A force multiplier for retail security and operations by Karl Pardoe, Account Executive at Genetec
Shoplifting has hit an all-time high, with over 530,000 offences reported in the year to March 2025, highlighting a growing need for security solutions that can proactively identify suspicious behavior, such as someone standing near a clothing rack for too long.
Whether it’s spotting a potential shoplifter before they leave the store, identifying hazards like spills in an aisle, or tracking patterns in customer flow to adjust staffing, AI-powered video analytics can help retailers respond to incidents faster, reduce false alarms, and gain insights that improve security, operations, and customer experience.
AI vs. IA: Clarifying the terminology
While they may seem similar, AI and Intelligent Automation (IA) play different interconnected roles in providing benefits to the retail industry. AI involves teaching technology to spot patterns, make decisions, and carry out tasks by feeding it information. For example, an AI-powered video security system can identify and flag suspicious activity to a security team, freeing up time they would have otherwise spent watching multiple hours of video.
IA, on the other hand, uses AI and takes it a step further by including human decision-making, so the security team can choose the best next steps.
Empowering loss prevention teams with AI-enabled technology
Retail loss prevention teams face mounting challenges, from ORC to internal theft. Strengthening processes and deploying advanced monitoring systems can help retailers become more resilient against threats.
Key benefits of AI-powered video analytics include:
Reducing friction for loss prevention teams
Retailers accumulate vast amounts of video footage every day, but manually sifting through it is time-consuming and inefficient. By using AI-enabled features of their video management or physical security system to analyse video streams, loss prevention teams can identify potential issues faster and focus on actual risks instead of monitoring multiple cameras at once.
For example, automated alerts can notify operators of potential threats, such as individuals lingering in high-value areas. Once flagged, management can review the incidents and decide if further monitoring or intervention is needed. This proactive approach reduces false alarms and allows teams to respond swiftly to credible threats.
Accelerating investigations
Manual video reviews after an incident can stretch for hours or even days. AI-powered video analytics can help streamline this process with forensic search functions. Investigators can use natural language prompts such as "woman in a red jacket" or "white truck near entrance" to easily locate relevant footage within a specific time frame. This rapid access to evidence not only aids internal investigations but also streamlines collaboration with law enforcement. Once the footage is located, loss prevention teams can securely share evidence with law enforcement using a digital evidence management system (DEMS).
Detecting vehicle license plates
ORC often involves repeat offenders operating across multiple stores in a region. In this case, an automatic license plate recognition (ALPR) solution enables retailers to track vehicle plates linked to previous incidents. For example, when a flagged vehicle enters a property, staff receive immediate alerts, and the information can be shared with law enforcement and nearby participating retailers. This collaboration strengthens defenses against ORC and helps retailers build stronger cases against offenders.
Enhancing the customer experience
Many people enjoy going into a store, whether it’s to see and touch a product before buying it (46%) or to take it home right away (40%), according to a PwC Survey. By showing when stores are busiest, how shoppers move through the aisles, and where staff are most needed, AI-powered video analytics can give retailers the information they need to create experiences that match what customers want.
Here are four ways retailers can improve customer experience by deploying AI-powered video analytics:
Understanding traffic patterns
By using AI to analyse data from in-store cameras, retailers can get a clearer picture of how customers move through the space. This helps identify high-traffic areas, optimise store layouts, and ensure essential items are within easy reach. For instance, a retailer can use these insights to determine the best placement for seasonal or clearance items, ultimately creating a smoother and more enjoyable customer experience.
Improving marketing and promotions
Video data can be used to monitor customer movement and help evaluate the effectiveness of product displays and promotional campaigns. By correlating customer behavior with sales data, retailers can determine which displays convert browsers into buyers. Additionally, comparing traffic patterns before, during, and after a promotion can provide valuable insights into what strategies work best for attracting and retaining customers.
Keeping shelves stocked and stores clean
Video analytics can help detect when stock is low or displays are disorganised. In this case, automated alerts can notify staff when restocking is needed or when a display has been disrupted, ensuring products are always available and the store stays presentable.
Managing checkout lines and customer service
Video analytics can detect long lines at registers and alert staff, prompting the opening of additional registers to ensure the customer experience is not negatively impacted. Similarly, by monitoring store zones, staff can be notified when a customer appears to need assistance, helping to close more sales and provide a personalised experience.
Building a responsible approach to AI
Retailers are increasingly using AI for large-scale data analysis and automation, aiming to maximise security investments and support loss prevention teams. However, AI is not a one-size-fits-all solution, and its success depends on thoughtful planning and human oversight.
Some key considerations include:
- Data privacy: Retailers must prioritise the protection of customer data by following strict data protection regulations and implementing strong security measures. Access to sensitive information should be carefully controlled to prevent misuse.
- Transparency and fairness: AI models should be rigorously tested to ensure fair, unbiased outcomes. Retailers should choose vendors that rigorously test AI models to minimise bias and ensure accurate, explainable results.
- Human decision-making: While AI can process information faster than any human, final decisions – especially those involving security – should involve human judgment. AI serves as a tool to inform and support decision-making, not replace it.
Preparing for tomorrow’s retail landscape
To fully benefit from AI advancements, retailers should consider physical security platforms with built-in AI capabilities. In these modern systems, AI isn’t a separate add-on, it’s embedded into the software, quietly powering key functions like video analytics.
Ultimately, the key to success lies in blending innovative technology with a thoughtful, human-centered approach. By doing so, retailers can protect their assets and create a shopping environment that’s both safe and enjoyable for customers.