Structured data plays a narrower role in answer engine optimization than most checklists claim: it is well documented as an input to Google's and Microsoft's search infrastructure, and essentially undocumented as an input to LLM-based engines like ChatGPT and Perplexity [8][9]. Google states that no special schema.org markup is needed to appear in AI Overviews or AI Mode; a page only needs to be indexed and eligible to show a snippet [3]. Microsoft is the clearest advocate, saying schema helps AI systems understand content and confirming that the LLMs behind Copilot consume it [6][7]. Controlled testing supports the skeptical reading elsewhere: Ahrefs tracked 1,885 pages that added JSON-LD and found citation changes indistinguishable from noise across AI Overviews, AI Mode, and ChatGPT [10], while live-retrieval tests show AI assistants reading visible HTML and ignoring JSON-LD when they fetch a page [12]. Structured data still earns its implementation cost through rich results, entity clarity, and Bing's AI pipeline, so the accurate framing is low-cost foundation rather than citation lever [1][6].
\n","summary":"Schema markup is one of the most repeated AEO recommendations and one of the least tested. This answer compares what Google, Microsoft, OpenAI, and Perplexity actually document against three independent studies from 2025 and 2026, explains why Bing is the outlier that openly feeds schema to its LLMs, identifies the five schema types still worth implementing, and lays out an implementation approach that holds up whether or not LLM-based engines ever read your JSON-LD.","publishedAt":"2026-08-09T21:03:29.347","verifiedAt":"2026-08-09T00:00:00","editorialStatus":"APPROVED","lastReviewedAt":"2026-08-09T00:00:00","nextReviewDueAt":"2026-11-09T00:00:00","templateVersion":"v2","aliases":["Does schema markup help AEO?","Schema markup for AI search","Structured data for AI answer engines","Do AI chatbots read schema markup?","Does structured data help you get cited by AI?","Schema markup and LLM visibility","JSON-LD for answer engine optimization","Schema markup in generative engine optimization","Does schema help ChatGPT cite your site?","Structured data and Google AI Overviews","Which schema types matter for AI search?","Is schema markup worth it for AI visibility?"],"confidenceScore":80,"confidenceLabel":"High","canonicalUrl":null},"contributor":{"id":"ec39deab-44fe-48d8-9029-fefe993ab85a","slug":"answer-stack","displayName":"AnswerStack","websiteUrl":null},"contributorOrganizationProfile":{"entityId":"ec39deab-44fe-48d8-9029-fefe993ab85a","legalName":null,"description":null,"websiteUrl":null,"imageUrl":null,"slogan":null,"subtitle":null,"facts":[],"coiNote":null,"foundingDate":null,"numberOfEmployeesText":null,"contactPoint":null,"address":null,"headquartersText":null,"organizationType":null},"contributorPerson":{"slug":"answerstack-editorial-team","displayName":"AnswerStack Editorial Team"},"sections":[{"id":"99f232fc-0202-4b5c-8cea-ca324eb00672","sectionKey":"what_structured_data_does","sectionType":"markdown_section","heading":"What does structured data actually do for an answer engine?","introMarkdown":"Structured data gives crawlers a machine-readable description of what a page contains, most often schema.org vocabulary embedded in the page source as JSON-LD [1][5]. Google defines it as \"a standardized format for providing information about a page and classifying the page content\" [1]. Schema.org, the shared vocabulary behind nearly all of it, was founded by Google, Microsoft, Yahoo, and Yandex, and reports that over 45 million web domains carry more than 450 billion schema.org objects [5]. That scale explains why schema appears in every AEO checklist. What the checklists usually skip is that the major answer engines consume it in very different ways, and some appear not to consume it at all.\n\nThe split runs along infrastructure lines. Engines that inherit a mature search index treat schema as a documented input: Google parses it for rich results and entity understanding [1], and Microsoft recommends it in its official guidance for getting cited in AI answers [6]. Engines built directly on LLM retrieval are a different story. OpenAI's crawler documentation for ChatGPT search covers robots.txt access without ever mentioning structured data [8], Perplexity's crawler documentation is just as silent [9], and testing shows that when an assistant fetches a page live during a conversation, it reads the visible text and skips JSON-LD entirely [12].\n\nEven where schema is definitely consumed, it is not an AI visibility switch. Google states there is \"no special schema.org structured data that you need to add\" to appear in AI Overviews or AI Mode, because a page only needs to be indexed and eligible for a snippet [3]. The largest controlled test to date, covering 1,885 pages that added JSON-LD, measured citation changes small enough to classify as noise [10]. So the honest role of structured data in AEO is infrastructure: it supports the search layer most answer engines are built on and keeps entity facts unambiguous, but it has not been shown to directly earn AI citations.","introHtml":"Structured data gives crawlers a machine-readable description of what a page contains, most often schema.org vocabulary embedded in the page source as JSON-LD [1][5]. Google defines it as "a standardized format for providing information about a page and classifying the page content" [1]. Schema.org, the shared vocabulary behind nearly all of it, was founded by Google, Microsoft, Yahoo, and Yandex, and reports that over 45 million web domains carry more than 450 billion schema.org objects [5]. That scale explains why schema appears in every AEO checklist. What the checklists usually skip is that the major answer engines consume it in very different ways, and some appear not to consume it at all.
\nThe split runs along infrastructure lines. Engines that inherit a mature search index treat schema as a documented input: Google parses it for rich results and entity understanding [1], and Microsoft recommends it in its official guidance for getting cited in AI answers [6]. Engines built directly on LLM retrieval are a different story. OpenAI's crawler documentation for ChatGPT search covers robots.txt access without ever mentioning structured data [8], Perplexity's crawler documentation is just as silent [9], and testing shows that when an assistant fetches a page live during a conversation, it reads the visible text and skips JSON-LD entirely [12].
\nEven where schema is definitely consumed, it is not an AI visibility switch. Google states there is "no special schema.org structured data that you need to add" to appear in AI Overviews or AI Mode, because a page only needs to be indexed and eligible for a snippet [3]. The largest controlled test to date, covering 1,885 pages that added JSON-LD, measured citation changes small enough to classify as noise [10]. So the honest role of structured data in AEO is infrastructure: it supports the search layer most answer engines are built on and keeps entity facts unambiguous, but it has not been shown to directly earn AI citations.
\n","outroMarkdown":null,"outroHtml":null,"contentJson":{},"configJson":{},"noteMarkdown":null,"noteHtml":null,"sortOrder":0},{"id":"19d19539-6095-4def-a088-274f08a4d310","sectionKey":"platform_evidence_table","sectionType":"table_section","heading":"What each platform says about structured data","introMarkdown":"The table separates what platforms document from what independent testing has found. The sections that follow walk through each row.","introHtml":"The table separates what platforms document from what independent testing has found. The sections that follow walk through each row.
