AI in Retail: 10 Use Cases and an Implementation Guide (2026)

AI in retail isn’t new. In the early 2000s, many leading retailers began finding AI solutions: Amazon introduced its groundbreaking recommendation engine, Walmart transformed inventory management, and Macy’s implemented dynamic pricing—all with the help of artificial intelligence.

While advancements in AI were gradual until 2022, the launch of ChatGPT marked a significant turning point. Its introduction accelerated the integration of artificial intelligence across industries, and the retail sector was no exception. Since then, AI technologies and tools have enhanced nearly every facet of retail operations.

Retailers have swiftly embraced these innovations, and the results are already coming to fruition. As of 2025, 87% of retailers report that AI has had a positive impact on revenue, and 94% have seen it reduce operating costs. Unsurprisingly, 97% plan to increase their AI spending in the next year

This article will explore the future of AI in retail, provide recent examples to inspire your business, and discuss how applications of artificial intelligence benefit the retail industry as a whole.

What is AI in retail?

AI in retail is about using predictive technology to improve everything from customer experience personalization to supply chain optimization. AI applications can run virtual shopping assistants that offer 24/7 support, and they can automate reordering or otherwise optimizing retail operations.

AI tools sift through huge amounts of customer and sales data in real time. They can use this information to predict demand for a product, tailor promotions for a specific shopper, and even adjust pricing on the fly. These applications help retailers spot consumer behavior patterns and forecast trends while improving operational efficiency. 

For example, an AI system can automatically adjust inventory levels based on sales patterns or deliver hyperpersonalized product suggestions that boost conversion rates.

Adoption of AI in retail

Businesses in virtually every industry are adopting tools that leverage artificial intelligence. In 2025, 71% of organizations report using generative AI in at least one business function. The highest adoption rates have been in marketing, sales, and product development

In retail specifically, nearly 90% of retailers either actively use AI in their operations or are assessing AI projects. Spending is expanding enterprise-wide, with retail executives expecting AI spending outside of traditional IT to surge by 52% in the next year. 

The investment is also being pulled by strong consumer demand. A 2025 Capgemini study found that 71% of consumers, with even higher interest from Gen Z and millennials, want generative AI (GenAI) integrated into their shopping experiences.

The surge in AI innovation has already transformed how retailers operate internally and interact with customers. A 2025 Nvidia report found the most common GenAI use cases include:

Marketing content creation (60%)
Personalized marketing and advertising (42%)
Predictive analytics (44%)
Digital shopping assistants and copilots (40%)
Customer analysis and segmentation (41%)

While retailers are adopting tools to improve operations and create convenient experiences for customers, AI tools are moving the innovation needle one more notch forward for retail businesses. Advancements in artificial intelligence also introduce a new kind of customer for retailers to cater to—the “machine customer.”

Machine customers are AI-driven entities that autonomously make transactions for consumers. For example, a smart refrigerator can order groceries, a home assistant can stock up on house supplies, and a smart printer can reorder ink when toner is low—all without any human intervention.

So, is AI the future of retail? In a word, yes. 

In the past, using AI in retail was a shimmer on the horizon. Now, it’s ubiquitous in retail. And it’s quite possibly the entire future—not only from a retailer’s perspective, but from consumers’ too.

10 use cases for AI in retail

While we haven’t quite reached the phase where robots run every aspect of retail, AI has already improved several critical, time-consuming operations. 

Here are some prime examples of AI use cases in retail:

Demand forecasting
Inventory management
Merchandising
Supply chain management
Dynamic price optimization
Customer service chatbots
Personalization
Sentiment analysis
Frictionless checkout
Loss prevention

1. Demand forecasting

AI can predict future product demand by analyzing historical sales data and market trends. This helps retailers optimize stock levels and reduce waste. These predictive analytics, along with AI-driven customer analysis, are key tools retailers use to create more accurate sales and demand forecasts. 

For example, Shopify retailer Doe Beauty leverages Shopify’s AI-driven tools to efficiently manage inventory across their global supply chain. They save $30,000 each week, and about four hours of work, thanks to Shopify Flow and automation. 

2. Inventory management

For physical retailers, AI-driven inventory management is one of the most common applications of AI. Real-time inventory monitoring is possible with AI, which automates restocking and reduces the chances of stockouts or overstock situations. 

