[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"blog-schema-doesnt-boost-ai-citations":3,"latest-blogs-home":110},{"message":4,"data":5},"Blogs retrieved successfully",{"blog":6,"latest_blogs":29},{"id":7,"author_id":8,"title":9,"slug":10,"content":11,"short_summary":12,"featured_image":13,"status":14,"meta_title":9,"meta_description":15,"canonical_url":16,"keywords":16,"blog_type":17,"is_featured":18,"word_count":19,"published_at":20,"created_at":21,"updated_at":22,"deleted_at":16,"author":23,"categories":28},348,9,"Adding Schema Doesn’t Boost AI Citations","schema-doesnt-boost-ai-citations","\u003Ch1>\u003Cspan style=\"color: rgb(0, 0, 0);\">Adding Schema Doesn’t Boost AI Citations\u003C\u002Fspan>\u003C\u002Fh1>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Ahrefs, the SEO and AI search analytics platform, published a study on May 11 that tested one of the more popular assumptions in AI visibility advice: that adding schema markup to a page will get it cited more often in AI responses. The study tracked 1,885 pages that added JSON-LD schema between August 2025 and March 2026, matched them against 4,000 control pages with similar citation history, and measured what happened to AI citations on Google AI Overview, AI Mode, and ChatGPT in the 30 days before and after schema was added. Adding schema produced no meaningful citation boost on any of the three platforms.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">That finding cuts against a lot of marketing content from the past 18 months that has positioned schema as an AI visibility lever. The argument usually starts with a correlation: pages cited by AI are much more likely to have schema markup than pages that aren’t. The Ahrefs data confirms the correlation. What it doesn’t confirm is the cause-and-effect story people have been telling about it.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">The correlation everyone has been quoting\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Before the study, the team at Ahrefs analyzed six million URLs and found that schema markup is much more common on AI-cited pages than on pages that aren’t cited. Among non-cited pages, only 18.5% had JSON-LD schema. Among AI-cited pages, 53.6% had it for reference citations and 71.7% had it for inline citations. AI-cited pages were almost three times more likely to have schema than non-cited ones.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">That gap is the kind of stat that gets shared in conference slides and LinkedIn carousels as evidence that schema is the unlock for AI visibility. But correlation and causation are two different things, and a difference this clean usually has a confounding explanation. Schema markup tends to live on better-maintained, more technically sophisticated sites. Those same sites publish stronger content, build more authority, earn more links, and do all the other things that get pages cited. So the team designed an experiment to find out whether schema specifically was doing the work, or whether it was just along for the ride.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">The study Ahrefs ran on 1,885 pages\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The team built a dataset of 1,885 pages that added JSON-LD schema between August 2025 and March 2026. They identified the exact date schema went live on each page by combing through historical crawl data and spotting the first day the page had a JSON-LD script tag.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">For each of those treated pages, they then picked three control pages from different domains with similar pre-period citation levels, none of which had added schema during the same window. The matched pairs let them compare apples to apples: a page that added schema versus a similar page that didn’t, both starting from a comparable baseline.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The reason for the matched comparison comes down to how noisy AI citation data has been over the past year. AI Overview citations were contracting during the study period while AI Mode citations were expanding. If the team had just compared each page to itself before and after schema, they would have been measuring the platform-wide trend, not the effect of schema. The matched comparison let them strip out those platform swings and isolate what adding schema actually did.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">The numbers across three AI platforms\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">After running the matched comparison across the full dataset, the results came out like this:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">•\u003C\u002Fspan>\u003Cspan style=\"font-size: 7pt; color: rgb(0, 0, 0); font-family: "Times New Roman";\">             \u003C\u002Fspan>\u003Cspan style=\"color: rgb(0, 0, 0);\">\u003Cstrong>Google AI Overview\u003C\u002Fstrong>: citations on treated pages fell 4.6% relative to control pages. The decline was small but statistically significant.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">•\u003C\u002Fspan>\u003Cspan style=\"font-size: 7pt; color: rgb(0, 0, 0); font-family: "Times New Roman";\">             \u003C\u002Fspan>\u003Cspan style=\"color: rgb(0, 0, 0);\">\u003Cstrong>Google AI Mode\u003C\u002Fstrong>: citations on treated pages rose 2.4% relative to control pages. The result is statistically indistinguishable from zero.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">•\u003C\u002Fspan>\u003Cspan style=\"font-size: 7pt; color: rgb(0, 0, 0); font-family: "Times New Roman";\">             \u003C\u002Fspan>\u003Cspan style=\"color: rgb(0, 0, 0);\">\u003Cstrong>ChatGPT\u003C\u002Fstrong>: citations on treated pages rose 0.8% relative to control pages. Also indistinguishable from zero.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Two of the three numbers are essentially noise. The AI Overview decline is real in the sense that it’s unlikely to be random chance, but the absolute size is small (an average loss of around 12 daily citations on pages that were already getting hundreds), and the team can’t cleanly attribute the gap to schema itself. Treated and control pages were both already on a downward trajectory in AI Overview citations during the study window, which suggests something else (a Google update, content staleness, AI Overview pulling back from certain content types) may explain part of the decline.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">To make sure the conclusion held up, the team ran the test four different ways: a basic t-test, a difference-in-differences analysis, an event study tracking week-by-week trends, and a symmetric-window version of the DiD analysis. All four pointed in the same direction. No citation growth in AI Mode, no growth in ChatGPT, and a small AI Overview decline that’s real but can’t be definitively pinned on schema.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">The small AI Overview decline that nobody can explain\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The 4.6% AI Overview decline deserves a closer look, since it’s the one finding that isn’t statistical noise. The team flagged it as real but unexplained. A few possible explanations:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The pages in the study were already getting heavy AI Overview citations going in. Every page in the dataset had over 100 AI Overview citations in February 2025 before any schema was added. Pages that high in the citation pool tend to be the ones AI Overview is actively reviewing and refreshing, which means small changes to those pages get noticed. It’s possible that adding schema triggered some kind of re-evaluation that pulled some of these pages slightly out of favor.