AI Plans Outrun Store Realities as Bad Stock Data Can Cost Large Retailers Millions Each Year
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 /CNW/ -- 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," said Samuel Mueller, CEO and co-founder of Scandit. "Associates are spending valuable time repairing these gaps, yet much of that work disappears into unofficial tracking systems. Before retailers ask AI to make more decisions based on inaccurate store data, they need a shared, accurate view of what is actually happening on the shelf and across their stores."
Associates' trust in stock data does not build gradually. The survey highlights it stays low until the data is very good. Only 8% of associates completely trust their store's stock information, and only one in four say it is accurate more than 70% of the time. Among associates who put accuracy anywhere up to 50%, roughly a quarter report high trust, and that figure barely moves until accuracy passes 70%, when it rises sharply.
"This is not a gradual improvement problem," said Andrew Smith, Managing Partner and co-founder of ThinkUncommon and a RETHINK Retail Top Expert since 2021. "Moving stock accuracy from 46% to 60% may look like progress in a board report and still change nothing on the floor. Trust only begins to move once accuracy clears 70%, and until then associates do what sensible people do: they check the shelf themselves and either correct the system or just do what they need to do to help the customer in front of them."
The consequences extend beyond productivity. Associates report spending an average of 4.1 hours each week handling customer questions that store technology should be able to answer, and are asked to physically check whether an item is in stock 5.85 times a week on average. More than one in five say they make those checks more than ten times a week.
The findings point to a practical lesson for technology deployment: associates who participated in a pilot reported using most store tools at a rate of 74%, compared with 61% among those who were consulted before rollout and those who were never asked. Half of pilot participants reported high trust in their stock data, compared with 25% of those who were never asked for input.
Read The Store Truth Gap report here.
Asked what would make inventory management workflows easier, associates said responded with the following:
- "Get the stock counts actually right so I'm not chasing ghost inventory."
- "A lot of times the back stock isn't in the system so I go and have to physically check more often than I'd like."
- "More accurate layouts. Sometimes it sends me to a location that doesn't have product."
- "More consultation from head office with us, who are the ones facing the customer."
*Estimate is based on an average self-reported $18 hourly wage
About the survey
Scandit commissioned Sapio Research to survey 2,200 store associates online in September 2026. All worked in physical stores for retailers with more than 1,000 employees across the U.S., Germany, UK, France, Italy, Canada and Poland. The research covered grocery, mass merchant and department store, hypermarket and supercenter, DIY and home improvement, convenience, and drugstore and pharmacy formats.
About Scandit
Scandit is the leader in smart data capture, powered by vision AI, giving superpowers to workers, customers, and businesses by providing actionable insights and automating end-to-end processes. Its Smart Data Capture platform enables smart devices, such as smartphones, handheld computers, drones, digital eyewear, robots, and fixed cameras to interact with physical items by capturing data from barcodes, text, IDs, and objects with unmatched speed, accuracy, and intelligence.
Scandit enables innovation that delivers significant cost savings, increases employee retention and customer loyalty. The company partners with customers at every step with trials, solution design, integration, and customer success support included.
Visit scandit.com to learn why market leaders across retail, transport and logistics, healthcare and manufacturing like Instacart, Carrefour, Ahold Delhaize, Albertsons, Levi Strauss & Co., Sephora, Lufthansa, and FedEx trust Scandit.
SOURCE Scandit
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