Generative AI in Retail: 7 Use Cases & Examples
Retail businesses strive to enhance customer experiences and loyalty. This requires producing attractive content in various formats, effective marketing efforts, and exceptional customer service.
With generative AI, retailers can address most of these issues through automation, particularly by enhancing their ability to analyze customer data to deliver more personalized experiences.
See the examples and benefits of generative AI in retail:
7 Use Cases of Generative AI in Retail
1- Product and display design
Generative AI can create new product designs based on the analysis of current market trends and customer interactions, consumer preferences, and historical sales data. The AI model can generate multiple variations, allowing companies to shortlist the most appealing options. Creating designs for clothing, furniture, or electronics can be an option.
Figure 1: Product design can be the number one use case of generative AI in retail.1
Personalizing the display options according to customer choice is another option. The video below demonstrates an example of AI-generated 3D models that can be integrated into product displays.
For more information, check out generative AI in fashion.
2- Automated content generation
Generative AI produces marketing content at scale, including product descriptions, email campaigns, social media posts, and advertising copy. This automation allows retailers to maintain consistent brand voice while personalizing messages for different customer segments and channels.
Figure 2: ChatGPT content creation is an example of using generative AI in retail.
3- Personalized marketing
AI can generate personalized customer experiences through the marketing content for individual customers, such as emails or ads. These are produced based on customer data, including past purchasing behavior and preferences.
AI can predict what kind of promotional content will most appeal to each customer, increasing the effectiveness of marketing campaigns.
4- Product recommendations
Using generative models, AI can suggest new or alternative products to customers that they might be interested in, based on their buying history and preferences. It can also anticipate their future needs and preferences, thereby improving the shopping experience.
5- Inventory management & supply chain optimization
Generative AI can help forecast product demand, generating predictions based on historical sales data, trends, seasonality, and other factors. This can improve inventory management, reducing instances of overstock or stockouts.
Generative AI can be an essential tech to invest in for many supply chain operations, including:
- Demand forecasting
- Supplier risk assessment
- Anomaly detection
- Transportation and routing optimization
6- Visual Search and Virtual Try-On
AI-powered visual search allows customers to find products by uploading images, while virtual try-on technology lets them see how products will look before purchasing. These technologies reduce uncertainty in online shopping and improve customer confidence.
Generative AI can also power conversational virtual assistants that assist customers throughout their shopping journey, generating responses to their queries and guiding them through the purchasing process.
7- Customer service automation
AI-powered chatbots and virtual assistants handle customer inquiries, provide product information, and guide customers through the purchasing process. Advanced systems can understand context and provide human-like responses while escalating complex issues to human agents.
Modern AI customer service systems maintain conversation context, understand customer intent, and provide relevant product recommendations during support interactions.
Real-life generative AI in retail examples
1- ChatGPT for shopping
ChatGPT Shopping Research is an AI shopping assistant that asks questions, searches for product information online, and compares options:
- Personalized buyer’s guides: Creates customized guides that help users explore, compare, and discover products.
- Conversational product research: Users can describe what they are looking for in natural language, and the system asks follow-up questions about preferences, budget, or features to refine recommendations.
- Automated comparison of options: Gathers information from multiple sources and presents key differences, pros and cons, and trade-offs between products.
- Real-time product data: Searches online for up-to-date details such as prices, availability, specifications, images, and reviews while building recommendations.
- Interactive refinement of results: Users can provide feedback (e.g., “not interested” or “show similar items”), enabling the system to dynamically adjust recommendations during the search process.2
2- eBay’s AI Shopping Agent
eBay’s AI Shopping Agent is a conversational AI assistant that helps users find products by answering questions and giving guidance during the shopping process. Here is how it works:
- Hyper-personalized recommendations: Analyzes user preferences and behavior to suggest relevant products in real time.
- Predictive assistance during browsing: The AI appears throughout the buying journey, responding to queries or proactively surfacing suggestions while users explore the site.
