Computer Vision AI is rapidly expanding to solve a range of problems in physical retail stores, from loss prevention, operational efficiency and transaction accuracy through to customer analytics. But adoption remains fragmented across geographies, retail formats, and use cases. Vendors operating in this complex landscape need detailed market intelligence to identify which use cases are scaling fastest, where untapped opportunities lie, and how competitors are positioned.
This study covers computer vision deployment across six markets and five retail segments, analyzing eleven use cases driving adoption across the retail landscape.
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The study covers four solution categories subdivided into eleven use cases:
Loss Prevention
- At checkout
- In-aisle monitoring
Operational Efficiency and Safety
- Queue management and lane routing
- Layout and planogram optimisation
- Inventory management / stock checking
Transactional Accuracy
- Full basket recognition (checkout)
- In-cart “smart cart”
- Fresh produce identification
- Age verification at checkout
Customer Analytics
- Footfall analytics
- Dwell-time measurement
