From keywords to conversations

For years, Google trained shoppers to compress intent into three words. Now, tools like ChatGPT have flipped the script: a shopper types, “I need a flowy dress for a summer wedding under $150,” and expects a helpful, guided response. When sites still return literal, keyword-based results, shoppers bounce—to an AI agent that understands context.

What’s changing:

• Natural-language queries replace rigid filters.
• Expectations are set by AI assistants, not legacy site search.
• Patience is thin—if the right result isn’t in the first screen, customers leave.

Why the AI agent wins

An effective AI agent interprets intent, clarifies details, blends content and products, and personalizes results—just like an in-store associate. It also supports multimodal interaction (text, images, and voice), meeting shoppers where they are and how they prefer to communicate.

Key capabilities:

• Understands ambiguous requests (“guest dress for Spanish wedding”).
• Personalizes with first-party data and loyalty context.
• Presents rich, visual product cards, not just blue links.

Don’t just rank, be discoverable to AI agents

Discovery now starts beyond your domain. If your products aren’t understood by external AI agents (ChatGPT, Perplexity, voice assistants), you may never enter the consideration set.

Make products agent-discoverable:

• Structure your product data (rich attributes, clean taxonomy).
• Write conversational, FAQ-style copy that maps to questions an AI agent can summarize.
• Establish authority signals through consistent, accurate content.

The infrastructure shift: vector search + MCP

Delivering conversational commerce isn’t a copy change: it’s an architecture change.

1. Vector search
   If your search returns literal matches, shoppers feel the gap immediately. That’s why traditional keyword search has started to lag behind lately. A modern stack uses semantic/vector retrieval to map “pretty summer wedding guest dress” to relevant results, even if those exact words aren’t in the title.
   
2. Model Context Protocol (MCP)
   To transact across a growing ecosystem of AI agents, expose commerce capabilities (search, cart, checkout, order history) through a standard interface. MCP acts like “USB-C for AI,” letting any compliant AI agent discover products and complete tasks. Major players are aligning around this approach, and brands that implement MCP-style endpoints will be easier for agents to work with—meaning more visibility and conversions.

Voice is next (and natural)

Conversational discovery will increasingly be spoken. Voice lowers friction and fits how people actually ask for help. Your AI agent experience should support voice input and responsive, visual output (cards, carousels, video) to keep the journey fluid.

Two places to win today

• Off-site, via third-party ai agents: Ensure agents can understand, rank, and recommend your products.
• On-site, via your own agent: Blend chat and search into a single, visual, guided experience that feels like a great store associate.

Your 90-day action plan

• Upgrade search to a vector/semantic engine.
• Restructure data and enrich product attributes.
• Rewrite content in conversational, FAQ-friendly formats.
• Expose APIs (search, cart, checkout, account) with MCP-style tooling.
• Prototype an AI agent UI that merges chat, results, and product cards—desktop and mobile, text and voice.

Bottom line: Agentic commerce isn’t a future bet—it’s the current customer expectation. Brands that become AI-agent ready now will own discovery, loyalty, and growth as this shift accelerates.

For even more details, watch the full recording of our webinar:

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