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Is your store invisible to AI?

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How to Optimise Your E-Commerce Store for AI Shopping Agents: A Complete Guide

The landscape of online shopping is undergoing a fundamental transformation. With major platforms like Shopify, Google, and OpenAI introducing AI-powered shopping agents, e-commerce merchants face a critical question: How do you ensure your products are recommended by these intelligent agents?

This isn’t about gaming a new system—it is about fundamentally rethinking how you present product information to serve both AI agents and the customers they represent.

Understanding Agentic Commerce

Agentic commerce represents a paradigm shift in how consumers discover and purchase products online. Unlike traditional search engines that simply match keywords and return links, AI agents function as personal shopping assistants, analysing product data, comparing options, and making recommendations based on user preferences.

The key difference? AI agents are the shopper’s representative. They are not just crawling your site for ranking purposes—they are actively making decisions about whether your products meet their user’s needs.

Why Traditional SEO Thinking Doesn’t Apply

Many merchants are approaching agentic commerce the same way they approached search engine optimisation: trying to “rank” in ChatGPT or other large language models. This misses the fundamental shift taking place.

The question isn’t “How do I rank higher?” but rather “Are my products genuinely the ones these agents should be recommending to their users?”

This distinction matters. While traditional SEO focused on optimising for bots that served human decision-makers, agentic commerce requires you to provide the information agents need to make informed recommendations on behalf of humans.

Critical Elements for Agent-Ready Stores

  1. GTINs Are Non-Negotiable

Global Trade Item Numbers (GTINs) have become essential infrastructure for agentic commerce. Many merchants skip adding GTINs, often due to limited resources or simply not prioritising the task. This is a critical mistake.

If you’re selling products that other retailers also carry, lacking GTINs means AI agents won’t even know you have those products available. Agents need to extract data quickly and efficiently—without proper identifiers, your inventory becomes effectively invisible.

Action Item: Prioritise adding GTINs to all products, especially those sold by multiple retailers. For unique products you manufacture, ensure you have proper identifiers in place.

  1. Structured Data Over Narrative Descriptions

Product specifications buried in flowing prose don’t serve AI agents well. Instead, structure is paramount:

  • Place specifications in dedicated fields or metafields
  • Use tables in product descriptions when appropriate
  • Organise information so agents can easily extract specific data points
  • Ensure technical details are consistently formatted across products

Think of it this way: An agent needs to answer “Does this product have X feature?” in milliseconds. Make that information instantly accessible.

  1. Store Policies as Trust Signals

Recent developments have highlighted the importance of comprehensive store policies. Shipping information, refund policies, privacy statements, and terms of service now function as trust signals to AI agents.

These policies serve a similar role that brand reputation and reviews play in traditional e-commerce. They help agents assess whether your store is trustworthy and whether they should recommend your products to their users.

Essential Policies to Implement:

  • Clear shipping policies and timeframes
  • Transparent refund and return procedures
  • Privacy policy
  • Terms of service
  • Contact information

  1. Rich, Extractable Content

Beyond basic specifications, provide comprehensive information that agents can use to match products to user needs:

  • Multiple high-quality product images
  • Detailed use cases and applications
  • Material compositions and dimensions
  • Compatibility information
  • Care and maintenance instructions

The goal is to anticipate questions an agent might need to answer and provide that information in a format that’s easy to extract and reference.

Monitoring AI Agent Activity

Understanding which AI agents are visiting your site and what they are examining provides valuable insights into which products should receive optimisation priority.

Different AI bots identify themselves differently when crawling websites:

  • Amazon Bot
  • ChatGPT Bot (identified as GPTBot)
  • Various other AI agent identifiers

Note that some agents, like Google’s Gemini, don’t use separate identifiers and appear as standard Google bots. However, tracking the identifiable bots can still provide useful patterns about which products are being evaluated most frequently.

The Universal Commerce Protocol (UCP)

Google’s recently announced Universal Commerce Protocol represents an emerging standard for how e-commerce sites should structure data for AI agents. While still in early stages, UCP will become an important consideration for merchants.

Shopify plans to implement UCP natively, though there may be opportunities to enhance beyond the default implementation. Staying informed about UCP developments and planning for implementation will be crucial as this standard matures.

Assessing Your Store’s Agent Readiness

Consider evaluating your store across these dimensions:

Data Quality Score:

  • Are GTINs present on all applicable products?
  • Is product information structured in extractable formats?
  • Do products have sufficient descriptive content?

Trust Signals:

  • Are all major store policies published and accessible?
  • Is contact information clearly available?
  • Are shipping and return policies transparent?

Structural Completeness:

  • Is schema markup implemented correctly?
  • Are images optimised and sufficiently numerous?
  • Is technical information organised consistently?

Testing Product Discoverability

An effective approach to understanding how agents perceive your products is to simulate agent queries. Take common search queries from your analytics tools and evaluate:

  • Which of your products would match these queries based on current data structure?
  • Which products were close matches but didn’t quite meet the criteria?
  • What additional information would make your products better matches?

This exercise reveals gaps in how you’ve structured product information and highlights optimisation opportunities.

Leveraging Platform-Native AI Tools

For Shopify merchants, Sidekick represents a significant advantage. This built-in AI assistant can help with store optimisation tasks, including:

  • Enhancing product descriptions
  • Identifying missing information
  • Suggesting improvements to product pages
  • Assisting with policy implementation

While manual one-by-one optimisation through AI tools may work for small catalogues, merchants with large product inventories should explore bulk optimisation strategies or await API integrations that enable automated improvements.

The Road Ahead

Agentic commerce is still in its early stages, but the direction is clear: AI agents will increasingly mediate the relationship between consumers and products. The merchants who succeed will be those who:

  1. Provide comprehensive, structured product information
  2. Build trust through transparent policies and practices
  3. Make their inventory genuinely discoverable by AI agents
  4. Continuously optimise based on how agents interact with their stores

This isn’t about tricks or shortcuts—it’s about fundamentally serving customers better by ensuring the AI agents representing them have the information they need to make informed recommendations.

Taking Action

Start with these immediate steps:

  1. Audit your GTINs: Identify which products lack proper identifiers and prioritise adding them
  2. Review product data structure: Move specifications out of narrative descriptions and into structured fields
  3. Implement missing policies: Ensure all major store policies are published and accessible
  4. Test key queries: Simulate how agents would respond to common product searches in your category
  5. Monitor agent activity: Track which AI bots are visiting your site and what they’re examining

The future of e-commerce is being written now. Merchants who adapt their product data and store structure for agentic commerce will be best positioned to capture this emerging channel of customer acquisition.

Looking to test your store’s readiness for agentic commerce? Consider conducting a comprehensive audit of your product data structure, policy completeness, and technical implementation. The investment in proper product data infrastructure pays dividends across all customer acquisition channels, from traditional search to emerging AI agent recommendations. If you need assistance in this field, feel free to reach out to Jim Stewart at jim@stewartmedia.biz to discuss your needs.

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