From Keywords to Conversations: What an AI Shopping Assistant Is
An AI shopping assistant is a conversational, intelligent system embedded in e-commerce platforms that helps customers discover, compare, and evaluate products through natural language, images, and interactive tools instead of relying only on rigid keyword-based search boxes. In practice, this means shoppers can describe what they need in everyday language, refine results through follow-up questions, and receive personalized shopping recommendations that reflect their style, budget, and context. Major retailers are integrating these assistants directly into websites and apps so that the product search experience feels more like speaking with a knowledgeable store associate than typing into a search engine. Combined with other e-commerce AI tools, these assistants can power virtual try-ons, room visualizations, and side-by-side comparisons, turning static catalog pages into guided, responsive shopping journeys tailored to each visitor.
Retailers Race to Add AI to the Product Search Experience
Large retailers are rapidly adopting AI shopping assistants to modernize how customers browse and find products online. While traditional search forces shoppers to guess the right keywords, new LLM-based experiences can interpret detailed prompts about size, color, style, and budget in a single request and respond in seconds. Retailers are partnering with major cloud providers to build these e-commerce AI tools directly into their platforms, positioning AI as a core part of the product search experience rather than a separate feature. This shift also changes the role of search from retrieving lists of items to guiding decisions, surfacing inspiration, and narrowing overwhelming assortments. As AI systems gain multimodal capabilities, shoppers can use photos alongside text, allowing the assistant to match products to images, detect visual patterns in preferences, and keep the conversation coherent across devices.
Inside Kmart’s Joy: A Case Study in AI-Powered Shopping
Kmart’s new AI shopping assistant, Joy, shows how retailers are turning AI into a virtual shopping partner embedded across their online range. Joy is available on the Kmart website and app and can help shoppers discover products, compare options, and refine searches using natural language prompts. Customers can ask for items that match a specific size, style, color, and budget, then receive tailored suggestions in a few seconds. Joy also supports photo uploads, enabling more interactive and personalized shopping recommendations. According to Kmart Group Chief Customer Officer Bernard Wilson, “Customers aren’t just searching anymore; they’re engaging conversationally and looking for ideas and guidance.” Built with Google Cloud’s Gemini Enterprise for Customer Experience, Joy can support the customer lifecycle from discovery through post-purchase, showing how AI assistants are evolving from simple chatbots into end-to-end shopping companions.
Virtual Try-Ons and ‘See It in My Space’ Experiences
One of the most visible changes AI brings to online shopping is the blend of conversation and visualization. Through Joy, Kmart has introduced virtual try-on features so shoppers can see how selected products may look on them, closing the gap between browsing and trying items in-store. The “See It in My Space” function goes a step further by enabling customers to view furniture and home items within their own rooms before buying, reducing guesswork about fit and style. These features are integrated directly into the AI shopping assistant, so users can ask questions, switch colors, or compare items without leaving the experience. Combined with multimodal capabilities like photo uploads, this approach turns the product search experience into an exploratory, visual dialogue that helps customers feel more confident in their choices and reduces friction along the path to purchase.
The Future of E-Commerce: AI as the Default Search Interface
As AI shopping assistants become standard, they are reshaping expectations of what online shopping should feel like. Instead of typing short, keyword-heavy queries, customers can explain their needs in full sentences, refine results with follow-up questions, and receive ongoing personalized shopping recommendations. LLM-based search shifts from matching words to understanding intent, context, and constraints, allowing e-commerce AI tools to act more like digital stylists or planners than simple search engines. For retailers, this means product discovery, inspiration, and support can all be delivered through a single conversational interface that spans discovery, comparison, and post-purchase help. Over time, these assistants will likely become the default entry point into e-commerce platforms, turning the product search experience into a continuous, adaptive dialogue that learns from each interaction and brings shoppers back with smarter, more relevant suggestions.