AI In eCommerce: Use Cases, Examples, And What To Build First

AI in eCommerce is the use of machine learning and generative models across the buying experience and the operations behind it. For shoppers, that means product recommendations, search and discovery, cart and browse recovery, personalized messaging across channels, conversational shopping assistants, and post-purchase and replenishment. Behind the scenes, it supports demand forecasting, dynamic pricing, and fraud detection.

Because "AI" covers so much, it helps to separate the two types. Generative AI creates content, such as product copy, campaign variants, images, and answers to shopper questions. AI-driven selection decides which individual gets which experience, based on observed outcomes. This guide shows where each type helps in every use case, with real brand results, how to measure impact, and what to build first.

TL;DR

• AI in eCommerce is the use of machine learning and generative models across the buying experience and the operations behind it, from product recommendations to fraud detection.
• Generative AI creates content, such as product copy, campaign variants, and images. AI-driven selection, also called AI decisioning, decides which customer gets which version, on which channel, and when, based on observed outcomes.
• The six customer-facing use cases are product recommendations, search and discovery, cart and browse recovery, AI personalization across channels, shopping assistants and conversational AI, and post-purchase and replenishment.
• Measure AI against a conversion benchmark, track a specific rate for each use case, and use holdout groups to show what the AI caused.
• Start where your first-party data is strongest, add selection where volume supports learning, then expand into conversational AI and agentic pilots with clear guardrails.

What is AI in eCommerce?

AI in eCommerce is the use of machine learning and generative models across the buying experience and the operations behind it. On the customer-facing side, it recommends products based on what each shopper browses and buys, interprets what people mean when they search, recovers abandoned carts and browsing sessions, personalizes messages across email, push, SMS, and in-app, and powers shopping assistants that answer questions in conversation. On the operational side, it forecasts demand so inventory matches what customers are likely to buy, adjusts prices in response to demand and stock levels, and flags transactions that look fraudulent.

This guide covers that full landscape, then goes deepest on the customer-facing engagement layer, where AI decides what each shopper sees and hears across site, app, and messaging.

AI in eCommerce examples: use cases that move revenue

Customer-facing AI in eCommerce can help the shopper from their first search through to a repeat purchase, and the following six use cases map that journey. You'll see what triggers or feeds each example, how generative AI and AI-driven selection contribute, and which kinds of brands each one suits best. That makes it easier to judge which ones fit your business and what you'd need in place first.

Where a Braze customer has published results, the brand is named and linked. Where no published eCommerce result exists yet, a short scenario shows how the mechanics would work for a retail brand.

Product recommendations

What it is: Product recommendations put items in front of each shopper based on what they, and shoppers like them, have viewed, added to cart, and bought. They appear on product pages, in carts, and inside email, push, and in-app messages, and cross-sell placements give brands a lever on average order value.

What triggers or feeds it: Interaction data, such as views, add-to-carts, and purchases, joined to catalog data like category, price, and stock status. Braze AI Item Recommendations uses the last six months of item interaction data. It works best for catalogs of roughly a few hundred to 100,000 items.

Where generative AI helps: It writes the copy around each recommendation, such as the headline, a short product blurb, or a line on why the shopper might like the item, and adapts it by audience or language. The products themselves come from the recommendation model.

Where AI-driven selection helps: The recommendation model decides which products each person sees. A further layer of selection decides how those recommendations reach them: which message carries them, on which channel, and whether an offer goes alongside. BrazeAI Decisioning Studio™ makes these 1:1 decisions for brands and optimizes for any business KPI, from conversions to revenue.

Example: 24S, a luxury online retailer, curates more than 60 brands for a global audience. When a customer signs up for a back-in-stock alert, a triggered journey confirms the subscription and adds four products chosen by AI Item Recommendations, giving the shopper relevant alternatives while they wait. Over six months, that back-in-stock campaign delivered a 7% increase in add-to-cart rate. A second campaign in the same program used live inventory data to send abandoned-cart reminders when stock ran low, and delivered a 35% increase in purchase conversion rate (3-day purchase window) over six months.

Best for: Brands with broad or frequently changing catalogs, such as fashion, beauty, and home, where shoppers are choosing between many similar items.

Search and discovery

What it is: AI-powered search interprets what a shopper means, matches it to the catalog, and ranks the results. Discovery applies the same thinking to category pages, new-arrival feeds, and the products featured in messages.

What triggers or feeds it: Search queries, clicks on results, purchases that follow a search, and catalog data. Attribute quality carries a lot of weight here. A product tagged with color, fit, material, and occasion can match many more queries than one with only a title and a price.

