Generative Engine Optimization (GEO) refers to the practice of designing content structure and information so that a company's content is more likely to be cited, mentioned, and referenced by AI in search environments where AI itself generates the answers—such as ChatGPT or Google's AI Overviews.

The search entry point is shifting away from the traditional "choose from a list of links" format toward a "read a single AI-generated answer" format. When a BtoB buyer asks an AI assistant, "What are the best predictive maintenance tools for mid-sized manufacturers?"—whether or not your company appears in that answer is the new measure of visibility.

This article is written for marketing and web professionals at BtoB companies. It explains the definition of GEO, how it differs from traditional SEO, how AI cites information, common misconceptions, and five steps to get started. By the end, you should have a clear picture of where to begin in order to get cited by AI.

GEO is best understood as "optimization to be read by AI and cited by AI." Its goals and success metrics differ from those of SEO for traditional search engines. Let's start by defining the term and clarifying how it differs from the easily confused concepts of SEO, AEO, and LLMO.

GEO is an optimization practice aimed at increasing a company's visibility within AI-generated answers by ensuring its content is cited, mentioned, or referenced when generative AI or AI-powered search constructs a response. While traditional search optimization aimed to "rank a company's page at the top of search results," GEO aims to have "a company's information, data, and name appear within the answer text generated by AI."

The term GEO was coined in the academic paper GEO: Generative Engine Optimization (Aggarwal et al., accepted at KDD 2024), published in 2023. Under experimental conditions, the study reported that techniques such as citing sources explicitly, adding statistical information, and incorporating quotations from credible sources improved the visibility of content in generative engine responses by up to 40% (source: arXiv:2311.09735). However, this result reflects a visibility (impression) metric used in the experiment and does not mean that citation rates, clicks, or sales will uniformly increase by 40% in real-world services. GEO is not purely intuitive, but its effects should be understood as highly condition-dependent.

What matters here is that the goal of GEO is not "to be clicked," but "to appear in the answer itself." In AI-powered search, "zero-click" scenarios are increasing—where users compare and evaluate options without ever visiting individual websites. Even if no link is clicked, being mentioned in an AI response—such as "Company A's approach is well known in this field"—creates a touchpoint that can lead to brand awareness, branded searches, and sales conversations. GEO is the practice of designing where and how your company can enter the process by which an LLM (large language model) generates a response.

Note that GEO as used in this article is a practical umbrella term for efforts to increase the likelihood of being cited, mentioned, or referenced within AI-generated answers. Neither Google nor OpenAI officially endorses any optimization method called "GEO," and the citation logic of each AI search service remains undisclosed and continues to evolve. GEO practices should be understood not as "guaranteed methods to be cited," but as information design that makes content easier for AI to discover, understand, and reference.

To state the conclusion upfront: SEO, AEO, GEO, and LLMO are not opposing concepts. It is more accurate to think of them as a continuous spectrum in which the target of optimization gradually expands from search results pages to AI-generated answers.

All of these terms are relatively new, and their definitions have not been fully standardized even within the industry. Some practitioners use AEO and GEO almost interchangeably, while others treat LLMO as a broader concept that encompasses GEO. There is no need to draw rigid distinctions between these terms.

The one practical point to keep in mind is this: whereas SEO was about "getting evaluated by a search engine algorithm," GEO is about "being read, summarized, and cited by a language model—an LLM." When the evaluating party changes, so does the way content needs to be crafted to be well-received. SEO knowledge remains a valid foundation, but GEO adds another layer on top of it: designing content to be read by AI. That is the role GEO plays.

The growing attention around GEO is rooted in a structural shift: the search entry point has changed. BtoB buying processes in particular are susceptible to the influence of AI assistants. Let's examine the reasons from two angles.

Buyers no longer necessarily "search, open links, and compare on their own." They are now asking AI directly and completing the entry point of their decision-making with summarized answers.

Research firm Gartner has predicted that by 2026, traditional search engine usage will decline by 25%, with that share shifting to AI chatbots and other virtual agents (Gartner, press release, February 2024). This is a forecast, not a confirmed fact, but it is already becoming commonplace for B2B procurement managers to ask AI assistants things like "What SaaS tools are recommended for the XX industry?" or "What's the difference between Company A and Company B?" in the early stages of vendor selection.

