AI-powered search \u2014 ChatGPT, Google’s AI Overviews, Perplexity, and more \u2014 is spreading rapidly.<\/p>\n\n\n\n
In a survey conducted in November 2025, a combined 64% of generative-AI users said they were satisfied with the AI summaries shown in search results and stopped researching further without clicking through to a link (Source: Mobile Society Research Institute<\/a>).Another study found that a combined 23.9% of users completed their search using only the generative AI’s answer or additional AI follow-up questions alone (Source: ONEDER<\/a>).<\/p>\n\n\n\n Until now, driving traffic to a website has depended on “ranking high in search results.” But the spread of this kind of “zero-click search” has created a new challenge for corporate websites: if a company isn’t cited by AI, it risks losing its very point of contact with users.<\/p>\n\n\n\n This is why the concepts of GEO (Generative Engine Optimization) and LLMO (Large Language Model Optimization) are drawing attention. This article explains what GEO and LLMO are, how they differ from SEO, and concrete measures you can start on today \u2014 backed by supporting data.<\/p>\n\n\n\n SEO (Search Engine Optimization) refers to efforts aimed at ranking highly in search engines like Google.<\/p>\n\n\n\n GEO and LLMO, on the other hand, are approaches designed to make it more likely that generative AI will cite or reference your site as a “trustworthy source” when composing its answers. The objectives are different.<\/p>\n\n\n\n Traditional SEO has centered on:<\/p>\n\n\n\n GEO and LLMO, by contrast, focus on:<\/p>\n\n\n\n This doesn’t mean SEO becomes unnecessary. Going forward, designing a “site that AI chooses” \u2014 in addition to SEO \u2014 will be key to competitiveness.<\/p>\n\n\n\n The experience rate of generative AI usage grew from 3.4% in March 2023 to 56.3% in a June 2026 survey (Source: Nippon Research Center<\/a>) \u2014 approaching half of the target population in roughly three years.<\/p>\n\n\n\n Amid this shift, if a company’s website is never cited in an AI’s answer, users may finish their research without ever learning the company exists. This affects content marketing and lead generation as well.<\/p>\n\n\n\n Not just “being clicked,” but “being cited by AI” is becoming an important KPI in its own right.<\/p>\n\n\n\n None of the generative AI providers has published the details of its citation logic, but reliable sources currently point to the following factors:<\/p>\n\n\n\n Well-organized information with a clear heading structure. Pages that state the answer to a question clearly at the outset are easier for AI to process when summarizing or citing. The “inverted pyramid” style \u2014 stating the conclusion first, then following with details \u2014 is a writing approach repeatedly recommended in guides on AI search optimization.<\/p>\n\n\n\n “Experience,” “Expertise,” “Authoritativeness,” and “Trustworthiness” \u2014 the qualities Google emphasizes \u2014 are considered important in AI search as well. Who wrote the content, what expertise it’s based on, and whether it cites trustworthy sources are all thought to factor into evaluation.<\/p>\n\n\n\n Implementing schema.org structured data \u2014 such as FAQPage, HowTo, and Article \u2014 via JSON-LD makes it easier for AI to mechanically grasp a page’s content and entities (company names, product names, people, etc.).<\/p>\n\n\n\n One important point to get right here: Google has not officially recognized structured data itself as a direct ranking factor<\/strong>. <\/p>\n\n\n\n Google Search Central’s own documentation states clearly that if there’s a problem with structured data and a manual action is taken, the page will simply stop appearing as a rich result \u2014 it will not affect the page’s actual ranking in Google web search (Source: Google Search Central<\/a>).\u00a0<\/p>\n\n\n\n Google also narrowed FAQ rich results in August 2023 \u2014 citing a desire to simplify search results \u2014 to a limited set of highly authoritative sites such as government and healthcare organizations, and in May 2026 eliminated FAQ rich results entirely (Source: Search Engine Journal<\/a>). The era in which “if you implement it, it’s guaranteed to stand out” is already over.<\/p>\n\n\n\n In other words, the value of structured data isn’t in directly boosting rankings \u2014 it functions as a “guide rail” that helps AI read a page’s content without misunderstanding it. Since implementation cost is relatively low, a practical priority order is to start with Organization, Article, and FAQPage. Note that BreadcrumbList is considered to offer relatively little benefit for AI comprehension, so it can reasonably be given lower priority.<\/p>\n\n\n\n “llms.txt” \u2014 a Markdown-format file for communicating site structure to AI, proposed in 2024 by Answer.AI’s Jeremy Howard \u2014 has also been seeing wider adoption in 2026.<\/p>\n\n\n\nThe Difference Between SEO and GEO\/LLMO<\/strong><\/h2>\n\n\n\n
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What Kind of Sites Does AI Cite?<\/strong><\/h2>\n\n\n\n
1. Information Architecture<\/strong><\/h3>\n\n\n\n
2. E-E-A-T<\/strong><\/h3>\n\n\n\n
3. Structured Data (schema.org \/ JSON-LD)<\/strong><\/h3>\n\n\n\n
4. llms.txt<\/strong><\/h3>\n\n\n\n