Designing for AI Shoppers: Data, UX, and Ranking Signals That Matter

Two recent pieces caught my attention. Commerce-UI published an analysis of AI in ecommerce and future trends, and Glance wrote a detailed explainer on how AI commerce actually works.

Both pieces go deep on what drives AI shopping recommendations. Not the hype, but the mechanics. Here is what ecommerce teams need to know and what to do about it.

How AI shopping actually decides

AI shopping systems are not magic. They ingest data, apply models, and surface products that best match the buyer intent. The quality of your input determines whether you get recommended.

Here is what matters.

Attribute completeness and structure

AI systems rely on structured product data to understand what you sell and who it fits. If your attributes are incomplete or inconsistent, the system cannot confidently recommend your product.

Commerce-UI highlights this as the foundation. Ambiguous or missing data gets filtered out early. Complete, structured data gets evaluated further.

What to fix:

Map every attribute your category expects. Materials, dimensions, compatibility, care, fit, use case.
Use consistent terminology across your catalog. Do not mix units or naming conventions.
Make variants explicit. Size, color, and options need to be machine readable.

Query understanding and intent matching

Glance explains that AI commerce works by matching buyer intent to product attributes. The system analyzes what the shopper asked for and ranks products by how well they fit.

This is different from keyword matching. The AI understands context. If someone asks for running shoes for trail running in wet conditions, it looks for attributes like traction, waterproofing, and stability, not just the phrase "trail running shoes."

What to fix:

Describe your products in terms of what problems they solve and when they are used.
Include context in your descriptions. Who is this for? What situations does it handle well?
Test how your products respond to natural language queries, not just keyword searches.

Source authority and trust signals

Both Commerce-UI and Glance emphasize that AI systems weigh external sources heavily. Reviews, comparisons, buying guides, and editorial mentions influence whether your product gets recommended.

The logic is simple. If trusted sources describe your product positively, the AI treats that as a strong signal. If no one mentions your product, the AI has less confidence to recommend it.

What to fix:

Earn citations from credible publications in your category.
Secure detailed reviews that describe specific use cases and product attributes.
Monitor which sources AI systems cite when recommending competitors. Then figure out how to get mentioned by those same sources.

Real time personalization

Glance goes into detail on how AI commerce personalizes recommendations in real time based on the shopper's history, preferences, and context. This is a competitive advantage for platforms with deep user data, but it also means your product data needs to support a wide range of intents.

If your attributes are narrow, you only show up for narrow queries. If your data is rich, you show up across more contexts.

What to fix:

Describe your products broadly. Do not assume the buyer knows exactly what they need.
Include cross sell and bundle information. AI systems use this to recommend combinations.
Test how your products appear for related but not identical queries.

The weekly testing discipline

Commerce-UI and Glance both stress that AI models evolve constantly. What works today might not work next month. That means you need a testing rhythm.

Here is a simple weekly discipline:

Pick five buyer intents relevant to your top category.
Run those queries in ChatGPT, Perplexity, and other AI shopping interfaces.
Document which products appear and what attributes they highlight.
Compare your products to what showed up. What do they have that you do not?
Fix one or two gaps and retest the following week.

This loop compounds. Small fixes add up to meaningful visibility gains over a quarter.

UX considerations for agent driven shopping

When AI agents drive discovery, the traditional product detail page matters less. Shoppers do not browse. They ask, get recommendations, and decide.

That shifts where you invest in UX. Your product data and your checkout flow matter more than your homepage or category pages.

What to prioritize:

Fast, frictionless checkout that works when traffic comes from an agent.
Clear, complete product data that the agent can confidently describe.
Inventory accuracy so the agent does not recommend out of stock items.

How Wildcard helps teams execute this

The hardest part of this work is knowing where you stand and what to fix first. Wildcard gives you visibility into your rankings, shows you what competitors have that you do not, and surfaces the attribute gaps that matter most.

Instead of guessing, you get a clear roadmap. Fix these five attributes. Earn citations from these three sources. Test these queries weekly. Ship it and measure the lift.

The takeaway

Commerce-UI and Glance both make the same point. AI shopping rewards structured data, source authority, and relentless testing. The brands that treat this as a discipline will win share. The brands that treat it as a side project will fade.

Pick one category. Fix the data. Earn the sources. Test weekly. Ship the improvements. Then move to the next category. The channel is here, and it is growing faster than most brands realize.