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">It’s also possible the decline reflects content-type patterns that happen to correlate with the kind of sites that add schema during the study period. A more granular follow-up looking at which schema types and which content categories drove the decline could clarify the question.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">For now, the only thing the team can say with confidence is that the decline is real, small, and not the kind of result anyone betting on schema as an AI visibility lever would have predicted.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">Where schema might still play a role\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The study has one important caveat that anyone reading the headline should keep in mind. Every page in the dataset was already being cited heavily by AI before any schema was added. These were pages already inside the consideration set, being crawled and surfaced by language models. The study tested whether adding schema pushed those pages higher. It did not test whether schema helps pages that aren’t being cited at all get into the consideration set in the first place.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">For pages that aren’t being seen by AI search, schema markup might still help with crawling, parsing, or indexing. A separate experiment from searchVIU tested whether five major AI systems (ChatGPT, Claude, Perplexity, Gemini, and Google AI Mode) actually parsed schema markup when fetching a page in real-time. None of them did. All five extracted only visible HTML content during direct retrieval, ignoring JSON-LD, hidden Microdata, and hidden RDFa.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">That doesn’t rule out schema playing a role in earlier stages of the pipeline (the crawl, the index, the entity recognition layer that decides what a page is about before any user prompt comes in). But it does suggest that at the moment of retrieval, when an AI system pulls content to compose a response, the content that matters is the content humans can see on the page.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">What the data suggests actually drives AI citations\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Schema being three times more common on AI-cited pages was the original observation that kicked off the schema-helps-AI-visibility theory. The study confirms the correlation and dismantles the causal claim built on top of it. So the question becomes: why are 53% of AI-cited pages running schema if schema isn’t what’s getting them cited?\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The Ahrefs analysis offers the answer directly. Sites that add structured data tend to also invest in technical SEO, publish authoritative content, build links, maintain their pages, and rank well in regular search. AI systems are more likely to retrieve and cite that kind of content. Schema and citation eligibility share a common cause, which is the broader investment in content quality and authority that makes a page citation-worthy in the first place.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">For brands trying to build AI visibility, the takeaway is to focus the investment on what causes citations rather than what correlates with them. \u003C\u002Fspan>\u003Ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"text-primary-blue-600 hover:underline\" href=\"https:\u002F\u002Fnobsmarketplace.com\u002Flink-building\">\u003Cspan style=\"color: rgb(0, 0, 0);\">Link building\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan style=\"color: rgb(0, 0, 0);\"> and \u003C\u002Fspan>\u003Ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"text-primary-blue-600 hover:underline\" href=\"https:\u002F\u002Fnobsmarketplace.com\u002Fdigital-pr\">\u003Cspan style=\"color: rgb(0, 0, 0);\">digital PR\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan style=\"color: rgb(0, 0, 0);\"> build the third-party authority signals that AI retrieval systems use to decide which pages to trust. \u003C\u002Fspan>\u003Ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"text-primary-blue-600 hover:underline\" href=\"https:\u002F\u002Fnobsmarketplace.com\u002Fguest-posting\">\u003Cspan style=\"color: rgb(0, 0, 0);\">Guest posting\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan style=\"color: rgb(0, 0, 0);\"> on credible domains and \u003C\u002Fspan>\u003Ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"text-primary-blue-600 hover:underline\" href=\"https:\u002F\u002Fnobsmarketplace.com\u002Flink-insertion\">\u003Cspan style=\"color: rgb(0, 0, 0);\">link insertions\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan style=\"color: rgb(0, 0, 0);\"> into authoritative content put a brand inside the citation pool through pages that retrieval systems already trust.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Schema can still earn its place on a page for other reasons. Rich results in regular search (where they still apply), voice assistant compatibility, knowledge graph contributions, and downstream entity recognition all benefit from structured data. But if the only reason for adding JSON-LD was to get more AI citations on pages already being indexed, the Ahrefs data doesn’t support that as a working theory.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The simpler story is the one the study lands on: pages that get cited tend to be pages whose owners do a lot of things right, and schema is one of those things, alongside everything else that actually moves the needle. Adding the marker without the underlying work isn’t going to get a page over the line.\u003C\u002Fspan>\u003C\u002Fp>","Ahrefs ran a difference-in-differences study on 1,885 pages that added JSON-LD schema between August 2025 and March 2026. Adding schema produced no meaningful boost in AI citations on Google AI Overview, AI Mode, or ChatGPT, despite schema being three times more common on AI-cited pages overall.","https:\u002F\u002Fwebsite-cdn.nobsmarketplace.com\u002Fuploads\u002Ffeatured-images\u002Fbottle-20260513133606-WOq2SzvQ.png","published","Ahrefs tracked 1,885 pages adding schema markup over eight months. The result: no meaningful citation gains on AI Overview, AI Mode, or ChatGPT. (",null,"blog",true,1504,"2026-05-13T13:14:52.000000Z","2026-05-13T13:36:11.000000Z","2026-05-13T13:39:00.000000Z",{"id":8,"name":24,"email":25,"about":16,"avatar":26,"created_at":27,"updated_at":16,"deleted_at":16},"Rasit Cakir","rasit@nobsmarketplace.com","https:\u002F\u002Fwebsite-cdn.nobsmarketplace.com\u002Frasit.webp","2026-01-26T11:10:22.000000Z",[],[30,33,72,94],{"id":7,"author_id":8,"title":9,"slug":10,"content":11,"short_summary":12,"featured_image":13,"status":14,"meta_title":9,"meta_description":15,"canonical_url":16,"keywords":16,"blog_type":17,"is_featured":18,"word_count":19,"published_at":20,"created_at":21,"updated_at":22,"deleted_at":16,"author":31,"categories":32},{"id":8,"name":24,"email":25,"about":16,"avatar":26,"created_at":27,"updated_at":16,"deleted_at":16},[],{"id":34,"author_id":35,"title":36,"slug":37,"content":38,"short_summary":39,"featured_image":40,"status":14,"meta_title":36,"meta_description":41,"canonical_url":16,"keywords":16,"blog_type":17,"is_featured":18,"word_count":42,"published_at":43,"created_at":44,"updated_at":44,"deleted_at":16,"author":45,"categories":51},347,3,"Even in AI Search, You Still Need Local Visibility","local-seo-ai-search","\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">\u003Cem>This post is an updated version of our post on the importance of\u003C\u002Fem>\u003C\u002Fspan>\u003Ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"text-primary-blue-600 hover:underline\" href=\"https:\u002F\u002Fnobsmarketplace.com\u002Fblog\u002Fwhy-is-local-link-building-important\">\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">\u003Cem> \u003C\u002Fem>\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; color: rgb(17, 85, 204); font-family: Arial, sans-serif;\">\u003Cem>\u003Cu>local link building\u003C\u002Fu>\u003C\u002Fem>\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">\u003Cem> that was published in 2022. Nearly four years later, AI has overhauled the SEO playbook down to the tiniest detail. This updated post will explain how to do local link building for AI search.