Where generative AI helps: It fills in and standardizes product attributes and descriptions across large catalogs, which gives search more to match against. It also powers natural-language search, where a shopper types a full request such as "red waterproof jacket for hiking in spring, size 14."

Where AI-driven selection helps: Ranking is a selection problem: deciding which products each shopper sees first, based on what led similar shoppers to click and buy. Away from the search bar, selection decides which categories or new arrivals to feature for each person in messages and on-site placements.

Example: Two shoppers on a fashion retailer's site could both search for "black dress" and see different results first. One has bought from premium designer labels before, so those styles rank at the top for her. The other usually buys sale items, so discounted styles lead her results. Generative AI can fill in missing product details, such as occasion or fabric, which gives the ranking more to work with.

Best for: Brands with large catalogs and high-intent shoppers, such as electronics, home improvement, grocery and fashion, where people often know what they want but not what it's called.

Cart and browse recovery

What it is: Cart recovery journeys bring back shoppers who left items in a cart or viewed products without buying. Browse abandonment works the same way, one step earlier in the funnel. For a deeper look at decisioning in this area, see how AI decisioning helps retail brands go beyond abandoned cart emails.

What triggers or feeds it: A cart with no purchase after a set window, a product view with no add-to-cart, or a change to an item the shopper showed interest in, such as a price drop or low stock. Real-time event and catalog data keep these messages accurate, so a shopper isn't reminded about an item that has already sold out.

Where generative AI helps: It produces variants of the reminder, including subject lines, push copy, and different tones, so teams have more than one template to test.

Where AI-driven selection helps: Selection decides, for each shopper, whether to send a reminder at all, which channel to use, when to send it, and whether an incentive is needed. Some shoppers return after a simple nudge, while others only respond to an offer. BrazeAI Decisioning Studio™ learns from outcomes which approach works for each person, helping hold discounts back from shoppers who would have bought anyway.

Example: A home retailer could follow up on browse abandonment when a product a shopper viewed drops in price. The price drop triggers the message, and AI-driven selection decides whether each shopper gets it by email or push, and when, based on how they've responded before.

Best for: Brands selling high-consideration or limited-stock products, such as luxury, fashion, home improvement, and electronics, where shoppers often research before they buy.

AI personalization in eCommerce across channels

What it is: Each customer gets messages shaped for each person, across email, push, SMS, WhatsApp, and in-app, with dynamic content, channel, timing, and frequency set at the individual level. For implementation of this kind of use case, see the guide to eCommerce personalization.

What triggers or feeds it: First-party data on the customer profile, including purchase history, browsing behavior, stated preferences, and engagement with past messages on each channel.

Where generative AI helps: It produces the volume of content that personalization needs, such as copy variants by audience, localized versions, and image options. Each still needs review against brand guidelines before it goes out.

Where AI-driven selection helps: Selection decides which of those variants each person receives. BrazeAI Decisioning Studio™ can decide channel, message, creative, offer, time, day, and frequency for each person.

Example: A beauty retailer launching a new collection could let selection decide how each member hears about it. A member who tends to engage with push on weekday mornings would get the announcement then, on push. A member who only engages with email would get it by email, with the subject line variant most likely to resonate with them. Marketers set the goal and the guardrails, and the system adjusts as each member's behavior and outcomes change.

Best for: Brands with large audiences active across several channels, such as beauty, fashion, and multi-category retailers, with enough send volume for the system to learn from outcomes and retailers with an in-store and eCommerce presence.

Shopping assistants and conversational AI in eCommerce

What it is: Conversational AI in eCommerce runs two-way conversations with shoppers on site, in-app, or over channels such as SMS and WhatsApp. Scripted chatbots follow fixed decision trees, while an AI shopping assistant can answer product questions, recommend items, and complete tasks like placing an order, all without the shopper switching channels.

What triggers or feeds it: The shopper's own words, plus catalog data, order history, and profile data, which allows the assistant to give answers specific to that person.

Where generative AI helps: This is where generative AI does its most visible work of reading a shopper's natural-language message, working out what they want, and writing a reply. Answers about price, stock, or compatibility should be drawn straight from catalog data, so the assistant can't invent them.

Where AI-driven selection helps: Selection decides which products to recommend within the conversation, and which customers to invite into one, such as who receives a two-way prompt after a purchase and on which channel.

Example: A consumer electronics retailer could let shoppers reply to a product launch message by text. If a shopper asks whether the new headphones work with their phone, an AI agent checks compatibility in the catalog, recommends the right model and accessories, and completes the order inside the thread once the shopper confirms.

