How multi-shopper AI is transforming retail store operations

Perfect real-time inventory accuracy is the next big frontier for retail. Current store-level inventory accuracy is estimated to be as low as 60 percent, and this gap represents a massive opportunity for growth. By unlocking complete visibility into products, shoppers, and store activity, retailers can effectively eliminate stockouts, optimise placement, and maximise overall sales.

The business impact is significant. Inventory distortion, including out-of-stocks and overstocks, costs retailers approximately $1.7 trillion globally each year. While modern stores generate vast amounts of data through cameras, sensors, point-of-sale systems, and digital channels, most of it remains siloed. This visibility gap makes it almost impossible for retailers to respond dynamically as floor conditions change throughout the day.

Where intelligence meets the shop floor

Multi-shopper AI, combining computer vision and real-time analytics to interpret activity across the store, can be a solution. By analysing the behaviour of dozens or even hundreds of shoppers, retailers can gain a broader view of operations and customer behaviour, supporting faster and more informed decision-making.

Computer vision has gained momentum in retail over the last few years. The global market for computer vision in retail is projected to grow from approximately $1.7 billion in 2024 to more than $12.5 billion by 2033, reflecting increasing investment in technologies that enhance operational visibility and informed decision-making.

Translating live data into store floor action

AI-powered vision systems analyse live video feeds to understand how shoppers move through stores, interact with products, and respond to promotions and product displays. This visibility supports a range of operational improvements. Operations teams can identify congestion points, monitor shelf availability, and assess engagement with promotions. Leaders can analyse customer journeys without disrupting the shopping experience. If a popular product is running low, store teams can receive and respond to alerts before shelves are emptied. If queues begin forming at checkout, the floor manager can adjust staffing levels to match demand.

Retailers can understand which promotions attract attention, identify areas where congestion regularly occurs, and evaluate how shoppers move through different sections of the store. These insights support decisions on store layouts, staffing, and merchandising.

Move from passive operations to autonomous environments

Modern stores generate a continuous stream of operational data. Information flows from inventory systems, connected devices, digital shelves, customer touchpoints, and vision-based monitoring platforms throughout the day. AI combines these to provide leaders with a more complete view of their retail operations, store-by-store.

Paired with predictive analytics, timely insights support increasingly autonomous operations. Inventory can be replenished before shortages occur, while workforce allocation and operational planning can be adjusted using real-time information.

This shift is already delivering measurable results. As a case in point, Infosys helped a major retailer build a unified in-store AI-powered platform that combined computer vision, RFID, and edge technologies to improve real-time operational visibility. The solution demonstrated more than 90 percent real-time inferencing accuracy with only a few seconds of latency in a live store environment, highlighting how intelligent stores can translate real-time insights into operational action and enhance customer experience, inventory management, and store associate workflows.

Inventory visibility is one of the most practical applications of AI in retail. Systems can identify misplaced products, detect empty shelves, monitor product availability, and find unusual patterns that require attention. These capabilities improve operational efficiency, while ensuring that products are available when customers need them.

Bridge physical and digital retail

Retail leaders can build greater value by connecting digital commerce data with activity inside physical stores. Historically, online and in-store experiences have operated separately, resulting in fragmented views of customer behaviour. AI helps retailers build stronger links between these channels.

Leaders can also develop a more complete understanding of customer preferences and purchasing intent by combining in-store behavioural signals with digital interactions. Such a move supports more relevant engagement through personalised promotions, product recommendations, and services tailored to individual needs.

Customers benefit from greater consistency across physical and digital channels, while retailers gain deeper insight into purchasing patterns. As stores become more intelligent, they can offer many of the personalisation capabilities that consumers expect from eCommerce, while retaining the immediacy and convenience of in-person shopping.

The next phase of retail innovation will usher in stores that can think, see, and act in real time. As physical and digital retail become more intricately connected, store intelligence is rapidly gaining centre stage as a foundational capability for modern retail operations.