From Being Found to Being Chosen: Microsoft’s Guide to the New Rules of AI Search

Have you noticed that despite your website’s solid SEO, your products rarely appear in ChatGPT’s or Copilot’s recommendation lists? Your content ranks on Google’s first page, yet it’s absent from AI’s summarized answers. This isn’t an illusion; it’s evidence that the core rules of retail competition have fundamentally shifted.

This week, Microsoft released an official document titled “From discovery to influence: A guide to AEO and GEO,” which clearly maps this transformation. The battlefield of traditional Search Engine Optimization (SEO) was about being found. The new arena of AI-driven search is about being chosen.

The End of Traditional SEO? No, It’s Evolution.

For decades, SEO revolved around a core objective: ranking, clicks, page visits. We optimized keywords, built backlinks, and improved site speed—all to get users to click on our link among the ten blue links on a search results page.

However, AI search has rewritten the rules. When a user asks ChatGPT, “Recommend a men’s backpack suitable for rainy commutes,” the AI doesn’t provide ten descriptive links. It delivers a direct answer, a list of recommendations, or even a purchase decision executed by an AI agent.

This means mere “visibility” is no longer sufficient. The new competitive dimension is: Can AI systems clearly understand your product? Do they trust your brand? Can they act on your data?

This explains why many brands with strong traditional SEO have no presence in AI search. Yet, once they begin optimizing for AI’s “way of thinking,” their visibility improves rapidly. The underlying infrastructure of competition has changed.

Understanding the AI “Brain”: What Are AEO and GEO?

Microsoft’s guide introduces two core concepts: AEO and GEO. Think of them as two essential, complementary facets of AI search optimization.

AEO: Making AI “Understand” and “Act”

AEO (Answer / Agentic Engine Optimization) aims to optimize your content and data so that AI assistants and agents (like Copilot, ChatGPT, Gemini) can:

  • Find it
  • Understand it
  • Summarize it
  • Recommend it
  • Act on it (e.g., add to cart)

The core of AEO is clarity and machine-readability. It’s about whether your product descriptions are precise, your structured data is complete, and key information is easy for machines to extract.

GEO: Making AI “Trust” and “Cite”

GEO (Generative Engine Optimization) aims to optimize your content so generative AI search systems trust it as:

  • Authoritative
  • Credible
  • Citable

The core of GEO is credibility, reputation, and justification. It’s about whether your brand has third-party endorsements, if user reviews are genuine and plentiful, and if your content claims are well-supported.

Crucially, SEO is not obsolete. Microsoft explicitly states that SEO remains the foundation, but it is no longer the end goal. SEO is like laying a solid foundation, while AEO and GEO are about building the skyscraper on top that AI can recognize and favor.

How Does AI “Shop”? Demystifying Discovery and Decision-Making

Microsoft’s AI Shopping Ecosystem Diagram

To optimize, we must first understand AI’s discovery and decision pathways. Microsoft categorizes the AI shopping ecosystem into three overlapping systems:

  1. AI Browsers: Like Edge or Chrome with embedded AI. They can “see” and interpret the live webpage you are on in real-time.
  2. AI Assistants: Like Copilot, ChatGPT, Gemini. They answer questions, summarize options, and recommend products.
  3. AI Agents: This is the most advanced form. They can autonomously navigate websites, add items to carts, apply promo codes, calculate shipping, and even complete purchases.

For us, the key insight is:

“

The question is not “Which AI surface am I optimizing for?” but “What data can AI access, trust, and use?”

This is where most websites fail. The data exists, but it’s unstructured, inconsistent, or not presented in a way AI can reliably act upon.

The AI Decision “Fusion” Process

AI doesn’t recommend a product based on a single signal. Microsoft reveals a multi-stage reasoning process used by Copilot and Bing AI, which fuses at least three layers of data:

Layer 1: Crawled Web Data

  • Brand reputation
  • Category authority
  • Expert mentions
  • Historical understanding
    This shapes the AI’s baseline perception of your brand and category.

Layer 2: Product Feeds and APIs

  • Price, availability, product variants, key specifications
  • This is where competitive advantage is often built and where most brands are under-optimized.

Layer 3: Live Website Data

  • Real-time pricing, active promotions, user reviews, media assets, checkout functionality
  • This is the critical final step: if your live website is inaccessible or the transaction fails, the AI agent’s task fails, even if the first two layers of data were perfect.

Let’s examine an example from Microsoft: A user searches for “rain jacket under $200.”
The AI’s reasoning process might be:

  • “Patagonia and The North Face make quality jackets” (General knowledge)
  • “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” (Feed data)
  • “A competitor’s model is $199 and backordered” (Feed data)

Your product makes the top recommendation list because feed data + availability + price + context align perfectly. This also explains why content created solely to “rank,” without explaining, comparing, or justifying its value, rarely appears alone in AI answers.

From “Keyword” to “Trust Badge”: The Evolution Path SEO → AEO → GEO

Microsoft succinctly summarizes the progression:

  • Traditional SEO = Matching keywords

    “

    Example: “waterproof rain jacket”

  • AEO = Descriptive clarity

    “

    Example: “Lightweight, packable waterproof rain jacket with ventilation and reflective piping”

  • GEO = Justification and trust badges

    “

    Example: “Best-rated by Outdoor Magazine, 4.8 stars, 180-day return policy, 3-year warranty”

In simple terms, AEO drives understanding, GEO drives confidence. You need both to be recommended. This is why brands that combine long-form, intent-driven content with authoritative backlinks and mentions often outperform those relying on SEO alone.