\u003C\u002Fem>\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Search engines let people look for anything, anywhere, anytime. If anything, their invention made the world a smaller place because they can retrieve information from all corners of the known Web in less than a second. That’s how stories and brands from one part of the world reach the other and influence public opinion.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">But for the majority of users, they’re content with finding the closest clinic or the best eats in the area. Google knew this beforehand, having introduced the platforms that would later become Google Business Profile (GBP) and Local Services Ads (LSAs) in the early 2000s.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">With their inception came local search—and eventually, local SEO.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Fast forward to 2026, and local search is as strong as ever amid AI revolutionizing search. After all, the need to find the nearest establishment never goes away. However, it’s worth understanding that the rules of local SEO have changed.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"font-size: 1.5em; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">The Anatomy of AI-Powered Local Search\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">For our example, I asked Google to find the best tour packages to Hawaii.\u003C\u002Fspan>\u003C\u002Fp>\u003Cfigure data-type=\"image\" data-align=\"center\" style=\"display: inline-block; max-width: 100%; margin-left: auto; margin-right: auto;\">\u003Cimg class=\"max-w-full h-auto rounded-lg\" src=\"https:\u002F\u002Fwebsite-cdn.nobsmarketplace.com\u002Fuploads\u002Fblog-images\u002Ffireshot-capture-098-best-tour-packages-to-hawaii-google-search-wwwgooglecom-20260512082753-pu1B6gOU.png\" data-align=\"center\" style=\"display: block; margin-left: auto; margin-right: auto;\">\u003C\u002Ffigure>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">There’s more than just tour packages here. The AI also included other information such as the best time to visit, each island’s specialties, and the ideal travel duration. Technically, I didn’t ask for this information, but it helps plan my trip.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">It boils down to a concrete understanding of search intent. Natural language processing (NLP) enables AI models to learn and recognize the user’s objectives behind their search query. The AI determined that the additional information is helpful based on past similar searches, something that would’ve been hard to achieve with blue links alone.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">I’ll spare you the long, overly technical lecture on how AI does this since that isn’t the focus of this post. One thing you do need to know is the different types of search intent. They still work the same way for AI as they did in the age of traditional search.\u003C\u002Fspan>\u003C\u002Fp>\u003Cfigure data-type=\"image\" data-align=\"center\" style=\"display: inline-block; max-width: 100%; margin-left: auto; margin-right: auto;\">\u003Cimg class=\"max-w-full h-auto rounded-lg\" src=\"https:\u002F\u002Fwebsite-cdn.nobsmarketplace.com\u002Fuploads\u002Fblog-images\u002Fpicture28-20260512082856-dMxpecqp.png\" data-align=\"center\" style=\"display: block; margin-left: auto; margin-right: auto;\">\u003C\u002Ffigure>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Some queries carry more than one type of intent, called \u003Cem>mixed intent\u003C\u002Fem>. For example, “what are good laptops under $1,000” can be both informational and commercial.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Traditional SEO doctrine stresses the importance of making content that matches search intent, and that’s still the case for AI. The difference is that the latter is smarter in retrieving relevant content. It doesn’t care if the content is not in the top 10, as long as it’s relevant.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"font-size: 1.5em; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Why Local SEO is More Important Than Ever\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">The reasons for local SEO’s growth in importance are more or less the same as for SEO in general. To start with, AI summaries occupy the most strategic part of the search results page: the top. Professionals refer to this as “position zero.”\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Why zero? Because position one is the topmost blue link.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">If there’s one thing any expert has learned over the years doing SEO, it’s that people want answers \u003Cem>now \u003C\u002Fem>rather than later. That’s why in traditional SEO, the top result gets the most clicks. But with AI, many will be content with just reading the summary it generates.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Don’t take it from me; the latest numbers all point to this reality. A study by Whitespark in Q2 2025 looked at over 500 queries from three major U.S. cities and found that 7 out of 10 local queries returned AI Overviews. The difference between AI Overviews and local packs is more dramatic when looking at it by location and keyword.\u003C\u002Fspan>\u003C\u002Fp>\u003Cfigure data-type=\"image\" data-align=\"left\" style=\"display: inline-block; max-width: 100%; margin-left: 0px; margin-right: auto;\">\u003Cimg class=\"max-w-full h-auto rounded-lg\" src=\"https:\u002F\u002Fwebsite-cdn.nobsmarketplace.com\u002Fuploads\u002Fblog-images\u002Fpicture29-20260512083043-oJfP9DDw.png\" data-align=\"left\">\u003C\u002Ffigure>\u003Cp style=\"text-align: center;\">\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">\u003Cem>Data source:\u003C\u002Fem>\u003C\u002Fspan>\u003Ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"text-primary-blue-600 hover:underline\" href=\"https:\u002F\u002Fwhitespark.ca\u002Fblog\u002Fcase-study-the-prevalence-of-ai-overviews-in-local-search\u002F\">\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">\u003Cem> \u003C\u002Fem>\u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; color: rgb(17, 85, 204); font-family: Arial, sans-serif;\">\u003Cem>\u003Cu>Whitespark\u003C\u002Fu>\u003C\u002Fem>\u003C\u002Fspan>\u003C\u002Fa>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Does this mean local packs are on their way out? Not necessarily, but more on that later.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Of the AI Overview results, Whitespark reported that 60% retrieved information from third-party publishers. These include social media platforms like Reddit and Quora, as well as niche sites like HomeGuide, Yelp, and ZipRecruiter. This means a business can’t rely on posting content on its website alone for maximizing AI visibility.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">The rise of smartphones is also a strong case for considering, if not prioritizing, local link building. With these devices enabling Internet access anywhere, people can look for any nearby restaurant or store onsite.