Today’s ecommerce brands move faster, respond smarter, and personalize deeper with AI. Its adoption is rapidly expanding across departments—from marketing and operations to customer service and sales.
In our companion blog on AI shopping trends, we examined how shoppers in 2025 use and interact with AI daily. This time, we’re flipping the lens to show how ecommerce businesses are using AI to lead.
Whether you’re a marketplace merchant, a DTC brand, a B2B seller, or a multichannel powerhouse looking to streamline your stack, here are 7 ways ecommerce businesses are using AI to dominate in 2025.
#1 Optimizing for AI discovery
AI platforms now ingest merchant feeds, crawl product pages, interpret product attributes, and surface recommendations in response to queries like, “What’s a good everyday sneaker under $100 that will hold up in the rain?” or, “I need a lightweight backpack for hiking in warm weather, with plenty of space for holding water, but not too big or bulky, and has easily accessible pockets.” They’re intelligent interfaces built to understand natural language, evaluate intent, and return highly contextual, conversational results for customer queries.
Example of using AI chats to shop.
In 2025, ecommerce businesses are proactively feeding AI with structured and rich product information. As platforms like Perplexity, Gemini, and ChatGPT have more ingrained shopping features, and people use them to ask high-purchase-intent questions, brands are racing to ensure their products are visible and preferenced by them to ultimately drive sales.
The ecommerce SEO of 2025 includes feeding LLMs with complete, structured, and enriched product data. If your listings don’t have ample information and context, AI won’t recommend them.
Where it’s happening:
- Retailers are sending live product feeds directly to platforms like Perplexity, Google Gemini, and ChatGPT, ensuring their listings appear in AI-driven shopping responses.
- Feedonomics enables merchants to syndicate AI-optimized feeds—enriched with attributes like use case, audience, material, compatibility, and customer reviews—to AI-native environments.
- Marketplaces like Amazon (Rufus), Walmart (Sparky), and Alibaba (Wenwen) now have generative AI tools that shoppers can use to fine-tune their product hunt.
Why it matters:
- 39% of consumers already use generative AI for online shopping—and over half plan to do so this year.
- Generative AI-driven shopping traffic surged 1,300% during the 2024 holiday season, according to Adobe Analytics.
- Amazon’s generative AI assistant, Rufus, was poised to be used by 33% of Amazon Prime members during Prime Day, according to a May 2025 survey by Tinuiti.
What can businesses do to stay visible and persuasive in the AI economy?
- Product feeds enriched for AI with detailed, structured data: Titles, descriptions, specs, attributes, and images—that generative tools can parse and recommend.
- Use feed management platforms like Feedonomics to format, segment, and distribute feeds across channels that integrate with LLMs (large language models).
- Ensure your listings include granular product attributes such as compatibility, material, dimensions, and special features—so AI can confidently match your product to shopper intent.
- Include ratings, reviews, and social proof in your PDPs to give AI systems additional trust signals and context, and improve your ranking in AI-generated recommendations.
- Test LLM visibility by asking AI platforms product-related questions—see where you appear and why.
Enrich your product data for 300+ global channels with Feedonomics
#2 Generating product content at scale with AI
Writing product titles, descriptions, meta tags, and variant-specific copy for thousands of SKUs is one of ecommerce’s most stubborn bottlenecks—and AI has blown it wide open.
In 2025, businesses are using AI to create product content faster and multiply it across formats, tones, and channels. Paired with a feed management platform like Feedonomics for control over how that data’s optimized, distributed, and even synchronized, and you have your hands on power equivalent to The One Ring (the one to rule them all, bring them all, and in their darkness bind them.)
When your product content is accurate, scalable, and channel-ready, you don’t just move faster—you grow faster.
Where it’s happening:
- Ecommerce merchants are increasingly using AI-powered plugins and apps to automate content creation—generating product titles, descriptions, SEO tags, and even variant-specific copy at scale for their online stores. These tools, often available through platform app stores, help streamline catalog management and improve time-to-market.
- Feedonomics clients are pairing that content with powerful feed rules to determine which version of a title, description, or image is sent to each channel—ensuring marketplace compliance, SEO consistency, and channel-specific tone.
- Merchants are now using AI image tools to create alternate product photos, background-swapped visuals, and lifestyle imagery that aligns with each platform’s creative norms.
- AI-generated copy and visuals are being localized and adapted for different markets, shopper personas, and sales campaigns—then strategically manipulated and deployed via Feedonomics.
