Differences Between SEO and AI Search Strategies (LLMO/AIO/AISEO/GEO)

"Tell me about recommended services"

Recently, more people are searching with ChatGPT and Gemini. Furthermore, Google searches now display AI-generated overviews instead of SEO results. The action of typing keywords into the search box and opening blue links in order from the top has become less frequent. Instead, we throw questions at AI and choose from the 3 to 5 options it returns.

Here, a quiet problem is arising.

Your company is not among those 3 to 5 options.

If you search for your company name on Google, it appears at the top. You have invested in SEO. Yet, when you ask AI, "What do you recommend?", your name doesn't even come up. It's not that you're losing in comparison; you aren't even seated at the comparison table in the first place.

Why is this happening? To understand this, you need to know that "SEO" and "AI search optimization" operate on completely different logics, even though they may seem similar. In this article, I will explain these differences as gently as possible, using analogies.

First, let's clarify the terms. LLMO? AIO? GEO? They're all the same.

The world of AI search optimization is currently filled with various names.

• LLMO(Large Language Model Optimization)

• AIO(AI Optimization)

• AISEO(AI Search Engine Optimization)

• GEO(Generative Engine Optimization)

While the names differ, they refer to almost the same thing: "the optimization to make your company appear in the answers generated by AI such as ChatGPT, Gemini, Perplexity, and Google AI Overview."

 The reason there are so many names is simply that the field is too new, and the industry's terminology has not yet solidified. In the early 2000s, terms like "homepage optimization," "search engine optimization," and "search marketing" proliferated before converging into "SEO." The same phenomenon is happening again now.

 In this article, I will collectively refer to it as "AI search optimization (LLMO)." (The AI search optimization service we provide, umoren.ai, is also a service in this field.)

What matters is not the name, but the content. And that content is not an extension of SEO.

The conventional wisdom of SEO - A game of "popularity voting"

First, let's talk about the SEO we are familiar with.

The origin of SEO is the "PageRank" algorithm devised by Google's founders. The concept comes from the world of academic papers. A good paper is cited by many other papers, and the more authoritative the citing papers, the higher the value. PageRank was a translation of this concept to the web.

 

In essence, SEO is about accumulating letters of recommendation. A page that receives many links (recommendations) from trustworthy sites is displayed higher as "a good page recognized by everyone." It's similar to a popularity vote in a class meeting, but the weight of votes differs by person. The vote of the most trusted class president counts many times more than an ordinary vote. It's that kind of game.

 Of course, today's Google does not operate solely on PageRank. Content quality, E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness), user behavior, mobile compatibility… it is said that over 200 factors are involved.

 And here’s the important point— Google does not disclose the full picture.

 The reason SEO is called a "black box" is that Google does not reveal the details of its scoring criteria. Therefore, the SEO industry has spent over 20 years observing ranking fluctuations and building know-how by piecing together guesses from the outside, saying things like "this is probably effective" or "this update seems to emphasize this factor." It's similar to weather forecasting. While we cannot see the contents of the sky directly, we can make fairly accurate predictions by accumulating observational data. SEO insights are the result of this vast accumulation of observations.

This is a respectable system in its own right. However, this system is solely for the purpose of navigating "Google's popularity vote." AI responses are generated through a different mechanism than this popularity vote.

The true nature of AI search - The mechanism of "RAG"

When ChatGPT, Perplexity, or AI Overview generates answers, behind the scenes, a mechanism called " RAG(Retrieval-Augmented Generation) " is at work.

 It sounds complicated, but what it does is surprisingly simple. Let's use a library as an analogy.

 Imagine you ask a librarian, "Can you recommend some accounting software?" A competent librarian won't just answer from memory. They will first go to the stacks and pull out a few relevant books and materials. Then, while comparing the materials they have brought back, they will construct an answer like, "In this field, companies A and B are well-regarded. Company A is for sole proprietors..."

 This is exactly what RAG does.

1. Retrieval - Gathering relevant information from the web or indexes based on the question

2. Augmented - Arranging the retrieved information at hand

3. Generation - Writing an answer using "only" that information as material

 Here’s a crucial point.

 Books that the librarian did not bring from the stacks have zero chance of appearing in the answer.

 No matter how good a book is, if it is not retrieved from the depths of the stacks, it is as if it does not exist. No matter how well-crafted your company's site is, if it is not picked up in the "retrieval" stage of RAG, it will not be reflected in the AI's answer at all.

