By Steve Merrill, Founder of WRKNG Digital | June 7, 2026
We've audited over 200 Shopify stores for AI shopping visibility this year. The average score was 23 out of 100.
That number isn't surprising anymore. Most stores weren't built for AI discovery. They were built for Google. And the signals those two systems care about are different enough that strong Google performance tells you almost nothing about where you stand with ChatGPT or Perplexity.
Most guides on this topic are either too vague ("improve your product descriptions!") or too technical for a store owner who just wants to know what to actually do. This post covers the specific process, in order, with the reasoning behind each step. The order matters. Don't skip ahead.
What Does "AI Shopping Discoverability" Actually Mean?
AI shopping discoverability is the degree to which your products show up when AI assistants answer shopping questions. When someone asks ChatGPT "what's the best trail running shoe under $150," the model doesn't run a Google search. It draws from its training data, its real-time browsing index, and structured sources like Google Merchant Center feeds and product schema markup.
I've watched stores with perfect Google rankings get ignored completely by ChatGPT Shopping. Different inputs, different outputs.
ChatGPT Shopping, which OpenAI expanded in April 2025, surfaces product cards based on structured product feeds and schema data. Perplexity's shopping tab works similarly. These aren't search engines in the traditional sense. They're recommendation engines. That distinction changes everything about what you need to do to show up.
Step 1: Fix Your Product Data First
Product data is the foundation. Everything else sits on top of it.
AI models match products to queries using the attributes in your feed. If your product title says "Blue Jacket - M" instead of "Men's Insulated Hiking Jacket, Waterproof, Size Medium, Navy Blue," you won't appear for the natural-language queries that actually lead to purchases. The model can't make the inference for you. The data has to say what the buyer is asking.
Five fields that matter most for AI discoverability:
- Title: Specific enough to match natural-language queries. Include material, use case, key specs, and color where relevant.
- Description: Answers real buyer questions (what's it made of, who's it for, what does it fit or work with). Cut the marketing copy. Write what a buyer would actually search.
- Category: Mapped to Google's product taxonomy, not a custom category tree you invented. AI models recognize the taxonomy. They don't recognize yours.
- Brand: Present on every product, not just inherited from the store name.
- GTIN or MPN: Present and accurate. AI models treat this as a trust signal. Missing GTINs often mean lower recommendation priority.
Go through your feed and check every product against these five fields. Fix them before moving to step 2. Better schema on bad data doesn't move the needle. It just makes the wrong information more legible to machines.
Step 2: Add Product Schema Markup
Schema is how you tell AI systems what your products are, not just show them a page.
Google's Product schema documentation is the reference point here. The fields AI models weight most heavily are: price, availability, brand, aggregateRating, and GTIN. If those aren't present in your schema, you're losing discoverability to competitors who have them.
For Shopify stores, the fastest path is either the Schema Plus for SEO app or editing your theme's product JSON-LD block directly. Either way, verify your output in Google's Rich Results Test after making changes. A schema error that silently fails is worse than no schema at all, because you won't know it's broken until you check.
We ran a before-and-after on a home goods store last quarter. After adding complete Product schema with aggregateRating, their AI citation rate across Perplexity and ChatGPT went from 4% to 31% on tracked queries over six weeks. The product data was already solid. Schema was the missing piece. One change, documented difference.
Step 3: Publish an llms.txt File
Most Shopify stores haven't done this. That's the opportunity right now.
An llms.txt file is a plain-text document at your domain root (yourstore.com/llms.txt) that tells AI crawlers how to understand your site. Think of it as robots.txt, but written for language models rather than traditional search crawlers. The format is straightforward: your brand description, your main product categories with URLs, your top products, and your sitemap location.
There's no locked-down official standard yet. The working convention, proposed by fast.ai founder Jeremy Howard, puts high-level context at the top and links to detailed product and category pages below. For a Shopify store, a solid llms.txt covers five things:
- Who you are and what you sell (2-3 factual sentences, no marketing language)
- Your main product categories with direct URLs
- Your top 10-20 products with links
- Your sitemap URL
- Any crawl restrictions for AI agents
Build the file, upload it as a static asset, and add a route in your theme. Takes about an hour from scratch. The stores doing this now are building a small but real advantage that compounds as AI crawling matures.
