AI's Rush to Transform Retail: The Costly Gap in Stock Data Accuracy
In the fast-evolving landscape of retail, companies are increasingly relying on artificial intelligence (AI) to streamline operations and enhance customer experiences. However, a recent study by Scandit reveals that the move toward AI adoption is far outpacing the accuracy of stock data within stores. This discrepancy could lead to significant financial repercussions for large retailers, amounting to millions in lost operational efficiency each year.
According to Scandit’s survey, less than half (46%) of store associates find that stock records align with the actual inventory on the shelves. This leaves a staggering 54% of stock data potentially misleading. Furthermore, a mere 8% of those surveyed expressed complete trust in the accuracy of the stock data, indicating a crucial trust gap that not only affects employee morale but also has direct financial implications. Retailers are faced with a costly conundrum: the technologies employed to improve operations are severely handicapped by an unreliable foundation of data.
The report highlights that associates often spend substantial time rectifying discrepancies in stock information. On average, they estimate regaining over 4.4 hours of work per week if they could depend on accurate data. For a retailer with around 10,000 employees, this time savings translates into an astonishing cost of over $41 million annually in labor. Extending this issue to the entire retail industry in the United States, the potential loss soars to an estimated $22 billion each year due to reliance on flawed stock data.
The current environment forces associates to adopt alternative methods for inventory management. Surprisingly, nearly half (49%) of associates report taking it upon themselves to correct records, while 44% advocate for stock adjustments. Yet, among those who act, many do so outside official channels—42% of associates bypass the system entirely, resorting to informal methods such as paper notes, team chats, personal photographs, and mental shortcuts. This has led to the emergence of a ‘shadow store’ where crucial inventory information exists outside the systems that headquarters, management dashboards, and AI technologies rely on.
Samuel Mueller, the CEO and co-founder of Scandit, shed light on this issue by stating that the real challenge lies not in the adoption of AI tools but in the reliability of the information that retailers have at their disposal. "Associates are investing their time in bridging the data gap; however, much of this effort is lost in unofficial systems that go unnoticed," he mentioned. Building a trustworthy view of store operations is essential for effective AI deployment and decision-making.
The survey findings indicate that the trust in stock data is not something that develops gradually; instead, it hinges significantly on high accuracy levels. Interestingly, once data accuracy crosses the 70% threshold, associates exhibit a marked increase in their trust levels, highlighting the direct correlation between reliable data and employee confidence.
Andrew Smith, a Managing Partner and co-founder of ThinkUncommon, emphasized the importance of achieving satisfactory accuracy levels. He noted that mere incremental improvements in data accuracy do not equate to meaningful change on the sales floor. Instead, trust and efficiency radically improve once data accuracy surpasses that crucial 70% mark. Until that point is achieved, associates will continue to perform manual checks to reconcile discrepancies and assist customers directly, ultimately detracting from overall productivity.
The implications of inaccurate data reach beyond operational efficiency, as associates also find themselves spending an average of 4.1 hours weekly addressing customer inquiries that technology should auto-resolve, along with nearly six physical checks for stock per week. With more than a fifth of associates reporting upwards of ten checks weekly, the demand for reliable inventory data underscores the operational bottleneck faced by retailers.
The practical implications of the research also extend to how technology is rolled out across stores. In a pilot program, associates who were involved in the consultation for technology deployment reported using store tools at a significantly higher rate than those who were not included in the discussion. Half of the pilot participants reported having trust in their stock data, contrasted with only 25% of those uninvolved in the planning phase.
As a solution, associates voiced the need for accurate stock counts to eliminate efforts spent chasing phantom inventory, improved layout accuracy, and enhanced communication from head office to field staff to ensure operational success. These measures could facilitate a more efficient inventory management workflow, ultimately leading to a better experience for both employees and customers alike.
As the retail landscape continues to evolve, it is imperative for companies to address the foundational issues surrounding stock accuracy to fully leverage the potential of AI-driven solutions—before they incur further costs due to inefficiencies and inaccuracies in their data.
According to Scandit’s survey, less than half (46%) of store associates find that stock records align with the actual inventory on the shelves. This leaves a staggering 54% of stock data potentially misleading. Furthermore, a mere 8% of those surveyed expressed complete trust in the accuracy of the stock data, indicating a crucial trust gap that not only affects employee morale but also has direct financial implications. Retailers are faced with a costly conundrum: the technologies employed to improve operations are severely handicapped by an unreliable foundation of data.
The report highlights that associates often spend substantial time rectifying discrepancies in stock information. On average, they estimate regaining over 4.4 hours of work per week if they could depend on accurate data. For a retailer with around 10,000 employees, this time savings translates into an astonishing cost of over $41 million annually in labor. Extending this issue to the entire retail industry in the United States, the potential loss soars to an estimated $22 billion each year due to reliance on flawed stock data.
The current environment forces associates to adopt alternative methods for inventory management. Surprisingly, nearly half (49%) of associates report taking it upon themselves to correct records, while 44% advocate for stock adjustments. Yet, among those who act, many do so outside official channels—42% of associates bypass the system entirely, resorting to informal methods such as paper notes, team chats, personal photographs, and mental shortcuts. This has led to the emergence of a ‘shadow store’ where crucial inventory information exists outside the systems that headquarters, management dashboards, and AI technologies rely on.
Samuel Mueller, the CEO and co-founder of Scandit, shed light on this issue by stating that the real challenge lies not in the adoption of AI tools but in the reliability of the information that retailers have at their disposal. "Associates are investing their time in bridging the data gap; however, much of this effort is lost in unofficial systems that go unnoticed," he mentioned. Building a trustworthy view of store operations is essential for effective AI deployment and decision-making.
The survey findings indicate that the trust in stock data is not something that develops gradually; instead, it hinges significantly on high accuracy levels. Interestingly, once data accuracy crosses the 70% threshold, associates exhibit a marked increase in their trust levels, highlighting the direct correlation between reliable data and employee confidence.
Andrew Smith, a Managing Partner and co-founder of ThinkUncommon, emphasized the importance of achieving satisfactory accuracy levels. He noted that mere incremental improvements in data accuracy do not equate to meaningful change on the sales floor. Instead, trust and efficiency radically improve once data accuracy surpasses that crucial 70% mark. Until that point is achieved, associates will continue to perform manual checks to reconcile discrepancies and assist customers directly, ultimately detracting from overall productivity.
The implications of inaccurate data reach beyond operational efficiency, as associates also find themselves spending an average of 4.1 hours weekly addressing customer inquiries that technology should auto-resolve, along with nearly six physical checks for stock per week. With more than a fifth of associates reporting upwards of ten checks weekly, the demand for reliable inventory data underscores the operational bottleneck faced by retailers.
The practical implications of the research also extend to how technology is rolled out across stores. In a pilot program, associates who were involved in the consultation for technology deployment reported using store tools at a significantly higher rate than those who were not included in the discussion. Half of the pilot participants reported having trust in their stock data, contrasted with only 25% of those uninvolved in the planning phase.
As a solution, associates voiced the need for accurate stock counts to eliminate efforts spent chasing phantom inventory, improved layout accuracy, and enhanced communication from head office to field staff to ensure operational success. These measures could facilitate a more efficient inventory management workflow, ultimately leading to a better experience for both employees and customers alike.
As the retail landscape continues to evolve, it is imperative for companies to address the foundational issues surrounding stock accuracy to fully leverage the potential of AI-driven solutions—before they incur further costs due to inefficiencies and inaccuracies in their data.
Topics Consumer Products & Retail）
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