Generative AI in eCommerce: Use Cases, Examples, Benefits

This may not come as shocking news to eCommerce business owners, but according to eCommerce Benchmarks, 70% of online shopping carts are abandoned before shoppers complete their purchase. Why? Sometimes it’s extra costs and taxes, sometimes it’s unnecessary suggestions, lack of trust, and even a poor shopping experience. More often than not, customers expect shops to personalize and streamline their shopping path. 

Imagine a shopper visiting an online store at 11:00 PM—they’re looking for a specific type of attire, but they’re met with static grids of product and rigid filters. What many eCommerce businesses do to improve customer experience is to utilize generative AI tools for content creation, for providing product details, and for personalized recommendations. So, instead of rigid filters and static grids that may result in abandoned carts, an AI-driven shopping experience could simply be typing a message: “I’m attending a beach wedding in Sicily next month; I want something breathable but elegant, and I need shoes I can actually walk on sand in.” In seconds, the storefront could be transformed, not with the simple list of blue dresses, but a curated, complete look, product images, and recommendations. 

eCommerce businesses have officially moved past “suggested for you” sections and simple product information chatbots and started shifting toward a “created for you” approach. Thanks to Generative AI, businesses can tailor experiences and increase customer satisfaction, building loyal and recurring relationships. In this article, we’ll walk you through practical examples and case studies to help you build a solid foundation so you can also use AI for your eCommerce business.

What is Generative AI in eCommerce and How Does it Work?

Generative AI is a type of artificial intelligence that creates entirely new content (lifelike images, human-sounding text, etc.). It works by training on massive amounts of customer data and patterns, learning the “grammar” of everything from fashion trends to shopper behavior. By utilizing natural language processing, the AI can understand and respond to human prompts with startling nuance, making it feel less like a computer and more like a creative partner. 

When a generative AI system is integrated into the company’s tech stack, it acts as a powerful engine for operational efficiency, capable of instantly generating marketing copy, predicting shifts in the supply chain, and automating complex tasks. 

In eCommerce companies, Gen AI:

• Connects raw information to customer-facing experiences, shifting the daily workflow of a brand’s managing teams 
• Transforms the product lifecycle by automating product data and removing the need to manually add tags, SEO-friendly titles, and technical specifications
• Produces creative assets, reducing the time spent on photoshoots and editing
• Creates an efficient customer feedback loop by ingesting thousands of customer comments, summarizing core sentiment, and suggesting specific improvements 

There’s more, and to understand what these tools can truly do, we’ve prepared the top 5 use cases of generative AI in e-commerce.  

Top 5 Uses of Generative AI in eCommerce 

E-commerce platforms have shifted the baseline requirement for digital success by implementing advanced AI models. According to NVIDIA’s State of AI in Retail and CPG Annual Report, 98% of retailers plan to invest in the power of generative AI to meet customer demands and improve their experience.  Statements and reports like this highlight how AI solutions have changed from a competitive advantage to a necessity. 

To effectively use generative AI in eCommerce, brands are shifting toward agentic AI —autonomous systems that suggest actions and execute them. However, in the sea of generative AI solutions and AI use cases, it’s difficult to find a single point to which you should pay attention. Do you want to improve customer interactions? Do you need to update your product catalog or predict market trends? Perhaps your marketing campaigns could benefit from the potential of generative AI? 

These top 5 use cases of AI technology in the e-commerce sector represent the most transformative ways retailers are deploying AI to drive loyalty and revenue. They’re here to help you understand how your e-commerce business could benefit from AI assistants, and where you should put your energy when integrating AI. 

Hyper-personalized product recommendations

Have you ever noticed that when you buy (let’s say) a toaster, for the next three weeks, every ad you see is for more toasters? It’s nothing new; we’ve all been there, and we’ve all experienced traditional recommendations—they simply look at what you just bought instead of recommending what you may need next. Generative AI-powered hyper-personalization changes this, becoming a personal shopper who knows the customer’s style, budget, and the fact that they have a summer vacation coming up in Sicily. 

AI models build 360-degree profiles of customers, combining reviews, social media interactions, and even live chat sentiments, and match the products to intent (instead of simply suggesting “people also bought” sections). 

Why do it? Because the data shows it’s what shoppers actually want: According to McKinsey, 71% of consumers expect companies to deliver personalized interactions, and 76% get frustrated when this doesn’t happen. BCG research also shows that the use of generative artificial intelligence for personalized product suggestions increases revenue by 5% to 10%.

Who’s doing it right?

• Amazing and Sephora: Amazon has a legendary engine, but Sephora takes it further with their “Color iQ” and personalized skincare and makeup suggestions based on a user’s specific skin tone and past concerns.
• Stitch Fix built a “Style Shuffle” game to predict exactly what a user will keep before the box even ships.
• Adore Me lingerie brand uses AI to personalize the “showroom” experience for every user.

AI-powered virtual styling & try-ons

If you’ve ever done deep research on consumer psychology, you know about the “click-and-pray” anxiety, when shoppers are ordering attire and wondering if it’ll fit, if the color is true to the picture, etc. Virtual styling and try-ons use Generative AI to kill that uncertainty. Instead of customers looking at models that look nothing like them, AI capabilities allow them to see how the garment actually drapes, folds, and fits on their body or a digital twin that matches their proportions. 

eCommerce teams use Generative Adversarial Networks (GANs) and diffusion models to overlay 3D digital versions of clothing onto photos of real people. The benefits are huge for both the shopper’s confidence and the brand’s bottom line: 25% reduction in returns and fewer abandoned carts, with 56% of shoppers feeling more confident about product quality and sizing.

Who’s doing it right?

