AI in Retail: Where Does It Actually Pay Off?

If AI has suddenly become one more thing your retail team is expected to figure out, we get it. There are vendor demos, competitor launches, internal questions, and probably a few very confident opinions about what you should be doing already.

That can make AI in retail feel like a decision you’re late to. You’re not. There are useful applications already, and there is no reason to chase them all at once.

A useful place to look first is where AI already has a clear job and a result you can measure. In retail, that usually means five areas across the customer journey and day-to-day operations: discovery, personalization, merchandising, support, and operations.

We’ll go through each one, look at what AI can improve there, and spell out what needs to be true about the data or systems before it can do much for you.

What AI in retail actually covers

You probably know most of this already, so let’s put the terms in order before we go any further.

“AI in retail” refers to systems that help retailers predict, rank, recommend, generate, or respond using store data. Some of those are the machine learning tools that retailers have used for years for forecasting demand, ranking products, and spotting patterns.

Generative AI in retail handles a different kind of work, such as writing product content, answering customer questions, or turning a natural-language request into something the system can act on.

We’ll be dealing with both throughout this article. And a good place to see how that plays out is the part customers meet first: finding the right product.

#1 Discovery: helping shoppers find the right product

Imagine someone looking for a floor lamp for a small reading corner. A person has a picture in their head, a rough budget, and absolutely no idea what the product in your catalog is called. This is where AI can make discovery feel much closer to how people actually shop.

Semantic and vector search

A user starts with “warm light floor lamp for a small dark corner under $150.”

Traditional keyword search has several words to match. Semantic and vector search can go further, surfacing products that match the request’s complex meaning even when titles use terms like “ambient lighting” or “reading lamp.”

Target is an example of a brand that uses hybrid search. The latter combines keyword and vector matching across millions of products. The change in the search logic did pay off: the company reported a 20% improvement in product discovery relevance after rebuilding its search experience. 

For that to work, the catalog has to give the search system enough information. Useful attributes, categories, descriptions, prices, and availability give it what it needs to make those connections.

Query understanding

A single request contains more than a product name. Partially, that shift was driven by more active use of AI search and Gemini being built into Google Search.

The system has to recognize the product type, intended use, size constraints, and budget. If shoppers regularly search for things like “lamp for a dark corner” and end up buying floor lamps with warm light, that history helps the store understand what those words usually mean in practice.

This is why query volume and search history matter. They show how customers describe products, and they don’t use the same language as the merchandising team.

Visual search

Suppose a day later, our shopper sees almost exactly the lamp she wants in a coffee shop. Describing the shade, base, material, and shape sounds like too much text, so they take a photo instead.

Amazon Lens is built for exactly this kind of moment. It can match a photo or live camera view against visually similar products, and Amazon says photo searches through Lens have more than doubled since 2023. 

Here, the catalog images are doing part of the searching. Consistent, high-quality product photography gives visual search much more to work with.

Zero-result recovery

Back home, a user tries one last search: “brass mushroom lamp under $150.”

Maybe the store has no product using the word “mushroom.” It does have several dome-shaped brass lamps that fit the rest of the request.

A useful search experience can recover from that mismatch instead of ending with “0 results.” Adobe reports that one furniture retailer reduced zero-result searches by 57% in its first month of testing semantic search. 

There still has to be something relevant to recover into. Catalog breadth and good product data decide whether the system can offer a sensible alternative or simply turn an empty page into a bad recommendation.

#2 Personalization: more relevant experiences

Personalization helps decide which relevant products get more attention for each shopper. It now covers AI product recommendations, customer segmentation, on-site ranking, and the wider browsing experience. The quality of those decisions depends on having enough browsing and purchase data to learn from.

You can see how that plays out in these personalized search examples, or take the idea further with hyper-personalization in e-commerce.

#3 Merchandising: pricing, assortment, and content at scale

AI has plenty of work to do behind the storefront too. AI merchandising can help teams handle pricing and assortment decisions, category order, product content, and the constant stream of smaller updates around them.

There is room to save serious time here. McKinsey estimates that retail merchants could reclaim up to 40% of their time by moving repetitive manual work to AI and using that time for higher-value commercial work. 

Dynamic pricing

AI can use demand signals, competitor prices, inventory levels, and previous price performance to recommend or trigger an update. The precondition is straightforward: competitor and demand feeds have to arrive fast enough for the decision to still matter. Yesterday’s picture is a poor basis for today’s price.

Assortment and category ordering

Merchandising teams also have to decide what deserves more space, what should move up a category page, which products are losing momentum, and how the assortment should change with the season.

AI models can help with those calls when there is enough sales history to compare performance over time and enough seasonality data to separate a lasting pattern from a short spike. A retailer with several seasonal cycles recorded gives AI much more to work with than one with a few weeks of sales.

AI-generated product content

Content becomes another workload once a large catalog starts changing frequently. New launches can mean descriptions, localized versions, product attributes, campaign imagery, and updates across existing pages.

