5 Transformative Use Cases of Computer Vision in Retail

Due to the nature of the retail industry, computer vision is one of the most important technologies for companies to adopt. By making it possible to collect and analyze visual data at scale, it enables powerful capabilities across the whole retail value chain, from the warehouse to the store. 

In retail, both customers and companies are excited about innovation. With such enthusiasm among buyers and sellers, it’s not surprising to see research findings like Nvidia’s, showing that:

• The retail and consumer packaged goods sectors have the highest potential for AI and analytics among all industries.
• AI has a significant positive impact on revenue and operating costs.
• Improving operational efficiencies is retail’s biggest AI opportunity.

Customers appreciate retailers’ use of technology so much that they often reward it with loyalty. In a survey of 12,000 consumers across 12 countries, Capgemini found that tech-enabled in-store experiences are among the most powerful drivers of loyalty for retailers:

• 60% of consumers regularly go to physical stores, and 46% regularly shop online, making in-store shopping the most important experience for the majority of consumers.
• 60% of consumers prefer a tech-enabled buying experience in grocery, home improvement, electronics, and specialty stores.
• 72% prefer it in apparel and fashion stores, making it a standout among other sectors.

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But, as all retailers know, nowadays it’s not physical or digital – it’s all about the omnichannel experience. And computer vision enables innovative customer experiences in the two most important channels: physical stores and mobile applications. 

What is computer vision in retail?

Computer vision is basically a type of AI that enables machines to understand images and videos. In retail, that might translate to recording shelves to identify inventory gaps, watching shoppers to spot suspicious behaviour, or letting customers search for products with photos instead of text.

Before we move on to the benefits of retail computer vision, it’s important to understand how incredible of an achievement it is.

Research on computer vision began in the 1940’s. One of the biggest breakthroughs came in 1989, when Yann LeCun introduced Convolutional Neural Networks (CNN). But early CNNs were limited by the technology of their time. 

The power of CNNs was finally proven in 2012, when researchers showed their astonishing capabilities at recognizing objects in images. Today, CNNs are still the backbone of most computer vision algorithms. 

Were all those decades of research worth it? What can this technology actually do? 

Computer vision has many applications that can be beneficial for all kinds of businesses:

• Object Classification – what broad category of object is in the image? 
• Object Identification – which type of a given object is in the image? 
• Object Verification – is the object in the image?
• Object Detection – where in the image is a specific object? 
• Object Recognition – what objects are in the image and where are they?
• Object Tracking – tracking a specific object across a series of images 
• Semantic/Instance Segmentation – breaking down the object into its components including counting 
• Object Character Recognition (OCR) – what is written in a particular image (i.e. text and numbers)?
• Document Analysis – Analyse a document and provide me the information I enquired about
• Facial Recognition – identify gender, age, cultural appearance, emotions, etc.
• Action Recognition – identify a specific action/gesture of a person
• Mood and Sentiment – forecast someone’s reactions or current mood
• Crowd Dynamics – count people and track their density / direction

The general applications of computer vision provide some idea of what’s possible with retail image recognition technology, but let’s get more specific. 

According to data from 2024 collected from 400 companies, many retailers are investing in different capabilities that are based on computer vision:

• 53% are investing in store analytics and insights
• 39% are investing in stockout and inventory management
• 35% are investing in loss prevention and asset protection
• 28% are investing in augmented reality experiences
• 24% are investing in visual search
• 21% are investing in autonomous checkouts
• 19% are investing in pick-and-place robotics

In another report, Grand View Research estimated the size of the global market for computer vision AI in retail at $1.66 billion in 2024, projected to reach $12.56 billion by 2033. They identified five key areas where computer vision is applied in retail:

• Customer Experience Enhancement
• Inventory Management
• Checkout and Payments
• Marketing and Merchandising
• Operations Optimization

Clearly, there are a lot of uses for it and computer vision, along with other types of AI and machine learning in retail, is spreading quickly. It’s not because of hype, but because it delivers results. According to Lenovo’s research, 96% of AI deployments in retail are either meeting or exceeding expectations, which is the highest satisfaction score among all industries.

What are the benefits of computer vision in retail?

Computer vision in retail enables innovative customer experiences and operational excellence. Here are several popular use cases that enable retailers to extract the most benefits from computer vision.

Visual Search makes it easier than ever to find products

Visual search in retail means customers no longer have to figure out how a product is called, search for different keywords or browse catalogs to find what they need. You can just take a photo or a screenshot – computer vision AI will analyze it, find matching products, and take you straight to them.

