Can AI Cameras Really Stop Shoplifting? What They Catch and What They Can't

Introduction

Shoplifting remains a major concern for U.S. retailers, from neighborhood convenience stores and grocery chains to clothing boutiques and big-box stores. The National Retail Federation reported that retailers experienced a 93% increase in average annual shoplifting incidents in 2023 compared with 2019. Source: NRF's retail theft research.

Conventional surveillance cameras enable the owners to view the recorded footage in case anything goes wrong; however, these cameras are not effective in preventing any loss if there is no one viewing the feed. Here, AI surveillance cameras for retail come into play.

Through analyzing video footage, recognizing any actions that fall under certain predetermined risk patterns, and generating alerts for their evaluation, AI technology may allow for an earlier reaction of store staff to any situations that arise. Nevertheless, such technology is not infallible, and it cannot establish any person’s intent or the occurrence of any crime through analyzing video footage.

Therefore, can AI security cameras actually prevent shoplifting? They may help reduce certain opportunities for stealing, but the most effective solution lies in combining technology with qualified staff.

How Do AI Surveillance Cameras for Retail Work?

Traditional CCTV systems primarily record video for later viewing. AI-enabled video analytics add another layer by analyzing camera footage to identify events or patterns that may need attention.

Depending on the system, capabilities may include:

• Suspicious activity alerts: Flagging specific movements or behaviors that match configured detection rules.

• High-risk area monitoring: Highlighting activity around frequently targeted merchandise, stockrooms, or checkout zones.

• Self-checkout monitoring: Helping identify possible missed scans, product switching, or unusual checkout activity when supported by suitable analytics and transaction data.

• Point-of-sale monitoring: Connecting video with transactions such as voids, refunds, discounts, and no-sale events.

• Real-time notifications: Sending relevant clips or alerts to designated staff or monitoring operators.

Not every AI camera will have all these capabilities. Some cameras just analyze video feeds, some connect to POS software, some connect to human monitoring services, and so on.

The objective for the retailer is not to collect more videos. The aim is to detect meaningful events faster and give the proper individual the necessary context to react properly.

What Can AI Cameras Catch in a Retail Store?

1. Possible Concealment and Suspicious Merchandise Handling

There is the possibility to set the AI video analytics to detect specific actions that may indicate possible hiding, such as suspicious manipulation with products and activities related to bags and clothes.

For instance, if the actions of a customer match the configuration of a particular detection algorithm in the high-theft aisle, the store manager will be notified. A worker will be able to watch the recorded video and decide on further actions.

Nevertheless, taking and putting back a product into a shopping bag or manipulating with merchandise is not always an indication of theft. There are many other possible explanations for such actions performed by customers.

The key benefit: AI can draw attention to activity that deserves review, rather than requiring employees to watch every camera continuously.

2. Self-Checkout Errors and Potential Missed Scans

It may be hard to supervise self-checkout lanes during peak hours because there might be some problems with barcodes, wrong products being scanned, and putting the products in the bag before they have been scanned at all.

The type of solution used will determine if artificial intelligence-assisted video analytics may detect the potential exception or unusual handling of products.

For instance, it may register the situation when a product is detected to move to the bagging zone without being scanned.

However, this alert may not provide the full picture because there may be some blind spots for the camera, problems with barcodes, delays in scanning and other system malfunctions.

3. Repeated Activity in High-Risk Areas

Some stores experience recurring losses around particular aisles, displays, or merchandise categories.

AI-enabled video monitoring can help highlight patterns across recorded events, depending on its analytics and reporting capabilities. Managers may use these insights to review camera placement, improve visibility, adjust staffing, or reconsider how high-value items are displayed.

This is particularly useful for U.S. convenience stores, pharmacies, grocery stores, and retailers with multiple locations.

Rather than relying on assumptions about individual shoppers, managers can focus on documented incidents, vulnerable areas, and measurable patterns.

4. Suspicious Point-of-Sale Activity

Shoplifting is not the only source of retail shrink. Employee errors, policy violations, and internal theft can also contribute to inventory losses.

If video analytics are applied to the POS system, then it will allow for a faster investigation of suspicious activities. Such activities may consist of numerous refunds, voided sales, suspicious discounts, or opening of cash drawers without a purchase taking place.

A suspicious transaction does not necessarily mean that the employee did something wrong.

Connecting transaction records with video provides additional context, helping authorised managers investigate consistently and fairly.

What AI Cameras Can't Do

There are certain limitations associated with the use of AI for surveillance. Awareness about these limitations will help retailers set realistic expectations.

1. They Cannot Reliably Read Someone's Intentions

It is the actions seen by a camera and not the thoughts of the individual. The customer who is loitering around a very expensive product may be making comparisons, waiting for their family, or deciding to buy.

The AI system will be able to identify actions that suggest the possibility of a crime being committed, but it won’t be able to detect the intention of committing a crime.

The retailers need to consider the alerts as signals to verify situations.