Why it matters:
- 47% of ecommerce sellers already use AI-generated product content
- Additional findings from Liquidweb show 63% of businesses report higher engagement—more clicks, longer time on page, better scroll depth—on listings enhanced with AI-generated copy and imagery, and 41% report higher revenue.
- The generative AI market for ecommerce content is projected to hit $1.04 billion in 2025, as retailers shift from manual listing updates to automated content engines.
What can businesses do to build content with AI?
- Start by using AI to generate baseline content at scale, then apply human review or business logic to fine-tune it.
- Standardize structured data across your product catalog so AI tools can accurately pull attributes into content.
- Customize templates for each channel to meet SEO, character length, and category requirements automatically.
- Pair with Feedonomics for distribution control: Decide exactly which version of your AI-generated title, description, or image goes to each destination—so Meta ads can feature lifestyle-driven copy, Google Shopping gets keyword-rich local language, and TikTok Shop displays short, snappy spec lists—all while meeting each channel’s requirements and best practices.
#3 Delivering hyper-personalized experiences with AI
In 2025, businesses are using AI to tailor product recommendations, segment customers, and deliver more precisely targeted content.
The magic lies in AI’s ability to process enormous volumes of data—think browsing behavior, purchase history, location, session activity, and electromagnetic-induced behavioral changes caused by the solar cycle to anticipate what customers want. Instead of generic “You might also like” listings, shoppers now get relevant bundles, dynamic pricing, tailored promotions, and 1:1 messaging that feels more like a concierge and less like a spray-and-pray banner ad.
AI also gives ecommerce brands the tools to recover lost sales and build lasting customer relationships through more personalized re-engagement. Two of the most common areas are cart abandonment and loyalty building.
Let’s start with cart abandonment. AI can now analyze session behavior, cart value, urgency signals (like limited stock), and user history to craft the right message—via email, SMS, or push—at the right time. Offers, reminders, and trust-building content are dynamically tailored to bring shoppers back and close the loop.
Now let’s talk loyalty. Static, point-based programs are fading fast. In 2025, loyalty strategies can be enhanced with AI, using customer lifetime value (CLV), churn risk, and purchase frequency to deliver rewards and incentives. AI-driven programs adjust tiers, offers, and messaging based on what drives repeat purchases and emotional stickiness.
These tools stop revenue leaks and turn casual shoppers into loyal fans.
Where it’s happening:
- Amazon, Walmart, and large DTC brands are using AI recommendation engines to power dynamic product carousels, “complete the look” bundles, and real-time add-on suggestions that boost AOV and conversion.
- Platforms like Dynamic Yield, Bloomreach, and Nosto help businesses tailor offers, messaging, and layouts based on behavioral triggers, geolocation, traffic source, or device type.
- Platforms like Klaviyo and Treasure Data enable businesses to segment customers by purchase patterns, engagement signals, churn risk, and lifecycle stage—feeding into automated campaigns across email, SMS, and web.
- With Feedonomics, merchants tailor feed content per channel—e.g., keyword-rich titles for search/display and titles aligned with site product pages for SMS/email—and leverage supplementary feed attributes for display ads, turning cleaner data into better personalization.
Why it matters:
- Personalized product recommendations account for up to 31% of ecommerce revenue.
- Shoppers who receive relevant suggestions are more likely to convert, with AOV gains as high as 369%.
- 76% of consumers feel frustrated when a site lacks personalization, which can negatively impact loyalty and retention.
- AI-powered personalization has been shown to increase email open rates, click-through rates, and conversions by delivering timely, relevant messages.
What can businesses do to personalize and segment smarter?
- Enrich product data with detailed attributes and custom labels so AI can deliver more relevant, timely recommendations.
- Centralize and unify customer data across systems—web analytics, CRM, email platforms—to allow AI to cluster users by behavior, lifecycle stage, or predicted value (not just standard demographics).
- Automate experiences based on segment intent: Trigger different journeys for first-time buyers vs. repeat customers, or serve tailored promotions based on past engagement or category interest.
- Test and optimize recovery timing, channels, and messaging
- Identify high-value customers and churn risks with predictive models—and trigger loyalty rewards, surprise rewards, and reactivation incentives accordingly
#4 Streamlining merchandising and operations with AI
AI-driven merchandising tools help businesses move at the speed of the market, adjusting prices, forecasting demand, and managing inventory with the accuracy of that one Turkish Olympian who crushed it with the air pistol.