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">I’m talking about the well-known “near me” searches. Once taken too literally by search engines (i.e., looking for a business with “near me” in its name), they now understand the intent behind the key phrase.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Miriam Ellis, an independent SEO consultant who served as a local SEO expert for Moz, wrote in her\u003C\u002Fspan>\u003Ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"text-primary-blue-600 hover:underline\" href=\"https:\u002F\u002Fsearchengineland.com\u002Fguide\u002Fnear-me-search-optimization\">\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\"> \u003C\u002Fspan>\u003Cspan style=\"font-size: 11pt; color: rgb(17, 85, 204); font-family: Arial, sans-serif;\">\u003Cem>\u003Cu>Search Engine Land \u003C\u002Fu>\u003C\u002Fem>\u003Cu>article\u003C\u002Fu>\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">:\u003C\u002Fspan>\u003C\u002Fp>\u003Cblockquote>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">\u003Cem>While a complete local search marketing campaign will strive to offer content that matches all intent phases of a customer journey, it’s that final phase (transactional intent) that has the highest conversion potential because people using the “near me” modifier are on the verge of choosing a local business for fulfillment.\u003C\u002Fem>\u003C\u002Fspan>\u003C\u002Fp>\u003C\u002Fblockquote>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">If I were touring an unfamiliar city and suddenly craving pasta, you bet I’d fire up Google on my phone and search for “Italian restaurants near me.” And if you run one such restaurant, you'd better hope that you’re nearby and popular enough to be most people’s first choice.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"font-size: 1.5em; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Steps to Local Link Building Success\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Despite AI bringing sweeping changes to SEO, the techniques for local link building remain more or less the same. One reason is that AI models struggle with retrieving local results, especially for “near me” queries. Some of these include:\u003C\u002Fspan>\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">AI summaries lacking pinpoint map locations\u003C\u002Fspan>\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">AI citing establishments that aren’t nearby\u003C\u002Fspan>\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">AI being unable to detect the user’s location\u003C\u002Fspan>\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Ful>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">But more importantly, it’s how AI in search works. Just as AI generates summaries based on search results, AI summaries for local search still rely on local packs. Returning to the Whitespark study, it shows a major disparity between AI Overviews and local pack results.\u003C\u002Fspan>\u003C\u002Fp>\u003Cfigure data-type=\"image\" data-align=\"left\" style=\"display: inline-block; max-width: 100%; margin-left: 0px; margin-right: auto;\">\u003Cimg class=\"max-w-full h-auto rounded-lg\" src=\"https:\u002F\u002Fwebsite-cdn.nobsmarketplace.com\u002Fuploads\u002Fblog-images\u002F3-1-1024x576-20260512083118-tzRKnb6Z.jpg\" data-align=\"left\">\u003C\u002Ffigure>\u003Cp style=\"text-align: center;\">\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">\u003Cem>AI Overview prevalence. Source: Whitespark\u003C\u002Fem>\u003C\u002Fspan>\u003C\u002Fp>\u003Cfigure data-type=\"image\" data-align=\"left\" style=\"display: inline-block; max-width: 100%; margin-left: 0px; margin-right: auto;\">\u003Cimg class=\"max-w-full h-auto rounded-lg\" src=\"https:\u002F\u002Fwebsite-cdn.nobsmarketplace.com\u002Fuploads\u002Fblog-images\u002F4-1-1024x576-20260512083142-sFvzQcoR.jpg\" data-align=\"left\">\u003C\u002Ffigure>\u003Cp style=\"text-align: center;\">\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">\u003Cem>Local pack results prevalence. Source: Whitespark\u003C\u002Fem>\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">This is why you can’t discount GBP and LSAs just yet. Many local queries still return a map and directory of local businesses, just as they did years ago. To that end, you need to:\u003C\u002Fspan>\u003C\u002Fp>\u003Ch3>\u003Cspan style=\"font-size: 1.25em; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Ensure Your GBP is Up-to-Date\u003C\u002Fspan>\u003C\u002Fh3>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Your GBP is the heart of your local link building efforts. It’s how Google answers questions like “Where is this business located?” or “Are there businesses like this near me?” Without a profile, your local search visibility might suffer.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"text-primary-blue-600 hover:underline\" href=\"https:\u002F\u002Fsupport.google.com\u002Fbusiness\u002Fanswer\u002F7091?hl=en#zippy=\">\u003Cspan style=\"font-size: 11pt; color: rgb(17, 85, 204); font-family: Arial, sans-serif;\">\u003Cu>Google says\u003C\u002Fu>\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\"> the first step is to ensure that your profile information is up-to-date. Details like the business’s name, full address, and contact information should be updated after any changes (e.g., moving to a new location). Other than this:\u003C\u002Fspan>\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Verify your business with Google\u003C\u002Fspan>\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Update regular and special store hours (if applicable)\u003C\u002Fspan>\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Respond to reviews from customers\u003C\u002Fspan>\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Add photos and videos to your profile\u003C\u002Fspan>\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">If you’re in retail, display your products in the profile\u003C\u002Fspan>\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Ful>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">\u003Cem>Note: The last tip is currently only possible in select countries and regions\u003C\u002Fem>\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Exercise caution in editing your GBP, as experts warn that it can cause your profile to be suspended and rendered invisible to local search. They advise against making sweeping edits at the same time, instead waiting at least a week following each change.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch3>\u003Cspan style=\"font-size: 1.25em; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Research for the Ideal Local Keywords\u003C\u002Fspan>\u003C\u002Fh3>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Local search works a bit differently from general search, specifically in the type of query often used. Keywords with local intent tend to be short and locked to a specific location, such as “seo firm in delaware.” Posing the query as a question is almost unnecessary (unless in AI Mode), as search engines understand local intent.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">However, know that there are other ways for a local keyword to appear.\u003C\u002Fspan>\u003C\u002Fp>\u003Cfigure data-type=\"image\" data-align=\"left\" style=\"display: inline-block; max-width: 100%; margin-left: 0px; margin-right: auto;\">\u003Cimg class=\"max-w-full h-auto rounded-lg\" src=\"https:\u002F\u002Fwebsite-cdn.nobsmarketplace.com\u002Fuploads\u002Fblog-images\u002Fpicture30-20260512083222-S1YozHDV.png\" data-align=\"left\">\u003C\u002Ffigure>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Start your research from the typical local keyword—as in the business type and location—and work your way from there. If you provide services, make sure your choice of locations are within your business’s area of operation. For retail businesses, they have to ensure their location can serve the adjacent areas or have branches in distant places.