• The Pioneer has a “Be Your Own Model” feature. With the use of AI, shoppers can see how the clothes fit in their own photos.
• Louis Vitton and Dior are also using generative AI to produce “try on” features for expensive watches, sneakers, and handbags.
• Zenni Optical has 3D Virtual Try-Ons. Their AI can analyze and map the face to show exactly how frames sit on the nose and ears.

Automated high-quality product descriptions

E-commerce operations often rely on strong marketing and content teams. Your team members probably spend hours creating product pages, drafting product listings, and writing descriptions. It’s especially daunting when you need to create different descriptions for five hundred slightly different pairs of socks. Generative AI provides automation and reduces the bottlenecks caused by manual content writing. 

Businesses feed their product data into AI applications that have been trained on their specific brand voice. The AI identifies and uses technical information and raw materials, and turns them into persuasive, brand-aligned copy. If you use AI to automate this process, you’re significantly decreasing the speed to market. According to the McKinsey report about The Economic Potential of Generative AI, Gen AI can reduce the time spent on content creation by up to 70%. 

Who’s doing it right?

• Amazon perfectly showcases the traditional AI use for content generation. The company has new AI tools that allow resellers to take a simple image or a few keywords and generate a complete, professional product listing.
• H&M uses AI to help generate localized product descriptions for its global markets.
• Casper is a mattress brand that has experimented with AI implementation and successfully automated content writing. Their AI creates conversational and informative copy across thousands of retail partner sites.

Conversational commerce (advanced chatbots)

With this use case, we’re breaching the subject of conversational AI. Conversational commerce is a blend of both generative and conversational artificial intelligence, with GenAI serving as the “brain” and conversational AI acting as the “interface.” Advanced AI chatbots are one of the most popular AI use cases that allow e-commerce brands to reap the benefits of AI development. 

Advanced chatbots use Large Language Models (LLMs) to understand context, slang, and intent. For example, if a customer says, “I’m looking for something modest for a garden party,” the AI knows exactly what “modest” means in a fashion context and can curate a selection instantly. 

Businesses that leverage the power of generative and conversational AI shift from basic “old school” chatbots to new and advanced ones. The necessity of this shift is backed by explosive market growth and reports that show a 15-20% increase in total sales conversion with AI-driven personalization. 

Who’s doing it right?

• L’Oréal recently introduced its “Beauty Bestie” chatbot that uses GenAI to hold full conversations about skincare routines, analyze users’ concerns, and recommend a personalized, multi-step regimen just like a consultant at a counter would.
• REI has experimented with AI tools like conversational AI to help hikers and campers find the right gear based on trail conditions and weather forecasts. Their conversational AI shopping assistant moved beyond “filters” to “advice.” 
•  For eBay, AI is transforming how users navigate millions of listings. Their “ShopBot” helps customers find rare collectibles or the best deals by asking clarifying questions about condition, price, and year. 

Dynamic pricing and promotional strategy

Have you ever noticed how a flight price jumps the second time you check it, or how an Uber price climbs during a rainstorm? That’s dynamic pricing. In eCommerce, it used to be a simple game of “undercut the competitor by 5 cents.” With Generative AI, it’s evolved into a sophisticated strategy that balances inventory levels, competitor moves, and even “willingness to pay” of individual customer segments—all in real time and without manual efforts. 

Generative AI can analyze unstructured data (social media trends, local weather patterns, etc.) and the sentiment of new reports to predict demand spikes. Instead of a blanket 20% off sale that eats into your margins, the AI might suggest a personalized 10% discount for a specific “at-risk” customer who hasn’t purchased in 30 days, while keeping the price stable for others.

According to BCG, retailers that use AI to monitor and power their pricing and promotional strategies typically see a 2% to 5% increase in EBIT margins (Earnings Before Interest and Taxes). It’s evident that when AI automates these pricing decisions, companies can reduce the time spent on manual price adjustments. 

Who’s doing it right?

• It’s no secret that Amazon changes prices millions of times a day. Their AI can reflect on competitor stock levels and users’ browsing history to ensure they are always the most “attractive” option at the moment the customer is ready to buy.
• Zara, a fast-fashion giant, uses AI to track how quickly items are selling in specific regions. If a dress is flying off the shelves in London but sitting still in Madrid, the AI adjusts the promotional strategy locally to clear stock without a global markdown.
• Airbnb, while not a traditional retail business, has a “Smart Pricing” feature that uses AI to suggest prices to hosts based on local events, seasonality, and real-time search volume in the area.

Ready to Get Started with Your Own E-Commerce AI Agent? 

It is no coincidence that the eCommerce sector has become one of the largest and most aggressive adopters of generative AI in the world. This is an industry where speed, precision, and customer connection are everything, and AI has proven to be the ultimate automating force. 

Think about the traditional retail timelines: manually tagging a thousand new SKU items, writing SEO-optimized product copy, or analyzing months of messy customer feedback to spot a single product flaw. These processes usually take hours, weeks, and sometimes even months of human labor. AI automates these workflows in seconds, simplifies the high-stakes world of product development, delivers tailored product recommendations, and ensures every shopper feels like the store was built specifically for them. 

If you’re ready to bring such transformation to your business, CogniAgent is the perfect solution to turn these insights into real actions. 

CogniAgent is a sophisticated Agentic AI solution. While the standard AI can “talk,” CogniAgent can communicate, analyze, and act. Think of it as an autonomous extension of your team:

• Built for every scale 
• Has turnkey integration 
• Offers lightning-fast setup 
• Transforms insights into action

Sounds like something you’d like to explore? Get started today and sign up. It takes only a few minutes to put your growth on autopilot.