AI in Retail Examples: 18 Real-World Use Cases

AI in retail is no longer just a buzzword—it’s a business strategy. From product discovery and customer service to visual merchandising and supply chain automation, artificial intelligence is transforming how retailers operate, personalize, and grow.

Major brands like Amazon, Sephora, and Walmart are already leveraging AI to create faster, smarter, and more human shopping experiences. And AI-native platforms like Glance AI are showing what’s next: real-time personalization, virtual try-ons, and intent-based recommendations at scale.

In this guide, we’ll explore 20 impactful AI use cases in retail—grouped by function, supported by real brand examples, and linked to measurable benefits like higher conversions, better inventory control, and improved customer satisfaction.

Want the bigger picture? Read AI in E-Commerce: A Winning Combination
See how Glance personalizes retail: AI-Designed Clothes and Style Commerce

AI in Inventory & Operations – 5 Smart Retail Examples

Inventory is the backbone of retail—and AI is making it leaner, smarter, and far more responsive. Here are five examples of how AI is transforming inventory, supply chains, and operations:

1. Walmart – AI for Demand Forecasting

Problem: Over/under-stocking across 4,700+ stores.
 Solution: Walmart uses AI models to analyze local weather, events, and real-time sales for hyper-local demand predictions.
 Impact: Improved in-stock rates, reduced waste, optimized shelf space.

Source: Walmart’s Retail AI Strategy (Forbes)

2. Zara – AI for Automated Inventory Replenishment

Problem: Missed sales due to slow restocking cycles.
 Solution: Zara implemented AI-driven inventory management to track sell-through rates and auto-trigger replenishment by store.
 Impact: Faster turnaround, lower markdowns, higher availability of trending items.

3. Amazon India – Robotics and Warehouse AI

Problem: Managing real-time delivery logistics across Tier 1–3 cities.
 Solution: Amazon India uses AI-powered Kiva bots and ML for warehouse route optimization, dynamic SKU placement, and peak time planning.
 Impact: 1-day/2-day Prime delivery scalability in metro + regional zones.

Source: Amazon India Press Centre

4. DMart – AI-Driven Pricing & Restocking

Problem: Fluctuating demand in discount-driven retail..
 Solution: DMart applies ML to analyze foot traffic, SKU rotation, and store geography to automate pricing and restocking frequency..
 Impact: Maximized sell-through, reduced working capital blocks.

Source : Avenue Supermarts Investor Presentation

5. Target – AI for Shelf Scanning & Audit

Problem: Inconsistent stock visibility at store level.
 Solution: Target uses AI-equipped robots and shelf cameras to detect out-of-stock items and planograms in real time.
 Impact: Increased planogram compliance, reduced manual audits.

Indian retailers are adopting AI at scale—blending hyper-local nuance with predictive logic to streamline inventory and operations.

AI in Personalization & Recommendations – 5 Use Cases

Personalization is no longer a luxury—it’s an expectation. AI helps retailers deliver curated experiences, increase conversions, and build loyalty by tailoring content, products, and offers to individual preferences.

Here are five impactful use cases:

1. Amazon – Personalized Homepage and Product Feeds

Problem: One-size-fits-all product listings underperform.
 Solution: Amazon uses collaborative filtering and neural networks to recommend items based on browsing, purchase history, and real-time behavior.
 Impact: Over 35% of Amazon’s revenue comes from its recommendation engine.

Source: McKinsey on Personalization in Retail

2. Myntra – AI-Powered Style Recommendations

Problem: Users struggle to discover fashion that matches their style.
 Solution: Myntra’s “Style Suggestions” engine uses computer vision, ML, and behavior mapping to recommend looks and bundles.
 Impact: Higher engagement, improved repeat purchase rates.

Source: Myntra Engineering Blog

3. Glance AI – Visual Personalization via Avatars

Problem: Traditional personalization misses visual cues like skin tone, body shape, or style identity.
 Solution: Glance AI creates AI avatars from selfies and suggests personalized outfits, styles, and “shop similar” items based on fashion preferences.
 Impact: Hyper-personalized discovery, reduced decision fatigue, and better fit-to-preference alignment.

4. Sephora – Beauty Profile + AI Skin Scan

Problem: Customers are unsure which beauty products suit their skin.
 Solution: Sephora’s Color IQ and skin diagnostic AI tools offer product suggestions based on tone, skin type, and past purchases.
 Impact: Reduced returns, increased product confidence, and higher customer satisfaction.

Source: BuiltIn – AI in Retail

5. Tata Neu – Cross-Brand Recommendation Engine

Problem: Multiple verticals (fashion, grocery, electronics) lack cross-learning on consumer intent.
 Solution: Tata Neu’s AI platform analyzes cross-category behavior to recommend complementary offers across its apps (Croma, BigBasket, Westside).
 Impact: Higher basket size, increased average order value (AOV), and ecosystem stickiness.

