AI Video Analytics: What It Is and Why US Retailers Can’t Afford to Skip It in 2026
Store theft in the USA has emerged as one of the most up to date operations challenges among the store owners. Retail losses are costing businesses more than 100 billion USD, every year, as reported by the National Retail Federation due to shoplifting, organized retail crime and operational inefficiencies.
While CCTV systems are already established by most of the retailers to handle these risks, the industry is becoming awakened to the fact that traditional surveillance cameras were implemented to record and not prevent incidents. That strategy is already starting to become obsolete in 2026.
Lately, retailers are seeking technologies that are able to identify risks at an earlier stage, enhance operations in the store, and generate insights about the customer behavior, something like an AI-enabled video analytics and modern retail video analytics software.
What Is AI Video Analytics?
AI video analytics is the technology that recognizes the patterns, behaviors, or anomalies in video footage of CCTV cameras and attempts to identify them automatically. So, rather than leaving the process of watching recordings to security teams, AI-enabled video analytics processes live video feeds and marks activity that needs to be addressed. These retail video analytics software allows retailers to have CCTV systems that are also an intelligent monitoring tool for both security and operational insights.
An AI-enabled video analytics can detect:
– Footfall Traffic of customers.
– Checkout queue congestion
– Suspecting or suspicious behavior.
– Movement within confined space.
– Customer interaction at points of display of products.
These newer AI-enabled video analytics allow the use of AI face detection and AI face recognition as well. With AI face detection, retailers can now identify human faces in camera footage to monitor restricted zones or high-risk areas. Meanwhile, AI face recognition technology can help identify repeat offenders and track known individuals across multiple cameras. This capability is specially becoming increasingly relevant because external theft, including organized retail crime (ORC), accounts for nearly 36% of total retail shrink, making it one of the largest sources of loss for retailers.
In short, ai-enabled video analytics and retail video analytics software is transforming ordinary surveillance cameras into advanced systems that enable retailers to make faster and more informed decisions.
The reason why Retailers are resorting to AI Video Analytics.
Retailers are being pressured to manage losses, enhance efficiency of stores as well as provide quality customer experiences despite having few staffs available and ai-enabled video analytics can solve a number of these challenges at the same time. Here are some-
1. Improved Loss Prevention
AI-enabled video analytics systems are able to identify suspicious movement patterns, uncharacteristic behaviour around high-value products, or uncharacteristic activity that are not part of the usual operating trends.
For example, retail video analytics software can detect when someone repeatedly lingers near a restricted shelf or behaves differently from normal customer patterns and because there is real time alert activation, store teams are able to act sooner and minimize the losses that are likely to arise. This ability can have a quantifiable effect on the overall security performance of retailers who operate across locations.
In addition, integrating AI face detection and AI face recognition allows security teams to track individuals who repeatedly appear in suspicious incidents.
2. Knowledge on Customer Behavior in the Physical Stores.
Behavioral data has not been new to online retailers who have been utilizing it to streamline the online shopping experience. They are aware of how the customers wander, what they are clicking on and where they are losing.
Physical stores have traditionally enjoyed much less insight into this customer experience. To help this, retail video analytics software gives retailers a better understanding of the customer flow in their stores.
Through movement patterns and dwell time, retailers could be in position to know:
– What parts of the store do you have the most visitors.
– What displays get one interested?
– What are the common sections that have been overlooked?
– Aisle navigation by customers.
Put simply, ai-enabled video analytics can assist retailers to enhance store format, better merchandising tactics, and develop more efficient store experiences.
3. More Productive Store Processes.
According to industry estimates, process and control failures account for about 13.7% of total retail shrink. This puts managers into a task of determining better when to add more checkout counters, the places where to assign the staff in the peak time, and even how to handle the number of customers in the peak hours and retail video analytics software will be able to automate most of these observations.
As an example, the AI-enabled video analytics can monitor the checkout queues, and once they reach a certain limit, it will alert the personnel to open more registers. It will also be able to detect high shopping times and give historical traffic data. By combining these insights with AI face detection and traffic monitoring, managers gain real-time visibility into store activity without manual observation..
4. Getting the Most out of the Cameras You already have.
The benefit of the infrastructure already owned by many retailers is one of the biggest upsides of the use of retail video analytics software and a majority of the stores have a CCTV system or any form of business security cameras. These CCTVs can be integrated with the ai-enabled video analytics that can provide an intelligence layer without having to replace the hardware used by retailers. This renders adoption much easier.
The practice of AI Video Analytics.
Retail video analytics software continuously analyzes real-time feeds of existing CCTVs in a store. The AI-enabled video analytics uses machine learning models to find patterns, anomalies, and actionable insights to camera footage. As an alternative to manual monitoring, these retail video analytics software automatically monitor major indicators of operations.
To add to that, artificial intelligence has penetrated all aspects of contemporary retail business, including demand prediction and inventory control, as well as marketing automation. Within such a setting, it can become more and more restrictive to rely only and only on cameras, which are only able to record the footage.
Through the retail video analytics software with integrated AI face detection, and AI face recognition, the CCTV cameras are able to actively analyze the activities better and provide insights that enable quicker responses by the retailer. The bottomline is that the usage of surveillance cameras as passive recording devices is increasingly becoming akin to applying yesterday technology to the current retail problems.
Conclusion
The retail stores have had CCTV cameras over the decades. But their role is evolving. It is no longer a thing to just capture footage when the modern retail video analytics software can do so much more. Retailers will be able to make smarter decisions, have stronger loss prevention, and have an improved in-store customer experience with the assistance of these ai-enabled video analytics, which will turn their current camera security system into a tool. As the retailing market itself is still undergoing transformation, the companies that will use advanced video analytics will be in a better place to conduct their business and react fast to the issues in the store floor.
In the event that you are considering retail video analytics software to guide your retail operations, you can consider organizations such as JARVIS by Staqu as a platform to get your traditional CCTV systems to enable real-time insights of your cameras, you already have.
FAQs
What is AI-enabled video analytics?
AI video analytics applies AI to real-time CCTV camera footage in order to process video data and identify patterns, anomalies, or threats to security. Jarvis by Staqu does exactly this, delivering real-time intelligence across your camera network without requiring manual monitoring.
What is the difference between AI face detection and AI face recognition?
AI face detection identifies the presence of human faces in images or video footage, helping retailers monitor restricted zones and activity across cameras. AI face recognition goes a step further by identifying specific individuals using facial features, allowing retailers to track repeat offenders and strengthen store security.
What is the benefit of retail video analytics to stores?
Video analytics in retail stores assists outlets to track customer flow and activity, store queues, and shopper behavior within the store. Jarvis by Staqu brings all of this into a single dashboard, giving store managers actionable insights on footfall, dwell time, and shopper patterns without sifting through hours of footage.
Can AI video analytics be used with current CCTV systems?
Yes. The majority of video analytics software can be used together with current CCTV systems so that a business can upgrade its existing infrastructure without replacing cameras. Jarvis by Staqu is built for exactly this, it integrates with your existing setup, making the transition seamless and cost-efficient.\
Can AI video analytics only be afforded by large retail stores?
No. AI-powered video analytics solutions can help retailers of any size, particularly those looking to enhance visibility and security across their stores. Jarvis by Staqu offers scalable deployment, whether you operate one outlet or a hundred, the solution adapts to your scale and budget.