And your product could be an excellent match, yet, still be absent from the answer. WHY??

AI Shopping Is Already Moving Beyond Traditional Search

Shopify says AI-driven traffic to Shopify stores increased eightfold year over year in Q1 2026, while orders attributed to AI-powered searches grew nearly thirteenfold.

Google is also preparing AI-performance reporting in Merchant Center covering discovery through AI Mode, AI Overviews and Gemini. Its planned reporting includes share of voice, shopping-funnel performance, product terms and product-attribute completeness.

So e-commerce businesses need to start thinking toward:

Customer need → AI understands request → AI evaluates products → AI recommends products → customer buys

The question is whether your product data gives the AI enough evidence to put you into that recommendation.

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Example:

Your product page may be like:

Model X500 Premium Countertop Oven
Powerful performance with innovative technology for today’s modern kitchen.

However, customers nowadays may ask AI:

“Recommend a countertop oven under $250 that fits on a 20-inch-wide counter, has convection cooking, can fit a 12-inch pizza and is easy to clean.”

Your product may satisfy every requirement. But does your website clearly state:

Price: $229
Width: 18.5 inches
Cooking: Convection
Pizza capacity: 12 inches
Interior: Non-stick
Removable crumb tray: Yes

If those facts are missing, buried inside an image, inconsistent across your feed, or expressed only through vague marketing copy, an AI system may have difficulty establishing that your product matches the request.

Google explicitly says structured technical specifications can improve its ability to show individual products based on user queries, including on AI-driven surfaces.

That’s the important shift:

Good product content isn’t only about convincing customers anymore. It’s also about helping machines understand exactly when your product is relevant.

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How Can You Test Whether AI Recommends Your Products?

Start with your 10 most important products.

Step 1 — Stop Searching for Your Brand

This is crucial.

Don’t ask: “What is the best [YOUR BRAND] product?”

Of course the AI now knows which brand you’re looking for.

Instead, behave like a customer who has never heard of your company.

For a vacuum cleaner, for example: “What’s a good cordless vacuum under $500 for a house with two cats?”

For cookware: “Recommend a cookware set for an induction stove that’s dishwasher safe and under $400.”

For skincare: “What moisturizer would you recommend for very dry skin that feels lightweight and is fragrance-free?”

For office furniture: “I need an ergonomic office chair under $600 for someone who works from home eight hours a day and is 6 feet tall.”

These are intent-based prompts and they’re much closer to how AI shopping can work.

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Step 2 — Test Multiple Types of Questions

Don’t run one prompt and declare success or failure. For each product, test several scenarios.

Broad discovery: “What are some good air fryers under $200?”

Problem-based: “What’s a good air fryer for a family that wants to cook dinner quickly on weekdays?”

Specification-based: “Recommend a 6-quart or larger air fryer under $200 with dishwasher-safe parts.”

Comparison: “Which air fryers should I consider if easy cleaning and capacity matter more than having lots of cooking presets?”

Situational: “I have four people in my family and very little counter space. What air fryer should I buy?”

Notice that the customer isn’t necessarily searching for a product. They’re describing a situation.

Your product information therefore needs to communicate not only what the product is, but also when it’s a good fit.

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Step 3 — Test Across More Than One AI Platform

Run similar prompts through several relevant AI/search environments, because different AI systems can use different data sources, retrieval methods, shopping integrations and ranking logic.

Create a simple spreadsheet:

Run the test again periodically and look for patterns.

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Step 4 — Ask the Most Valuable Question: Why Them, Not Me?

Don’t stop at “AI doesn’t recommend us.”, look at what was recommended.

Then you may find the competitor A might clearly publish: dimensions, capacity, materials, compatibility, warranty, included accessories, use cases, availability, price, customer reviews

while your product page only provides: product name + five marketing bullets + price.

You have now discovered something actionable.

Build a Product Recommendation Gap table:

Now AI visibility stops being mysterious and you have identified specific information gaps you can fix.

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Step 5 — Fix the Data, Then Run the Test Again

This is the part many businesses will miss.

AI recommendation testing should be:

TEST → IDENTIFY GAP → IMPROVE → RETEST → MEASURE

For example, suppose customers commonly ask: “Will this fit under my kitchen cabinet?”

But your product page only gives dimensions in a downloadable manual.

Move the dimensions into clearly structured product information. Add:

Product Dimensions
18.5″W × 14.2″D × 12.8″H

Recommended Clearance
3 inches above unit.

Add an FAQ:
Will this fit underneath standard kitchen cabinets?

Then make sure the same facts are represented correctly in your product feed and structured data.

Google specifically recommends accurate and comprehensive product attributes, noting that providing relevant attributes increases the chances of products appearing for relevant queries.

Google also supports product structured data for information such as price, availability, condition, SKU and GTIN, helping its systems reliably understand information on product pages.

Then wait for systems to recrawl/update the information and repeat your test.

Did anything change?

That’s the beginning of AI product optimization.