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch3>\u003Cspan style=\"font-size: 1.25em; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Prioritize Local Backlinks in Your Content\u003C\u002Fspan>\u003C\u002Fh3>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">I understand that getting backlinks from the likes of Forbes or US News can be tempting. But for local SEO, it’s better to get them from local publishers like community news sites and niche blogs. While these sites may not be as authoritative, they fulfill one of Google’s fundamentals for ranking in local search: relevance.\u003C\u002Fspan>\u003C\u002Fp>\u003Cfigure data-type=\"image\" data-align=\"left\" style=\"display: inline-block; max-width: 100%; margin-left: 0px; margin-right: auto;\">\u003Cimg class=\"max-w-full h-auto rounded-lg\" src=\"https:\u002F\u002Fwebsite-cdn.nobsmarketplace.com\u002Fuploads\u002Fblog-images\u002Fpicture31-20260512083240-GTTXJCei.png\" data-align=\"left\">\u003C\u002Ffigure>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">As for content, the typical how-to and listicle-style articles can still work for local SEO. The topics, however, need to be relevant to the location you’re ranking for. If a local festival or event is just around the corner, consider making content that talks about it in some form. If possible, consider participating in said event as a sponsor for extra brand exposure.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Besides a GBP, you’d also want to build and maintain a profile in local directories, if there are any. Note that some sites may require you to become a member of a local organization.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"font-size: 1.5em; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">Local Visibility Is Still Key\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"font-size: 11pt; color: rgb(0, 0, 0); font-family: Arial, sans-serif;\">It’s interesting how local SEO remains the same amid AI making drastic changes in search. Keeping your GBP updated and ranking for local keywords is still as effective as they were before the rise of AI. Whether ranking in the top ten or getting mentioned in AI summaries, local SEO is still worth doing.\u003C\u002Fspan>\u003C\u002Fp>","Amid the continued growth of AI search, where does local link building fit in? Surprisingly, it's still relevant in this day and age, including its tried-and-true techniques.","https:\u002F\u002Fwebsite-cdn.nobsmarketplace.com\u002Fuploads\u002Ffeatured-images\u002Felf-moondance-strategy-6725105-1280-20260512082022-4BLEhyXp.png","Despite AI making sweeping changes to how search works, local search has more or less stayed the same. Here’s a look at why it still matters.",1495,"2026-05-12T16:33:00.000000Z","2026-05-12T08:33:41.000000Z",{"id":35,"name":46,"email":47,"about":48,"avatar":49,"created_at":50,"updated_at":50,"deleted_at":16},"Jonas Trinidad","jonas@nobsmarketplace.com","","https:\u002F\u002Fwebsite-cdn.nobsmarketplace.com\u002Fblog-authors\u002F2023\u002F05\u002Fjonas-trinidad.jpg","2025-10-26T11:10:22.000000Z",[52,56,60,66],{"id":53,"name":54,"slug":17,"created_at":50,"updated_at":50,"deleted_at":16,"pivot":55},1,"Blogs",{"blog_id":34,"category_id":53},{"id":35,"name":57,"slug":58,"created_at":50,"updated_at":50,"deleted_at":16,"pivot":59},"SEO","seo",{"blog_id":34,"category_id":35},{"id":61,"name":62,"slug":63,"created_at":64,"updated_at":64,"deleted_at":16,"pivot":65},8,"Link Building","link-building","2025-10-26T11:10:26.000000Z",{"blog_id":34,"category_id":61},{"id":67,"name":68,"slug":69,"created_at":70,"updated_at":70,"deleted_at":16,"pivot":71},16,"Educative Content","educative-content","2026-02-10T11:18:29.000000Z",{"blog_id":34,"category_id":67},{"id":73,"author_id":8,"title":74,"slug":75,"content":76,"short_summary":77,"featured_image":78,"status":14,"meta_title":74,"meta_description":79,"canonical_url":16,"keywords":16,"blog_type":17,"is_featured":80,"word_count":81,"published_at":82,"created_at":83,"updated_at":83,"deleted_at":16,"author":84,"categories":85},346,"FAQ Schema May Matter More for AI Than for Search","faq-pages-still-matter-for-ai","\u003Ch1>\u003Cspan style=\"color: rgb(0, 0, 0);\">FAQ Schema May Matter More for AI Than for Search\u003C\u002Fspan>\u003C\u002Fh1>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Google’s May 7, 2026 announcement that FAQ rich results are no longer appearing in Search was widely covered as the end of a SERP feature. That framing misses what the documentation actually says. Google removed the visible rich result. It explicitly committed to continuing to use FAQ structured data to better understand pages. The visible payoff is gone. The underlying function is not.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The interesting question for anyone running an AI visibility program is whether the FAQ format has more value for AI retrieval today than it ever had for the rich result that just got retired. The answer appears to be yes, and the reasoning has nothing to do with Google specifically. It has to do with how language models retrieve and cite content across ChatGPT, Gemini, Perplexity, and every other AI search surface.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">Google removed the feature but kept the function\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The deprecation notice draws a clean line between two things that often get conflated. Schema markup tells a search engine what a page is about in machine-readable form. Rich results are a display feature that uses some of that data to render visual SERP elements. Removing the visual feature is a product decision. Continuing to use the data is a technology decision.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">For FAQ specifically, the schema describes a page as containing question and answer pairs, with each question explicitly paired with its corresponding answer in a structure a machine can parse without ambiguity. That structure remains useful for any system trying to understand the page, including the systems that decide which content to retrieve and cite in generative responses.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Google made the distinction explicit. Other AI platforms have not commented directly on FAQ schema, but the way their retrieval systems work suggests they value Q&A content for reasons that have little to do with whether Google displays a rich result.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">How AI systems decompose user questions\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The 1.4 million ChatGPT prompt study from Ahrefs that we covered earlier this year revealed something that changes how to think about content structure for AI visibility. When a user asks ChatGPT a question, the model does not search the web for that exact query. It generates a set of narrower sub-questions internally (sometimes called fanout queries) and searches for pages relevant to each one separately.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">A user asking “what is the best CRM for small businesses” might trigger internal sub-questions like “CRM pricing comparison for small teams,” “CRM features for sales pipeline management,” and “CRM integrations with accounting software.” ChatGPT retrieves pages for each sub-question independently and assembles the final answer from the combined results.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Cited pages in the Ahrefs study scored 0.656 on title-to-fanout-query similarity using cosine similarity, while non-cited pages scored 0.484. The gap was significant enough that title alignment with sub-questions emerged as one of the strongest predictors of whether a page got cited in a ChatGPT response.