Top 10 AI in Retail Examples You Should Know

Introduction

We’ve all heard the buzzwords—personalization, automation, virtual assistants. But what does AI in retail actually look like when the rubber meets the road? It’s not just tech hype. It’s changing what we buy, how we discover, and even how we feel while shopping.

In this blog, we’ll explore 10 real-world AI in retail examples that aren’t just futuristic—they’re already live. From global giants like Amazon and Sephora to Indian innovators like Glance AI, this is a breakdown of what’s working and why it matters.

And if you’re looking for the full context—how AI is transforming retail from inventory to innovation—bookmark our AI in Retail Pillar Page for a deeper dive.

Let’s get into it.

1. Amazon’s Recommendation Engine: The OG AI Powerhouse

If AI in retail had a Hall of Fame, Amazon’s recommendation system would be front and center. It's not just one of the first successful implementations of retail AI—it's arguably the most influential.

Amazon uses machine learning to analyze:

• Browsing behavior
• Purchase history
• Search terms
• Real-time product interaction

The result? Amazon’s recommendation engine drives 35% of its total sales by personalizing “You may also like” and “Customers also bought” sections. Powered by AI, these suggestions evolve in real time—making every experience feel intuitive, not salesy.

2. Glance AI: Personalized Fashion on Your Lock Screen

Let’s bring it closer to home.

Glance AI is redefining how Indian consumers discover fashion—by moving retail to the lock screen. Yes, before you even open your phone, you're met with AI-generated fashion looks tailored to your persona, preferences, and browsing habits.

The magic lies in:

• Gen AI-powered styling
• Visual curation in a magazine-like format
• Passive discovery that doesn’t require a search

What makes Glance AI special isn’t just the personalization—it’s the context-awareness. You get relevant, swipe-worthy looks without asking. Soon, you’ll be able to try-on AI-curated outfits and tap to buy, creating a seamless journey from curiosity to conversion.

This is AI commerce in action—intuitive, embedded, and emotionally intelligent.

3. Sephora’s Virtual Artist: Beauty Meets Computer Vision

Trying on lipstick online used to be a guessing game. Now, thanks to AI, it’s pretty much magic.

Sephora’s Virtual Artist lets users:

• Upload a selfie
• Virtually try on makeup
• Get personalized shade recommendations

It uses computer vision and facial recognition to map your features and match products accordingly. Add to that NLP-based chat support and AI-generated beauty tutorials, and you get a brand that truly understands and educates its customers.

It’s not just cool—it reduces product returns and increases customer confidence. AI makes beauty inclusive and accessible, especially for online shoppers who can't visit a store.

4. Zara’s Demand Forecasting: Fast Fashion at AI Speed

Zara doesn’t just follow trends—it reacts in real time. How? With AI-powered inventory forecasting and trend analysis.

Here’s what they do:

• Monitor social media, store behavior, and sales data
• Predict what styles will pop regionally and seasonally
• Adjust manufacturing and distribution instantly

Instead of guessing what customers might want, Zara uses data to know what they’ll want next week. The result? Fewer markdowns, faster product cycles, and leaner inventory—all thanks to AI’s predictive prowess.

Fast fashion isn’t just about speed anymore—it’s about smart speed.

5. Lenskart’s AI Try-On: Making Eyewear Interactive

Eyewear is personal—and online shopping for it used to be a shot in the dark. Lenskart’s AI try-on feature changed that.

It lets users:

• Scan their face using their phone camera
• Try on hundreds of frame styles virtually
• Get smart suggestions based on face shape

This is AI + AR at its best. It’s frictionless, accurate, and weirdly fun. The company has seen massive engagement with the feature, which also improves conversion and reduces return rates.

More importantly, it builds confidence—and that’s gold in retail.

6. H&M’s AI-Powered Chatbot: Real-Time Style Help

You want help, but you don’t want to call or email support. We get it. That’s where H&M’s AI chatbot comes in.