- Improved product discovery: Helps shoppers locate items across eBay’s large inventory and provides curated suggestions such as gifts or outfits.
- Agentic commerce platform: Connects eBay’s data, infrastructure, and AI models to support personalized shopping experiences and integrate with external AI agents.
- Responsible AI framework: All AI features are developed with oversight focused on safety, fairness, transparency, and accountability.
eBay also uses AI to simplify product listings. Sellers can start listings with photos and titles, while AI fills in product details and descriptions.
Figure 3: eBay AI agent’s chat user interface.3
3- Shopify Magic
Shopify Magic is a built-in suite of AI tools that helps merchants create content, design stores, analyze customers, and manage operations more efficiently.
- AI text generation: Automatically generates content such as product descriptions, blog posts, page text, headings, and email subject lines using the information provided by the merchant.
- Sidekick AI assistant: An AI-powered commerce assistant that understands Shopify’s features and store data to provide personalized help and suggestions for running the store and completing tasks.
- Media generation tools: Creates or edits visual content used in an online store, helping merchants produce images or banners more easily.
- Theme and theme-block generation: Generates store design elements, such as themes and blocks, to simplify building or customizing a store’s layout.
- App review summaries: Summarizes app reviews to help merchants understand feedback and evaluate Shopify apps.
- Customer insights and segmentation: Analyzes customer data, creates customer segments, and project metrics like expected spending per customer to support marketing decisions.
Figure 4: Shopify reply generation example.4
4- Stitch Fix: Personalized Styling Recommendations
Stitch Fix uses generative AI to create personalized style profiles for each customer. The AI analyzes customer feedback, purchase history, style preferences, and even social media activity to recommend clothing and accessories. The system generates detailed style profiles that help human stylists make better selections, resulting in higher customer satisfaction and lower return rates.
5- The North Face: Interactive Shopping Assistant
The North Face uses IBM’s Watson-powered AI to offer a conversational shopping assistant on its website. The AI assistant asks customers a series of questions about their preferences, planned activities, and intended usage for outdoor gear, and then generates product recommendations based on the responses. By leveraging generative AI, The North Face enhances the online shopping experience, making it more interactive and tailored to individual needs.
Figure 5: North Face conversational AI assistant example.
6- Sephora Virtual Artist
Sephora’s Virtual Artist app uses facial recognition and AR technology to let customers try on makeup virtually. The AI analyzes facial features, skin tone, and lighting conditions to provide realistic previews of how different products will look. Customers can experiment with various combinations before making purchases.
7- Peter Sheppard Footwear
This luxury retailer implemented AI chatbots on their Shopify website to match the level of personalized service provided in their physical stores. The AI system includes product recommendations, sizing advice, and care instructions while maintaining the brand’s premium service standards.
Benefits of generative AI for the retail industry
- Efficiency and cost reduction: Generative AI in retail can automate various tasks, such as content creation, customer service, and inventory management. This saves time, reduces labor costs, and enables businesses to focus more on strategic decision-making and other key tasks.
- Increased personalization: Generative AI can create highly personalized content and recommendations for individual customers. This can enhance customer experience, increases customer loyalty, and can lead to higher sales.
- Improved customer service: By utilizing generative AI in retail, businesses can offer 24/7 customer support. AI-powered chatbots can respond to customer queries in real time, resolve issues, and provide information. Thus, it helps to improve customer satisfaction.
- Innovation and product development: Generative AI can provide new product designs or variations based on market trends and customer preferences, fostering innovation and potentially leading to more successful products.
FAQ
Generative AI is a form of artificial intelligence that creates new content by learning patterns from existing data. In the retail sector, it is employed to generate product descriptions, personalized recommendations, realistic images, and even entire marketing campaigns. Generative AI models, such as OpenAI’s GPT, utilize deep learning techniques to generate human-like text and visuals, enabling retailers to create engaging customer experiences and enhance operational efficiency.
Reference Links
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.
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