AI-powered Shopping: How to Ensure Agents Find Your Products

SEO still matters. But focusing solely on traditional search engines misses a major shift in how people find products online: shoppers are asking AI assistants what to buy. Salesforce’s 2025 Connected Shoppers Report says 39% of consumers (and most Gen Z) already use AI for discovery.

This changes the playbook. The goal is to be more than just the blue link served up by Google. The goal is to be the answer and the cited source.

AEO + GEO: what’s the difference (and why you need both)

• Answer Engine Optimization (AEO) is about making your content the answer AI systems surface (Google’s AI Overviews, ChatGPT, Alexa). Instead of chasing links, you optimize content so facts, summaries, and product data get pulled directly into the response (snippets, AI cards, spoken answers).

• Generative Engine Optimization (GEO) is about getting referenced and quoted by those same systems. You structure and substantiate your content so LLMs treat it as credible, current, and easy to cite (showing up in personalized AI responses with attribution).

Think of AEO as “be selected” and GEO as “be trusted and cited.” You want both.

Shoppers ask an AI agent to “find a 110-inch green sectional under $1,500 that’s pet-friendly,” or “show me hiking boots for wet terrain that can arrive within three days.” The agent returns a short list, with reasons and links. If your catalog and content are messy, you’re not on that list. Not because your products aren’t right, but because the AI can’t parse your data.

This post is about fixing that. Make your products findable and trusted (to humans and AI assistants) and you’ll lift conversion, lower returns, and protect margin, without touching media spend.

The New Reality: Shoppers Are Consulting AI 

Adobe Analytics reports traffic to U.S. retail sites from generative-AI sources jumped 1,200% YoY in March 2025 and 4,700% YoY by July. AI agents are now an integral part of the shopping journey and ignoring them will lead to soft traffic and slumping conversion.

Think of ChatGPT, Gemini, and other AI agents as the smart shopping buddy who reads the internet so your customer doesn’t have to. They narrow choices, explain trade-offs, and avoid sketchy outcomes. They love structure (clear specs, consistent units, availability) and reward clarity (compatibility, sizing, care, warranty). Publish that clearly and you get recommended instead of skipped over.

Mindset shift: You’re not just convincing a person anymore. You’re convincing their AI assistant to put you in the top three and cite you.

What “Findable” Means Now

• Your data means something: One unit system for dimensions. Compatibility is explicit (“fits openings up to 110 in”). Variants are linked cleanly. Required fields aren’t optional.
• Your pages travel well: Ship structured data (JSON-LD for Product/Offer/Review/FAQ). Make comparison and fitment charts actual HTML tables, not screenshots. Keep URLs and IDs stable so assistants can cite you without guessing.
• Your naming clears things up: Product titles like "Brand Model–Core Spec," plus small fields that disambiguate: size, connectors, “works with,” finish, etc. Round out titles with details that matter.

It’s not rocket science, but it does require discipline.

The Kind of Content Assistants Serve Up

Start by publishing content that answer the questions buyers are already asking your sales or support teams every day:

• Short buying guides by use case (“small-room seating,” “pet-friendly fabrics,” “starter vs. pro”).

• Head-to-head comparisons with clear “choose this if / choose that if".

• Compatibility/fitment tables that prevent returns.

• Policy snippets on the PDP (“30-day returns, free parts, two-year warranty”).

If a human can decide in 30 seconds, an AI assistant can recommend you in just three. If it’s cleanly structured and sourced, it’s GEO-ready to be quoted.

GEO basics (so models trust & quote you)

• Structure & schema: JSON-LD (Product, Offer, Review, FAQ/HowTo), machine-readable spec tables, canonical IDs.

• Clarity & brevity: scannable intros, bullets, definitions, comparison grids.

• Authority signals: named author/brand, last-updated dates, consistent terminology, links to primary sources where relevant.

• Accuracy & freshness: keep specs/pricing/policies current (stale or contradictory info gets down-weighted).

• Consistency across pages: the same attribute names/units everywhere (don’t make the model reconcile “width” vs. “overall width”).

Governance 

This is where findability becomes a system, not a one-off clean-up:

• Keep a living synonym list (brand slang, common names, misspellings). Prioritize fixing the top zero-result queries every week.

• Curate facets that are human-readable, non-overlapping, and in a sensible order.

• Be explicit about ranking

  • Start with relevance: Show the best semantic match to the query first. Use title/description/attribute matches, synonyms, and user behavior (clicks on similar queries). If an item isn’t a strong match, it shouldn’t appear up top no matter how great the margin is.

  • Layer in trust signals. Among similarly relevant items, give a bump to:
    • In-stock (and available in the shopper’s location)
    • High review score/volume
    • Low return rate for that category
    • Complete specs (no missing dimensions, materials, etc.)
  • Then add business goals (without breaking relevance). Boost items that:
    • Have a healthy margin 
    • Are new or strategically important
    • Have owned-brand preference
    • Support upsell (compatible bundles)

The order matters: relevance is the gate, trust is the tie-breaker, business is the nudge. 

Merchandise for Margin in an Agent-led Journey

If an assistant is going to pick you, make it easy to justify:

• Your offer looks safe (in-stock, clear price, no gotchas in the return policy).

• The bundle makes sense (“includes mounting hardware,” “rug size that fits the sectional you chose”).

• Your PDP copy answers key buyer questions so the AI assistant can confidently put you forward.

How to Know it’s Working

You won’t get a tidy “AI agent” channel in Google Analytics, so watch the second-order effects:

• Findability: zero-result rate down, fewer query refinements, search success up.

• Revenue: conversion/AOV improve, margin per session climbs.

• Quality: returns drop on items featured in guides/comparisons.

• Clues: small spikes in "direct" traffic to specific guide URLs right after publish (assistants, PDFs, messaging apps often strip referrers). And try a simple copy-paste code in your content (e.g., AGENT-SEPT). If you see it in orders, that content/assistant likely influenced the journey.

Lightweight GEO checks: spot-check a few buyer-intent queries and see if AI Overviews or assistants paraphrase your language or cite your page. Keep an internal log of queries and whether your content appears or is quoted.

Bottom Line

AI assistants are already steering high-intent shoppers. AEO helps you become the answer. GEO helps you get quoted as the source. Make your catalog easy to parse, your pages easy to quote, and your offers easy to trust. Do that, and when someone asks an assistant, “What should I buy?”, the answer starts to sound a lot like… you.

Want a quick audit of agent-readiness? Reach out here for a complimentary 30-minute session.