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Updated on Apr 3, 2025

Top 5 Use Cases of Computer Vision in Retail in 2025

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Computer vision (CV) can be called the “eyes” of artificial intelligence (AI). It is revolutionizing almost every sector in the world, including retail. As more organizations recognize the potential of computer vision, they are investing more in improving their computer vision capabilities. The global computer vision market is projected to grow more than 300% to $41 billion by 2030.

See the top use cases of computer vision in retail to help business leaders make better decisions.

1. Improving store layout

With in-store computer vision-enabled cameras, retail store managers can track customers’ movements to identify patterns and repetitive behavior. This information can be used to mark hot areas and place relevant products to improve sales.

Through computer vision, retail store managers can gather information such as:

  • Which area of the store has the most traffic?
  • The average time a customer takes to complete a purchase
  • Is the time of day associated with product purchase?
  • Which products have the highest put-backs?

Example:

Samsung uses computer vision to track customer movement in its retail stores.

If you are a CPG (consumer packaged goods) producer and want to work with a service provider to manage the retail end of your supply chain. Here is a list of services that offer retail and planogram audits.

2. Inventory management

Computer vision is being used to improve efficiency in omnichannel retail systems. Smart cameras and sensor systems track inventory levels and products on shelves in real time.

For example, Sam’s Club, an American retail company, uses inventory scanning robots to track and share information on inventory levels, price accuracy, product location, etc.

3. Automated stores

The increasing trend towards customer service automation also leads to greater use of computer vision systems for automated checkout systems in retail stores.

Computer vision systems enable in-store cameras and sensors to track products, shelves, and customers accurately. Based on CV and AI, the system automatically charges the customer for the marked products upon their exit from the store.

Example:

Amazon Go is an excellent example of automated or cashier-less retail stores. These automated walkout stores are being considered the future of the convenience store market, and according to Forbes, they will revolutionize the retail sector. The stores use computer vision and AI technology to fully automate customer checkouts.

Similarly, an autonomous convenience store at San José State University uses AI and computer vision without facial recognition to ensure higher customer privacy and faster checkouts.

Check our article on hyperautomation in retail for more examples.

4. Retail theft prevention

Computer vision systems also help prevent shoplifting by tracking each product and the customer’s behavior.

Example:

Vaak Eye is a shoplifting prevention system based on computer vision. It is installed with retail store cameras and observes customer behavior to identify potential shoplifting activities.

Walmart is also using AI-enabled surveillance cameras to eliminate shoplifting from its stores:

5. Smartphones for barcode scanning

Computer vision-enabled smartphone apps are also being used in the retail sector. One of the most important applications is obtaining product information, such as product reviews and detailed information, in brick-and-mortar stores, which is normally available online.

Similarly, computer vision-enabled smartphone applications can perform tasks such as counting inventory and searching for products.

Example:

Samsung devices provide similar solutions based on computer vision and AR to improve retail operations and customer experience.

To find the option that best suits your annotation needs, you can also check our sortable and filterable list of:

Further reading

If you have any questions regarding computer vision in retail, please don’t hesitate to reach out:

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
Özge is an industry analyst at AIMultiple focused on data loss prevention, device control and data classification.

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