Retail AI Faces a Store Data Gap, Scandit Survey Finds
Retail stock records match what store associates find on shelves only 46% of the time, according to global research released by Scandit. The company says the gap is undermining AI and other technology investments while forcing workers to rely on informal records.
- Stock information matched shelf findings only 46% of the time.
- Only 8% of associates completely trusted their store's stock data.
- Scandit estimates a retailer with 10,000 associates faces more than $41 million in annual labor costs tied to the data gap.
- 68% of associates who took action used at least one informal tracking method.
- Pilot participants reported using most store tools at a rate of 74%.
Inaccurate records create a costly gap
Scandit commissioned Sapio Research to survey 2,200 store associates online in September 2026. Participants worked in physical stores for retailers with more than 1,000 employees across the U.S., Germany, the 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.
Only 8% of associates said they completely trust their store's stock information, while one in four said it is accurate more than 70% of the time. Associates estimated that they could recover more than 4.4 hours each week if they had data they could trust, equivalent to nearly six working weeks a year.
Scandit estimates that a retailer with 10,000 store associates faces more than $41 million a year in labor costs connected to compensating for inaccurate data. The estimate uses an average self-reported hourly wage of $18. Extrapolated across the U.S. retail workforce, Scandit said inaccurate data could cost the industry as much as $22 billion annually.
Workers build a ‘shadow store’ outside official systems
When inventory systems are wrong, 49% of associates correct the record themselves and 44% raise a stock adjustment. Among associates who take action, 42% bypass the official system entirely. In addition, 68% use at least one informal method, such as paper notes, team chats, personal phone photos, apps or memory.
Scandit describes the result as a “shadow store”, where information exists outside the records used by head office, dashboards and AI systems. Samuel Mueller, Scandit's CEO and co-founder, said, “Retailers do not have an AI adoption problem as much as a store truth problem.”
The data gap also affects customer service. Associates reported spending an average of 4.1 hours a week handling customer questions that store technology should answer. They were asked to physically check whether an item was in stock 5.85 times a week on average, while more than one in five said they performed these checks more than ten times a week.
Consulting associates was linked to higher tool use
The findings suggest that deployment processes can affect whether store technology is used. 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.
Andrew Smith, Managing Partner and co-founder of ThinkUncommon and a RETHINK Retail Top Expert since 2021, said trust rose sharply only after accuracy passed 70%. Associates surveyed by Scandit asked for accurate stock counts, better back-stock records, more accurate layouts and greater consultation from head office.
For retailers, the research frames reliable store data as a prerequisite for AI systems that make decisions using inventory information. It also points to involving store associates in technology pilots as one way to improve adoption and trust, although the survey reports an association rather than proving that consultation caused the difference.