 "It's not that you're losing; you're not even in the game." - This is what is happening in AI search.

Therefore, the logic is fundamentally different

Let's outline the differences between SEO and AI search optimization. 

Especially impactful is the difference in "how to lose."

 In the world of SEO, even if your ranking drops, your existence does not disappear. If you are in 10th place, you can still see it by scrolling, and you are technically still present on the second page. This is a gradient way of losing.

 The world of AI search is different. It’s either cited in the answer or not at all. Zero or one. When AI talks about "top 3 recommendations," the fourth option does not get "slightly displayed lower," but rather, it is not mentioned at all. The opportunity for users to even see it disappears.

 Moreover, users tend to make significant decisions once they read the AI's answer. Before being searched for by name or opening comparison sites, candidates are narrowed down at what could be called "page zero." Companies not included here will not be visited, no matter how much they refine their landing pages afterward.

What does "analyzing RAG" mean?

So, how can you get included in AI responses?

It is not impossible to adopt the method of "watching ranking fluctuations and accumulating empirical knowledge" as with SEO. However, there is a significant difference in AI search.The mechanism of RAG itself is published in research papers and technical documents. Unlike Google's rankings, where the contents are fundamentally opaque, RAG's structure is clear in terms of "how information is retrieved and how answers are constructed."

This means that instead of relying on vague empirical rules, there is room to logically reverse-engineer from the mechanism.

 Specifically, you will need to analyze at least three checkpoints.

 Checkpoint 1

What "search queries" is AI converting? Even if a user vaguely asks, "I want to make accounting easier," AI will internally convert that into specific search terms to retrieve information. The raw question from the user and the actual query that AI throws are different. The first dividing line is what queries your company can potentially accommodate.

 Checkpoint 2

Is your company page being "retrieved"? RAG does not read pages in their entirety but breaks down text into fragments called "chunks," picking only those fragments relevant to the question (detailed explanation of the chunking and citation mechanism). It’s like handing over cut-out cards instead of a whole book. Text that does not make sense without surrounding context or a structure where the conclusion is not presented until the end is judged as "not usable" as a standalone card and is less likely to be picked up.

 Checkpoint 3

Once retrieved, is it being "cited"? From the cards arranged at hand, AI selects which ones to adopt in the answer. Clear facts are preferred over vague expressions, and primary information is favored over unsubstantiated claims. Only when selected here does your name finally appear in the answer.

 Where you are falling short among these three checkpoints cannot be understood just by observing from the outside. You need to throw a large number of questions at AI and observe "how many times you were mentioned," "how many times competitors appeared," and "which pages were cited" to finally see your current position. Just like you cannot decide on a diet plan without undergoing a health check, AI search optimization also starts with current analysis (AI search diagnosis).

Will SEO become useless?

After reading this far, you might think, "So is SEO over?" The answer is no.

In the "retrieval" stage of RAG, many AIs refer to existing search engine results. In other words, being easily found on Google remains one of the tickets to entry for being retrieved by AI. The assets built through SEO are not wasted.

However, simply having a ticket does not mean you can participate in the game; this is the new reality. A chunk structure that is easy to retrieve, descriptions that are easy to cite, and alignment with the queries AI throws. An additional layer of optimization beyond SEO is now necessary. SEO and LLMO are not in opposition but are two sides of the same coin. Companies that are only focusing on one side will quietly disappear from the candidate list.

Conclusion (First, know the "now" when asked by AI)

Let’s summarize.

SEO was about navigating the "popularity vote" that starts with PageRank. The criteria are undisclosed, and the industry has built know-how through empirical reasoning. On the other hand, AI search optimization (LLMO/AIO/AISEO/GEO) faces a published mechanism called RAG. Competing for citations rather than rankings, when you lose, it’s not "lower visibility" but "absence." That’s why an approach that analyzes the behavior of RAG itself, rather than relying on intuition, is necessary. 

We at Queue Inc. provide an AI search optimization service called umoren.ai, centered on reverse engineering RAG.

If you are even slightly concerned about "Will my company appear when asked by AI?", we offer a free AI search diagnosis. We will throw 100 non-branded questions related to your company at AI and provide a report summarizing how many times your company was mentioned, how many times competitors appeared, and which pages were cited. You cannot decide on actions without knowing your current position. Start with the diagnosis; feel free to reach out.

 Has your company been asked by AI today?