What AI Shopping Assistants Are—and Why They Matter Now
AI shopping assistants are conversational, task-focused systems embedded into online stores that can answer questions, compare products, surface personalized recommendations, and complete shopping actions on a shopper’s behalf, using natural language, customer data, and real-time product information to create a more responsive, guided and personalized shopping experience than traditional search and filter tools. This is the heart of a shift toward “agentic commerce,” where software agents handle a growing share of the work we put into finding, evaluating, and buying products online. Instead of clicking through dozens of pages, customers describe what they need—size, style, budget, context—and the assistant narrows the options in seconds. Retailers see these tools as a way to deepen engagement, reduce choice overload, and convert browsing into purchases through tailored support at every step of the digital journey.
Amazon Turns Its Agentic Shopping Tech into a Service
Amazon Web Services is turning its internal retail AI into a product for other merchants with the AWS Agentic Shopping Assistant. Built on the same underlying technology as Alexa for Shopping on Amazon.com, it lets retailers add AI shopping assistants that can chat with customers, answer product questions, and recommend items based on their own catalog and rules. AWS says a retailer can have a branded assistant running on its site in about 60 days, with control over customer data and inventory logic. A notable early tester is fashion brand Kate Spade, whose parent Tapestry created an AI gift concierge that asks about the occasion, recipient, and style before suggesting products. According to Amazon, Alexa for Shopping drove nearly USD 12 billion (approx. RM55.2 billion) in incremental sales last year, underscoring the commercial stakes of this technology.
Kmart’s Joy Shows How Visual and Conversational AI Can Blend
Kmart’s new AI shopping assistant, Joy, shows how retail AI technology is moving beyond text search to richer, multimodal experiences. Available on its website and app, Joy combines conversational recommendations with visual tools, including virtual try-on for selected products and a “See It in My Space” feature that lets shoppers place items in their own rooms before buying. Customers can describe what they want in everyday language—size, style, colour, and budget—or upload photos to receive tailored suggestions, while Joy can present side-by-side comparisons across Kmart’s online range and marketplace brands. These capabilities are powered by Gemini Enterprise for Customer Experience from Google Cloud, described as an agentic solution that supports AI agents throughout the customer lifecycle, from discovery to post-purchase support. The result is a more immersive, responsive, and personalized shopping experience that aims to reduce returns and increase confidence in purchase decisions.
The Rise of Agentic Commerce and a New Personalization Race
Amazon and Kmart’s moves signal a broader shift toward agentic commerce, where AI agents do far more than answer FAQs. They guide customers through the whole journey: discovering ideas, refining needs, comparing options, and resolving issues after purchase. Accenture estimates that by 2030, more than 30% of online commerce could run through AI agents, representing about USD 3.1 trillion (approx. RM14.27 trillion) in transactions, so the race to build retail AI technology is intense. Tech giants are offering ready-made platforms, while retailers seek to keep ownership of customer data and brand experience. For shoppers, this means online stores that feel less like catalogs and more like helpful assistants. For retailers, the promise is higher conversion rates, larger baskets, and richer insight into intent—but also new questions about trust, data control, and how much autonomy to give AI agents in shaping the buying path.
What Shoppers Can Expect Next from AI Shopping Assistants
As AI shopping assistants mature, your online experience is likely to feel more like an ongoing conversation than a series of isolated searches. Instead of starting from scratch every visit, assistants could remember preferences, occasions, and constraints, then suggest timely ideas—outfits for upcoming events, home upgrades that match existing decor, or gifts tailored to a recipient’s tastes. Visual features such as virtual try-on and in-room views will make online shopping more exploratory and less risky, while side-by-side comparisons and budget-aware suggestions can cut research time. Retailers will also experiment with post-purchase agents that track orders and handle returns. The challenge will be balancing helpfulness with transparency: making sure recommendations reflect your interests and the store’s inventory without feeling pushy. If done well, agentic commerce could turn the familiar online store into a personal, always-on shopping companion.
