The commerce landscape is undergoing a fundamental transformation. As AI assistants like ChatGPT, Perplexity, and Google Gemini become the primary interface for product discovery, brands face a new challenge: how do you ensure your products are found, understood, and recommended by AI?

This shift represents more than just a new marketing channel—it's a complete reimagining of how consumers discover and purchase products. Welcome to the age of agentic commerce, where AI agents act as trusted shopping advisors, and product visibility is determined not by ad spend, but by how well your catalog speaks to machines.

The Rise of AI-Powered Product Discovery

More than 700 million people use ChatGPT weekly, and according to Adobe, over half of consumers anticipate using AI assistants for shopping by the end of 2025. The numbers tell acompelling story: traffic to US retail sites from generative AI browsers increased 4,700% year-over-year in July 2025. Perhaps most significantly, a Profound study found that 58% of consumers used an answer engine like ChatGPT in a recent product research journey, with nearly 80% making at least half of their buying decisions inside those experiences. This isn't speculation about the future—it's happening right now.

Understanding Agentic Commerce

Agentic commerce refers to AI assistants that don't just answer questions about products—they actively guide the entire purchase journey. These AI agents understand context, remember preferences, compare options, and can even complete transactions without users leaving the conversational interface. OpenAI's recent launch of ChatGPT Instant Checkout exemplifies this trend. Users can now discover products from Etsy and over a million Shopify merchants, and complete purchases entirely within their ChatGPT conversation. Perplexity has launched similar "Buy with Pro" functionality, and Google is expanding its AI Mode shopping interface. The key difference from traditional e-commerce: product recommendations are organic and unsponsored, ranked purely on relevance to the user's needs. This levels the playing field but also demands a new approach to product visibility.

  Why Traditional SEO Falls Short

  Search Engine Optimization was built for humans typing keywords into search boxes. Generative Engine Optimization (GEO) is fundamentally different. AI assistants don't crawl your website looking for keywords—they parse structured product data, understand attributes and use cases, and synthesize information from multiple sources. Traditional SEO tactics like keyword stuffing, meta descriptions, and backlink building have limited impact on AI recommendations. Instead, what matters is:

 • Product data richness: Detailed specifications, clear use cases, and contextual descriptions

• Attribute clarity: Machine-readable properties that AI can parse and compare

• Semantic relevance: How well your product information aligns with natural language queries

• Source credibility: The trustworthiness and authority of your product catalog

Optimizing Your Catalog for AI Discovery

 Success in agentic commerce requires rethinking how you structure and present product information. Here are the critical elements:

  1. Comprehensive Product DescriptionsDon't just describe features—explain use cases, ideal customers, and problem-solving capabilities. AI assistants need context to make relevant recommendations.

 2. Structured AttributesUse clear, standardized product attributes. Instead of burying specifications in paragraph text, use structured fields that machines can easily parse and compare.

3. Rich Metadata: Include detailed categorization, tags, and semantic markup. Help AI understand not just what your product is, but why someone would choose it.

4. Use Case DocumentationExplicitly document scenarios where your product excels. When someone asks an AI assistant for "the best running shoes for marathon training," your product should have that context clearly defined.

Measuring Share-of-Recommendations

In agentic commerce, the key metric isn't page views or search rankings—it's share-of-recommendations. This measures how often your products appear in AI-generated shopping responses

compared to competitors. AgentPay helps brands track this critical metric, monitoring product visibility across ChatGPT, Perplexity, Gemini, and other AI platforms. You can see which products are getting recommended, for what queries, and how you stack up against competition.

The Competitive Advantage

Without intervention, brands risk being reduced to background utilities in agent-controlled marketplaces. According to BCG, the brands that will thrive are those that make their products easy for AI to find, understand, and recommend. The opportunity is significant: users coming from generative AI browsers spend 32% more time on site and have a 27% lower bounce rate than traditional search traffic. They're more engaged, more informed, and more ready to purchase.

Taking Action Today

The time to optimize for agentic commerce is now, not later. Start by auditing your product catalog through an AI lens:

• Ask ChatGPT and other AI assistants to recommend products in your category

• See which competitors get recommended and why• Identify gaps in your product data that prevent AI understanding

• Enrich your catalog with detailed, contextual information• Monitor your share-of-recommendations over time

The brands winning in agentic commerce aren't necessarily the biggest or best-funded—they're the ones that understand how AI agents think and have optimized their product data accordingly. As conversational AI becomes the primary interface for commerce, product discovery is no longer about gaming search algorithms or buying ad placements. It's about making your products genuinely discoverable, understandable, and recommendable by the AI assistants that consumers increasingly trust to guide their purchasing decisions. The question isn't whether to prepare for agentic commerce, but how quickly you can adapt to this new reality.