\n","outroMarkdown":"Two platforms document consuming schema, two say nothing, and direct testing fills in the rest.","outroHtml":"Two platforms document consuming schema, two say nothing, and direct testing fills in the rest.
\n","contentJson":{"rows":[{"cells":["Google Search, AI Overviews, AI Mode","Structured data powers rich results and helps Google understand page content [1], but \"no special schema.org structured data\" is needed for AI features [3]","Documented for search; explicitly optional for AI features"]},{"cells":["Microsoft Bing and Copilot","Schema \"helps search engines and AI systems understand your content\" [6]; a Bing principal product manager confirmed schema feeds the LLMs behind Copilot [7]","Documented and openly endorsed"]},{"cells":["ChatGPT search (OpenAI)","Crawler documentation covers robots.txt and IP ranges only; structured data is never mentioned [8]","Silent"]},{"cells":["Perplexity","Crawler documentation covers bot access and firewall configuration; no schema guidance exists [9]","Silent"]},{"cells":["Live-fetch assistants (Claude, Gemini in chat)","No published guidance; testing shows JSON-LD is not read during direct page fetches [12]","Tested, mostly negative"]}],"columns":["Platform","What its documentation or engineers say","Evidence status"]},"configJson":{},"noteMarkdown":null,"noteHtml":null,"sortOrder":10},{"id":"e68d590c-7060-4f22-8b6d-5d8b169d1746","sectionKey":"google_evidence","sectionType":"markdown_section","heading":"What does Google do with structured data?","introMarkdown":"Google consumes structured data more thoroughly than any other platform, and it is also the platform most direct about schema being optional for its AI surfaces. On the search side, Google's gallery currently supports roughly 30 structured data features, including Article, Product, Organization, and Breadcrumb, each of which can trigger an enhanced result appearance [2]. The documented payoff is real: Google's own case studies report that Rotten Tomatoes measured a 25% higher click-through rate on pages with structured data and Food Network saw a 35% increase in visits after marking up 80% of its pages [1]. Google also uses the markup to gather information about entities on the web [1].\n\nFor AI features, the guidance is unambiguous. Google's documentation states: \"You don't need to create new machine readable files, AI text files, or markup to appear in these features,\" and adds that there is no special schema.org structured data to add [3]. Eligibility for AI Overviews and AI Mode requires only that a page is indexed and eligible to appear in Search with a snippet, with \"no additional technical requirements\" [3]. Limits on AI use of content run through snippet controls and the Google-Extended token, not through markup [3].\n\nGoogle has also been narrowing rich results rather than expanding them. In August 2023 it restricted FAQ rich results to \"well-known, authoritative government and health websites\" and began winding down HowTo rich results [4]. The FAQPage markup remains valid vocabulary, but for most sites it no longer produces any visible search feature [4].","introHtml":"Google consumes structured data more thoroughly than any other platform, and it is also the platform most direct about schema being optional for its AI surfaces. On the search side, Google's gallery currently supports roughly 30 structured data features, including Article, Product, Organization, and Breadcrumb, each of which can trigger an enhanced result appearance [2]. The documented payoff is real: Google's own case studies report that Rotten Tomatoes measured a 25% higher click-through rate on pages with structured data and Food Network saw a 35% increase in visits after marking up 80% of its pages [1]. Google also uses the markup to gather information about entities on the web [1].
\nFor AI features, the guidance is unambiguous. Google's documentation states: "You don't need to create new machine readable files, AI text files, or markup to appear in these features," and adds that there is no special schema.org structured data to add [3]. Eligibility for AI Overviews and AI Mode requires only that a page is indexed and eligible to appear in Search with a snippet, with "no additional technical requirements" [3]. Limits on AI use of content run through snippet controls and the Google-Extended token, not through markup [3].
\nGoogle has also been narrowing rich results rather than expanding them. In August 2023 it restricted FAQ rich results to "well-known, authoritative government and health websites" and began winding down HowTo rich results [4]. The FAQPage markup remains valid vocabulary, but for most sites it no longer produces any visible search feature [4].
\n","outroMarkdown":null,"outroHtml":null,"contentJson":{},"configJson":{},"noteMarkdown":null,"noteHtml":null,"sortOrder":20},{"id":"b6e90ad9-2d6e-4e49-997c-df8a9ef45980","sectionKey":"bing_copilot_evidence","sectionType":"markdown_section","heading":"How do Bing and Copilot use schema markup?","introMarkdown":"Microsoft is the only major platform whose engineers have said on record that schema markup feeds their LLMs. At SMX Munich in March 2025, Fabrice Canel, a principal product manager at Bing, confirmed that schema markup helps Microsoft's LLMs, including Copilot, understand site content [7]. He also connected freshness to AI behavior: \"Gen AIs value fresh content in particular, partly as a reference check of their LLM training data,\" and recommended pushing updates through the IndexNow API [7].\n\nMicrosoft followed the conference statement with written guidance. An October 2025 post by Krishna Madhavan, a principal product manager on the Bing team, explains that \"schema is a type of code that helps search engines and AI systems understand your content\" and that it can label content as a product, review, FAQ, or event, \"turning plain text into structured data that machines can interpret with confidence\" [6]. Markup that Bing parses at indexing time can reach Copilot's answer layer, which makes Bing the strongest documented case for schema in AEO [6][7]. How much weight that deserves depends on how much of your audience uses Copilot and Bing-powered experiences.","introHtml":"Microsoft is the only major platform whose engineers have said on record that schema markup feeds their LLMs. At SMX Munich in March 2025, Fabrice Canel, a principal product manager at Bing, confirmed that schema markup helps Microsoft's LLMs, including Copilot, understand site content [7]. He also connected freshness to AI behavior: "Gen AIs value fresh content in particular, partly as a reference check of their LLM training data," and recommended pushing updates through the IndexNow API [7].
\nMicrosoft followed the conference statement with written guidance. An October 2025 post by Krishna Madhavan, a principal product manager on the Bing team, explains that "schema is a type of code that helps search engines and AI systems understand your content" and that it can label content as a product, review, FAQ, or event, "turning plain text into structured data that machines can interpret with confidence" [6]. Markup that Bing parses at indexing time can reach Copilot's answer layer, which makes Bing the strongest documented case for schema in AEO [6][7]. How much weight that deserves depends on how much of your audience uses Copilot and Bing-powered experiences.
