I keep hearing about AI shopping assistants becoming the next big thing in ecommerce.
What I’m seeing:
My concern: If users ask AI “what’s the best running shoe for marathon training” and we’re not in the response, we lose the sale before they ever visit our site.
Our current situation:
Questions:
Ecommerce is our entire business. Can’t afford to miss this shift.
AI shopping is different from Google Shopping in fundamental ways:
Google Shopping:
AI Shopping Assistants:
Why this matters for optimization:
Google Shopping: “Is your feed accurate?” AI Shopping: “Is your product the best match for this user’s need?”
What AI shopping assistants evaluate:
| Factor | Weight | How to Optimize |
|---|---|---|
| Product-need match | Very High | Clear use case descriptions |
| Reviews/ratings | High | Strong review profile |
| Specifications | High | Complete, structured specs |
| Price/value | High | Transparent pricing |
| Brand reputation | Medium | Third-party mentions |
| Availability | Medium | Real-time stock data |
| Comparison clarity | Medium | How you differ from alternatives |
The AI is trying to answer: “For this person’s specific need, which product is best?” Your job is making it easy for AI to match your product to specific needs.
On product data structure - this is crucial:
Product schema example:
{
"@type": "Product",
"name": "Marathon Pro Running Shoe",
"description": "Designed for marathon training and racing...",
"brand": {
"@type": "Brand",
"name": "Your Brand"
},
"offers": {
"@type": "Offer",
"price": "149.99",
"priceCurrency": "USD",
"availability": "InStock",
"priceValidUntil": "2026-12-31"
},
"aggregateRating": {
"@type": "AggregateRating",
"ratingValue": "4.7",
"reviewCount": "342"
},
"additionalProperty": [
{
"@type": "PropertyValue",
"name": "Best For",
"value": "Marathon training, Long distance running"
},
{
"@type": "PropertyValue",
"name": "Drop",
"value": "8mm"
}
]
}
Key elements:
Without this structure, AI has to guess if your product matches user needs. With it, AI can match confidently.
Reviews are massive for AI shopping recommendations.
Why: AI shopping assistants heavily weight user reviews because:
Review optimization for AI:
Encourage reviews that mention:
AI shopping assistants extract these details. The more you have, the more matches AI can make.
This is really helpful. We have good reviews but they’re mostly on Amazon, not our site.
Question: Does it matter where reviews are - our site vs Amazon vs Google Reviews? Can AI shopping assistants access all of these?
Great question. Review source matters:
What AI can access:
| Source | AI Access | Impact |
|---|---|---|
| Your site reviews (with schema) | Direct | High - clearly attributed to your product |
| Amazon reviews | Indirect | High - cited frequently in recommendations |
| Google Reviews | Direct | Medium - for brands with Google profiles |
| Third-party review sites | Direct | High - especially for consideration content |
The challenge with Amazon-only:
Recommendation:
For AI shopping specifically, review sites like Wirecutter, RunRepeat (for running shoes), etc. are highly cited. One strong review site placement can be more valuable than 100 more Amazon reviews for AI visibility.
Comparison content is gold for AI shopping.
When user asks: “What’s the best running shoe for marathon training?”
AI needs to:
Where does AI get comparison info?
What you can create:
“Best for” pages
Comparison pages
Use case guides
When AI searches for comparison content, you want YOUR comprehensive guide to be cited, not just competitor reviews.
Making sense. Here’s my action plan:
Product data (Week 1-2):
Reviews (Ongoing):
Content (Month 1-3):
Measurement:
Question: How quickly can we see impact from these changes?
Timeline expectations:
Product schema changes: 2-4 weeks
Review site coverage: 2-6 months
Comparison content: 4-8 weeks
Overall trajectory:
This isn’t overnight. But ecommerce brands starting now will have advantage over those who wait.
One more consideration: category positioning.
AI shopping assistants categorize products. How you’re categorized affects which queries you appear for.
What to ensure:
Clear category placement
Subcategory specificity
Cross-category potential
The risk: If AI categorizes you wrong, you’re recommended for wrong queries (or not at all).
Check how AI currently describes your product. If it’s categorizing you incorrectly, adjust your product positioning to fix this.
The big picture on AI shopping:
Today: AI shopping assistants are helpful research tools Soon: AI will handle complete purchase journeys Eventually: AI agents will buy on users’ behalf
Implication: The products that AI knows well and trusts will win.
Think about it: When AI can autonomously purchase based on user preferences, which products get selected?
The brands that establish this trust now will be positioned for AI-driven commerce. Those that don’t will be invisible to an increasingly important purchase channel.
Start optimizing today.
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