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">This decomposition pattern is not specific to ChatGPT. Gemini, Perplexity, and other AI retrieval systems all break user prompts into narrower internal queries before searching, with implementations that vary in detail but follow the same underlying logic. A user prompt rarely matches a page title directly, so the system breaks the prompt into more granular questions that are more likely to align with how content actually gets written.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">Q&A structure as a map of AI retrieval queries\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">A page structured as explicit question and answer pairs is, by design, a list of narrowly scoped questions with corresponding answers. That structure maps almost directly to the fanout queries an AI retrieval system generates from a broader user prompt.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">A prose article about CRM software might cover pricing, features, and integrations across paragraphs that flow into each other without explicit question markers. A page with FAQ markup covers the same topics but presents them as discrete questions: “How much does CRM software cost for a 10-person team?” “What CRM features support sales pipeline management?” “Does this CRM integrate with QuickBooks?” Each question is paired with a direct answer that a retrieval system can extract cleanly.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The first version is harder for an AI to match against a fanout query. The retrieval system has to infer where the answer to a specific sub-question lives within the prose. The second version is easier. The questions are explicit, the answers are bounded, and the alignment between sub-question and content is direct.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The implication is that FAQ-structured pages have a structural advantage in AI citation, separate from any direct benefit FAQ schema provides as a retrieval signal. Even setting the schema markup aside, content organized as explicit Q&A pairs maps more cleanly to how AI retrieval systems search for information.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">FAQ schema as a comprehension signal for AI\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The schema markup itself adds a second layer of value on top of the structural advantage. When a page includes FAQPage schema with properly marked Question and Answer entities, the markup tells any system parsing the page exactly which strings represent questions and which represent answers. There is no inference required. The structure is explicit, the entities are typed, and the relationships between them are unambiguous.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Whether AI retrieval systems use \u003C\u002Fspan>\u003Ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"text-primary-blue-600 hover:underline\" href=\"http:\u002F\u002FSchema.org\">\u003Cspan style=\"color: rgb(0, 0, 0);\">Schema.org\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan style=\"color: rgb(0, 0, 0);\"> markup directly or simply benefit from the cleaner content structure that schema usage tends to correlate with is a question that lacks public confirmation from OpenAI, Google’s Gemini team, or any other major AI platform. What is clear is that schema markup signals an authoring decision: someone deliberately structured this content as Q&A pairs, which usually means the content actually works as Q&A pairs rather than being repurposed prose.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Google’s own statement that it will continue to use FAQ data to better understand pages includes Gemini and AI Overviews by extension, since both rely on Google’s content understanding layer. Even if competing AI systems do not parse \u003C\u002Fspan>\u003Ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"text-primary-blue-600 hover:underline\" href=\"http:\u002F\u002FSchema.org\">\u003Cspan style=\"color: rgb(0, 0, 0);\">Schema.org\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan style=\"color: rgb(0, 0, 0);\"> directly, the cleaner content structure that schema usage tends to indicate likely makes the page easier for any retrieval system to extract answers from.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">Genuine Q&A content versus FAQ markup decoration\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The argument for FAQ schema as an AI visibility signal only holds if the underlying content actually works as Q&A. Google’s content guidelines for FAQ markup, which remain in place even after the rich result deprecation, require that the questions and answers appear as visible content on the page, that the questions are written by the site rather than user-submitted, and that the answers are not promotional or repetitive.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">These guidelines existed to prevent abuse of the rich result feature. They now serve a different purpose. A page with FAQ schema that follows the guidelines presents genuine Q&A content that AI retrieval systems can extract from. A page with FAQ schema that violates the guidelines (artificial questions, padded answers, content added solely for SERP real estate) does not provide that benefit, because the underlying Q&A content does not actually answer any real user question.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The sites that benefited most from the 2023 rich result restriction were the ones whose FAQ content was actually useful. The same logic applies now. Sites with real Q&A content that aligns with questions users actually ask have content that serves AI retrieval well, with or without the schema markup. Sites with artificial FAQ sections do not benefit from the structure, because the structure does not contain answers that retrieval systems would want to surface.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">Sites that should expand FAQ content, not abandon it\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">For sites currently using FAQ markup, the May 2026 announcement is not a signal to strip the schema. It is a signal to reconsider whether the FAQ content itself is doing useful work. Sites with genuine, well-organized Q&A content should keep the markup and consider expanding FAQ sections to cover more of the specific questions users actually ask about their product, service, or topic.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">For sites without FAQ content, the announcement is also not a reason to avoid creating it. The rich result that originally motivated many FAQ pages is gone. The AI retrieval benefit, the content comprehension benefit, and the user experience benefit of having common questions answered clearly all remain. Adding a well-structured FAQ section to a product page, a service page, or a category page now serves AI visibility and user experience, even if it no longer earns SERP dropdown real estate.