As a metric for measuring this shift, a concept called "Share of LLM" (also referred to as Share of Model or LLM Share of Voice) has begun to gain traction. This concept indicates how frequently and in what context a company appears in AI-generated responses when questions are asked about a specific topic. It is, however, an emerging practical metric—not a KPI with a fixed definition established as an industry standard. Whereas search ranking was the representative metric for SEO, it is most accurate at this stage to understand Share of LLM as "one candidate metric for measuring the effectiveness of GEO."

The reason this shift matters for B2B companies lies in the nature of their buying cycles: long consideration periods and multiple stakeholders. If a company fails to appear on the "candidate list" that AI surfaces during a front-line researcher's initial investigation, it becomes difficult to even enter the arena for subsequent comparative evaluation. AI responses are, in effect, becoming a new engine for generating "long lists."

In the world of SEO, "domain authority" has long been considered a major factor in achieving high rankings. Large media outlets and portal sites with many inbound links and long operating histories tend to dominate the top positions in search results.

In AI search, however, this assumption is partially breaking down. While traditional search rankings and domain credibility remain important, primary information that directly answers a question—and content structured for easy extraction—also has room to become a citation candidate. Cases have been observed where a niche specialist site's "paragraph that pinpoints the answer to that specific question" is cited over a broad, general article from a major media outlet.

This represents an opportunity for B2B companies that have struggled to compete head-on with large players on domain authority. If a company possesses primary information—definitions, procedures, figures, and conditions—written concretely about a specialized domain where it actually does hands-on work, AI may pick it up as "readily usable material" for answering questions.

One must not, however, misunderstand this. It does not mean that authority or search rankings have become unnecessary. Research shows that many of the URLs cited in AI Overviews overlap with the top results in conventional search, meaning the credibility built through SEO continues to carry weight in AI search as well. More precisely: authority and search rankings remain important, and on top of that, "primary information that answers a query in depth" now has new citation opportunities. For small and mid-sized, specialist B2B companies, GEO can be a worthwhile arena to compete in.

To think through GEO tactics, it is first necessary to understand "how AI ingests external information and cites it in responses." Once the mechanism is clear, the points that need to be optimized will naturally come into focus.

Most AI search systems today do not rely solely on knowledge the LLM learned during training; instead, they search the web in real time for each response, read the retrieved pages, and summarize and cite them. This mechanism of "search, retrieve, and use in a response" is built on a technology called RAG (Retrieval-Augmented Generation).

In simple terms, AI search operates in the following flow:

1. Decompose the user's question into multiple search-friendly queries
2. Gather relevant pages via a search engine or proprietary index
3. Read the body text of the gathered pages and extract the portions relevant to the question
4. Piece together the extracted fragments and generate a response with citations

From a GEO perspective, steps 3 and 4 are what matter. AI does not read an entire page uniformly—it is looking for "usable fragments" that correspond to the question. This means that even if a company's page is picked up by search, if the body text is structured in a way that makes it "difficult to extract as a fragment," it will not reach the point of being cited. Pages where the conclusion does not appear upfront, definitions are vague, and key points are buried in long paragraphs may be indexed by search engines but are unlikely to be cited in AI responses.

GEO can be rephrased as the work of improving both "retrievability" and "usability as a fragment." Understanding how RAG works should make it intuitive why conclusion-first writing and structured content are effective.

The process by which AI connects its responses to external factual information is called "grounding." LLMs inherently only generate plausible text based on the probability distribution of their training data, with no guarantee that the content is factually accurate. By "grounding" responses in real web pages and primary sources, hallucinations (plausible-sounding errors) are suppressed and citations can be provided—this is grounding.

The reason grounding matters for GEO is that content that "AI can safely ground itself in as a source" is what gets cited. AI search presents citations in order to guarantee the reliability of its responses. To be selected as one of those citations, it helps for content to be verifiable—meaning figures are sourced, claims are backed by evidence, and the author is clearly identified.

Conversely, unsourced assertions, thinly supported generalizations, and articles with no identifiable author are content that AI finds "difficult to ground in." Since citing such content risks undermining the credibility of its own response, AI tends to avoid it.

The reason so many GEO tactics converge on "produce primary information," "attach sources," and "identify the author" is precisely because all of these directly affect how easily content can be grounded. For a deeper look at how grounding works, please also refer to AI Grounding and the LLM Fact-Verification Implementation Guide.