\n","outroMarkdown":null,"outroHtml":null,"contentJson":{},"configJson":{},"noteMarkdown":null,"noteHtml":null,"sortOrder":30},{"id":"a497d944-8cff-49fd-b995-dc57f784cca9","sectionKey":"llm_engines_evidence","sectionType":"markdown_section","heading":"Do ChatGPT, Perplexity, and Claude read schema at all?","introMarkdown":"Neither OpenAI nor Perplexity mentions structured data anywhere in its publisher-facing documentation, which is a meaningful absence given how specific both are about crawler access. OpenAI's stated requirement for ChatGPT search visibility is access, not markup: \"Sites that are opted out of OAI-SearchBot will not be shown in ChatGPT search answers\" [8]. Perplexity's documentation likewise covers PerplexityBot, which indexes pages for search and is \"not used to crawl content for AI foundation models,\" plus the on-demand Perplexity-User fetcher, and offers no schema guidance of any kind [9].\n\nDirect testing goes further than the silence. In October 2025, the analytics firm searchVIU planted product prices in eight formats on a test page, including visible HTML, JavaScript-rendered text, JSON-LD, Microdata, and RDFa, then asked each assistant to retrieve them [12]. No system extracted a fact that existed only in JSON-LD during a live fetch. Claude found none of the hidden formats, ChatGPT recovered three of eight prices, and Gemini recovered four of eight as the only assistant that rendered JavaScript [12].\n\nThe honest caveat is that these tests probe one phase of a multi-phase pipeline. The same researchers note that schema \"could very well be used in earlier phases,\" such as index construction or model training, where no outside tester can observe it [12]. The claim the evidence supports is narrower than \"schema is useless\": LLM-based engines do not document consuming it, and they demonstrably do not read it at answer time.","introHtml":"Neither OpenAI nor Perplexity mentions structured data anywhere in its publisher-facing documentation, which is a meaningful absence given how specific both are about crawler access. OpenAI's stated requirement for ChatGPT search visibility is access, not markup: "Sites that are opted out of OAI-SearchBot will not be shown in ChatGPT search answers" [8]. Perplexity's documentation likewise covers PerplexityBot, which indexes pages for search and is "not used to crawl content for AI foundation models," plus the on-demand Perplexity-User fetcher, and offers no schema guidance of any kind [9].
\nDirect testing goes further than the silence. In October 2025, the analytics firm searchVIU planted product prices in eight formats on a test page, including visible HTML, JavaScript-rendered text, JSON-LD, Microdata, and RDFa, then asked each assistant to retrieve them [12]. No system extracted a fact that existed only in JSON-LD during a live fetch. Claude found none of the hidden formats, ChatGPT recovered three of eight prices, and Gemini recovered four of eight as the only assistant that rendered JavaScript [12].
\nThe honest caveat is that these tests probe one phase of a multi-phase pipeline. The same researchers note that schema "could very well be used in earlier phases," such as index construction or model training, where no outside tester can observe it [12]. The claim the evidence supports is narrower than "schema is useless": LLM-based engines do not document consuming it, and they demonstrably do not read it at answer time.
\n","outroMarkdown":null,"outroHtml":null,"contentJson":{},"configJson":{},"noteMarkdown":null,"noteHtml":null,"sortOrder":40},{"id":"96bc7963-052e-4161-9dbd-973d3cfcd856","sectionKey":"independent_testing","sectionType":"markdown_section","heading":"What does independent testing show about schema and AI citations?","introMarkdown":"The largest controlled study found no meaningful citation effect from adding schema. In May 2026, Ahrefs tracked 1,885 pages that added JSON-LD between August 2025 and March 2026, matched them against 4,000 control pages with similar prior citation levels, and measured citations 30 days before and after [10]. Google AI Overviews moved by negative 4.6%, Google AI Mode by positive 2.4%, and ChatGPT by positive 2.2%, with the two positive figures small enough that the authors classify them as noise [10]. The same study also found that AI-cited pages were almost three times more likely to carry JSON-LD than non-cited pages, a gap the authors attribute to overall site quality rather than to the markup: \"Schema could be doing real work, but it could also just be riding the wave of every other signal\" [10].\n\nA smaller self-experiment points the same direction with one exception. OtterlyAI added five schema types to its own site in December 2025 and tracked brand visibility across 319 prompts for three months [11]. Google surfaces rose, with AI Mode up 42% and a large AI Overviews jump the researchers partly attributed to unrelated algorithm shifts, while ChatGPT, Copilot, and Gemini visibility fell and Perplexity did not move [11]. Their summary: \"Schema was invented for algorithms that couldn't handle unstructured text. AI Search is built to do exactly that\" [11]. Read together, the correlation data flatters schema because well-run sites tend to implement it, and controlled tests keep failing to find a citation lift outside Google's search-backed surfaces.","introHtml":"The largest controlled study found no meaningful citation effect from adding schema. In May 2026, Ahrefs tracked 1,885 pages that added JSON-LD between August 2025 and March 2026, matched them against 4,000 control pages with similar prior citation levels, and measured citations 30 days before and after [10]. Google AI Overviews moved by negative 4.6%, Google AI Mode by positive 2.4%, and ChatGPT by positive 2.2%, with the two positive figures small enough that the authors classify them as noise [10]. The same study also found that AI-cited pages were almost three times more likely to carry JSON-LD than non-cited pages, a gap the authors attribute to overall site quality rather than to the markup: "Schema could be doing real work, but it could also just be riding the wave of every other signal" [10].
\nA smaller self-experiment points the same direction with one exception. OtterlyAI added five schema types to its own site in December 2025 and tracked brand visibility across 319 prompts for three months [11]. Google surfaces rose, with AI Mode up 42% and a large AI Overviews jump the researchers partly attributed to unrelated algorithm shifts, while ChatGPT, Copilot, and Gemini visibility fell and Perplexity did not move [11]. Their summary: "Schema was invented for algorithms that couldn't handle unstructured text. AI Search is built to do exactly that" [11]. Read together, the correlation data flatters schema because well-run sites tend to implement it, and controlled tests keep failing to find a citation lift outside Google's search-backed surfaces.
\n","outroMarkdown":null,"outroHtml":null,"contentJson":{},"configJson":{},"noteMarkdown":null,"noteHtml":null,"sortOrder":50},{"id":"37f7abfc-cd37-410f-87c9-43eca86e70d2","sectionKey":"schema_types_table","sectionType":"table_section","heading":"Which schema types are worth implementing?","introMarkdown":"The priorities below reflect what platforms document consuming, not what markup generators can output. Each type gets a fuller explanation in the sections that follow.","introHtml":"The priorities below reflect what platforms document consuming, not what markup generators can output. Each type gets a fuller explanation in the sections that follow.