For customers, it makes for a more rewarding search experience and easier product discovery. Retailers get to benefit from higher conversion rates and greater customer satisfaction.

Visual search in the Home Depot app is a great example of how visual search can bridge the gap between digital and in-store customer experiences. 

When you’re fixing things around the house, you don’t want to get stuck researching specific products that you need to get the job done. Home Depot solves this with their mobile app, by letting you take a photo and finding the right things for you. Once you get to the store, you don’t even have to wander much, because they also have a ‘Product Locator’ feature that navigates you to the correct products. 

Amazon offers visual search as well, called Amazon Lens. It offers a lot of different functionalities:

• Circle to Search lets customers draw a circle around a specific product in a picture and search for just that
• Camera Search lets customers take a photo and immediately find similar products on Amazon, with the option to add text in order to narrow down the search
• Barcode scan provides an easy way for customers to find items they bought in the past and purchase them again

To get an idea of what it takes to implement visual search in-house, you can check out this article from the Etsy engineering blog. It explains how the Etsy team turned a proof-of-concept hackathon project into a feature that surfaces visually similar results from almost 100 million listings in a fraction of a second.

Virtual try-on is blending reality with the digital world

Buying glasses, makeup, or furniture online has always meant taking a leap of faith. Will that lipstick shade actually complement your skin tone? Will those glasses look ridiculous on your face? Is that sofa going to overwhelm your living room? These uncertainties have traditionally driven customers back to physical stores, or worse – resulted in costly returns that ate into retailers’ margins.

Virtual try-on technology powered by computer vision and augmented reality is eliminating this guesswork. By overlaying digital products onto live images of customers or their spaces, these tools let shoppers visualize exactly how products will look before they commit to a purchase. 

It used to be a novelty, but today it’s becoming a standard, and retailers are competing to offer the best virtual try-on experiences. Walmart and Amazon are investing heavily in these solutions, each with the goal to out-do the other. Amazon keeps expanding virtual try-ons for clothes, cosmetics and accessories, as well as furniture and home decor with the ‘View in Your Room’ feature. IKEA rolled out a digital experience for scanning your room and redecorating it.

As of 2026, virtual try-on solutions have reached a stage where they’re just as good as physically trying on products. Because of this, popular eyewear brand Warby Parker and Amazon have both discontinued programs where they mailed physical products to customers’ homes to try out without committing to a purchase. For Warby Parker, this may translate to as much as $100 million in savings per year.

Meanwhile, Google keeps releasing their own answers to virtual try-ons, with features that let users try clothing or furniture from product listings in search, and even an experimental app called Doppl that can generate videos of you wearing specific clothes.

Virtual try-ons work remarkably well on most modern smartphones, making them accessible to the vast majority of customers. It’s becoming an essential part of the standard shopping experience that modern customers expect.

Automated, cashierless stores and smart carts enable a seamless shopping experience

Standing in checkout lines is one of the biggest frustrations of physical retail. Computer vision-powered cashierless stores, automated checkout systems and smart carts are addressing this problem by letting customers get in, buy what they need, and get out quickly.

In cashierless stores, cameras combined with weight sensors and AI track what customers pick up, what they return to shelves, and what they take with them. When shoppers exit, they’re automatically charged through an app, and they receive a digital receipt within minutes. It’s as seamless as online shopping, but with the immediacy of a physical store.

Amazon’s Just Walk Out technology has become the most visible example of this transformation. In 2024, they unveiled an updated, more powerful version of their AI system that enables a cashierless experience. At the time, the technology powered over 170 third-party locations at airports, stadiums, universities, hospitals, and other places across the U.S., UK, Australia, and Canada. 

The results of implementing Just Walk Out speak for themselves – one store reported an 85% increase in transactions and a 112% increase in sales, and another has been able to serve 300% more customers on their busiest days and has increased annual revenue by 56%.

For larger grocery formats, smart shopping carts are emerging as the preferred solution. For example, the Amazon Dash Cart uses the same computer vision tech as Just Walk Out stores, but packed into a smart shopping cart. 

It’s not just Amazon. Instacart’s Caper smart cart technology also uses built-in cameras and sensors to automatically identify items as shoppers add them to their carts. They make the shopping experience easier for the customers, and drive more sales for stores. 

One retailer reported a 30% increase in average basket size when shoppers use smart carts. Plus, smart carts create additional opportunities for personalized deals and recommendations, offering new ways of in-store advertising and cross-selling.

