A supermarket refrigeration system with four glass doors displays a variety of products. Several AI inference rectangles and labels are placed on products and sections of the cooler to indicate that a computer vision‒based system is being used to complete automated inventory management

Artificial Intelligence (AI) in Retail

Learn how AI is empowering retailers to automate, innovate, and meet changing customer expectations like never before. Discover how Intel can support your AI-in-retail initiatives.

An Overview of AI in Retail

  • Types of AI used in retail include machine and deep learning, conversational AI, and computer vision.

  • AI helps retailers enhance customer experiences, reduce shrinkage, and improve profits through better inventory management.

  • AI in retail use cases include frictionless self-checkout, smart shelves, and automated inventory management.

  • Intel offers a portfolio of hardware and software technologies and partner offerings to advance AI innovation in retail.



Create Frictionless Shopping and Checkout

Whether it’s a small boutique or a multinational superstore, retailers work hard to create shopping experiences that are convenient, personalized, and enjoyable. However, these shopping experiences are no longer enough to satisfy today’s tech-savvy, on-the-go customers. These customers now seek out frictionless shopping and checkout experiences—where most, if not all, interactions with the retailer are streamlined via technologies like AI, computer vision, deep learning, sensors, and software solutions to make the shopping journey as seamless as possible. By automating most of the transactional interactions, employees can focus on helping customers and other high-value tasks.

Nourish + Bloom grocery, the first autonomous grocery store in the Southern US, is an example of a truly AI-powered, frictionless, and contactless shopping experience. This store is designed for customers to shop with no checkout lines and no cashiers. To shop, customers download the store’s app, scan their phone when entering the store, load groceries into their cart, and walk out. The frictionless experience is powered by a combination of AI technologies, including:

Watch this video for an inside look at the Intel® AI technology that makes frictionless shopping at Nourish + Bloom possible.

Provide Unforgettable, Personalized Customer Experiences

In addition to seeking out frictionless shopping, customers desire experiences that are tailored to their preferences. For retailers, that means personalizing shopping with AI. Digital signage embedded with computer vision can boost customer engagement and serve up real-time advertising that speaks to a particular audience. It can also be used to collect data about which types of customers are shopping and when. This information can be used, for example, by merchandising teams to make better decisions about product promotions.

Personalization can also be enabled through point of sale (POS) systems that can capture data about what was purchased and use that information to generate new product recommendations for each customer. Personalization also benefits retailers. Capturing and analyzing all this data leads to more-accurate segmentation and experiences that are tailored to a customer’s patterns and preferences, which in turn can help build brand loyalty, improve customer retention, and grow revenue.

Enhance Loss Prevention Efforts

Product loss and theft—also known as retail shrink—is a rapidly growing challenge for today’s retailers. In 2021, retail shrink cost United States-based retailers nearly USD 100 billion.2 By integrating AI, retailers can leverage object detection, motion analytics at the self-checkout station, and digital sensors to support loss prevention. When used with computer vision, these checkout systems can help mitigate product loss in near-real time.

Improve and Automate Inventory Management

Maintaining an accurate inventory is a major challenge for retailers. By connecting more parts of their operations and applying AI, retailers gain a comprehensive view of stores, shoppers, and products to help with inventory management.

Intel-enabled responsive retail technologies make it possible to collect and process information from sensors, cameras, and other sources. Designed to bridge islands of technology and eliminate data silos, this platform supports sensors and software from a variety of third parties.

Another type of AI inventory management uses smart shelves to quickly identify out-of-stock items and pricing errors. Inventory robots can alert staff to low stock or misplaced items for more-up-to-date inventories. As a result, retailers can run stores more efficiently and free up associates’ time to focus on improving the shopping experience.

Demand Forecasting and Merchandising

The more you understand customer behaviors and trends the better you can meet demand and present the best possible products. AI helps retailers improve demand forecasting, make pricing decisions, and optimize product placement. As a result, customers connect with the right products, in the right place, at the right time. Predictive analytics can help you order the right amount of stock so that stores won’t end up with too much or too little. AI can also track data from online channels, informing better e-commerce strategies.

AI at the retail edge can help you recognize customer intent and optimize the shopper’s journey accordingly. One example is heat mapping in the store. The combination of cameras and computer vision reveals which products are picked up, which are returned, and where the customer goes after leaving the shelf. You can use this intelligence to create experiences that promote engagement with products and help shoppers learn more.

Sales revenue is a key performance metric, but in-depth analysis of poor sales performance is rare. By combining vision analytics with transaction data, you can gain insights into sales performance during periods of high and low traffic for each store.

Intel® Technologies for AI in Retail

Intel offers a deep portfolio of flexible AI hardware technologies and optimized AI software solutions to help you more easily develop and deploy AI at your store, hotel, or restaurant.

Intel® Partner Platforms and Solutions for AI in Retail

At Intel, we work with innovators in the retail ecosystem to deliver integrated, AI-powered solutions that can be deployed quickly and cost-effectively. These partner solutions apply our wide range of AI capabilities, from computer vision at the edge to machine learning in the cloud. To find prevalidated solutions through our partner ecosystem, explore the following emerging use case solutions or visit the Intel® Partner Showcase.

  • Intelligent digital signage ads use AI inferencing to understand customer engagement and interest. The platform adapts content to the audience in near-real time.
  • Smart shelves instantly check product availability so that items can be quickly replenished.
  • Endless aisle kiosks let customers see more products available at other locations. They also enable cross-selling and upselling opportunities.
  • Smart self-checkout systems accept loyalty cards, coupons, and transactions via mobile phone or through touchless technologies. Integrated video analytics identify products when a barcode is missing or unreadable.
  • Digital and touchless kiosks recognize speech and gestures and allow for hyperconvenient checkout options, self-service wayfinding, or in-store product research.
  • Autonomous mobile service robots interact with customers and perform simple tasks to enhance the shopping experience.

Retail Thrives on AI

AI in retail opens up a world of new possibilities and opportunities for businesses to authentically connect with their customers, turn their data into powerful new insights, and take operations to the next level. Intel, alongside our partners, is here to help you bring your AI in retail ideas to life.

Frequently Asked Questions

The technologies and solutions used to enable AI in retail vary widely depending on the business challenge being solved or project being deployed. However, the technologies most commonly used across AI use cases include:

  • General-use processors: These processors are created to be used for a variety of general-purpose workloads, such as web browsing or word processing, where acceleration may not be required. However, in some cases, FPGAs can be added to these processors to increase system performance so they can handle AI workloads.
  • AI processors: These processors are purpose-built to handle compute-intensive, specialized AI workloads. These processors can include integrated accelerators to provide extra performance gains.
  • GPUs: Although not always required to perform AI tasks, GPUs are sometimes added because of their ability to perform parallel processing when training deep learning models. GPUs can be integrated into CPUs or added as a discrete product.
  • Cameras, sensors, or other IoT technologies: To enable machines to “see” computer vision tasks, cameras and sensors are used to gather visual data for processing.
  • AI software: Specialized AI software solutions are essential for writing, training, and optimizing the AI algorithms used in machine learning, deep learning, computer vision, and more.