AI shopping
Definition
AI shopping is AI-powered product discovery, comparison, and purchasing inside conversational interfaces. Instead of browsing listings, users describe what they want and an AI assistant recommends products, summarizes tradeoffs, and increasingly helps complete the purchase. Surfaces include ChatGPT, Perplexity, Google AI Mode, and dedicated shopping agents.
How it works
In AI shopping, a user states needs in natural language, such as a budget, use case, or constraints, and the assistant retrieves candidate products, compares specifications and reviews, and synthesizes a recommendation. Many experiences support follow-up questions, letting shoppers refine by price, features, or availability across the conversation.
Increasingly, these systems move beyond recommendation toward action. Agentic shopping flows can place items in carts, apply filters, or complete checkout on the user's behalf using function calling and connected commerce APIs. Product data, structured feeds, and review signals feed the assistant's reasoning.
Why it matters for AI visibility
When an assistant recommends a short list, being one of the surfaced products is decisive, much like ranking once was, but compressed into a handful of synthesized picks. Products absent from the assistant's consideration set are effectively invisible to the shopper.
Earning that visibility depends on structured product information, accurate availability and pricing data, strong third-party reviews, and clear, extractable content that AI systems can ground on. Brands optimizing for AI shopping treat conversational and agentic commerce surfaces as a distinct channel from classic ecommerce search.
Frequently asked questions
What is AI shopping?
AI shopping is using AI assistants to discover, compare, and buy products through natural-language conversation. The assistant recommends options based on stated needs and can increasingly help complete the purchase.
Which platforms support AI shopping?
ChatGPT, Perplexity, and Google AI Mode offer conversational shopping experiences, and dedicated shopping agents are emerging. Each surfaces and recommends products in different ways based on its data sources and partners.
How do products get recommended by AI shopping assistants?
Assistants retrieve candidates from product data and reviews, then synthesize a recommendation matched to the user's stated needs. Structured product feeds, accurate pricing and availability, and strong review signals improve the odds of being surfaced.
How is agentic shopping different from AI shopping?
Agentic shopping is the action-taking subset of AI shopping, where the assistant not only recommends but executes steps like adding to cart or checking out, often via function calling and commerce APIs.
AI search
AI search is a search paradigm where AI assistants and engines synthesize a direct answer from multiple sources rather than returning a ranked list of links. Platforms like ChatGPT, Perplexity, Google AI Mode, and AI Overviews interpret intent, retrieve relevant passages, and generate a conversational response, often with inline citations to the sources used.
AI agent
An AI agent is a software system that uses a large language model (typically GPT-4o, Claude 3.5 / 4 Sonnet, Gemini 2.5, or open-source equivalents) to plan, decide, and act over multiple steps to complete a goal — calling tools, retrieving data, and producing outputs without step-by-step human supervision. Agents are the working surface of agentic AI in 2026.
Agentic workflows
Agentic workflows are AI architectures in which a model autonomously plans, calls tools, browses the web, executes code, and completes multi-step tasks with limited human input. Rather than producing a single answer, the system loops — observing results, revising its plan, and acting again — marking the shift from AI chat to AI work that carries out goals on a user's behalf.
Function calling / tool use
Function calling, also called tool use, is an AI capability that lets a model invoke external functions, APIs, and services to accomplish tasks beyond text generation. The developer describes available tools and their inputs; the model decides when to call one, emits structured arguments, receives the result, and uses it to continue. This connects language models to live data, code execution, and real-world actions.
Visual search
Visual search is AI-powered search that uses images as input rather than text. A user submits a photo and the system identifies objects, finds visually similar items, or answers questions about the image. It powers product identification, visual matching, and multimodal queries in tools like Google Lens, Pinterest Lens, and multimodal AI assistants.
Schema markup
Schema markup is structured data added to web pages using the schema.org vocabulary that tells search engines and AI systems exactly what the content represents — a product, an article, a recipe, an FAQ, a person. It powers rich results in Google, drives entity understanding in knowledge graphs, and increasingly determines whether content is cited in AI Overviews and LLM-generated answers.