Best Practices
If you lead e-commerce, digital or merchandising for a retailer, AI trends in retail are no longer optional reading: they're reshaping how customers find, evaluate and buy products.
The global AI in retail market is expected to reach USD 14.24 billion in 2025, growing at a 46.5 % CAGR to USD 96.13 billion by 2030 . That growth tells you one thing: AI is scaling really fast and it’s genuinely changing how retailers think, work, and serve their customers.
This article breaks down five AI trends in retail for 2026, with real-world examples and what each one actually depends on to work.
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Trend 1: How Are Retailers Using AI for Personalisation?
Retailers use AI for personalisation by analysing customer data, browsing behaviour, purchase history and even location, to surface the right product at the right moment. Companies that deliver personalisation at scale typically see 5 to 15% higher revenue growth, and retailers strong at AI-driven personalisation can generate up to 40% more revenue than less advanced competitors, according to Deloitte Digital.
Amazon, ASOS and Sephora lead the way on personalisation. Today's customers expect brands to recognise them and respond to their preferences, which makes personalisation a direct driver of loyalty and conversion, not a nice-to-have.
Sephora: How Is AI Used in Retail for Personalisation?
Sephora shows how AI in retail works in real life. With tools like Virtual Artist and Smart Skin Scan, Sephora uses artificial intelligence to recommend products and match skin tones. Their MACH-based system helps them deliver personalised experiences across all their stores and online.
Smart Skin Scan
This AI-powered tool assesses seven key categories, including fine lines, dark spots, texture, and more. Using deep learning and a database of over 70,000 medical-grade images, it delivers fast and accurate skin analysis for all skin types. The system is proven reliable (95% test-retest rate) and follows best practices for data privacy.
Source: https://www.sephora.com/beauty/skin-analysis-tool
Sephora Virtual Artist
Bringing virtual reality into beauty, this app uses facial recognition to let customers try on products anywhere, exploring endless shades and looks virtually, comparing brands side-by-side, and even following step-by-step tutorials tailored to their own face. It removes the guesswork and improves customer satisfaction.
Source: https://www.sephora.sg/pages/virtual-artist
Here’s How Sephora Delivers Personalisation With AI at Scale
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ModiFace for Virtual Try-ons: AI-driven facial recognition and AR for real-time product trials.
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Dynamic Yield: Analyses customer data to instantly recommend relevant products.
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commercetools: MACH-based architecture supports real-time, scalable personalisation both online and in-store.
Why it Works
Personalisation builds loyalty and drives sales. Research shows 81% of consumers are more likely to buy from a brand that “gets” them, while 70% prefer brands that remember previous interactions. AI in retail turns personalisation from a ‘nice-to-have’ into a revenue driver.
Discover more real-world AI use cases in retail by downloading our free e-book.
Trend 2: How Is AI Improving Product Search and Discovery?
AI improves product search and discovery by helping shoppers find what they want even when their query is vague, mistyped or conversational, such as "what's a good birthday gift for a science-mad 12-year-old?" Walmart, The Home Depot and Zalando all use AI to return better results faster, which lifts conversion rates and cuts abandoned baskets.
Zalando: Smarter Search with AI
Zalando built an AI assistant trained specifically on fashion data: product descriptions, user behaviour and platform-specific tags, not generic search patterns. It uses natural language processing and semantic search to interpret intent, context (budget, season) and attributes (colour, fit, style), so it can handle a query like "red dress under €50 with long sleeves" even when the customer doesn't use precise terms.
Recent updates to the assistant include a redesigned interface, deeper personalisation that connects recommendations to shopping history, and context awareness that recognises what a customer is browsing and prompts accordingly. In pilot testing, deeper personalisation led to a 40% increase in high-value interactions, such as adding items to a cart or liking a product.
Better search improves conversion and reduces bounce, and it addresses a real expectation gap: 31% of shoppers expect help from a virtual assistant when choosing what to buy.
Why it Works
Better search improves conversion rates and reduces bounce. Customers find what they need faster and spend more when search understands their intent — which matters, as 31% of them expect help from a virtual assistant when choosing what to buy.
Trend 3: How Is AI Automating Product Content Generation?
AI automates product content generation by creating and translating listings in bulk, letting retailers scale product content without adding manual work. Managing product descriptions, translations and localised content by hand is time-consuming at any real catalogue size, and this is where generative AI use cases in retail have the biggest day-to-day impact.
eBay and Decathlon use generative AI to keep content up to date and accurate, which speeds up launches and improves the customer experience.
What Does AI-Driven Content Automation Look Like Inside a PIM?