\n","outroMarkdown":"Everything else is situational: a local business should add LocalBusiness markup and an events company Event markup [2], but the five types above cover most companies practicing AEO.","outroHtml":"Everything else is situational: a local business should add LocalBusiness markup and an events company Event markup [2], but the five types above cover most companies practicing AEO.
\n","contentJson":{"rows":[{"cells":["Organization","Who publishes the site: name, logo, sameAs profile links","Feeds entity understanding at Google [1] and machine-readable labeling at Bing [6]"]},{"cells":["Article / BlogPosting","Headline, author, publish and modified dates","Supported Google rich result [2]; date signals matter to generative AI freshness checks [7]"]},{"cells":["Product and Review","Prices, availability, ratings","Supported rich results [2]; two of the four content labels Microsoft names for AI answers [6]"]},{"cells":["FAQPage / QAPage","Question-and-answer pairs","Rich results now restricted [4], but FAQ remains a label Microsoft cites for AI parsing [6]"]},{"cells":["Breadcrumb and structural types","Where a page sits in the site hierarchy","Supported in Google's gallery [2] and nearly free to maintain"]}],"columns":["Schema type","What it declares","Why it still earns a place"]},"configJson":{},"noteMarkdown":null,"noteHtml":null,"sortOrder":60},{"id":"01e7e092-f9ad-40a5-b623-a537bf14dc74","sectionKey":"organization_schema","sectionType":"markdown_section","heading":"Organization schema","introMarkdown":"Organization markup identifies the real-world entity behind a site, with properties for the legal name, logo, and sameAs links to official profiles. Google supports it as a structured data feature and uses markup like it to gather information about entities across the web [1][2], which matters because answer engines assemble claims about brands from many sources and benefit from an unambiguous statement of who you are. Implement one Organization object sitewide, reference it from the publisher field of your Article markup, and keep every value identical to your visible about and contact pages [1].","introHtml":"Organization markup identifies the real-world entity behind a site, with properties for the legal name, logo, and sameAs links to official profiles. Google supports it as a structured data feature and uses markup like it to gather information about entities across the web [1][2], which matters because answer engines assemble claims about brands from many sources and benefit from an unambiguous statement of who you are. Implement one Organization object sitewide, reference it from the publisher field of your Article markup, and keep every value identical to your visible about and contact pages [1].
\n","outroMarkdown":null,"outroHtml":null,"contentJson":{},"configJson":{},"noteMarkdown":null,"noteHtml":null,"sortOrder":70},{"id":"1cb8a783-83f5-4e5f-99af-296f0848376b","sectionKey":"article_schema","sectionType":"markdown_section","heading":"Article and BlogPosting schema","introMarkdown":"Article markup declares a page's headline, author, publication date, and modification date, and it remains a supported Google rich result feature [2]. Its relevance goes beyond the rich result: Bing's Fabrice Canel notes that generative AI systems value fresh content \"partly as a reference check of their LLM training data\" [7], and a machine-readable dateModified is the cleanest way to state freshness. Use real dates rather than scripts that bump the date on every deploy, and keep the visible byline and date matching the markup [1].","introHtml":"Article markup declares a page's headline, author, publication date, and modification date, and it remains a supported Google rich result feature [2]. Its relevance goes beyond the rich result: Bing's Fabrice Canel notes that generative AI systems value fresh content "partly as a reference check of their LLM training data" [7], and a machine-readable dateModified is the cleanest way to state freshness. Use real dates rather than scripts that bump the date on every deploy, and keep the visible byline and date matching the markup [1].
\n","outroMarkdown":null,"outroHtml":null,"contentJson":{},"configJson":{},"noteMarkdown":null,"noteHtml":null,"sortOrder":80},{"id":"04e2136f-7806-4b94-b929-1ea1ba141b2e","sectionKey":"product_review_schema","sectionType":"markdown_section","heading":"Product and Review schema","introMarkdown":"Product and Review markup carries prices, availability, and ratings, and it is the category with the most concrete platform support. Google lists Product and Review snippet features in its supported gallery [2], and Microsoft names product and review among the four labels that turn \"plain text into structured data that machines can interpret with confidence\" for AI answers [6]. Commerce is also where structured data has documented dependency: shopping surfaces need exact prices and availability, which prose cannot deliver reliably. Keep on-page markup consistent with any merchant feeds you submit, and only mark up reviews that actually appear on the page [1].","introHtml":"Product and Review markup carries prices, availability, and ratings, and it is the category with the most concrete platform support. Google lists Product and Review snippet features in its supported gallery [2], and Microsoft names product and review among the four labels that turn "plain text into structured data that machines can interpret with confidence" for AI answers [6]. Commerce is also where structured data has documented dependency: shopping surfaces need exact prices and availability, which prose cannot deliver reliably. Keep on-page markup consistent with any merchant feeds you submit, and only mark up reviews that actually appear on the page [1].
\n","outroMarkdown":null,"outroHtml":null,"contentJson":{},"configJson":{},"noteMarkdown":null,"noteHtml":null,"sortOrder":90},{"id":"fa786d70-ff4e-4a99-9d81-acc8bff692c7","sectionKey":"faq_qa_schema","sectionType":"markdown_section","heading":"FAQPage and QAPage schema","introMarkdown":"FAQPage markup lost most of its search payoff in August 2023, when Google limited FAQ rich results to \"well-known, authoritative government and health websites\" [4]. For a typical business site the markup now produces no visible Google feature, so it belongs lower on the priority list than the marketing around it suggests. It has not become worthless: FAQ is one of the content labels Microsoft names in its guidance for AI answer inclusion [6]. The larger AEO value is the visible question-and-answer structure itself, because assistants that fetch your page read the rendered text, not the markup [12]. Write the Q&A for readers first and treat the FAQPage block as an inexpensive add-on.","introHtml":"FAQPage markup lost most of its search payoff in August 2023, when Google limited FAQ rich results to "well-known, authoritative government and health websites" [4]. For a typical business site the markup now produces no visible Google feature, so it belongs lower on the priority list than the marketing around it suggests. It has not become worthless: FAQ is one of the content labels Microsoft names in its guidance for AI answer inclusion [6]. The larger AEO value is the visible question-and-answer structure itself, because assistants that fetch your page read the rendered text, not the markup [12]. Write the Q&A for readers first and treat the FAQPage block as an inexpensive add-on.
