Microsoft Releases Official AEO/GEO Guidelines: A Mature Methodology That Manifests Differently Across Diverse Markets.

Recently, Microsoft officially released its official guidelines on AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) titled From Discovery to Influence. This document systematically elaborates on how brands and products should be "understood, evaluated, and recommended" in the era of AI search, AI assistants, and generative recommendation.

I only learned about the release of these guidelines the day before yesterday. Taking advantage of having no major distractions today after sending my father-in-law back to Guangdong, I specially retrieved the original text and read it carefully. Friends in need can reach out to me for the original document.

I. What Core Problem Do Microsoft's Guidelines Actually Solve?

Let’s first briefly explain the concepts of AEO and GEO:

• AEO: Focuses on "whether content can be correctly understood by AI and used to answer questions". Its core lies in clear structure, accurate information, and direct quotability, addressing the question of "can AI mention you correctly".

• GEO: Goes a step further to focus on "whether AI is willing to select and recommend you amid multi-source information comparison". Its core is authority, trust signals, and brand credibility, addressing the question of "why AI should mention you".

Overall, Microsoft's guidelines do not focus on "ranking tactics", but on a more fundamental issue: in AI-driven information distribution and decision-making assistance scenarios, how can products and brands be correctly understood, accurately compared by systems, and ultimately trusted.

Microsoft breaks down the sources of information accessed by AI into three categories:

• Crawled web content

• Proactively submitted product Feeds and API data

• Real-time page data seen by AI Agents when accessing websites

Based on this, they propose a clear evolutionary logic:

• SEO: Addresses "whether discovered"

• AEO: Addresses "whether understood"

• GEO: Addresses "whether worthy of recommendation"

Within the context of overseas independent websites, e-commerce official sites, and Microsoft's advertising ecosystem, this logic is highly self-consistent—even arguably the de facto "standard answer" for the current stage.

II. The Difference Lies Not in the Methodology Itself, But in Market Premises

The problem lies in the prerequisite conditions. Microsoft's AEO/GEO methodology implies several default assumptions that do not always hold true in the Chinese mainland market.

1. Structured Data: Not a "Universal Language" in China

Microsoft repeatedly emphasizes the importance of structured data, including schema types such as Product, Offer, Review, AggregateRating, and JSON-LD as the core expression method.

However, in the Chinese mainland, the scope of structured data's role is relatively ambiguous:

• Baidu has its own parsing and standard system

• Internal data of e-commerce platforms is not open to the public

• There is no clear consensus on whether different AIs follow a unified schema

Therefore, in the domestic context, structured data is more reflected as normative construction at the engineering and SEO levels, rather than directly participating in AI's product understanding and recommendation decisions as it does overseas. This does not mean it is "valueless", but its role is not entirely consistent with what is set out in Microsoft's document.

2. Key Scenarios for Purchasing Behavior Do Not Lie in Independent Websites

Microsoft's AEO/GEO system is built on an important reality: users complete the full purchase journey on brand official websites or independent sites.

In the Chinese mainland, however, most C-end users' shopping behavior is highly concentrated on major third-party platforms:

• Searching, price comparison, and order placement all occur on platforms like Tmall, Taobao, and JD.com

• Independent websites mostly serve the roles of brand display, information supplementation, or business communication

In such an environment, "enabling AI Agents to place orders directly on websites" is not a mainstream scenario, and thus hardly becomes the core optimization goal for most enterprises.

For B-end users, real transactions are almost never completed with direct payments on e-commerce platforms or corporate official websites. B-end transactions typically involve corporate transfers, customized requirements, multiple rounds of communication and decision-making. Corporate official websites serve as the basis for judging "whether worth contacting and cooperating with", rather than a checkout button.

Of course, some may raise counterexamples: domestic AI Agents already have the capability of "direct ordering", such as Qianwen within Alibaba's ecosystem. However, it is important to note that such AI Agents essentially operate within the platform ecosystem, and the final transaction is completed on platforms like Taobao, Tmall, and Fliggy—not on enterprises' own websites. This distinction is crucial.

3. Information Presentation Methods Are Deeply Influenced by Platform Rules

Microsoft advocates in its guidelines: highlighting core product values upfront, clarifying usage scenarios, and enabling AI to quickly understand "what problems this product solves".

In contrast, on product detail pages of mainstream domestic e-commerce platforms, promotional information, platform activities, and preferential rules are often placed in the most prominent positions. This is not because merchants ignore product information, but a long-term adaptive result under platform rules and conversion pressure.

In the domestic e-commerce environment, "whether conducive to immediate transactions" often takes precedence over "whether conducive to AI understanding". This is an ecological difference, not a capability difference.

4. Trust Sources Are Mostly Fulfilled by Platforms

In the design of GEO, Microsoft attaches great importance to trust signals such as third-party authoritative endorsements, media reviews, and expert citations. In the Chinese mainland, however, the core trust sources in C-end consumers' decision-making are often the platforms themselves: genuine product guarantees, platform after-sales services, self-operated labels, and credit systems.

These factors largely replace the role of external authorities in decision-making. For this reason, domestic enterprises' GEO-related construction mostly occurs in scenarios of cognitive and trust supplementation outside platforms, rather than directly affecting order placement behavior.

For B-end enterprises, while they are easily influenced by news sites, industry media, and authoritative reports, B-end users do not place orders through corporate websites. External trust signals such as news media coverage, in addition to being used for GEO optimization to improve brand visibility, also play a key role in commercial procurement decisions—this is an important link to enhance trust endorsements externally.

III. From a Practical Perspective, Where Lies the Real Divergence?

Methodologically, I fully agree with a point repeatedly emphasized by Microsoft in the document:

Search competition is shifting from "acquiring traffic" to "whether correctly understood and trusted by AI".

This trend holds true in any market.

The real differences lie in, across different markets:

• How AI acquires information

• Where users complete decision-making

• Who endorses trust

In the domestic market, AEO/GEO is more of a long-term capability for cognitive and foundational construction, rather than a short-term tool. Combining my practical experience in website construction, press release distribution, and search marketing, I can help enterprises enhance their brand's visibility and trustworthiness in AI search and generative recommendation. Enterprises with needs can reach out to me for a discussion.