Les technologies basées sur l'IA permettent d'analyser en temps réel les images de vidéosurveillance, les interactions avec les produits, les déplacements et les opérations de caisse. Cela peut aider les commerçants à repérer plus rapidement les éventuelles anomalies, les comportements suspects et les erreurs humaines qu'avec la surveillance traditionnelle seule.
Les caméras de sécurité traditionnelles jouent un rôle important dans le commerce de détail, mais leurs capacités sont limitées lorsqu'elles servent principalement à enregistrer des événements déjà survenus. Il faut toujours quelqu'un pour visionner les images, repérer les activités inhabituelles et déterminer si un incident nécessite une intervention.
Cela devient de plus en plus difficile à mesure que les caisses en libre-service se généralisent. Les caisses libre-service offrent aux clients plus de flexibilité et peut aider les commerçants à améliorer l'efficacité du passage à la caisse, mais cela crée également des risques d'oubli de scan, de saisies incorrectes et d'autres formes de fraude à la caisse. Rien qu'en 2025, il a été rapporté qu'aux États-Unis seulement, environ $48 billion dollars were lost due to retail crime.
It is forecasted that theft and fraud will only increase if no actionable solutions are implemented. This is where the conversation around theft prevention needs to evolve. Retailers are not simply trying to see more. They need technology that can understand more.
Adding more cameras does not necessarily mean adding more visibility. When teams have hours of footage to review, important activity can easily be missed. AI changes this by continuously analyzing activity and identifying patterns that may warrant attention.
ELERA’s approach can begin with a silent-mode observation period, allowing the technology to monitor activity before full deployment. Over an initial 30 to 60 day period, the platform will examine patterns, product interactions and customer activity to establish a better understanding of the environment. This observation period is important because every retail store operates differently. Once those patterns are understood, AI can help retailers move to a more proactive solution. Instead of relying on employees to watch every interaction, technology can continuously monitor activity.
If a customer places a banana in the self-checkout area but enters a different product, instead of relying solely on the customer to select the correct produce code, AI-powered computer vision can analyze the item and identify that it appears to be a banana.
The real value of AI-powered loss prevention comes from its ability to connect what the camera sees with what is happening in the store in real time. AI can analyze signals such as movement, object handling, product interactions, timing and activity around the checkout area. Computer vision systems are increasingly capable of recognizing concealment gestures, identifying objects and comparing physical activity with checkout events.
The goal is not to have technology make every decision independently. Instead, AI provides retailers with better information at the moment it matters, allowing employees to focus their attention where it is most needed and allow retailers to make more informed decisions based on collected data.
Loss prevention however is only one part of the opportunity of utilizing AI-detection in your stores. AI-powered product recognition can also make the self-checkout experience faster and easier. Produce is a good example because customers traditionally need to identify an item and manually enter the corresponding product number or search through a list.
With AI-powered recognition, a camera can identify the product based on its visual characteristics and help match it to the appropriate item. That means less manual input, fewer opportunities for incorrect entries and less need for employees to intervene when customers are unsure which product code to select.
Over time, these small improvements can have a meaningful operational impact. Based on the ELERA information based on a major grocery retailer, after using the technology within the first few months, they reported an average of approximately 3.28 labor hours per day saved.
For customers, the benefit is simple, it allows for less friction at checkout, and for employees, it means spending less time correcting produce entries or assisting with routine checkout questions.
One of the biggest advantages of an AI-powered approach is that retailers do not need to abandon the processes they already have in place. AI can work alongside existing cameras, point-of-sale systems, self-checkout technology and loss-prevention teams. Instead of creating another disconnected system, the goal is to connect existing store activity with intelligent data and actionable insights.
This can help retailers better understand what is happening across their operations, from checkout behavior to customer interactions and product activity. The result is a more adaptable approach to retail management. Employees can spend less time manually reviewing activity and more time acting on information that has already been analyzed.
Shrink does not come exclusively from theft. Inventory loss can also result from errors, inaccurate product entries, administrative mistakes and other operational issues. AI can help retailers address some of these preventable losses by identifying discrepancies earlier.
At self-checkout, product recognition can help reduce incorrect produce entries. At the same time, computer vision can help identify potential mismatches between what a customer handles and what is recorded in the transaction. Reducing these errors has a direct connection to profitability. Every prevented loss protects more of the value already generated by the store.
There is also a labor benefit because when employees are not constantly required to correct avoidable errors or manually monitor every checkout interaction, retailers can use their teams more efficiently.
Better accuracy, better visibility and more efficient labor allocation can ultimately contribute to a more consistent customer experience. When products, prices and transactions are accurately represented, customers can move through the store with greater confidence.
ELERA can operate in a silent observation period before full deployment, allowing the system to analyze store-specific activity and establish patterns. A 30 to 60 day observation period can help retailers understand normal activity and improve how the technology is configured for their environment.
Yes. AI-powered produce recognition can help identify products automatically, reducing the need for customers to manually enter produce codes. This can make checkout faster while reducing incorrect entries and the need for employee assistance.
Real-time data allows retailers to respond to potential issues while they are happening rather than discovering them later through inventory counts or manual footage reviews. This can help retailers reduce preventable losses and make faster operational decisions.