Shopify retailer Incu has brought the very latest in international fashion and lifestyle products through their 10 retail stores on Australia’s east coast. They have automated several aspects of the business using AI technology, one of which is inventory management. This has helped the retailer boost sales by 300% year over year. 

3. Merchandising

AI is also reshaping how retailers approach merchandising, enabling more strategic product selection and placement. By analyzing consumer behavior and shopping patterns, AI helps retailers anticipate demand, optimize inventory, and ensure the right products are featured at the right time. This leads to more relevant shopping experiences and improved sales performance.

High-fashion retail brand Antonioli, for example, utilized Shopify and AI to optimize their merchandising strategy. They began by evaluating both the front-end and back-end organization of their product collections, with the goal of making them dynamic and personalized for shoppers, yet orderly and easy to manage for employees. 

Shopify has enabled Antonioli to centralize and operate more efficiently. Both ecommerce and warehouse management are unified and brought under one roof. And with automatic enrichment of product data, the user experience is also significantly improved, facilitating navigation and purchasing on an international scale. 

4. Supply chain management

AI is hugely advantageous when it comes to supply chain management. The supply chain is becoming increasingly complex and challenging, making AI a welcome partner in managing it. 

Nearly 6 in 10 retailers say AI improves operational efficiency and throughput, and 45% say it helps them reduce supply chain-related costs. Another 42% are incorporating more of the technology to meet changing consumer expectations. 

One analysis found that GenAI tools can reduce raw material costs by 5%—largely because they shorten the process of researching new products from weeks to just days. 

Sustainable sheets brand Boll & Branch successfully employed AI and Shopify to optimize their complex supply chain. They built a comprehensive enterprise resource planning (ERP) integration to connect data from order sources to their supply network. 

The integration made strategic customer experience initiatives possible, including features for automated inventory tracking, checkout optimization, order tracking, and shipping. Today, the company’s annual revenue exceeds $100 million.

5. Dynamic price optimization

Adaptive advertising, promotions, and pricing optimization all rank among the most-used AI applications, both online and in-store. The majority of mature AI programs already connect price moves to demand, inventory, and campaigns. 

What else is changing in retail with AI applications? Grocers using electronic shelf labels are now changing prices dozens of times per day. In fact, chains in Norway have reported up to 100 price changes per day. Major US retailers are piloting similar tech, but many are wary of the surge-pricing backlash. 

💡Looking to adopt AI pricing automations like this? Keep in mind: After 2024’s inflation shock, 44% of consumers compare prices more and 30% switch retailers over price, so dynamic pricing must not exploit people’s pockets. 

6. Customer service chatbots

Customer service chatbots continue to make their mark on the customer experience aspect of retail. Since Cyber Monday 2024, retailers have increased their use of generative AI and chatbot agents by 23%. And those that used these technologies for customer service during the holiday season saw nearly double the engagement growth compared to those without these capabilities (38% versus 21%). 

AI-powered chatbots provide shoppers with instant assistance, which can boost baseline customer satisfaction. They can improve the customer experience, offer personalized recommendations, boost conversions, and mitigate issues like returns. According to McKinsey, one global lifestyle brand developed a GenAI-powered shopping assistant that drove a 20% increase in conversion rates.

Luxury retailer Peter Sheppard Footwear added chatbots to improve customer service on their Shopify website—so it could be on par with the level of service provided in-store. This move helped drive a 30% increase in revenue. 

7. Personalization

As many as 42% of retailers use personalized marketing and advertising powered by generative AI. Retail AI enhances personalized shopping experiences by suggesting products based on customer data, boosting sales, and improving customer satisfaction. Personalized recommendation systems like Netflix’s AI suggest products that match consumer preferences. 

Take a look at Shopify retailer BÉIS as another example. The travel and lifestyle brand used Nosto, an AI-powered customer experience app available in the Shopify store, to create personalized experiences customized to shopper behavior. This helped the brand customize targeting for specific products during a customer’s most ripe buying period, supporting the brand’s double-digit growth.

8. Sentiment analysis

Retailers can gauge public sentiment about products or brands through AI analysis of customer reviews and social media posts, informing decisions about product offerings and marketing strategies. In fact, valuable insights and analytics are one of the most common use cases for AI in retail.

For example, makeup and skincare retailer Sephora uses AI to analyze customer feedback, which helps improve product recommendations and store layouts by identifying trends and preferences in large data volumes.