Instead of relying on fixed pricing rules or static stock levels, AI analyzes changing inputs—like competitor prices, site traffic, conversion rates, inventory, seasonality, and even time of day—to optimize merchandising decisions automatically.
The result? Better margins, fewer stockouts, faster fulfillment, and reduced carrying costs.
Where it’s happening:
- Amazon and Walmart update prices multiple times per day using AI-driven pricing engines that factor in demand, competitor moves, and margin goals.
- Retailers integrate tools like Prisync, Omnia Retail, and Competera for dynamic pricing at scale.
- Brands forecast demand with AI tools like NetSuite’s for optimizing SKUs across warehouses and 3PLs.
- Feedonomics clients use event-based sync to synchronize inventory, pause out-of-stock ads, and reprice products across Google Shopping, Amazon, and Meta.
- Use RAAP (regional availability and pricing) in Google Merchant Center to localize product pricing and delivery visibility.
Why it matters:
- Higher margins and revenue growth through dynamic pricing
- AI-driven dynamic pricing engines have been shown to boost profit margins by up to 25%, increase average order value by 13% during peak periods, and reduce overstock by 6% in a single quarter, according to Onramp’s ecommerce case studies.
- Improved conversions and more efficient customer support via automation
- AI tools in ecommerce help lift conversion rates by 3–10%, increase AOV by 5–12%, and deflect 20–50% of customer support tickets, freeing up teams and improving CX.
- Wider revenue and cost benefits from AI-powered merchandising and forecasting
- Retailers that deploy AI merchandising and inventory systems see an average 20% increase in revenue and an 8% reduction in costs, while AI forecasting cuts stockouts by up to 65% and warehousing expenses by 10–40%. Full stats available from DemandSage and ArtSmart.ai.
What can businesses do to optimize operations with AI?
- Use AI-powered pricing tools to test and adjust product pricing based on live market signals—automatically.
- Sync inventory and pricing data across all channels (site, ads, marketplaces) to avoid overselling, mispricing, or revenue loss.
- Incorporate operational insights into marketing decisions: suppress ads for low-stock SKUs, or run clearance promos for overstocked items (you can do this using custom labels)
See how Feedonomics helped this retailer improve profitability
#5 Preventing fraud and protecting business with AI
AI-powered fraud detection tools analyze thousands of data points—device type, geolocation, velocity patterns, behavioral anomalies, historical orders, and more—to flag suspicious activity before it impacts your business. Unlike traditional rule-based systems that require manual tuning and still miss edge cases, AI models continuously learn from new data and adapt to emerging threats. And perhaps most importantly, AI fraud prevention helps reduce false declines. That means fewer angry “Why was my order canceled?” emails and more smooth, frictionless transactions for legitimate customers.
Where it’s happening:
- Ecommerce businesses use tools like Sift, Riskified, and Signifyd to score and approve transactions based on real-time risk analysis.
- AI models integrate with payment gateways, CRMs, and fulfillment systems to block fraud across the entire journey—not just at checkout.
- Marketplaces and DTC brands alike rely on AI to protect against chargebacks, account takeovers, return fraud, triangulation schemes, and more.
Why it matters:
- AI protects revenue across the entire journey by detecting fraud at account creation, checkout, returns, and beyond.
- AI reduces false declines, helping legitimate customers complete purchases without unnecessary friction.
- AI improves operational efficiency by automating transaction review and letting fraud teams focus on high-risk cases.
What can businesses do to stay protected?
- Implement AI fraud tools that use behavioral analytics, pattern recognition, and global threat data to detect fraud in real time.
- Use fraud prevention tools that support adaptive approval workflows, automatically approving safe orders, declining risky ones, and flagging edge cases for review.
- Monitor fraud metrics (chargeback rates, approval delays, false positives) and use AI to strike the right balance between security and experience.
#6 Analyzing sentiment and reviews to improve the customer experience
Your customers are telling you exactly how they feel—you just need the right tools to listen.
Instead of manually combing through star ratings and comment threads, AI sentiment analysis tools scan millions of data points across marketplaces, social platforms, and brand-owned channels to detect emotion, urgency, themes, and intent. The goal? Understand why customers are happy, frustrated, or confused—and how to improve customer satisfaction.