Best for: Brands whose products prompt questions before purchase, such as electronics, beauty, and furniture, and that already run two-way channels like SMS or WhatsApp.

Post-purchase and replenishment

What it is: Post-purchase journeys cover what happens after an order: confirmations, setup and care content, cross-sell, review requests, and replenishment reminders for products that run out.

What triggers or feeds it: A completed purchase, a delivery confirmation, or the typical time it takes to use up a product. Purchase history and catalog data, such as pack size and usage cycle, set the timing.

Where generative AI helps: It writes the supporting content, including how-to guides, routine tips, and reminder copy, tailored to what each customer bought.

Where AI-driven selection helps: Selection decides who needs an incentive to reorder, and how much. BrazeAI Decisioning Studio™ can choose the offer and incentive for each individual, so a customer who would reorder anyway gets a simple reminder, while one who is drifting away gets a stronger reason to return.

Example: A skincare brand could replace one blanket replenishment email with a tiered approach. Customers most likely to reorder receive a reminder with no discount, others get a modest incentive, and those least likely to respond get a deeper offer or no message at all. Selection makes those calls person by person and learns from what each customer does next.

Best for: Brands selling consumables or products with a natural reorder cycle, such as beauty, skincare, pet supplies, and grocery.

Generative AI in eCommerce vs. AI-driven selection

Generative AI produces the assets an eCommerce team works with, including product descriptions, subject lines and other campaign variants, product and lifestyle images, and answers to shopper questions. For retailers with large catalogs, that means writing and updating copy for thousands of products, or producing several versions of a campaign for different audiences and markets, much faster than a team could manage by hand.

AI-driven selection, also known as AI decisioning, decides which individual gets which of those variants, through which channel, and at which time, based on observed outcomes. For example, one customer might receive a new arrivals message by push on a weekend morning, while another gets a discount-led version by email midweek, because those are the formats and times each has responded to before. Every send adds to what the system knows about each customer, so selection improves as outcomes accumulate. Platform-native AI acts on the same customer profile the channels write to, which removes the gap between insight and action.

The two types depend on each other. Generation gives selection a wider range of options to choose from, and the outcomes selection records show which kinds of content are worth producing more of.

Each also has limits. Generated content needs human review and brand guardrails, such as approved product claims and tone rules, before it reaches customers. Selection needs traffic volume and observed outcomes to learn from, so a retailer selling a small number of high-ticket items will wait longer for reliable decisions than one processing thousands of everyday orders a week. Until there's enough data, rules and tested defaults have to carry more of the load. And neither fixes a catalog with bad data. Missing attributes, out-of-date prices, and inaccurate stock levels can end up in generated copy and in the options selection chooses between. Shoppers then see the wrong details or get pointed toward items they can't buy, which costs both the sale and their trust in the next message. Selection also learns from those failed sends, so the errors carry into future decisions too.

Where agentic AI is taking eCommerce

Agentic AI moves AI from making decisions to taking actions on a person's behalf. In eCommerce, that's happening on both sides of the transaction, with shopper-side agents that search and buy, and brand-side agents that run campaigns and conversations under guardrails people set.

On the shopper side, agents research products across retailers, compare prices, and complete purchases within limits the shopper has approved. Adoption is early but climbing. In the Braze 2026 Retail Customer Engagement Review, 19% of surveyed consumers said they currently use AI intermediaries in their interactions with brands, while 46% said they either do or expect to by the end of the year — so things are changing fast. Shoppers want to stay in charge, though. Only 10% would let AI agents operate independently when dealing with a brand. For retailers, that means increasingly more purchases will begin with software reading their product data, so complete and accurate catalog information now has a direct impact on sales.

On the brand side, agents can hold two-way conversations with customers. In healthcare, an adjacent industry, Aeroflow Health used AI agents built in BrazeAI Agent Console™ to turn one-way SMS reorder prompts into two-way conversations for its Mom & Baby division, which helps pregnant and nursing mothers get insurance-covered medical devices such as breast pumps. When a customer replies asking for a different size in her own words, the agent interprets the request, matches it to the catalog, and places the order inside the text thread. Compared with the previous approach, the program delivered an 8.8% lift in repurchase conversions. The same pattern could apply to eCommerce reorders of consumables, from skincare to pet food.

Agents are also taking on parts of campaign work. BrazeAI Decisioning Studio™ uses agents that run continuous experiments and adapt as customer behavior changes, with results measured against control groups. The marketer sets the goal, the options, and the limits, and the agents work out what to try within them.

On both sides, the human role moves toward setting boundaries and having oversight. Teams decide which actions an agent can take without approval, which offers it's allowed to make, and the point at which a person steps in. With shopper-side agents, a brand's influence comes earlier, through the quality of its catalog data and how clearly it states its policies on pricing, returns, and delivery.