9. Frictionless checkout

AI technology enables automated checkout experiences, removing the need for manual scanning or cashier interaction, thus speeding up the shopping process and reducing wait times.

Home and design brand The Conran Shop adopted a unified commerce approach across their B2B, point of sale (POS), and online experiences to offer seamless checkout. The retailer has seen a 50% reduction in their total cost of ownership (TCO), plus a 54% increase in conversion rates and a 23% increase in email marketing revenue since replatforming to Shopify.

10. Loss prevention

AI can detect and prevent theft and fraud by monitoring in-store activity and identifying suspicious behavior, reducing losses. For example, drugstore chain Walgreens employs AI to analyze security footage and detect potential shoplifting incidents in real time. 

This technology uses machine learning algorithms to monitor video feeds, identify suspicious behavior, and instantly alert security personnel. The system improves its accuracy over time by learning from past incidents.

How to use AI in retail

Now that you’ve looked at some concrete examples of AI working in the retail world, consider how to use it in your own stores:

More accurate inventory counts

AI technology eliminates human error by automating real-time inventory tracking and management. Machine learning algorithms analyze sales data, customer demand, and stock levels to ensure correct inventory levels and counts. The result? Reduced overstocking and stockouts.

For example, Target has successfully implemented an automated inventory management system known as the Inventory Ledger. This system uses advanced machine learning models and IoT devices to provide accurate inventory data in real time across 2,000 stores.

To quantify its effectiveness, the Inventory Ledger processes up to 360,000 inventory transactions per second and handles as many as 16,000 inventory position requests per second—a task only a machine could handle. 

Higher shopper engagement

AI technology is also a win in terms of boosting shopper and customer engagement. Machine learning algorithms analyze customer data to offer tailored product suggestions, anticipate needs, and provide personalized promotions.

For example, Sephora uses AR and AI-driven tools like virtual try-ons and personalized skincare recommendations based on customer data and preferences. These tools make it easy for customers to select the right product for their unique skin type—without having to set foot in a store.

Improve customer experience 

With AI, it’s easier now than ever before to cater to customer demands. AI technology can keep up with simultaneous customer support requests around the clock. AI automates responses, reduces (or even eliminates) wait times, and personalizes interactions.

MakerFlo is a constantly evolving ecommerce brand with a track record of success. They use Yotpo to collect reviews with AI-powered review widgets that drive social proof. Reviews are displayed and integrated into social media and Google, where it’s easy to stand out with Google Seller Ratings and Google Shopping Ads. This has improved the customer experience, driving loyalty and converting more customers.

Store assistance 

AI also helps retailers enhance their in-person and online stores by augmenting skill sets they may not possess. For example, Shopify offers retailers the help of its AI tool, Shopify Magic.

Shopify Magic leverages AI to help you with tasks such as generating and editing professional product photos, writing more effective product descriptions, enhancing email communication with customers, and converting live chats into sales opportunities.

How to implement AI solutions in your retail business
1. Identify key business challenges and opportunities 

Start by identifying the problems you want to solve. If you’re stuck, look where AI is already proving its value. Various reports show the top functions are marketing and sales, service operations, product development, and software engineering. 

To identify these opportunities, here are some questions you can ask your team:

Why is our cart abandonment rate so high? Are we failing to show the right products or offers?
How many sales did we lose because our top-selling items were out of stock? 
What is our sell-through rate? Where is cash trapped in warehouses in the form of slow-moving inventory?
What is our agent turnover rate? Are we burning out our best people on repetitive, low-value tasks that we could be automating?
What is our first-contact resolution rate? How many customers have to call back, getting angrier and more expensive each time?

As a retailer, prioritize experiments in conversational commerce. The Shopify and OpenAI partnership, which enables in-chat checkout directly within ChatGPT, demonstrates that AI can now own the entire journey from discovery to purchase.

“Shopping is changing fast. People are discovering products in AI conversations, not just through search or ads,” says Vanessa Lee, VP of Product at Shopify. “This will let our merchants show up naturally in those moments and give shoppers a way to buy without breaking their flow. It’s a really exciting shift for commerce.”

2. Ensure data readiness and quality 

A successful AI strategy is built on a foundation of high-quality data. Before you start, define your success metrics. McKinsey notes that fewer than one in five companies actually track AI KPIs, which is the main reason they fail to see any impact on their bottom line.