Merchandising teams use sentiment data to refine product descriptions. CX teams spot issues early before they snowball. Marketing can highlight top-rated product traits. AI even pinpoints review patterns across different geographies, customer types, or device types.
In short: feedback becomes a competitive advantage, not a backlog item.
Where it’s happening:
- Cross-channel customer insight at scale: Retailers like Walmart and Amazon use sentiment AI to analyze reviews, surveys, and social media, surfacing patterns around pricing frustration, product quality concerns, or delivery issues. These insights inform merchandising, CX, and ops decisions in near real time.
- Proactive customer support and churn prevention: Online retailers like Zappos use AI sentiment analysis to scan support interactions for early signs of dissatisfaction. They found that customers using certain negative phrases were 80% more likely to return products, allowing proactive outreach that reduced return rates by 15% within three months.
- Merchandisers and copywriters feed sentiment insights into product pages, using customer words to sharpen value props and reduce purchase hesitation.
Why it matters:
- AI can process millions of reviews in real time, revealing patterns human analysts would miss.
- Brands use sentiment overlays to improve trust, conversion, and CX by showcasing top-rated product features and addressing negative feedback head-on.
- Real-time sentiment monitoring helps companies detect product quality issues faster and make more confident roadmap decisions.
- AI-powered QA teams use this data to prioritize fixes or elevate features based on customer language—not just internal assumptions.
What can businesses do to make feedback actionable with AI?
- Connect review and support data to sentiment platforms and tag it by product, category, and channel.
- Track shifts in sentiment over time—especially after product launches, policy changes, or seasonal promotions.
- Visualize top drivers of satisfaction and frustration using AI-powered dashboards and share insights across marketing, product, and CX teams.
- Incorporate sentiment-rich language into PDPs, ad copy, and SEO—use your customers’ words to win new ones
#7 Using AI agents to perform a series of complex tasks
Agentic AI refers to autonomous or semi-autonomous systems that can perform tasks, make decisions, and adapt dynamically to changing inputs without constant human intervention. In ecommerce, these agents are being embedded across the stack—not just to recommend or flag, but to act.
Whether it’s optimizing feeds, rerouting orders, or managing campaigns, AI agents are taking over complex workflows that used to require human hands. Businesses aren’t waiting for a full AI checkout revolution—they’re deploying task-specific agents today to scale operations, increase efficiency, and respond faster to changing market conditions.
Where it’s happening
- Dynamic order routing and fulfillment: Agentic AI systems like UPS’s ORION autonomously optimize delivery routes in real time, adapting to traffic, delivery volumes, and weather—saving UPS 100 million miles and $300 million annually while cutting CO₂ emissions significantly.
- Real-time inventory forecasting and shelf management: Retail giants like Walmart use agentic AI to dynamically forecast demand, adjust stock levels across locations, and deploy smart shelf monitoring to flag restocking needs—boosting availability and reducing waste.
- Dynamic and context-aware pricing: Fashion marketplace Zalando employs agentic AI pricing agents that continuously adjust prices based on competitor activity, inventory levels, and demand signals—leading to a 12% increase in revenue per SKU and improved margins.
- AI-driven omnichannel assistants and “super-agents”: Walmart’s AI super agents like “Sparky” act autonomously to personalize shopping, manage orders, assist suppliers, and streamline operations for staff, partners, and developers.
Why it matters
- Agentic AI reduces operational overhead by taking over routine decision-making across fulfillment, merchandising, and marketing.
- Businesses using AI agents scale faster without adding headcount—and respond in real time to changing conditions like price shifts, stockouts, or ad fatigue.
- Autonomous workflows free teams to focus on strategy, not micromanagement—turning ecommerce into a self-tuning system.
What businesses can do to prepare for agentic AI
- Identify high-volume, rule-based workflows that can be safely delegated to agents (e.g., feed cleaning, budget pacing, order routing).
- Connect product, inventory, and performance data across systems to enable real-time decision-making.
- Pair task-specific AI agents with human QA checkpoints at first, then gradually expand autonomy as confidence grows.
- Use platforms like Feedonomics to build agent-ready workflows with conditional logic, dynamic rules, and multi-channel data access.
Conclusion
While both shoppers and businesses are tapping into AI’s capabilities, their goals differ: shoppers seek convenience, personalization, and confidence in purchases; businesses aim to increase efficiency, reduce costs, and better understand customer behavior. Understanding these distinct use cases can help you identify the right AI tools for your ecommerce strategy in 2025 and beyond.