\n","outroMarkdown":null,"outroHtml":null,"contentJson":{},"configJson":{},"noteMarkdown":null,"noteHtml":null,"sortOrder":100},{"id":"172a6f28-1692-48bc-9744-935998295778","sectionKey":"breadcrumb_structural_schema","sectionType":"markdown_section","heading":"Breadcrumb and other structural types","introMarkdown":"Breadcrumb markup describes where a page sits in your site hierarchy, and it stays on the list because it is supported in Google's feature gallery and costs almost nothing [2]. Most modern CMS platforms generate it automatically, along with basic WebSite and WebPage objects. The benefit is contextual: hierarchy markup helps a crawler understand that a pricing page belongs to a product section, which supports accurate snippets in the search layer Google's AI features draw from [2][3]. Verify your CMS output in a validator once, then leave it alone.","introHtml":"Breadcrumb markup describes where a page sits in your site hierarchy, and it stays on the list because it is supported in Google's feature gallery and costs almost nothing [2]. Most modern CMS platforms generate it automatically, along with basic WebSite and WebPage objects. The benefit is contextual: hierarchy markup helps a crawler understand that a pricing page belongs to a product section, which supports accurate snippets in the search layer Google's AI features draw from [2][3]. Verify your CMS output in a validator once, then leave it alone.
\n","outroMarkdown":null,"outroHtml":null,"contentJson":{},"configJson":{},"noteMarkdown":null,"noteHtml":null,"sortOrder":110},{"id":"c4b5567c-bc2d-40b5-b63b-91a141df1320","sectionKey":"implementation_guidance","sectionType":"markdown_section","heading":"How should you implement structured data for AEO?","introMarkdown":"Implementation is a half-day project for most sites if you follow the constraints the platforms actually publish.\n\n1. Use JSON-LD. Google recommends it over Microdata and RDFa because it lives in a script tag instead of being woven through your visible HTML [1].\n2. Describe only what a reader can see. Google's guidelines prohibit marking up information users cannot see on the page, even when it is accurate [1].\n3. Validate before shipping. Google's Rich Results Test previews which supported features your markup is eligible for [2].\n4. Keep decisive facts in server-rendered visible HTML. Live-fetch assistants read rendered text only, and in testing just one of them executed JavaScript [12]. A price or definition that exists only in markup is invisible to most assistants.\n5. Let the crawlers in. Allow OAI-SearchBot in robots.txt for ChatGPT search visibility [8] and PerplexityBot for Perplexity [9], since access is the gating requirement both platforms document.\n6. Push updates through IndexNow, which Bing recommends for signaling the fresh content its engineers say generative AI weighs heavily [7].\n\nThe markup itself serves the search-index engines that document reading it, while the visible-content and access rules serve the LLM engines that do not.","introHtml":"Implementation is a half-day project for most sites if you follow the constraints the platforms actually publish.
\nThe markup itself serves the search-index engines that document reading it, while the visible-content and access rules serve the LLM engines that do not.
\n","outroMarkdown":null,"outroHtml":null,"contentJson":{},"configJson":{},"noteMarkdown":null,"noteHtml":null,"sortOrder":120},{"id":"5221b8c4-1ff6-498f-a134-f2cb12db7d3e","sectionKey":"trade_offs","sectionType":"markdown_section","heading":"Trade-offs and open questions","introMarkdown":"The main trade-off is opportunity cost. Schema implementation competes for the same developer and content hours as producing answerable content, and the only controlled tests available found no citation lift from markup outside Google's surfaces [10][11]. A team that spends a sprint on exotic schema types instead of publishing well-structured answers has probably optimized the wrong layer.\n\nMarkup drift is the quiet operational risk. Schema that stops matching the visible page as content changes violates Google's guidelines [1], and stale prices or dates in markup hand wrong facts to the one platform family that definitely reads them.\n\nMeasurement is unstable enough that single-site results deserve suspicion. OtterlyAI attributed part of its own AI Overviews jump to unrelated algorithm shifts [11], and Ahrefs found a small, statistically significant AI Overviews decline among schema-adding pages that it does not present as causal [10]. Before-and-after citation numbers on one site rarely isolate the markup.\n\nThe evidence window is also early. Current tests can only observe the live-fetch phase of AI retrieval, and schema could be consumed invisibly during indexing or training [12]. Microsoft's public commitment suggests at least one major AI pipeline intends to rely on it more over time [6], so the cheap position is to maintain accurate core markup and re-evaluate as testing matures.","introHtml":"The main trade-off is opportunity cost. Schema implementation competes for the same developer and content hours as producing answerable content, and the only controlled tests available found no citation lift from markup outside Google's surfaces [10][11]. A team that spends a sprint on exotic schema types instead of publishing well-structured answers has probably optimized the wrong layer.
\nMarkup drift is the quiet operational risk. Schema that stops matching the visible page as content changes violates Google's guidelines [1], and stale prices or dates in markup hand wrong facts to the one platform family that definitely reads them.
\nMeasurement is unstable enough that single-site results deserve suspicion. OtterlyAI attributed part of its own AI Overviews jump to unrelated algorithm shifts [11], and Ahrefs found a small, statistically significant AI Overviews decline among schema-adding pages that it does not present as causal [10]. Before-and-after citation numbers on one site rarely isolate the markup.
\nThe evidence window is also early. Current tests can only observe the live-fetch phase of AI retrieval, and schema could be consumed invisibly during indexing or training [12]. Microsoft's public commitment suggests at least one major AI pipeline intends to rely on it more over time [6], so the cheap position is to maintain accurate core markup and re-evaluate as testing matures.
\n","outroMarkdown":null,"outroHtml":null,"contentJson":{},"configJson":{},"noteMarkdown":null,"noteHtml":null,"sortOrder":130},{"id":"cbb7fc82-14a0-44f4-a7da-207a5653b5af","sectionKey":"what_it_is_not","sectionType":"markdown_section","heading":"What structured data is not","introMarkdown":"### Not a requirement for AI visibility\n\nGoogle states directly that no special markup, machine-readable file, or schema.org addition is needed to appear in AI Overviews or AI Mode [3]. A vendor pitch framing schema as the price of entry to AI search contradicts the platform's own documentation.\n\n### Not something chatbots read at answer time\n\nWhen ChatGPT, Claude, or Perplexity fetches your page during a conversation, testing shows it processes the visible text and ignores JSON-LD, Microdata, and RDFa [12]. Facts you want quoted need to exist in readable prose.\n\n### Not a proven citation lever\n\nThe correlation between schema and AI citations is real, but the controlled test that isolated markup found effects indistinguishable from noise [10]. Cited pages tend to have schema because well-maintained sites have schema.\n\n### Not invisible metadata you can stuff\n\nGoogle's policies require markup to describe content users can actually see on the page [1], so schema cannot serve as a side channel for claims your page does not make.\n\n### Not obsolete\n\nGoogle still builds roughly 30 search features from it [2], Microsoft actively recommends it for AI answer inclusion [6], and adoption spans tens of millions of domains [5]. The case for schema has weakened as an AI citation tactic, not as web infrastructure.","introHtml":"Google states directly that no special markup, machine-readable file, or schema.org addition is needed to appear in AI Overviews or AI Mode [3]. A vendor pitch framing schema as the price of entry to AI search contradicts the platform's own documentation.
