The Shift Nobody Saw Coming
I stumbled across something this week that most people seem to have missed entirely. Microsoft quietly released a document called “From discovery to influence: A guide to AEO and GEO” and it basically spells out exactly how to get traffic from AI assistants.
The central message is surprisingly blunt: retail competition is shifting from “being found” to “being chosen.”
Traditional SEO optimized for ranking, clicks, and page visits. AI-driven shopping replaces all of that with answers, recommendations, and agent-led decisions.
How AI Actually Decides What to Recommend
This was the most interesting part of the document. Microsoft outlines a multi-stage reasoning process that Copilot and Bing AI use. AI does not rely on one data source. It fuses three distinct layers:
1. Crawled Web Data
This is what AI learned during training plus what it finds via real-time web search:
- Brand reputation
- Category authority
- Expert mentions
- Historical understanding
Your baseline brand perception gets shaped here. Traditional SEO still matters for this layer.
2. Product Feeds and APIs
Structured data you actively provide:
- Price and availability
- Variants and inventory
- Key specs and GTINs
This is where competitive advantage often comes from. Most brands massively under-invest here.
3. Live Website Data
What AI agents see when they actually visit your site:
- Real-time pricing
- Current promotions
- Reviews and media
- Checkout functionality
If agents cannot transact, influence stops at recommendation.
The Rain Jacket Example
Microsoft gives a concrete example that made this click for me. User query: “rain jacket under $200.”
AI reasoning includes:
- “Patagonia and North Face make quality jackets” (general knowledge from training)
- “Hiking jackets need to be lightweight and waterproof” (category understanding)
- “Brand X is known for hiking equipment” (brand positioning)
- “Your model is $179 and in stock” (feeds)
- “Competitor is $199 and backordered” (feeds)
Your product makes the recommendation because feeds plus availability plus price plus context all align. Content that simply “ranks” but does not explain, compare, or justify rarely shows up in AI answers.
The Three Pillars Microsoft Prescribes
This is the actionable part. Microsoft breaks down execution into three pillars.
Implement AI Search Optimization
Microsoft's three-pillar approach to getting recommended by AI assistants
Technical Foundations and Structured Data
AI requires structure and consistency, not creativity.
Required schema types:
- Product and Offer
- AggregateRating and Review
- Brand and ItemList
- FAQ
Dynamic fields that matter:
- Price and availability
- Size, color, SKU, GTIN
- dateModified
- Localized pricing via priceCurrency
One quote stood out: “Never serve different HTML to bots than to users.”
Intent-Driven Content Enrichment
AI interprets intent over keywords. Microsoft recommends:
- Front-load descriptions with who it is for, what problem it solves, why it is better
- Use-case framing like “Best for day hikes above 40 degrees”
- Headings that mirror real questions
- Modular, citable content blocks
- Q&A sections and comparison content
- Video transcripts and detailed image alt text
Trust and Credibility Signals
AI systems prioritize verifiable truth.
Verified social proof:
- Review volume and sentiment extraction
- Review and AggregateRating schema
Authoritative brand identity:
- Expert reviews and press mentions
- Certifications and sustainability badges
Content integrity:
- Avoid exaggerated claims
- Maintain consistent brand voice
- Provide structured FAQs
Microsoft notes that “AI penalizes low-trust language.” Vague or hyperbolic claims actively hurt you.
The SEO to AEO to GEO Journey
Microsoft summarizes the progression pretty clearly:
SEO = matching keywords “Waterproof rain jacket”
AEO = descriptive clarity “Lightweight, packable waterproof rain jacket with ventilation and reflective piping”
GEO = justification and trust “Best-rated by Outdoor Magazine, 4.8 stars, 180-day returns, 3-year warranty”
AEO drives understanding. GEO drives confidence. You need both to get recommended.
What This Means for Developers
If you are building anything with a product catalog, user-generated content, or service offerings, this applies to you. The question is not “which AI surface am I optimizing for?” The question is “what data can AI access, trust, and use?”
Most sites break here. The data exists, but it is not structured, consistent, or surfaced in a way AI can reliably act on.
Does traditional SEO still matter for AI search?
What is the difference between AEO and GEO?
Which schema types should I implement first?
How do I know if AI can understand my content?
The Bottom Line
Microsoft is formally telling retailers that SEO alone is no longer enough. Feeds are now a competitive moat. Trust is algorithmic. AI assistants are the new gatekeepers of demand.
If AI cannot clearly understand your products, justify recommending them, and act on your data in real time, you will not have a meaningful presence in AI-driven commerce.
The brands that treat data as a product, feeds as strategic assets, and content as machine-readable infrastructure will win this shift. Everyone else will wonder why their traffic keeps declining while their rankings stay the same.
Key Takeaways
- AI shopping replaces rankings with recommendations and agent-led decisions
- You need to control three data layers: crawled web data, product feeds, and live website data
- AEO makes content machine-readable; GEO makes it trustworthy and citable
- Required schema types include Product, Offer, AggregateRating, Review, Brand, and FAQ
- Intent-driven content should answer who, what problem, and why better
- Trust signals like verified reviews and expert mentions directly affect AI recommendations
- Test your optimization by asking AI assistants about your products