What vision AI can actually do for retail businesses

Retail shrink doesn't happen in one place, and it doesn't happen slowly. Loss accumulates across the checkout and the aisle, and the challenge is having the shop-wide visibility to act in the moment before the loss is complete.

This is where Vision AI is invaluable, using computer vision to read what’s happening in the store, from every scanned item to shelf interactions and even queues forming at the checkout. It then converts those signals into decisions that teams can immediately act on and reduce shrinkage.

This piece covers what that looks like in practice: how Vision AI operates across the store, where it's most reliable, and what it means operationally for your store.

Real-time loss detection at self-checkout

There’s significant opportunity for recovering value at self-checkouts. At this point, a transaction is still open, the item is still present, and customers can self-correct, all without impacting the in-store experience.

Vision AI observes the full transaction as it happens, including which items are scanned and bagged and discrepancies that need to be addressed.

Discrepancies here represent loss patterns, from deliberate fraud to genuine mistakes. Some of the patterns Vision AI detects include:

• Non-scan and scan avoidance: Items missed, passed, or manipulated at the scanner
• Ticket switching: Barcodes swapped with those of cheaper items
• Basket and cart-based loss: Unscanned items left inside or beneath baskets and carts
• Abandoned transactions: Customers leaving the POS with unpaid items before payment completes
• Fresh produce misidentification: Items placed on the scale that don't match what's selected

When a loss pattern is detected, the first response is a soft nudge, a screen prompt that gives the customer the chance to self-correct before completing payment, without staff involvement. Associates are only brought in when the situation genuinely requires it.

These patterns are detected across all self-checkouts, and when they occur, customers are immediately prompted to self-correct. Only in rare cases is staff intervention necessary.

How much is recoverable at self-checkout with Vision AI?

Across Everseen's network of more than 10,000 stores and 150,000 checkouts, Vision AI intervention translates to over $500 million in recovered sales annually.

High-value aisle protection

Loss doesn't only happen at the checkout. External theft accounts for 36% of retail shrink, and the shelf is where most of it starts. Vision AI monitors high-value aisles continuously, analysing shopping behaviour against patterns associated with theft. With Everseen deployed across more than 10,000 stores, those patterns strengthen with every location added to the network.

When a pattern consistent with loss is identified, shoppers receive an immediate in-aisle audio alert. In most cases that's sufficient; loss is prevented without confrontation, and teams are only brought in when the situation genuinely calls for it.

Customer flow monitored before queues build

Many queue management strategies still involve a staff member noticing a build-up and responding. But that means the queue has already formed, and shoppers are waiting. Add the extra time it takes for a response to arrive, and a meaningful window has passed.

This is another touchpoint where Vision AI’s continuous monitoring improves efficiency. Queue length, wait time, and lane utilisation are all tracked in real time across the entire checkout estate for every moment the store is open.

Based on live customer flow data, Vision AI forecasts queue demand before it builds, giving checkout managers enough notice to open lanes and allocate staff ahead of time.

Adyen Research found that 2 out of every 3 shoppers abandon a purchase because of long queues, so lower average queue wait times across your large store estate means an increase in completed transactions.

Real-time, actionable data for retail teams

Each capability we’ve covered operates across a different part of the store and delivers value on its own: loss detection at checkout, shop floor theft prevention, and queue management.

But the stronger case for Vision AI is the connected intelligence layer: what you get when all of it is connected

For example, a nudge at SCO helps recover value, and teams can get ahead of queue build up with real-time lane forecasting.

Both are useful on their own, but together, they can identify patterns that individual store teams can’t catch in real time.

Vision AI monitors the store and converts what it sees into action fast enough for teams to do something about it.

How vision AI operates across the store

Everseen is Vision AI at scale

Everseen's platform brings together everything covered in this piece: checkout loss detection, aisle protection, queue forecasting, and connected intelligence. It is deployed across more than 10,000 stores and 150,000 live checkouts, processing over 15 million transactions every day. This scale means the model is constantly improving and getting better at differentiating loss patterns from normal shopping behaviour.

Book a demo to see how this works in practice.