Step 4: Build External Citation Signals
AI models don't just look at your site. They look at what the rest of the web says about your products.
Third-party citations carry significant weight in AI recommendations. Review sites, editorial roundups, comparison pages, press mentions with product-specific language. If ChatGPT is deciding between your product and a competitor's, and the competitor has 20 editorial citations to your 3, the competitor wins more often. This is the part of AI discoverability that overlaps most with traditional PR, and the part most ecommerce operators haven't started yet.
Where to focus first:
- Product review sites in your category (Wirecutter, Gear Patrol, or niche-specific editorial sites)
- Reddit threads where your category is actively discussed (r/running, r/homeimprovement, etc.)
- YouTube reviews from creators with engaged audiences in your space
- Press coverage that names specific products, not just the brand
You can't build this overnight. But you can start outreach now, and the stores doing it today will have a meaningful citation lead six months from now when the competition catches up to steps 1-3.
Step 5: Monitor Your AI Visibility and Adjust
You can't manage what you don't measure. Obvious statement, but most stores skip this entirely.
Run regular prompt tests across ChatGPT, Perplexity, and Google AI Mode. Use queries your actual customers would ask. "Best [product type] for [use case] under [price]" surfaces shopping-intent results most reliably. Do this monthly at minimum. Weekly if you're actively working through steps 1-4.
Track three things when you run these tests:
- Whether your products appear at all
- Which competitors are cited most consistently
- What language the AI uses to describe your product category (because that language tells you what your product data and descriptions should say)
Document your results in a simple spreadsheet. Date, query, result, notes. Over time you'll see the trend, and the trend tells you whether your changes are working or whether you need to go back and tighten something up in an earlier step.
How Long Before You See Results?
Most stores see measurable changes within 4-8 weeks of completing steps 1-3. Product data and schema changes get picked up by AI crawlers relatively quickly. External citations take longer because you're building something from scratch rather than correcting existing data.
The stores that try to skip steps 1 and 2 and go straight to llms.txt or citation outreach get worse results, consistently. The process works in order. Don't shortcut it.
Frequently Asked Questions
Does improving AI shopping discoverability hurt my Google SEO?
No. The changes here (better product data, accurate schema, external citations) are things Google values too. You're not making a tradeoff. In most cases, stores that go through this process see their Google performance hold steady or improve alongside their AI visibility.
Do I need to be on Google Merchant Center for AI shopping visibility?
Google Merchant Center helps, but it's not the only path. ChatGPT Shopping and Perplexity can surface products based on schema and crawl data without a Merchant Center feed. That said, a clean and complete Merchant Center feed makes the whole process easier and gives you broader distribution across AI shopping surfaces that pull from it directly.
How is this different from regular SEO?
Traditional SEO targets Google's ranking algorithm. AI shopping discoverability targets the recommendation logic inside models like ChatGPT and Perplexity. Where SEO weights backlinks and on-page signals, AI discoverability weights structured product attributes, schema completeness, and third-party citation quality. There's overlap, but enough difference that it requires its own process and its own tracking.
My store has thousands of products. Where do I start?
Start with your top 20 products by revenue. Fix their data and schema first. The process is the same at scale, but prioritizing your best sellers means you'll see results faster while you work through the rest of the catalog. Don't let scale become an excuse to not start.
Will this process work in every product category?
The five steps apply broadly across categories. Electronics, apparel, and home goods have more established AI shopping infrastructure, so you may see results faster there. Niche categories sometimes require more work on external citations because the AI training data is thinner and the recommendation surface is smaller. But the framework holds.
Want to Know Where Your Store Actually Stands?
Before you start making changes, it helps to know your baseline. We run a full AI Commerce Audit for Shopify stores that gives you a scored breakdown across product data, schema, llms.txt, and external citations. You'll know exactly which steps will move the needle fastest for your specific store.