GEO is a new domain, and as such, it is prone to misunderstandings born from extreme interpretations or the repurposing of outdated methods. Before getting started, I want to clear up two particularly common misconceptions.

"GEO is the future, so SEO is no longer necessary"——this is the most common, and most dangerous, misconception.

The reason is simple. As mentioned earlier, most AI search engines use conventional search engines and indexes internally to gather pages. Pages that are not picked up by search are never even considered as candidates for AI reference. SEO, which ensures crawlability, indexability, and discoverability in search, remains a prerequisite for GEO. Abandoning SEO is tantamount to closing off the very entry point through which AI can read your content.

Moreover, even as AI search becomes more widespread, traditional search will not disappear entirely. In the later stages of evaluation—when users are checking specific specifications, pricing, and case studies—they still visit and read individual sites directly.

The correct way to think about this is as follows: SEO and GEO are not a matter of "replacement" but of "addition." You first secure discoverability with SEO, then layer on top of it a "design to be cited by AI" with GEO. You cannot build a structure by demolishing its foundation.

Another misconception is treating GEO as "keyword stuffing for AI." This is reminiscent of old-school SEO tactics—pages that unnaturally repeat keywords, or schemes that deceive search engines with hidden text.

This approach tends to backfire. LLMs read text not as a "collection of keywords" but as a "web of meaning." Text that mechanically crams in keywords is semantically thin and difficult to summarize, making it precisely the kind of material that AI finds harder to cite.

Furthermore, AI search systems have their own quality evaluation mechanisms. Low-quality content that is clearly attempting to manipulate AI, or thin mass-produced articles, risk being downgraded by both search and generative engines alike.

What works in GEO is not superficial tricks. "Writing that a human reader finds concrete, accurate, and trustworthy is also writing that AI finds easy to cite." Even as the evaluator shifts from humans to models, the essence remains unchanged: "write substantive, first-hand information in a clear, well-structured way." GEO is not a hack—it is an extension of content quality.

From here, we move into the practical section. GEO can be approached as an extension of your existing website and content operations, without any specialized tools. Here, we explain five steps that B2B companies can begin without undue burden, organized into three blocks: "building out primary information and structured data," "designing content that is easy to cite," and "monitoring LLM visibility." Since these steps are meant to be built upon in sequence, please start from Step 1.

The first two steps lay the groundwork for being chosen by AI as a reliable source.

Step 1: Take stock of the primary information that only your company can provide. What AI wants to cite is not generic information found anywhere, but primary information accompanied by specific figures, procedures, and conditions. Implementation results, data obtained through testing, failures and countermeasures accumulated in the field, industry-specific decision criteria——list out the "knowledge your company has gained through hands-on experience." The knowledge held by sales, customer success, and technical teams does not exist on the web unless it is inventoried and put into words.

Step 2: Make the meaning of your pages easier for machines to understand. Clearly stating the author, supervisor, publication date, and last updated date, and setting up structured data (JSON-LD)—such as Article for articles and Organization for company information—helps search engines understand page content. However, there is an important caveat: Google has stated that no special schema.org structured data is required for content to appear in AI Overviews or AI Mode, and adding structured data does not guarantee AI citation. Structured data should be positioned not as a "direct path to citation," but as a foundation for clarifying a page's identity (who wrote it, when, and about what).

Note that FAQ structured data (FAQPage) exists as a Schema.org type, but Google Search's FAQ rich results are no longer displayed, so it should not be treated as a tactic for capturing Google's FAQ slots. That said, including an FAQ section itself remains valuable, as it offers the advantage of extracting key points in question-and-answer format for both users and AI alike.

These two steps may seem unglamorous, but skipping them tends to cause subsequent efforts to spin their wheels. Think of them as building the foundation that leads AI to judge your content as "worth reading and safe to reference as a source."

The next two steps involve shaping the content itself into a form that makes it easier for AI to extract as discrete fragments.

Step 3: Write conclusion-first. AI is looking for "usable passages" that correspond to a given question. At the beginning of each page and each heading, state the conclusion or definition in one or two sentences. Place a definitional sentence of the form "X is Y" as the very first line of the lead, and add a brief summary of each section directly beneath its heading. A structure where the conclusion only appears after a lengthy preamble is difficult for AI to extract from.