Available across platforms, it helps users:

• Find products by describing them
• Navigate size guides
• Track orders and returns

It uses natural language processing to understand user intent—not just keywords. If you type “I need a black dress for a wedding,” it can parse occasion + color + category, and give you curated suggestions instantly.

This is AI making customer support feel like conversation, not a queue.

7. Nike’s Smart Mirror and RFID: In-Store Gets a Brain

AI isn’t just online. Nike’s flagship stores are equipped with smart mirrors that use RFID technology and AI to:

• Instantly recognize the product you’re holding
• Suggest other items that match (e.g., shoes for those pants)
• Check stock availability in your size

It’s the ultimate phygital experience—bridging online personalization with offline immediacy.

Shopping in-store now feels like shopping on your phone, but with real fabric and fitting rooms. That’s AI bringing contextual intelligence to the physical world.

8. Reliance’s JioMart Personalization Engine: Scale Meets Precision

India’s retail titan Reliance isn’t sitting this one out. JioMart, their ecommerce platform, uses AI to power:

• Region-specific product rankings
• Dynamic pricing based on local demand
• Cross-sell suggestions based on cart history

Given India’s hyper-diverse demographics, this level of intelligent segmentation is crucial. AI allows JioMart to tailor its UX for a user in Pune differently than someone in Patna, without separate teams.

This isn’t personalization at scale. It’s personalization with cultural relevance—a uniquely Indian application of AI in retail.

9. Uniqlo’s StyleHint App: Search by Inspiration

Can a street photo inspire your next outfit? With Uniqlo’s StyleHint, yes.

Here’s how it works:

• Users snap a photo of a look they love
• The app finds visually similar pieces from Uniqlo’s inventory
• Suggests complete outfits based on the inspiration

Powered by computer vision and deep learning, this tool turns user inspiration into purchase-ready journeys. It’s AI meeting creativity, and it’s addictive in the best way.

The app doesn’t sell products—it translates emotion into merchandise.

10. Walmart’s Intelligent Store Cameras: AI as the New Manager

Walmart’s biggest AI play isn’t about style—it’s about logistics.

Their stores use AI-enabled cameras to:

• Monitor shelf stock in real time
• Detect checkout fraud
• Analyze shopper traffic flow

This isn’t surveillance—it’s operational intelligence. These systems reduce shrinkage, ensure product availability, and help with workforce planning.

It’s not flashy, but it’s the AI backbone of one of the world’s biggest retail ecosystems. Quietly powerful—and essential.

Final Thoughts: What These AI in Retail Examples Teach Us

From fashion-forward GenAI to invisible inventory tracking, these 10 AI in retail examples show the full spectrum of what’s possible. But here’s the twist: this list isn’t just about big names or cutting-edge tech. It’s about solving real problems in retail with empathy, speed, and intelligence.

If there's one pattern here, it's this: the best AI in retail doesn’t feel like AI. It feels like magic. Effortless, timely, human.

Want more strategic insights and use cases? Explore our comprehensive AI in Retail Pillar Page to see how this all connects—and what it means for the future of shopping.

FAQs

1. How is Zara using AI?
Zara uses AI for demand forecasting, inventory management, and trend prediction to streamline operations and improve customer experience.

2. What are the top 10 use cases for AI?

1. Product recommendations
2. Dynamic pricing
3. Chatbots
4. Inventory management
5. Fraud detection
6. Visual search
7. Demand forecasting
8. Customer sentiment analysis
9. Voice assistants
10. Personalized marketing

3. How can AI be used in retail stores?
AI powers smart shelves, cashier-less checkouts, personalized offers, inventory tracking, and customer analytics in stores.

4. What is an example of generative AI in retail?
AI-generated product descriptions and personalized ads are common examples.

5. How are brands using generative AI?
Brands use it to create marketing content, design products, generate visuals, and offer personalized customer interactions.