Agentic commerce gets a full breakdown in this guide, showing how eCommerce brands can prepare for agents acting on both sides of a purchase, from standardizing product data to staying discoverable to AI systems.

How to measure AI in eCommerce

Over the 12 months to September 2026, average eCommerce conversion rates ranged from under 1% of visitors for luxury and jewelry to over 5% for beauty and personal care, according to Dynamic Yield's benchmarks. Use a benchmark, whether from your own data or an industry source like this one, as a reference point, and judge an eCommerce AI program by how far it moves conversion against it. Activity metrics, such as messages sent or recommendations shown, only tell you the AI is running.

Conversion rate shows the overall result, but it can't tell you which part of your AI program made the difference. For that, measure each one separately. Every rate has a denominator, the number you divide by, and it decides what the rate tells you. Conversion rate divides purchases by every site visitor, while checkout conversion rate divides them by only the shoppers who start checkout, so the two give very different numbers. Match the denominator to the people the AI actually reached:

Product recommendations

Click-through rate: How often shoppers click a recommended product when it's shown to them.

Calculation: CTR = (clicks on recommendations ÷ recommendation impressions) x 100

Attach rate: This shows how often shoppers add a recommended product or add-on when they buy.

Calculation: Attach rate = (number of secondary products sold ÷ number of primary products sold) x 100

Search and discovery

Revenue per session: How much revenue each visit to your site or app brings in, on average.

Calculation: RPS = Revenue per session = total revenue ÷ total sessions in the same period

Cart and browse recovery

Abandoned cart recovery rate: The share of abandoned carts that go on to become completed orders after a reminder.

Calculation: Abandoned cart recovery rate = (recovered carts ÷ total abandoned carts, within the recovery program's attribution window) X 100

AI personalization in eCommerce across channels

Conversion rate: The share of people who received a message and went on to complete the action it was designed to drive, such as a purchase.

Calculation: Conversion rate = (conversions ÷ unique recipients) X 100

Shopping assistants and conversational AI in eCommerce

Containment rate: The share of conversations the AI resolves on its own, without passing the customer to a person.

Calculation: Containment rate = (conversations fully resolved by AI ÷ total conversations handled by AI) X 100

Customer satisfaction (CSAT): The share of customers who rate their experience as satisfied or very satisfied.

Calculation: CSAT = (satisfied responses ÷ total survey responses) X 100

Post-purchase and replenishment

Repeat purchase rate: The share of customers who buy from you more than once in a given period.

Calculation: Repeat purchase rate = (customers who made more than one purchase ÷ total customers in the same period) X 100

These rates show what changed, but not what caused the change, because some shoppers would have bought anyway. A holdout group, a randomly chosen share of eligible customers who don't get a specific AI-driven experience, shows what that experience added when you compare its conversion rate with the rate for customers who did get it. A global control group does the same across your whole program, holding back a small percentage of all users from every campaign so you can see the overall impact of your messaging over time.

How to start with AI for eCommerce: what to build first

You don't need to launch every use case at once. Start where your data is already in good shape, then add more as your data and channels mature. Here's a sensible order, with what you need in place before each stage.

Start where your first-party data is already strong

Product recommendations and cart recovery run on data most retailers already collect: product views, add-to-carts, purchases, and catalog details. That makes them the fastest place to show a measurable return. The prerequisite is clean catalog data and consistent event tracking across site and app, tied to a single customer profile.

Add AI-driven selection where volume supports learning

Once those journeys are running, let selection decide the channel, timing, and offer for each customer. Selection learns from outcomes, so it works best on journeys with enough customers and conversions to learn from quickly. Before you start, make sure each journey has that volume, along with a holdout group so you can measure how selection compares.

Introduce conversational AI where two-way channels are established

Conversational AI needs customers who already reply to you, so start with SMS or WhatsApp programs that have an engaged, opted-in audience. The assistant also needs accurate catalog and order data to draw its answers from. A clear handoff to a person for questions it can't resolve should be in place before launch.

Hold agentic pilots to journeys with clear guardrails

Agents that take actions on a customer's behalf are best tested on narrow journeys where the possible actions are limited and easy to reverse, such as reorders. The prerequisite is a defined set of rules covering what the agent can do without approval, which offers it can make, and when a person steps in. Expand only once those guardrails have held up in practice.

If you're comparing platforms to support this roadmap, start with what Braze offers retail and eCommerce brands.

See how consumer brands run AI-driven engagement across email, push, SMS, and in-app on Braze.