AI is enabling retailers to serve customers not just what they might want—but what’s most relevant to them in the moment, visually and behaviorally.

AI in In-Store & Omnichannel Experiences – 5 Use Cases

AI isn’t confined to digital screens—it’s reshaping physical retail too. From smart shelves to cashierless stores and cross-channel continuity, AI bridges the gap between online and offline shopping.

1. Amazon Go – Checkout-Free Retail Stores

Problem: Long checkout lines reduce in-store satisfaction.
 Solution: Amazon Go uses AI vision, sensors, and deep learning to automatically track what shoppers pick up and charge them via their Amazon app.
 Impact: No cashiers, no lines—just walk in, shop, and walk out.

2. Reliance Trends – AI for In-Store Product Allocation

Problem: Fast-moving styles not stocked in time at relevant locations.
 Solution: AI tracks sell-through velocity by store and allocates trending inventory across its vast retail network.
 Impact: Higher revenue per square foot and localized assortment optimization.

3. Glance TV – Smart Surface AI for Connected Retail

Problem: Idle screens in stores and homes go underutilized for engagement.
 Solution: Glance TV turns connected TV screens into dynamic content hubs, showcasing personalized product recommendations, live deals, and visual storytelling.
 Impact: Higher dwell time, cross-device awareness, and conversion lift from ambient product discovery.

4. Shoppers Stop – Virtual Mirrors & AI-Assisted Styling

Problem: In-store shoppers need style guidance without assistance fatigue.
 Solution: AI-powered mirrors suggest outfit combinations, alternatives, and “complete the look” items based on what shoppers try on.
 Impact: Improved average basket size and lower return rates.

5. Nike – Omnichannel AI for Store + App Sync

Problem: Disjointed experience between in-store and mobile shoppers.
 Solution: Nike’s AI integrates app browsing history, store availability, and real-time promotions to personalize both online and offline experiences.
 Impact: Seamless commerce across touchpoints and higher brand loyalty.

Source: Neontri – AI in Retail Trends

AI is making in-store shopping smarter, more connected, and more relevant—blurring the lines between physical and digital retail.

AI in Retail Customer Service – 3 Impactful Use Cases

Customer service is where loyalty is won—or lost. AI is powering faster, more efficient, and multilingual support that scales with demand and personalizes every interaction.

1. H&M – AI Chatbots for 24/7 Query Handling

Problem: High volumes of repetitive customer queries on orders, returns, and sizing.
 Solution: H&M uses AI chatbots to handle FAQs, track orders, and escalate complex issues to human agents.
 Impact: Reduced agent workload, improved first-response time, consistent support experience.

2. Ajio – Multilingual AI Customer Support

Problem: Diverse customer base across India’s language spectrum.
 Solution: Ajio deploys AI-powered bots and IVR systems that support Hindi, Marathi, Tamil, and other regional languages for order queries and status checks.
 Impact: Broader accessibility, higher CSAT in Tier 2/3 markets, faster resolution times.

AI customer service is helping retailers respond faster, personalize better, and scale across languages and intents—driving higher satisfaction and lower support costs.

FAQs: AI in Retail Examples

1. What are some real-world examples of AI in retail?

Brands like Amazon (AI fulfillment), Reliance (regional inventory allocation), and Glance AI (personalized shopping) use AI across discovery, inventory, and engagement.

2. How is AI used in retail stores?

AI powers smart shelves, checkout-free stores, visual search, in-store styling tools, and real-time stock tracking.

3. Which Indian brands are using AI in retail?

Reliance Retail, Tata Neu, Ajio, DMart, and Myntra all use AI for recommendations, pricing, localization, and operations optimization.

4. What is the impact of AI on retail operations?

AI improves demand forecasting, reduces waste, personalizes customer journeys, and boosts revenue through efficiency and automation.

5. Can AI help improve customer service in retail?

Yes. AI chatbots, virtual stylists, and multilingual support bots help scale service, reduce wait times, and personalize user support.

Final Takeaway

AI in retail is not theoretical—it’s everywhere. From hyper-personalized recommendations on Glance AI to inventory automation at Reliance and Amazon’s fulfillment AI, brands are building smarter, faster, and more responsive commerce systems.

The takeaway is clear:
 AI doesn’t replace retail. It elevates it.

By adopting AI for operations, personalization, omnichannel journeys, and customer support, brands can deliver experiences that are not only more efficient—but more human.

Keep exploring:
 AI Shopping Product Recommendations
 AI Inventory Management at Glance
 Virtual Try-On in India