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The brands building the strongest AI visibility positions tend to share a content pattern: clear questions in headers, direct answers in paragraphs, and topic coverage that aligns with what users actually want to know. FAQ markup is one specific implementation of that pattern, and one that Google has explicitly committed to continuing to use as a comprehension signal. \u003C\u002Fspan>\u003Ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"text-primary-blue-600 hover:underline\" href=\"https:\u002F\u002Fnobsmarketplace.com\u002Flink-building\">\u003Cspan style=\"color: rgb(0, 0, 0);\">Link building\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan style=\"color: rgb(0, 0, 0);\"> and \u003C\u002Fspan>\u003Ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"text-primary-blue-600 hover:underline\" href=\"https:\u002F\u002Fnobsmarketplace.com\u002Fdigital-pr\">\u003Cspan style=\"color: rgb(0, 0, 0);\">digital PR\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan style=\"color: rgb(0, 0, 0);\"> build the authority signals that determine whether AI systems trust a page enough to cite it. Q&A content structure determines whether those same systems can extract clean answers from the page once they decide to retrieve it. Both layers feed the citation pipeline.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The May 2026 announcement marked the end of FAQ rich results. The same week, it confirmed that FAQ structured data remains a useful signal for content comprehension. Sites that treat the two facts as one and remove their FAQ markup are making a SERP-feature decision in an environment where the SERP feature was never the most valuable thing the markup was doing.\u003C\u002Fspan>\u003C\u002Fp>","Google retired FAQ rich results in May 2026. The schema markup itself remains useful, and may have more value for AI citation than it ever had for the SERP feature. Structured Q&A pages align with how AI retrieval systems search for and surface information.","https:\u002F\u002Fwebsite-cdn.nobsmarketplace.com\u002Fuploads\u002Ffeatured-images\u002Fimg-5742-20260511070327-xgDnsVd6.PNG","Google killed FAQ rich results, but the schema may matter more for AI citation than it ever did for the SERP feature. The format aligns with AI retrieval.",false,1507,"2026-05-11T07:00:52.000000Z","2026-05-11T07:03:47.000000Z",{"id":8,"name":24,"email":25,"about":16,"avatar":26,"created_at":27,"updated_at":16,"deleted_at":16},[86,92],{"id":87,"name":88,"slug":89,"created_at":90,"updated_at":90,"deleted_at":16,"pivot":91},23,"AI","ai","2026-03-10T11:18:29.000000Z",{"blog_id":73,"category_id":87},{"id":35,"name":57,"slug":58,"created_at":50,"updated_at":50,"deleted_at":16,"pivot":93},{"blog_id":73,"category_id":35},{"id":95,"author_id":8,"title":96,"slug":97,"content":98,"short_summary":99,"featured_image":100,"status":14,"meta_title":96,"meta_description":101,"canonical_url":16,"keywords":16,"blog_type":17,"is_featured":18,"word_count":102,"published_at":103,"created_at":104,"updated_at":105,"deleted_at":16,"author":106,"categories":107},345,"Google Officially Kills FAQ Rich Results","google-officially-kills-faq-rich-results","\u003Ch1>\u003Cspan style=\"color: rgb(0, 0, 0);\">Google Officially Kills FAQ Rich Results\u003C\u002Fspan>\u003C\u002Fh1>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Google updated its Search Central documentation on May 7, 2026, with a deprecation notice for FAQ rich results. The update appears at the top of the FAQ structured data page and announces that as of that date, FAQ rich results are no longer appearing in Google Search. The notice also lays out what gets removed when, ending with a single line that changes how the announcement should be read: Google will continue to use FAQ structured data to better understand pages, even though the rich result feature is gone.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The deprecation completes a process that started in August 2023, when Google first restricted FAQ rich results to well-known authoritative government and health websites. For most of the web, FAQ rich results have already been gone for nearly three years. The May 2026 announcement removes them for everyone, including the sites that retained them after the 2023 restriction.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">The deprecation rolls out across three months\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The notice spells out a removal timeline that runs from May through August 2026.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The first change is already live. As of May 7, FAQ rich results no longer appear in Google Search. Sites that previously qualified for the feature, including the government and health domains that kept it after 2023, no longer see their FAQ markup rendered as expandable dropdowns in search results. The visual SERP feature is gone.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">In June 2026, Google will remove FAQ-related reporting from Search Console. The rich result status report for FAQ markup, which let site owners track how many of their FAQ-marked pages were eligible for the feature, will be retired. The Rich Results Test, the tool developers use to validate structured data, will also stop supporting FAQ markup at that point.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">In August 2026, Google will remove FAQ rich result support from the Search Console API. The three-month gap between the Search Console UI removal and the API removal gives developers time to adjust any automated reporting or monitoring that relies on FAQ rich result data.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The full timeline removes every visible trace of FAQ rich results from Google’s product surfaces. Search appearance, dashboards, testing tools, and API access all get retired in sequence.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">From restriction in 2023 to full retirement now\u003C\u002Fspan>\u003C\u002Fh2>\u003Cfigure data-type=\"image\" data-align=\"left\" style=\"display: inline-block; max-width: 100%; margin-left: 0px; margin-right: auto;\">\u003Cimg class=\"max-w-full h-auto rounded-lg\" src=\"https:\u002F\u002Fwebsite-cdn.nobsmarketplace.com\u002Fuploads\u002Fblog-images\u002Ffaq-rip-may8e-20260508221623-PGmMEMxQ.png\" data-align=\"left\">\u003C\u002Ffigure>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The May 2026 announcement is not a sudden change. The deprecation completes a process that began in August 2023, when John Mueller, a Search Advocate at Google, posted on the Google Search Central blog that FAQ rich results would only appear for well-known authoritative government and health websites going forward. The 2023 update was framed as a search appearance change, not a ranking change, and it was rolled out globally within a week of the announcement.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The 2023 restriction was a response to widespread abuse of FAQ schema. Sites had been adding artificial FAQ sections to inflate their SERP real estate, often with questions that did not match user intent or answers that existed only to occupy more pixels. Restricting the feature to government and health sites, where the questions and answers tend to address genuine public information needs, was Google’s way of cleaning up the SERP without removing the feature entirely.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Between 2023 and 2026, the restricted version remained available to qualifying sites. The May 2026 announcement removes it for those sites too. The reasoning is not spelled out in the documentation, but the practical effect is clear: FAQ rich results are no longer a Google Search feature for any category of website.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">Google’s commitment to keep using the data\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The deprecation notice includes a line that easily gets lost in coverage of the news but appears to be the most consequential part of the update. Google states that it will continue to use FAQ structured data to better understand pages, even though it will no longer display the rich result.