Step 4: Frame headings as questions and support them with evidence. The questions users ask AI tend to be natural-language queries like "What is ~?", "How do you ~?", or "What's the difference between A and B?" Aligning headings with this question format makes it easier for AI to map "this heading = the answer to this question." In addition, present comparisons in tables, and back up claims with figures and citations. Tables, definitional sentences, and figures with cited sources are all elements that AI can easily extract and quote.

This approach largely overlaps with "the craft of clear writing" rather than any specialized technique. The difference is that, with AI now among the audience, the discipline of "leaving no ambiguity," "stating conclusions first," and "always providing evidence" must be applied more rigorously. It is also effective to design each page to answer a single question in depth, rather than cramming too many points onto one page.

Step 5: Continuously monitor how your company appears in AI responses. GEO is not a one-time effort; it is a practice built through a loop of observation and improvement.

The most fundamental approach is to identify the questions that matter most to your business—queries that buyers are likely to actually ask, such as "What are the best △△ tools for the 〇〇 industry?" or "How do you do △△?"—and then regularly ask those same questions in ChatGPT, AI Overviews, Perplexity, and similar platforms, recording the responses each time. The key things to check are:

• Whether your company appears in the response (Share of LLM)
• The context in which it is mentioned (recommended, neutral, or negative)
• Whether your page is linked as a source
• How competitors appear

Manual spot-checks are sufficient to start. Simply listing 10–20 important queries and logging the responses on a monthly basis will reveal changes over time. Comparing how your company is mentioned before and after implementing changes will help you form hypotheses about what is working.

Note that AI responses can vary even for the same question, so it is important not to judge based on a single result—observe across multiple runs and multiple models. As you move into a full operational phase, building a system for continuously monitoring LLM outputs becomes worth considering. The Practical Guide to AI Observability is a useful reference for this approach.

Before getting started with GEO, here are answers to questions frequently asked by those responsible for B2B marketing.

Q1: If I work on GEO, will my content be cited in AI responses right away?

It is better not to expect immediate results. Because AI search reflects information through web recrawling and index updates, there is a time lag before content improvements show up in responses. Think of GEO not as a short-term traffic tactic, but as a practice to be cultivated over months—much like SEO.

Q2: How should I measure the effectiveness of GEO?

The central metric is "Share of LLM"—how frequently and in what context your company appears in AI responses to important queries. Supplementary indicators include referral traffic from AI, changes in branded search volume, and how often prospects say "I saw you on AI" during sales conversations. Measuring by clicks alone will cause you to miss much of GEO's value.

Q3: Can small and mid-sized B2B companies achieve results with GEO?

GEO is, if anything, well-suited to smaller and more specialized companies. AI citation tends to prioritize "the accuracy of the answer to a given question and the originality of the information" over domain authority, meaning that companies with deep expertise in a specific area can earn citations even against larger competitors.

Q4: Can our SEO team take on GEO as well?

Since the foundational skills overlap, having the SEO team handle GEO is the practical approach. However, the team needs to align on two key shifts: KPIs moving from "rankings and clicks" to "citations and mentions," and the evaluator changing from an algorithm to a language model.

Q5: Is it essential to adopt dedicated tools for GEO?

No. Steps 1–5 in this article can be started using your existing site and manual spot-checks. Once the number of queries you are tracking grows and your operations become more established, that is the time to consider introducing a visibility tracking tool.

As the entry point to search shifts from "a list of links" to "AI-generated answers," corporate visibility is beginning to be measured by a new metric: how frequently a company is cited in AI responses. Generative Engine Optimization (GEO) is the practice of adapting to this change—ensuring that AI correctly recognizes and cites your company's information.

To summarize the key points: GEO is not a replacement for SEO but an addition to it, and the SEO foundation remains indispensable. What gets cited by AI is not pages stuffed with keywords, but content that is "easy to ground"—content where primary information is specific, conclusions come first, and evidence and sources are provided. And GEO can be started with five steps that require no specialized tools: auditing your primary information, structuring your data, designing content conclusion-first, framing headings as questions, and monitoring continuously.

When it comes to adapting to AI search, the earlier a company starts, the more observational data it accumulates and the faster it can iterate on improvements. Begin by asking AI the questions that matter most to your business, and see how it answers them today.

If you would like to discuss how to approach GEO for your specific situation, or to talk through a content strategy for the age of AI search, please feel free to reach out via the contact page.