\nWhen ChatGPT, Claude, or Perplexity fetches your page during a conversation, testing shows it processes the visible text and ignores JSON-LD, Microdata, and RDFa [12]. Facts you want quoted need to exist in readable prose.
\nThe correlation between schema and AI citations is real, but the controlled test that isolated markup found effects indistinguishable from noise [10]. Cited pages tend to have schema because well-maintained sites have schema.
\nGoogle's policies require markup to describe content users can actually see on the page [1], so schema cannot serve as a side channel for claims your page does not make.
\nGoogle still builds roughly 30 search features from it [2], Microsoft actively recommends it for AI answer inclusion [6], and adoption spans tens of millions of domains [5]. The case for schema has weakened as an AI citation tactic, not as web infrastructure.
\n","outroMarkdown":null,"outroHtml":null,"contentJson":{},"configJson":{},"noteMarkdown":null,"noteHtml":null,"sortOrder":140},{"id":"feba9971-c179-4902-bad8-f6b756f18f2c","sectionKey":"contributor_perspective","sectionType":"markdown_section","heading":"How this answer was researched","introMarkdown":"This answer was produced as independent research with no commercial position on schema tooling, SEO platforms, or the answer engines discussed. Every platform document, engineering statement, and study cited here was fetched and verified live on August 9, 2026. The research deliberately separates what platforms publish in documentation, what named platform engineers have said on record, and what independent controlled testing has measured. Where those tiers disagree, the answer reports the disagreement rather than averaging it away, because the gap between vendor claims and tested behavior is the most useful thing a reader can take from this topic. Practitioners with relevant evidence are invited to contribute, particularly anyone holding before-and-after citation data from schema rollouts at scale. Qualified perspectives with disclosed affiliations strengthen records like this one.","introHtml":"This answer was produced as independent research with no commercial position on schema tooling, SEO platforms, or the answer engines discussed. Every platform document, engineering statement, and study cited here was fetched and verified live on August 9, 2026. The research deliberately separates what platforms publish in documentation, what named platform engineers have said on record, and what independent controlled testing has measured. Where those tiers disagree, the answer reports the disagreement rather than averaging it away, because the gap between vendor claims and tested behavior is the most useful thing a reader can take from this topic. Practitioners with relevant evidence are invited to contribute, particularly anyone holding before-and-after citation data from schema rollouts at scale. Qualified perspectives with disclosed affiliations strengthen records like this one.
\n","outroMarkdown":null,"outroHtml":null,"contentJson":{},"configJson":{},"noteMarkdown":"This answer was written and reviewed by the AnswerStack Editorial Team, which has no commercial stake in the products, companies, or methods discussed. Every claim is cited inline and verified on the dates shown.","noteHtml":"This answer was written and reviewed by the AnswerStack Editorial Team, which has no commercial stake in the products, companies, or methods discussed. Every claim is cited inline and verified on the dates shown.
\n","sortOrder":150}],"citations":[{"title":"Introduction to Structured Data","url":"https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data","excerpt":"Structured data is a standardized format for providing information about a page and classifying the page content.","quoteText":null,"sourceRole":"PRIMARY","verifiedAt":"2026-08-09T00:00:00","supportsText":"Definition of structured data; Google's use of it for rich results and entity understanding; JSON-LD as recommended format; requirement that markup match visible content; Rotten Tomatoes 25% CTR and Food Network 35% visit case studies","domain":"developers.google.com","publisherName":"Google"},{"title":"Structured data markup that Google Search supports","url":"https://developers.google.com/search/docs/appearance/structured-data/search-gallery","excerpt":null,"quoteText":null,"sourceRole":"PRIMARY","verifiedAt":"2026-08-09T00:00:00","supportsText":"The roughly 30 currently supported structured data features, including Article, Product, Organization, Breadcrumb, Event, and Review snippet; absence of FAQ and HowTo from the current gallery; Rich Results Test as the validation tool","domain":"developers.google.com","publisherName":"Google Search Central"},{"title":"AI features and your website","url":"https://developers.google.com/search/docs/appearance/ai-features","excerpt":"There's also no special schema.org structured data that you need to add.","quoteText":null,"sourceRole":"PRIMARY","verifiedAt":"2026-08-09T00:00:00","supportsText":"No special schema.org structured data or new markup is needed for AI Overviews or AI Mode; eligibility requires only an indexed, snippet-eligible page with no additional technical requirements; snippet controls and Google-Extended govern AI use of content","domain":"developers.google.com","publisherName":"Google Search Central"},{"title":"Changes to HowTo and FAQ rich results","url":"https://developers.google.com/search/blog/2023/08/howto-faq-changes","excerpt":"well-known, authoritative government and health websites","quoteText":null,"sourceRole":"PRIMARY","verifiedAt":"2026-08-09T00:00:00","supportsText":"August 2023 restriction of FAQ rich results to well-known, authoritative government and health websites and the wind-down of HowTo rich results","domain":"developers.google.com","publisherName":"Google Search Central Blog"},{"title":"Schema.org","url":"https://schema.org/","excerpt":"As of 2024, over 45 million web domains markup their web pages with over 450 billion Schema.org objects.","quoteText":null,"sourceRole":"PRIMARY","verifiedAt":"2026-08-09T00:00:00","supportsText":"Schema.org's founding by Google, Microsoft, Yahoo, and Yandex; adoption figures; its role as the shared structured data vocabulary","domain":"schema.org","publisherName":"Schema.org Community Group"},{"title":"Optimizing your content for inclusion in AI search answers","url":"https://about.ads.microsoft.com/en/blog/post/october-2025/optimizing-your-content-for-inclusion-in-ai-search-answers","excerpt":"Schema is a type of code that helps search engines and