Autonomous inventory management lets store owners easily track every item on their shelves

Keeping accurate track of inventory has always been a pain for retailers. Manual inventory audits take a lot of time, and error rates are high. The consequences are costly. Stockouts lead to lost sales, while overstocking ties up capital and leads to waste. 

Computer vision is changing this by enabling autonomous inventory management that monitors shelves around the clock, and catches problems before they impact the bottom line.

One solution involves autonomous robots that navigate store aisles, using advanced cameras and AI to scan products multiple times per day – like StockBot from PAL Robotics. 

Many retailers have already adopted robots for inventory management, and others are following suit. In 2025, one of the major grocery store chains in the US, Kroger, started a pilot program to test out robots from two different providers. One provider of such robots, Simbe, states that their robots can provide ‘full-store visibility with 98.7% SKU-level identification accuracy and over 99.3% shelf condition recall.’

But robots aren’t the only solution. Some retailers use fixed cameras mounted on shelves or ceilings that capture inventory data, and others are experimenting with wearable AI assistants that let store associates capture shelf data simply by walking their normal routes.

Wearables seem particularly enticing. Australia’s largest drug retailer, Chemist Warehouse, recently partnered with Augmodo, ‘the only real-time inventory and task tracker using wearable SmartBadges™ to create live 3D store maps.’ As employees walk the floor during their normal duties, the devices automatically scan shelves and alert them to gaps or misplaced items. 

What started as a pilot in four stores quickly grew to a full-chain expansion after Chemist Warehouse saw a 30% decrease in inventory gaps. The wearable, computer vision AI-powered badge reduces tasks, recommends actions to increase efficiencies, and continuously collects store data. Plus, it’s cheaper than most alternative solutions, and it might be the easiest to adopt – hardware is installed in 20 minutes. 

AI-powered security and loss prevention is solving the problem of shrinkage

Retail shrinkage has become a crisis that’s impossible to ignore. Just in the US, solving the issues that lead to shrinkage could help retailers save as much as $162.7 billion. Thanks to computer vision, AI-powered cameras can now catch what human eyes miss, identifying suspicious behaviors and verifying purchases in real-time. It’s a much-needed step-up from traditional surveillance.

Computer vision systems work by analyzing video feeds to detect patterns that signal theft or fraud. At self-checkout, overhead cameras can identify when items aren’t scanned properly, catch the infamous “banana trick” where expensive items are weighed as cheaper produce, and flag sweethearting (when cashiers deliberately undercharge friends). In a European retail chain study, AI technology monitoring 27 stores detected over 32,000 theft incidents among 2 million scanned items, catching fraud that would have otherwise gone unnoticed.

The ROI numbers are compelling. Companies that adopted retail loss prevention solutions from Vision AI provider Everseen achieved a 374% return on investment over three years, with systems paying for themselves in under six months. A separate implementation in European electronics stores reduced concealment-based theft by 41% by detecting suspicious behaviors like repeatedly glancing at security cameras or using clothing to hide products.

American big box store Sam’s Club took a different approach, deploying AI-powered exit verification in their stores. Instead of staff manually checking receipts, computer vision cameras capture images of shopping carts and instantly verify that members paid for everything. In locations where AI scanners were deployed, 50% of customers opted for them, reducing wait times for all customers by 23% while strengthening loss prevention.

Target has rolled out similar technology called TruScan, which detects unscanned items and tracks shoppers attempting to leave without properly checking out. It’s their response to customers exploiting self-checkout systems.

Privacy remains a key consideration. Ideally, theft-prevention systems should focus on tracking behaviors rather than identities, monitoring actions like product movements without using facial recognition or storing personally identifiable information. 

They should use techniques like video anonymization, and track shoppers by non-identifying details such as clothing color rather than biometric data. The goal is for retailers to gain valuable security insights while respecting customer privacy and maintaining trust.

Why retail and computer vision are a perfect match

Computer vision powers many of the most transformative innovations in modern retail. It enables retailers to address challenges that have been bringing the industry down for years, like shrinkage, inefficient inventory management, or costly returns from unsatisfied e-commerce buyers.

It’s also an essential technology for retailers to deal with the challenges of the future. At the 2025 edition of National Retail Federation’s event, all the big players in retail agreed – the border between physical and digital channels no longer exists. Retailers are ‘operating in a blended reality, where every space, interaction, and data point has commercial potential.’

Now, the goal is to provide dynamic, personalized experiences that transcend channels, and computer vision is one of the most important enablers for retailers that want to achieve this.