Winning the Three-Layered Data Battlefield

To win in AI search, retailers must establish a presence across three distinct data planes:

Plane 1: Crawled Data

  • What did AI learn during its training?
  • What does it find via real-time web search?
  • This forms the baseline brand perception. Traditional SEO still matters here.

Plane 2: Product Feeds and APIs

  • The structured data you actively provide.
  • This is the plane of precision and control.
    Feeds directly drive: product comparisons, rankings, and final recommendations. This is where many retailers under-invest.

Plane 3: Live Website Data

  • Everything the AI agent sees when it actually visits your site.
    Includes: user reviews, media, dynamic pricing, checkout capability.
  • If an AI agent cannot complete a transaction, your influence stops at “recommendation” and never converts to “sale.”

Microsoft’s Three Action Pillars for AI Optimization (An Executable Blueprint)

This is the most actionable part of the guide. To win in AI search, you must systematically build the following three pillars:

Pillar 1: Technical Foundations & Structured Data

AI craves structure and consistency, not creativity.

  • Core Requirement: Build machine-readable product catalogs.
  • Essential Dynamic Fields: Price, availability, size, color, SKU, GTIN, dateModified.
  • Use ItemList Markup for category pages.
  • Enable Localization via Schema: inLanguage (language), priceCurrency (currency).

Required Schema.org Types:

  1. Product
  2. Offer
  3. AggregateRating
  4. Review
  5. Brand
  6. ItemList
  7. FAQPage

One Critical Warning:

“

“Never serve different HTML to bots than to users.” (i.e., no cloaking)

Pillar 2: Intent-Driven Content Enrichment

AI interprets user intent, not just keywords.

Optimize Product Descriptions:

  • Front-load descriptions with: Who it’s for, what problem it solves, why it’s better.
  • Use use-case framing: e.g., “Best for day hikes above 40°F.”
  • Craft headings that mirror real user questions.

Build Modular, Citable Content Blocks. Microsoft explicitly encourages creating:

  • Q&A sections
  • Product comparison content
  • Detailed feature lists
  • “Goes well with” product relationship sections
  • Video transcripts
  • Detailed image alt text using ImageObject schema

This is content designed for extraction, not just human reading. Scale is key. One or two optimized pages won’t move the needle. Systems that produce dozens of structured, intent-mapped articles are what win.

Pillar 3: Trust & Credibility Signals (The Heart of GEO)

AI systems prioritize verifiable truth.

1. Verified Social Proof:

  • Genuine verified reviews
  • Substantial review volume
  • Sentiment extraction (e.g., “highly rated for comfort and fit”)
  • Correct implementation of Review and AggregateRating schema

2. Authoritative Brand Identity:

  • Expert reviews
  • Press mentions
  • Professional certifications
  • Sustainability badges
  • Official brand links

3. Content Integrity:

  • Avoid exaggerated or unsupported claims
  • Maintain a consistent brand voice
  • Provide structured FAQ and help content

A Particularly Noteworthy Point:

“

“AI penalizes low-trust language.”
This means vague, hyperbolic, or unprofessional language directly harms your reputation in the “eyes” of AI.

The Future is Here: Treat Data as a Product, Trust as an Algorithm

Microsoft’s conclusion is direct and profound: Retailers already possess most of the signals AI uses to rank and recommend. The winners in AI commerce will be the brands that make this shift:

  • Treat data as a product to be meticulously designed and maintained.
  • Treat product feeds as strategic assets, not just technical requirements.
  • Treat content as machine-readable infrastructure, not just marketing copy.
  • Treat trust as a measurable ranking factor, not a vague brand attribute.

This is what Microsoft calls “AI ranking readiness.”

The One Unavoidable Core Idea

If AI cannot clearly understand your products, justify recommending them, and act on your data in real-time, you will not be a legitimate presence in AI-driven commerce.

This document is Microsoft’s formal notice to all retailers:

  • SEO alone is good, but no longer sufficient.
  • Product feeds are now a competitive moat.
  • Trust is algorithmic.
  • AI assistants are the new gatekeepers of demand.

The wave of change has arrived. It’s time to re-evaluate your digital assets—not for being found, but for being chosen.

Frequently Asked Questions (FAQ) About AI Search Optimization

Q1: What’s the practical difference between AEO and GEO?
A: A simple analogy: AEO is about making the AI “read” your product manual (understanding what it is and what it does). GEO is about making the AI believe that manual comes from an authoritative source worth citing and recommending (trusting that it’s true and good).

Q2: My product information is already on my website. Why doesn’t AI understand it?
A: It’s likely your information is written for humans, not machines. Check if you use complete structured data (Schema markup), if your product descriptions are clear, specific, and front-loaded with key attributes, and if your content is modular for easy AI extraction.

Q3: I’m already doing SEO. How much extra work is needed for AEO/GEO?
A: This isn’t a completely new task from scratch; it’s an enhancement and extension of your existing work. Focus on: 1) Strengthening the completeness and accuracy of structured data on the technical side, 2) Shifting content strategy from keyword density to intent coverage and trust badge building, and 3) Elevating the management of product data feeds to a strategic level. It’s an upgrade and integration of current SEO practices.

Q4: Can AI really complete purchases? What does this require from my website technically?
A: Yes, AI agents are developing this capability. It requires your website’s checkout process to be stable, fast, highly compatible, and for real-time inventory and price data to be 100% accurate. Any technical issue that causes an AI “shopping failure” will directly impact your recommendation rate.

Q5: What exactly is “low-trust language”?
A: This typically refers to absolute claims without evidence (e.g., “world’s best”), vague marketing jargon (e.g., “revolutionary product”), or descriptions that clearly contradict user reviews or objective facts. AI identifies these inconsistencies by cross-referencing multiple data sources.