AI systems understand your content.","quoteText":null,"sourceRole":"PRIMARY","verifiedAt":"2026-08-09T00:00:00","supportsText":"Microsoft's official guidance that schema helps search engines and AI systems understand content; the product, review, FAQ, and event labels; schema turning plain text into machine-interpretable structured data","domain":"about.ads.microsoft.com","publisherName":"Microsoft Advertising (Bing)"},{"title":"Microsoft Bing/Copilot use schema for its LLMs","url":"https://searchengineland.com/microsoft-bing-copilot-use-schema-for-its-llms-453455","excerpt":"Gen AIs value fresh content in particular, partly as a reference check of their LLM training data.","quoteText":null,"sourceRole":"INDEPENDENT","verifiedAt":"2026-08-09T00:00:00","supportsText":"Fabrice Canel's SMX Munich (March 2025) confirmation that schema markup helps Microsoft's LLMs, including Copilot, understand content; his statement on generative AI valuing fresh content; the IndexNow recommendation","domain":"searchengineland.com","publisherName":"Search Engine Land"},{"title":"OpenAI crawlers (GPTBot, OAI-SearchBot, ChatGPT-User)","url":"https://developers.openai.com/api/docs/bots","excerpt":"Sites that are opted out of OAI-SearchBot will not be shown in ChatGPT search answers.","quoteText":null,"sourceRole":"PRIMARY","verifiedAt":"2026-08-09T00:00:00","supportsText":"OpenAI's three crawlers (OAI-SearchBot, GPTBot, ChatGPT-User); robots.txt access as the stated requirement for ChatGPT search visibility; complete absence of structured data or schema guidance","domain":"developers.openai.com","publisherName":"OpenAI"},{"title":"Perplexity crawlers (PerplexityBot, Perplexity-User)","url":"https://docs.perplexity.ai/guides/bots","excerpt":"not used to crawl content for AI foundation models","quoteText":null,"sourceRole":"PRIMARY","verifiedAt":"2026-08-09T00:00:00","supportsText":"PerplexityBot's role in surfacing sites in search results; Perplexity-User's on-demand fetching; absence of any structured data or schema markup guidance","domain":"docs.perplexity.ai","publisherName":"Perplexity"},{"title":"We Tracked 1,885 Pages Adding Schema. AI Citations Barely Moved.","url":"https://ahrefs.com/blog/schema-ai-citations/","excerpt":"Schema could be doing real work, but it could also just be riding the wave of every other signal.","quoteText":null,"sourceRole":"INDEPENDENT","verifiedAt":"2026-08-09T00:00:00","supportsText":"Controlled study of 1,885 pages adding JSON-LD versus 4,000 control pages; citation changes of -4.6% (AI Overviews), +2.4% (AI Mode), +2.2% (ChatGPT); the 3x JSON-LD correlation among cited pages and its interpretation as a site-quality proxy","domain":"ahrefs.com","publisherName":"Ahrefs"},{"title":"Schema Markup's Real Impact on AI Search","url":"https://otterly.ai/blog/schema-markup-real-impact-ai-search/","excerpt":"Schema was invented for algorithms that couldn't handle unstructured text. AI Search is built to do exactly that.","quoteText":null,"sourceRole":"INDEPENDENT","verifiedAt":"2026-08-09T00:00:00","supportsText":"Three-month self-experiment adding five schema types; Google AI Mode +42% and AI Overviews gains partly attributed to algorithm shifts; visibility declines on ChatGPT, Copilot, and Gemini; no change on Perplexity","domain":"otterly.ai","publisherName":"OtterlyAI"},{"title":"Schema Markup and AI in 2025: What ChatGPT, Claude, Perplexity & Gemini Really See","url":"https://www.searchviu.com/en/schema-markup-and-ai-in-2025-what-chatgpt-claude-perplexity-gemini-really-see/","excerpt":"JSON-LD Schema is NOT read by AI chatbots during direct fetch","quoteText":null,"sourceRole":"INDEPENDENT","verifiedAt":"2026-08-09T00:00:00","supportsText":"October 2025 retrieval test planting facts in eight formats; no assistant extracted JSON-LD-only facts during live fetch; Claude 0/8, ChatGPT 3/8, Gemini 4/8 with Gemini alone rendering JavaScript; caveat that schema could be used in earlier pipeline phases","domain":"searchviu.com","publisherName":"searchVIU"}],"revisions":[],"relatedAnswers":[{"id":"de717b06-01be-4de5-9285-bec6bebad067","slug":"biggest-aeo-mistakes-to-avoid","question":"What are the biggest AEO mistakes to avoid?","publishedAt":"2026-08-28T14:15:04.474","confidenceScore":87,"confidenceLabel":"High","industry":{"id":"70ea3802-1fc6-4fd7-a505-140d38d1c74a","slug":"digital-marketing","label":"Digital Marketing","description":"SEO, content, demand gen, and growth marketing"},"topic":{"slug":"answer-engine-optimization","label":"Answer Engine Optimization","description":"How B2B teams get content selected and cited by AI answer engines.","schemaKind":null},"contributor":{"id":"ec39deab-44fe-48d8-9029-fefe993ab85a","slug":"answer-stack","displayName":"AnswerStack","websiteUrl":null},"snippet":"Seven high-cost AEO mistakes, each with the evidence for why it hurts and the specific correction: chasing referral volume instead of buyer fit, blocking AI crawlers by accident at the robots.txt or CDN layer, mass-producing thin optimized pages, ignoring the third-party sources AI answers actually cite, running AEO apart from crawlability and indexing, publishing with no measurement baseline, and treating a page that earned a citation as permanently cited.","url":"/q/biggest-aeo-mistakes-to-avoid"},{"id":"e99a45a2-0ef5-45e5-bec8-f6d813825f8c","slug":"what-offsite-signals-matter-for-aeo","question":"What off-site signals matter for AEO?","publishedAt":"2026-08-26T14:15:07.644","confidenceScore":76,"confidenceLabel":"Medium","industry":{"id":"70ea3802-1fc6-4fd7-a505-140d38d1c74a","slug":"digital-marketing","label":"Digital Marketing","description":"SEO, content, demand gen, and growth marketing"},"topic":{"slug":"answer-engine-optimization","label":"Answer Engine Optimization","description":"How B2B teams get content selected and cited by AI answer engines.","schemaKind":null},"contributor":{"id":"ec39deab-44fe-48d8-9029-fefe993ab85a","slug":"answer-stack","displayName":"AnswerStack","websiteUrl":null},"snippet":"Branded mentions, review corpus, third-party comparisons, community threads, earned media, and entity consistency are the six off-site signals with published evidence behind them. The quality of that evidence is uneven: a little is documented platform behavior, some is buyer survey data, and most is correlation from companies selling AI visibility tools. This breaks down what each signal is actually supported by, how long it takes to move, and the order most teams should work through them.","url":"/q/what-offsite-signals-matter-for-aeo"},{"id":"051baa33-8c61-42b3-8763-ea8a0267f827","slug":"what-makes-ai-engines-recommend-a-brand","question":"What