Zalando shows how much time that can save. The retailer said generative AI had cut the time needed to produce imagery from six to eight weeks to around three or four days, while reducing production costs by 90%. The company also reported higher customer engagement with the generated content.

That speed only helps when the model knows how the brand is supposed to sound and what it is allowed to say. A defined style guide, approved terminology, formatting rules, claim restrictions, and review criteria keep the output usable. Otherwise, much of the saved production time comes back during editing.

#4 Support: chatbots, tickets, and order status

Support is another place where AI can take a very visible chunk of repetitive work off the team. McKinsey reports that generative AI copilots have reduced customer-care handling time by 40–60% in some retail deployments. 

The useful work here is usually pretty ordinary. Customers want to know where an order is, whether they can return something, or what happens next. Those questions become good candidates for automation when the answer already exists somewhere the system can reach.

Chatbots and order status

An AI chatbot for e-commerce becomes much more useful once it can query actual order and return data.

A customer asking “Where’s my package?” should be able to get the status of their own order. The same applies to questions about return eligibility or the next step after a refund request. That requires structured data and access to the systems where the answer lives.

A chatbot sitting in front of disconnected systems has very little to work with. It may sound conversational while still sending the customer to support for the information they came to get.

Ticket deflection and returns

Some support requests do not need to become tickets at all. 

Returns are a good example. If the system can query the order and apply the retailer’s return rules, it can confirm whether an item qualifies and guide the customer through the next action. That depends on two things being available: order data the system can query, and policies expressed clearly enough to apply as rules. When either is missing, automation quickly runs out of useful answers.

There is also a sensible stopping point. “Where’s my order?” can be straightforward. “Tracking says delivered, but nothing has arrived yet, and I need this resolved today” already requires judgment and discretion.

Ambiguous cases, complaints, and account-specific issues still need a person. A useful support system should recognize that point and hand the conversation over with the relevant context attached.

#5 Operations: forecasting, inventory, fraud, logistics

Operations is where small prediction errors get expensive fast. Order too much, and stock sits. Order too little, and the sale goes somewhere else. Put inventory in the wrong warehouse, and the product is technically be available while the customer still sees a long delivery window.

AI can help with those decisions, provided the systems behind them give it enough history and a current view of what is happening.

Demand forecasting

Retail demand rarely moves in a straight line. Weather changes, holidays shift buying patterns, promotions pull demand forward, and last year’s bestseller can suddenly lose momentum. AI can use historical sales and seasonality to estimate what is likely to happen next, giving purchasing and planning teams a better basis for stock decisions.

The quality of that forecast depends heavily on history. A retailer with several recorded seasonal cycles gives the model patterns to learn from. A new category with a few weeks of sales gives it much less to work with.

Inventory allocation

Having enough stock is only useful if it is somewhere it can be shipped without delays or complications. AI inventory management can help distribute items across stores, warehouses, and online channels as demand changes. That can also improve fulfillment decisions by choosing a location that has the product and can get it to the customer efficiently.

For that to work, inventory data has to be connected across locations. If the website sees one number, the warehouse sees another, and store stock updates later, the model starts from a shaky picture before it makes a single decision.

Fraud detection

Fraud detection gets tricky because unusual activity can still be perfectly legitimate. Someone buying an expensive laptop at two in the morning might seem suspicious for people with different sleeping schedules and decision-making patterns. But that alone isn’t enough to flag a transaction as suspicious.

AI can look across transaction history to find patterns associated with fraud and help flag the cases worth reviewing. The more relevant transaction history a retailer has, the better the system can learn what normal and suspicious activity look like for that business. 

Across these use cases, the same issue keeps coming back: AI needs reliable access to the data behind the decision. Before choosing another use case, it helps to look at those dependencies together.

How to make it all work

By now, the pattern is pretty clear. A successful implementation of every mentioned scenario depends on something already working underneath it.

• Product data quality provides search, recommendations, and generated content with reliable information to work with.
• Catalog structure helps systems understand how products relate to categories, attributes, and each other.
• Order and customer data enable personalization and support to access the history and status behind each interaction.
• System access and integrations give AI a way to reach the information it needs and, where appropriate, act on it across the commerce stack.

Commerce platforms such as Salesforce Commerce Cloud and BigCommerce can provide part of that setup, while the wider stack may also include a PIM, OMS, CRM, search, or inventory system.

When the work reaches beyond a single AI feature into the platform itself, our e-commerce development work covers that wider platform and integration layer.

Once you know which of these pieces are already in place, choosing where to start gets much easier.

Where to start

Pick two or three functions where the prerequisites are already strongest and the result is easy to measure. A well-structured catalog may put search high on the list. Accessible order data can make support a sensible first move. Years of sales history may point toward forecasting.

In practice, readiness does a lot of the choosing for you. If that part still feels fuzzy, checking how ready your commerce setup is can show which ideas already have the data and systems behind them. And suddenly, “What should we do with AI?” becomes a much smaller question: where can it do useful work right now?

If you already have a couple of answers, drop us a message, and our tech team will help work out what it would take to move them forward.