2. They Can Produce False Alerts

AI systems are capable of detecting legitimate behavior as suspicious or failing to detect genuine incidences. The success rate is contingent on the angle at which cameras are placed, lighting, quality of images, congestion, obstruction, and analytics in use.

One instance is where a customer reaches inside his or her backpack to grab the wallet.

Retailers should test systems in real store conditions, track false alerts, and review performance regularly. Facial recognition deserves particular caution because inaccurate matches can lead to serious consequences. The U.S. Government Accountability Office's research on facial recognition discusses accuracy and privacy concerns.

3. They Cannot Guarantee That Theft Will Never Happen

AI cameras may help employees notice certain events sooner, but they cannot eliminate every opportunity for theft.

A camera may have a blocked view. A person may act outside the system's detection capabilities. An alert may arrive when employees are already occupied, or a theft may occur in an area without adequate coverage.

Effective loss prevention therefore requires more than cameras. Store layout, employee awareness, inventory controls, appropriate product protection, and consistent incident procedures remain important.

4. They Cannot Replace Human Judgement

A responsible retail security process should include human review before consequential action is taken.

Employees should take everything into account, stick to the rules of the corporation, and refrain from provoking people just because the algorithm triggered an alarm. It would also be wise for companies to teach their workers to ensure security in cases of potential theft.

The purpose of AI is to support better decisions, not remove accountability from them.

How U.S. Retailers Can Use AI Cameras More Effectively

To get practical value from AI surveillance cameras for retail, store owners should start with a clear operational plan.

1. Identify your biggest loss risks. Review inventory discrepancies, past incidents, self-checkout issues, and vulnerable store areas before choosing a system.

2. Check camera coverage. Confirm that important areas have usable views, sufficient lighting, and appropriate image quality. AI cannot reliably analyse details it cannot see.

3. Choose features that match your needs. The local boutique will only require basic event notifications, whereas a multi-store retailer would gain advantage from centralized monitoring, POS integration, and incident reporting.

4. Require human verification. Determine who will be notified, who will review the videos, and how the staff should react. Determine whether notifications provide useful context without being overwhelming to the staff members.

5. Protect customer and employee privacy. Clearly specify the terms under which footage is to be accessed, stored, shared, and secured. Check any federal, state, and municipal regulations that may apply when implementing video analytics, audio recording, or facial recognition technology. Do not rely on AI alerts alone to charge or ban someone.

6. Measure results. Monitor occurrences of events that have been proven true, shrinkage in inventory, false alarms, reaction time, and employee burden. Monitor the effectiveness of the system over time to ascertain improvement.

Conducting a pilot in selected locations will assist the retailer in assessing his performance before expanding to the entire business.

How Survill Supports Retail Theft Prevention

For retailers looking to move beyond passive video recording, Survill combines AI-powered CCTV monitoring with risk alerts and human-verified monitoring workflows.

This technology system was designed to make sure that companies are able to identify issues as they arise rather than after the fact through viewing recorded footage. Depending on how it is implemented, it can be used for investigating incidents using video footage from point-of-sale terminals.

For multi-location operators, Survill's industry solutions provide a starting point for exploring surveillance and monitoring needs across retail and other business environments.

The important thing here is to find out whether the solution fits the specific requirements of your store regarding risk exposure, cameras, staff, and response procedure. It is important to evaluate the quality of alerts and results of operations rather than only statistics of accuracy.

Frequently Asked Questions

Can AI cameras detect shoplifting in real time?

There exist AI-powered devices capable of recognizing the suspicious behavior that might be related to stealing and giving notifications. It is all determined by the camera configuration, the algorithms used, and the environment inside the store. The notification itself cannot serve as proof of the crime.

Are AI security cameras better than traditional CCTV?

Systems equipped with AI functionality can be employed for automated event and alert detection in the context of video surveillance. Traditional closed-circuit television will do the trick for companies that only need video recording.

Can AI cameras detect employee theft?

There are some ways to identify any abnormal POS activity, which could be multiple void transactions, refunds, or no-sale incidents. All of these have to be investigated because even normal transactions may create the same trends.

Do small retail stores need AI surveillance?

On the other hand, however, all small businesses may not need AI analytics. Small businesses that have been consistently losing money, have inadequate monitoring staff, or operate from various locations may benefit from considering whether it is worth their while to analyze AI-driven monitoring compared to existing security costs.

Does AI surveillance replace loss-prevention employees?

No. AI may assist in prioritizing events so that one does not have to watch every stream continuously, but it cannot substitute human beings for alert triaging, decision making, and incident response.

Conclusion

Cameras powered by artificial intelligence will help American retailers detect any attempts of stealing goods, perform surveillance on self-checkout counters, analyze questionable operations, and mitigate any risks that can arise during these processes. Nevertheless, it is not within the power of AI cameras to prevent any thefts, prove one’s intentions, or replace human judgment.

What you need to do is use suitable cameras along with proper configuration of the analytics system and human verification.

If your store still relies on reviewing footage only after an incident, explore Survill's AI-powered video monitoring to see whether real-time alerts and connected video workflows could improve your retail security operations.