makes AI engines recommend one brand over another?","publishedAt":"2026-08-24T14:15:05.494","confidenceScore":82,"confidenceLabel":"High","industry":{"id":"70ea3802-1fc6-4fd7-a505-140d38d1c74a","slug":"digital-marketing","label":"Digital Marketing","description":"SEO, content, demand gen, and growth marketing"},"topic":{"slug":"answer-engine-optimization","label":"Answer Engine Optimization","description":"How B2B teams get content selected and cited by AI answer engines.","schemaKind":null},"contributor":{"id":"ec39deab-44fe-48d8-9029-fefe993ab85a","slug":"answer-stack","displayName":"AnswerStack","websiteUrl":null},"snippet":"A cross-platform evidence map of why ChatGPT, Gemini, Perplexity, and Google's AI answers surface one brand instead of another. It separates what platforms actually document (index eligibility, crawler access, organic shopping results) from what correlational studies suggest (mention volume, listicle presence, reviews, rankings, freshness), with effect sizes drawn from studies covering 680 million citations, 75,000 brands, and 1.4 million prompts, plus the volatility data showing why these drivers shift month to month.","url":"/q/what-makes-ai-engines-recommend-a-brand"},{"id":"027226b0-7f49-42e5-924e-5a870eef73d9","slug":"how-far-aeo-extends-into-buyer-journey","question":"How far does AEO extend into the buyer journey?","publishedAt":"2026-08-21T14:15:05.932","confidenceScore":85,"confidenceLabel":"High","industry":{"id":"70ea3802-1fc6-4fd7-a505-140d38d1c74a","slug":"digital-marketing","label":"Digital Marketing","description":"SEO, content, demand gen, and growth marketing"},"topic":{"slug":"answer-engine-optimization","label":"Answer Engine Optimization","description":"How B2B teams get content selected and cited by AI answer engines.","schemaKind":null},"contributor":{"id":"ec39deab-44fe-48d8-9029-fefe993ab85a","slug":"answer-stack","displayName":"AnswerStack","websiteUrl":null},"snippet":"AI assistants now answer questions at every stage of a purchase, from problem framing to checkout and returns. This answer maps the evidence stage by stage: what buyers ask assistants at each point, which sources answer engines actually cite as the journey progresses, and the content each stage requires, using verified 2025-2026 data from Forrester, Bain, G2, Salesforce, Semrush, Ahrefs, xfunnel, and platform documentation from Google, OpenAI, and Stripe.","url":"/q/how-far-aeo-extends-into-buyer-journey"}],"contributorStats":{"verifiedAnswers":269,"openDisputes":0},"schemaJson":{"@context":"https://schema.org","@type":"Question","name":"What role does structured data play in answer engine optimization?","text":"What role does structured data play in answer engine optimization?","url":"https://www.answerstack.io/q/structured-data-role-in-aeo","answerCount":1,"datePublished":"2026-08-09T21:03:29.347","author":{"@type":"Person","name":"AnswerStack Editorial Team","worksFor":{"@type":"Organization","name":"AnswerStack"},"url":"https://www.answerstack.io/contributors/answer-stack"},"about":[{"@type":"Thing","name":"Answer Engine Optimization"},{"@type":"Thing","name":"Digital Marketing"}],"acceptedAnswer":{"@type":"Answer","text":"Structured data plays a narrower role in answer engine optimization than most checklists claim: it is well documented as an input to Google's and Microsoft's search infrastructure, and essentially undocumented as an input to LLM-based engines like ChatGPT and Perplexity [8][9]. Google states that no special schema.org markup is needed to appear in AI Overviews or AI Mode; a page only needs to be indexed and eligible to show a snippet [3]. Microsoft is the clearest advocate, saying schema helps AI systems understand content and confirming that the LLMs behind Copilot consume it [6][7]. Controlled testing supports the skeptical reading elsewhere: Ahrefs tracked 1,885 pages that added JSON-LD and found citation changes indistinguishable from noise across AI Overviews, AI Mode, and ChatGPT [10], while live-retrieval tests show AI assistants reading visible HTML and ignoring JSON-LD when they fetch a page [12]. Structured data still earns its implementation cost through rich results, entity clarity, and Bing's AI pipeline, so the accurate framing is low-cost foundation rather than citation lever [1][6].","url":"https://www.answerstack.io/q/structured-data-role-in-aeo","upvoteCount":0,"datePublished":"2026-08-09T21:03:29.347","dateModified":"2026-08-09T00:00:00","author":{"@type":"Person","name":"AnswerStack Editorial Team","worksFor":{"@type":"Organization","name":"AnswerStack"},"url":"https://www.answerstack.io/contributors/answer-stack"},"citation":[{"@type":"CreativeWork","name":"Introduction to Structured Data","url":"https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data"},{"@type":"CreativeWork","name":"Structured data markup that Google Search supports","url":"https://developers.google.com/search/docs/appearance/structured-data/search-gallery"},{"@type":"CreativeWork","name":"AI features and your website","url":"https://developers.google.com/search/docs/appearance/ai-features"},{"@type":"CreativeWork","name":"Changes to HowTo and FAQ rich results","url":"https://developers.google.com/search/blog/2023/08/howto-faq-changes"},{"@type":"CreativeWork","name":"Schema.org","url":"https://schema.org/"},{"@type":"CreativeWork","name":"Optimizing your content for inclusion in AI search answers","url":"https://about.ads.microsoft.com/en/blog/post/october-2025/optimizing-your-content-for-inclusion-in-ai-search-answers"},{"@type":"CreativeWork","name":"Microsoft Bing/Copilot use schema for its LLMs","url":"https://searchengineland.com/microsoft-bing-copilot-use-schema-for-its-llms-453455"},{"@type":"CreativeWork","name":"OpenAI crawlers (GPTBot, OAI-SearchBot, ChatGPT-User)","url":"https://developers.openai.com/api/docs/bots"},{"@type":"CreativeWork","name":"Perplexity crawlers (PerplexityBot, Perplexity-User)","url":"https://docs.perplexity.ai/guides/bots"},{"@type":"CreativeWork","name":"We Tracked 1,885 Pages Adding Schema. AI Citations Barely Moved.","url":"https://ahrefs.com/blog/schema-ai-citations/"},{"@type":"CreativeWork","name":"Schema Markup's Real Impact on AI Search","url":"https://otterly.ai/blog/schema-markup-real-impact-ai-search/"},{"@type":"CreativeWork","name":"Schema Markup and AI in 2025: What ChatGPT, Claude, Perplexity & Gemini Really See","url":"https://www.searchviu.com/en/schema-markup-and-ai-in-2025-what-chatgpt-claude-perplexity-gemini-really-see/"}]}}}