The State of AI Shopping Agents in 2025: Market Overview
The Rise of AI Shopping Agents
In 2025, AI-powered shopping assistants have moved from novelty to necessity. Over 40% of online product discovery now involves an AI intermediary — whether through ChatGPT's product recommendations, Perplexity's shopping answers, Google's AI Overview in search, or Microsoft Copilot's Bing integration.
This represents a fundamental shift in e-commerce: the buyer no longer browses your site directly. Instead, an AI agent reads your product data, evaluates it, and decides whether to recommend it. If your store isn't optimized for these agents, you're invisible to a growing segment of consumers.
The Major AI Shopping Agents
ChatGPT Shopping (OpenAI) — Launched in late 2024, ChatGPT now recommends products directly in conversation. It relies heavily on JSON-LD structured data and product feeds. Merchants with proper schema markup see 3-5x higher recommendation rates.
Perplexity Shopping — Perplexity's "Buy with Pro" feature lets users purchase directly from search results. It crawls product pages and prioritizes stores with clean structured data, verified prices, and clear availability signals.
Google AI Overview — Google's AI-generated summaries now appear at the top of shopping queries. Products in Google Merchant Center with complete schema markup dominate these results.
Claude (Anthropic) — Anthropic's Claude provides thoughtful product analysis and comparison. It values verifiable credentials, transparent policies, and well-structured merchant data.
Microsoft Copilot — Integrated into Bing, Edge, and Windows, Copilot surfaces product recommendations across Microsoft's ecosystem. It relies on Bing's index and prioritizes stores with strong structured data signals.
What These Agents Look For
Despite different architectures, all AI shopping agents share common data requirements:
1. Structured product data (JSON-LD) — Name, price, availability, images, and descriptions in machine-readable format. This is the single most important factor.
2. Product identifiers (GTIN/MPN) — Unique identifiers allow agents to cross-reference products across merchants and verify pricing.
3. Clear policies — Return, shipping, and privacy policies signal merchant trustworthiness. Agents prefer recommending stores with transparent policies.
4. Technical health — HTTPS, fast response times, and security headers indicate a reliable merchant.
5. AI-specific signals — robots.txt directives for AI bots, merchantstamp.json manifests, and verifiable credentials provide explicit trust signals.
Key Takeaways for Merchants
The average e-commerce store scores just 34/100 on AI readiness. This means most online merchants are leaving significant revenue on the table by being invisible to AI shopping agents.
The good news: optimizing for AI agents is straightforward. A proper JSON-LD implementation, combined with product identifiers and clear policies, can dramatically improve your visibility. Tools like MerchantStamp automate this process, scanning your store and generating the structured data these agents need.