Feedonomics makes ecommerce success scalable for your business.
AI for ecommerce business FAQs
How are everyday shoppers interacting with AI tools today?
Shoppers are already deep into the AI experience—often without even realizing it. Many use generative AI platforms like ChatGPT, Perplexity, and Google AI Mode to get product recommendations, compare prices, or even complete purchases via agentic checkout features. Instead of searching “best hiking shoes” and wading through tabs, they ask AI something like, “What are the best trail shoes for Arizona summers under $120?”—and the AI delivers contextual results based on user behavior, preferences, and session history.
Shoppers are also using AI-powered virtual try-ons, voice search, visual search, and predictive reordering tools to enhance convenience and reduce friction. These natural language-based, context-rich interactions are shaping new expectations—and businesses that don’t align risk falling behind.
In short: shoppers expect AI to understand, respond, and act—and they’re bringing those expectations with them to every ecommerce store they visit.
Which departments in an ecommerce-focused business might these tools impact?
Pretty much all of them—AI isn’t just a marketing play. Here’s how different teams benefit:
- Marketing uses AI to drive customer segmentation, personalize marketing campaigns, and optimize ad copy based on real-time engagement.
- Merchandising teams use machine learning for pricing strategies, inventory management, and demand forecasting to avoid overstocking or stockouts.
- Operations and supply chain teams rely on AI for smarter fulfillment, predictive purchasing, and real-time inventory level updates.
- Customer support benefits from AI-powered chatbots, virtual assistants, and natural language processing (NLP) to handle queries at scale while maintaining a smooth user experience.
- CX and product teams use AI sentiment analysis to turn customer interactions into actionable improvements—whether that’s copy on a PDP or an actual product revision.
It’s not just about automation—it’s about unlocking more intelligent, cross-functional decision-making using real-time signals.
What kinds of AI’s are there, and which ones are used in ecommerce?
The artificial intelligence umbrella is big, but here’s a breakdown of the most relevant types for ecommerce:
- Generative AI creates content—product descriptions, ad copy, chatbot responses, even visuals—based on patterns in data. Tools like ChatGPT, Gemini, and Jasper fall into this camp.
- Predictive AI uses historical data, trends, and customer behavior to forecast outcomes—think inventory demand, lifetime value, or churn risk.
- Agentic AI doesn’t just recommend—it takes action, like adding to cart, tracking prices, or checking out for a shopper (e.g., Google AI Mode’s “agentic checkout”).
- Conversational AI powers chatbots, voice assistants, and customer service tools that use natural language processing (NLP) to resolve inquiries and guide shopping flows.
- Computer vision and visual AI power tools like virtual try-ons and visual search, enabling shoppers to interact through images instead of keywords.
In ecommerce, these AIs often overlap—for example, a chatbot that recommends a product, personalizes the message, and links to an agentic checkout is likely using several types at once.
How does a feed management platform play into optimizing and working with AI?
A feed management platform like Feedonomics acts as the connective tissue between your ecommerce data and the AI systems that rely on it.
These platforms ensure your product catalog is structured, enriched, and clean enough to be parsed by AI algorithms—whether that’s for use in a generative AI interface like Perplexity or in an ad platform’s personalization engine. Feed platforms also enable channel-specific optimization, meaning your listings can be adapted for each environment (Google Shopping, Meta, TikTok, marketplaces, etc.)—and synced in real time as inventory, price, or content changes.
In the age of AI, data quality = visibility. Without well-structured feeds, AI won’t understand what your product is—let alone recommend it.
What role does an ecommerce platform play in AI for ecommerce businesses?
Your ecommerce platform is the foundation of your AI stack. Platforms like BigCommerce, Shopify, and Salesforce Commerce Cloud play a critical role by:
- Hosting and surfacing your product data, order history, and customer interactions
- Supporting third-party AI tool integrations, including personalization engines, fraud detection tools, and AI-powered chatbots
- Powering workflows across marketing, operations, and support teams through apps, APIs, or native functionality
Some platforms now offer native AI capabilities—like dynamic search, upselling engines, and predictive analytics—but the real magic happens when they integrate seamlessly with feed management tools, marketing platforms, and conversational AI technology to create a fluid, responsive ecommerce ecosystem.
Muhammed is a content marketing specialist creating informative content to help ecommerce professionals solve industry challenges and stay ahead of the curve.