Content generation is only half the picture. A modern PIM for retail doesn't just generate text: it decides when generation should happen and where the result goes next, using the same event-based automation documented in Bluestone PIM's e-book, Automation in Product Information Management.
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Automation in Product Information Management
This e-book explains how automation works in product information management, which catalogue workflows can be automated today, and how an event-based architecture creates the foundation for the agentic PIM.
Two examples show what that looks like in practice:
- Moving products through content stages automatically. A team sets completeness requirements for each stage: data entry, AI-assisted description, translation, review. When a product hits the required threshold, Bluestone PIM moves it to the next stage on its own, so a content team never has to check readiness manually.
- Generating documents the moment content is ready. A branded PDF or product sheet template gets built once. When a product's content is complete or its workflow status changes, Bluestone PIM triggers document generation in the background and attaches the finished file to the product record automatically.
Bluestone PIM's own AI capabilities handle the generation step natively, bulk descriptions, translation and proofreading, inside the same platform that holds the product data, so generated content never has to be exported, edited and re-imported by hand.
Trend 4: How Is AI Improving Demand Forecasting and Inventory Management?
AI in retail is also transforming supply chain efficiency. By analysing sales trends, weather, and local events, artificial intelligence predicts demand and optimises stock.
Target, Nike, and Zara are using retail AI to reduce overstock and avoid empty shelves. This is one of the most practical AI use cases in retail for cutting costs and keeping customers satisfied.
Nike: Inventory Optimisation Powered by Predictive Analytics and AI
Nike has made strategic investments in artificial intelligence, generative AI, and advanced data analytics to streamline its entire supply chain and boost product innovation. Their approach centres on predictive analytics, using vast datasets, such as historical sales, seasonal trends, campaign performance, social media sentiment, and even weather patterns to accurately forecast demand.
Some of Nike’s standout AI-driven initiatives include:
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Nike Fit: Introduced in 2019, this combines computer vision and machine learning to help customers find the perfect shoe fit, reducing returns and supporting inventory accuracy.
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Real-Time Inventory Tracking: Nike maintains a live overview of stock levels across all stores and distribution centres, allowing rapid response to demand changes or supply disruptions.
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Dynamic Pricing and Automated Replenishment: AI models enable Nike to adjust prices and restock inventory based on real-time demand and competitor activity, all with minimal manual intervention.
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Supplier Collaboration: By sharing forecasts and inventory data with suppliers, Nike shortens lead times and improves production planning.
Why it Works
Smarter forecasting leads to better stock availability, fewer markdowns, and lower carrying costs. Using AI to predict supply needs can cut errors by 20–50% and reduce lost sales and stockouts by up to 65%. It also helps teams respond faster to demand shifts.
Trend 5: How Is AI Changing Dynamic Pricing in Retail?
AI in retail industry pricing is more dynamic than ever. AI models now adjust prices in real time based on demand, competitor activity, and stock levels.
Retailers like Wayfair and Instacart are seeing increased margins by trusting AI to manage pricing decisions. This approach is becoming standard in online retail and is a key part of the future of AI in retail.
What Do These Five AI Trends Have in Common?
Every trend above, personalisation, search, content generation, forecasting and pricing, runs on the same input: clean, structured, real-time product data. An AI model that recommends products from incomplete attributes, prices from stale stock data, or forecasts from an inconsistent catalogue produces bad output regardless of how good the model is.
This is the argument for treating product data as infrastructure, not admin. Bluestone PIM gives retailers that foundation through a headless, API-first platform: every attribute, price signal and stock status is available through the same 700+ task-level endpoints that feed a storefront, a search engine, a forecasting model or an AI agent.
That last category is the one changing fastest. Retail AI is moving from AI-assisted, where a person still runs the workflow, to agentic, where an AI agent can act directly on the catalogue under human governance. Bluestone PIM's MCP integration is built for that shift: an AI agent can already read and act on structured product data today, which is where these five trends are heading next. See how MCP connects AI agents to Bluestone PIM.
Why Retailers Need to Act Now
In 2025 AI is already helping leading retailers meet growing demands by streamlining operations, improving decision-making, and accelerating time to market. The retail AI market is expected to be worth approximately USD 127.2 billion in 2033, compared to USD 9.3 billion in 2023 (CAGR: 29.9%).
For company owners and e-commerce specialists, this means it’s time to pay close attention to what is the role of AI in online retail.
Our new e-book How Top Retailers Use AI: 7 Smart Use Cases for 2025 is packed with practical advice, generative AI use cases in retail, and lessons from brands already leading the way. If you want a deeper look at how artificial intelligence is changing retail, this is the resource for you.
Download the e-book today to see what’s working and how you can make the most of AI trends in retail for your business, or connect with our team for expert advice tailored to your needs.