You’ll also want to consolidate your data so you can trust it. AI can’t provide accurate predictions if your customer data is in one system, your orders in another, and your inventory in a third. 

Consider using a unified commerce platform like Shopify. It centralizes all your critical data, from a customer’s first in-store visit to their final delivery, in one place. Your AI tooling will always have access to fresh, accurate, and reliable data.

3. Choose the right AI tools and platforms 

The biggest mistake retailers make is buying a cool AI tool before they know what problem it will solve. Base the smart retail technology you choose on the problems identified in step one. 

Before you shop for a new system, check the tools you already pay for. This is the AI built directly into your core platforms, such as your ecommerce system, email marketing tool, and customer helpdesk. It’s the lowest-cost, lowest-risk, and fastest way to get started, as these tools are already integrated with your data and workflows.

If your problem is too complex for the built-in features, you’ll need a specialized add-on tool. When evaluating add-on tools, consider the following:

Does it integrate natively with your existing CX stack (Shopify, Klaviyo, etc.)?
Does it have a clear roadmap for agentic commerce?
Have you secured a cross-functional budget since AI spend is surging outside of IT?

Overall, the best AI tool isn’t the one with the most fancy buzzwords, but the one that integrates seamlessly with your tech stack and tackles the challenges you’ve targeted. 

4. Start with a pilot project and measure return on investment (ROI) 

Avoid the urge to change everything at once. While tempting, it’s better to start with one pilot project. Pick a single department like customer service and add AI into its daily workflow. 

Decide what you’re trying to improve, whether that’s average order value (AOV), conversion rate, inventory shrink, or warehouse picking productivity. Tracking specific retail metrics is the single best predictor of eventually seeing a positive impact on your company’s overall profit. 

Last up, set realistic expectations for your leadership team. Be clear that company-wide results will follow after you have proven the concept at a smaller scale. Over 80% of firms report that the earnings before interest and taxes (EBIT) impact isn’t material right away, so it’s important to be patient.

Overcoming the challenges of AI in retail
Addressing data privacy and security concerns 

The EU AI Act is now law. If you sell in the EU, you are legally required to map your AI tools to different risk tiers and prove you are managing them with human oversight and solid documentation.

In the US, the FTC is watching. If an AI vendor promises not to train on your data but does it anyway, your business is at risk. Add a no-training clause into your vendor contracts.

What to do: Don’t feed unnecessary data to the AI. Strip out all personally identifiable information (PII) before it gets to the model. If you use retrieval augmented generation (RAG) for chatbots, make sure it only pulls answers from your own secure documents.

Maintaining high implementation costs

Retail executives plan to increase AI spending in 2026, despite knowing that EBIT is slow to show up. Fund your AI projects in phases, with each new phase only getting a green light after the first one proves its ROI.

What to do: To control your TCO, don’t assume you need the biggest, most expensive AI model. 2024 data shows that many businesses get a great balance of cost and performance from smaller, more efficient models.

Bridging the AI talent and skills gap

A 2025 Bain report notes that 44% of executives are being slowed down by a lack of in-house expertise. There are a few options for closing that AI skill gap:

Build up your current team through training.
Buy a few expensive experts to lead adoption.
Borrow (or work with) consultants for a specific project. 

What to do: Train everyone based on their roles. For example, teach customer service agents how to write quality AI prompts and deescalate queries from your chatbot. Level up merchandisers by teaching them how to use AI-powered analytics to influence floor displays. Tie your upskilling programs to the KPIs from your pilot projects to know if you’re meeting your goals. 

The future of artificial intelligence in the retail industry

AI is fundamentally changing how businesses operate and how customers shop. With adoption rates surpassing those of smartphones and tablets, generative AI is becoming essential for staying competitive.

Forward-thinking retailers are using AI to transform every aspect of their business, including:

Personalizing customer experiences at scale
Automating routine tasks to free up staff
Making smarter inventory and pricing decisions
Predicting trends before they emerge

AI becomes even more powerful when built on the right foundation. Modern retail platforms like Shopify combine AI capabilities with a complete view of your business—from customer behavior to sales patterns to inventory levels. This means AI tools can work with accurate, real-time data to deliver better insights and automate more effectively, helping retailers make smarter decisions and serve customers better.

The most successful retailers will be those who embrace AI strategically, focusing on solutions that deliver real value for their business and customers. As AI technology continues to evolve, it will enable even more innovative ways to serve customers, optimize operations, and drive growth.