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The line confirms what some SEO professionals have argued since the 2023 restriction: structured data and rich results are two different things. Schema markup tells Google what a page is about in machine-readable form. Rich results are a display feature that uses some structured data to render visual SERP elements. Google can choose to stop showing the visual feature without abandoning the data that informed it.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">For FAQ markup specifically, the data describes a page as containing question-and-answer pairs, with each question and its corresponding answer clearly delineated. That structure is useful to Google’s understanding of the page regardless of whether the SERP includes a visual FAQ block. The model used to generate AI Overviews, the system that decides which pages to retrieve for a given query, and the algorithms that match pages to user intent all benefit from clearer signals about page content. FAQ markup contributes to those signals.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Whether the practical impact of FAQ markup on rankings or AI Overview citation probability is large or small is a separate question, and one Google has not directly addressed. What the documentation does say is that the schema continues to function as a comprehension signal, which is a different statement than “remove your FAQ markup because the rich result is gone.”\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">Sites with FAQ markup face a clear decision\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">For any site currently using FAQ structured data, the May 2026 announcement raises a practical question: keep the markup or remove it.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The argument for removing it is the simplest one. The rich result that originally motivated the markup is no longer available, and the Search Console reports that helped track its performance are being retired. From a pure SERP-feature ROI standpoint, the markup no longer earns its keep.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The argument for keeping it follows directly from Google’s own statement. The data still informs how Google understands the page. Removing the markup removes a signal that Google has explicitly committed to continuing to use. For a site where the FAQ markup accurately reflects on-page content, the cost of keeping it is minimal (a small amount of additional code in the page) and the upside is preserving a comprehension signal that may or may not show up in rankings or AI Overview citations.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The decision should also account for content quality. Google’s content guidelines for FAQ markup require that the questions and answers on the page actually exist as visible content, that the questions are written by the site rather than user-submitted, and that the answers are not promotional or repetitive. Sites with FAQ markup that does not meet those guidelines, or that was added purely to chase the rich result, may want to clean up the markup or remove it. Sites with genuine FAQ content that follows the guidelines are better off keeping the markup, since Google has committed to continuing to use the data.\u003C\u002Fspan>\u003C\u002Fp>\u003Ch2>\u003Cspan style=\"color: rgb(0, 0, 0);\">FAQ schema as a comprehension signal beyond rich results\u003C\u002Fspan>\u003C\u002Fh2>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The most useful framing of the announcement is probably not “Google is removing FAQ” but “Google is removing the visible feature while keeping the underlying data signal.” That distinction has implications for how to think about structured data more broadly.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Schema markup serves multiple functions. The most visible one is enabling rich results, the visual SERP features that capture extra real estate and click-through rate. The less visible function is helping search engines understand page content, classify it correctly, and match it to relevant queries. Rich results are the visible payoff. Comprehension is the underlying value.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">When Google removes a rich result feature, the visible payoff disappears. The underlying value does not necessarily disappear with it. Google’s explicit statement that FAQ data will continue to inform page understanding makes this distinction concrete for FAQ specifically, and it is a useful frame for thinking about other structured data types whose rich result features may be deprecated in the future.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">For brands building AI visibility through SEO, \u003C\u002Fspan>\u003Ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"text-primary-blue-600 hover:underline\" href=\"https:\u002F\u002Fnobsmarketplace.com\u002Flink-building\">\u003Cspan style=\"color: rgb(0, 0, 0);\">link building\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan style=\"color: rgb(0, 0, 0);\">, and editorial coverage, the comprehension layer matters. Pages that Google understands clearly are pages that get retrieved for relevant queries, that get cited in AI Overviews, and that contribute to the entity recognition signals that AI retrieval systems use across the broader ecosystem. Structured data, including FAQ markup that follows Google’s content guidelines, contributes to that comprehension layer.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The May 2026 announcement is the end of FAQ rich results. It is not the end of FAQ structured data as a useful signal. The two have always been distinct, and Google’s deprecation notice makes that distinction explicit by spelling out what gets removed (the visible feature) and what continues (the comprehension function). For sites with genuine FAQ content and properly implemented markup, the schema is still doing work behind the scenes, even when there is nothing visible on the SERP to show for it.\u003C\u002Fspan>\u003C\u002Fp>","On May 7, 2026, Google announced FAQ rich results are no longer appearing in Search. The full deprecation timeline removes Search Console reporting in June and API support in August. Google says the schema will continue to inform how it understands pages.","https:\u002F\u002Fwebsite-cdn.nobsmarketplace.com\u002Fuploads\u002Ffeatured-images\u002Ffaq-tombstone-final-20260508221602-ixdqt8zs.png","Google announced FAQ rich results are no longer appearing in Search. The full deprecation runs through August, but the schema still serves a purpose.",1397,"2026-05-08T21:48:59.000000Z","2026-05-08T22:11:57.000000Z","2026-05-08T22:16:29.000000Z",{"id":8,"name":24,"email":25,"about":16,"avatar":26,"created_at":27,"updated_at":16,"deleted_at":16},[108],{"id":35,"name":57,"slug":58,"created_at":50,"updated_at":50,"deleted_at":16,"pivot":109},{"blog_id":95,"category_id":35},[111,114,125],{"id":7,"author_id":8,"title":9,"slug":10,"featured_image":13,"published_at":20,"short_summary":12,"word_count":19,"author":112,"categories":113},{"id":8,"name":24,"avatar":26,"email":25},[],{"id":34,"author_id":35,"title":36,"slug":37,"featured_image":40,"published_at":43,"short_summary":39,"word_count":42,"author":115,"categories":116},{"id":35,"name":46,"avatar":49,"email":47},[117,119,121,123],{"id":53,"name":54,"pivot":118},{"blog_id":34,"category_id":53},{"id":35,"name":57,"pivot":120},{"blog_id":34,"category_id":35},{"id":61,"name":62,"pivot":122},{"blog_id":34,"category_id":61},{"id":67,"name":68,"pivot":124},{"blog_id":34,"category_id":67},{"id":95,"author_id":8,"title":96,"slug":97,"featured_image":100,"published_at":103,"short_summary":99,"word_count":102,"author":126,"categories":127},{"id":8,"name":24,"avatar":26,"email":25},[128],{"id":35,"name":57,"pivot":129},{"blog_id":95,"category_id":35}]