Bad Stock Data Can Cost Large Retailers Millions Each Year

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Scandit survey of store associates finds stock records match shelves less than half the time, fueling a 'shadow store' of paper, chats and photos that leaves AI ...

Scandit survey of store associates finds stock records match shelves less than half the time, fueling a 'shadow store' of paper, chats and photos that leaves AI working from an incomplete view of the store and business

ZÜRICH, Nov. 10, 2026 /PRNewswire/ -- Scandit, the leader in smart data capture powered by vision AI, today released the results of global research revealing that retail's race to deploy AI is running ahead of the data reality inside its stores. Store associates reported that stock information matches what they find on the shelf just 46% of the time while only 8% trust store data completely. This creates a costly gap between what the system says, what is actually in the store and what customers experience, while leaving AI and other technology investments to act on incomplete records.

That gap is already being absorbed by labor. Associates estimate they would each get back more than 4.4 hours a week, nearly six working weeks a year, if they had data they could trust. That time amounts to millions in recoverable labor hours. A retailer employing 10,000 store associates, for example, faces more than $41 million a year* in labor cost tied to compensating for the gap. Extrapolating that time across the entire pool of retail associates in the U.S., as tabulated by the U.S. Bureau of Labor Statistics, inaccurate data could cost the retail industry as much as $22 billion annually.

And when the system is wrong, associates do what they have to do to keep the store running. Nearly half (49%) correct the record themselves and 44% raise a stock adjustment. But among associates who take action, 42% bypass the official system entirely, while 68% use at least one informal method, including paper notes, team chats, personal phone photos and apps or memory. The result is a 'shadow store' in which vital information exists outside the record that head office, dashboards and AI systems rely on.

"Retailers do not have an AI adoption problem as much as a store truth problem,"…

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