--- title: "Google AI Overviews Explained: How They Work & SEO Impact (2026)" description: "Google AI Overviews launched broadly in May 2024 (formerly SGE). 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AI Overviews include numbered source citations linking to the underlying pages, and a portion of users do click through. However, the strategic value of an AI Overview citation extends beyond the immediate click — it establishes brand authority, surfaces your name to a high-intent user, and contributes to brand-search lift even when no click follows." } }, { "@type": "Question", "name": "Do AI Overviews kill SEO?", "acceptedAnswer": { "@type": "Answer", "text": "No, but they change the success metric on informational queries. Classic SEO fundamentals — entity clarity, organic ranking signals, Schema.org structured data, freshness, and E-E-A-T — are still the foundation, because AI Overviews use standard organic ranking signals as the retrieval base. The shift is that on TOFU informational queries, citation share inside the AI Overview becomes as important as the click-through rate beneath it." } }, { "@type": "Question", "name": "How do I track AI Overview performance for my site?", "acceptedAnswer": { "@type": "Answer", "text": "Combine four data sources. Use Google Search Console with the AI-feature dimension Google has added to performance reports. Run a fixed weekly prompt set across Google AI Overviews, ChatGPT, Perplexity, Claude, and Gemini (manually or via tools like Otterly.ai or Profound). Track branded search lift in Search Console and Google Trends. Add 'Google AI Overview' as an explicit option on your lead-form attribution field." } }, { "@type": "Question", "name": "What about AI Overview hallucination risks?", "acceptedAnswer": { "@type": "Answer", "text": "AI Overviews experienced public scrutiny over hallucination issues during the early broad rollout in May and June 2024, with widely-shared examples of low-confidence answers. Google has since tightened guardrails and reduced the frequency of low-confidence outputs. From a content-owner perspective, the directional principle is unchanged — stronger grounding signals (citations, schema, primary sources, clear entity definitions) reduce the chance of being misrepresented inside an AI Overview." } }, { "@type": "Question", "name": "Does Schema.org structured data help with AI Overviews?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. Google has confirmed Schema.org structured data is a supported signal for AI Overviews. 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[Home](/en) [Insights](/en/insights) [AI Search & LLMO](/en/insights/ai-search) Google AI Overviews Explained AI Search & LLMO Deep Dive Fresh · Last reviewed: 16 September 2026 · 6d ago # Google AI Overviews Explained ## TL;DR Quick Answer Cited by AI > Google AI Overviews are AI-generated answers that appear above organic results on a subset of Google searches. They launched broadly in May 2024 (rebranded from Search Generative Experience / SGE), are powered by Gemini, and include numbered citations to source pages. They trigger more often on informational and exploratory queries than on transactional ones. Google AI Overviews launched broadly in May 2024 as the rebranded successor to Search Generative Experience (SGE). They are powered by the Gemini family of models and appear above traditional results on a subset of queries. This deep dive explains how they work, when they trigger, and how to earn citation inside them. Google AI Overviews are AI-generated answer boxes that appear above traditional 'blue link' organic results on a subset of Google searches. Originally announced as Search Generative Experience (SGE) at Google I/O 2023, they were rebranded and rolled out broadly as 'AI Overviews' at Google I/O 2024 (May 14, 2024). They are powered by Google's Gemini family of large language models, draw on standard organic ranking signals plus additional grounding logic, and include numbered citations linking to source pages. ![Linus Ingemarsson - Author at Alice Labs](/images/linus-ingemarsson.png) Written by [Linus Ingemarsson ](https://www.linkedin.com/in/linus-ingemarsson/) ![Eric Lundberg - Reviewer at Alice Labs](/images/eric-lundberg.png) Reviewed by [Eric Lundberg ](https://www.linkedin.com/in/eric-lundberg-3530451bb/) Published May 6, 2026 · Updated September 16, 2026 12 min read May 2024 Google AI Overviews broad launch (rebranded from SGE) [Google I/O 2024](https://blog.google/products/search/generative-ai-google-search-may-2024/) ~60% Google searches ending without an open-web click (US/EU) [SparkToro / Datos 2024](https://sparktoro.com/blog/in-2024-we-finally-satisfyingly-measured-how-much-of-google-search-traffic-goes-to-google-properties/) Up to 40% Visibility lift from citation-rich content in generative engines [Aggarwal et al. 2024 (GEO paper)](https://arxiv.org/abs/2311.09735) What you'll learn(6 points) - What Google AI Overviews actually are and how they evolved from SGE (May 2023) to AI Overviews (May 2024) - How AI Overviews work under the hood — Gemini grounding plus standard organic ranking signals - When AI Overviews trigger and which query types are most exposed - How AI Overviews change SEO strategy — clicks decline, citations gain - How to optimize content for citation inside AI Overviews using E-E-A-T, schema, and citation-rich writing - How Alice Labs runs the LLMO Citation Benchmark across Google AI Overviews and the major AI assistants ## Key Takeaways - 01 Google AI Overviews were originally announced as Search Generative Experience (SGE) at Google I/O 2023 (May 2023, Sundar Pichai). They were rebranded and rolled out broadly as 'AI Overviews' at Google I/O 2024 on May 14, 2024. - 02 AI Overviews are powered by the Gemini family of models. Google has stated the system uses standard organic ranking signals plus additional grounding logic to verify and cite sources. - 03 AI Overviews trigger on a subset of queries — Google has confirmed they do not appear on every search. Industry observation indicates they are more common on informational and exploratory queries than on transactional or commercial ones. - 04 AI Overviews include numbered source citations linking to the underlying pages. Earning a citation — not just ranking — is now the strategic objective on informational queries. - 05 Aggarwal et al. (2024) found citation-rich content lifted generative-engine visibility by up to 40% (arXiv:2311.09735). Schema.org markup, E-E-A-T signals, and structured FAQ blocks are the highest-leverage levers. - 06 The Alice Labs LLMO Citation Benchmark tracks 100 SaaS brands quarterly across Google AI Overviews, ChatGPT, Perplexity, Claude, and Gemini — measuring citation share as the new equivalent of organic share-of-voice. ### Contents 12 min left - [01 What Are Google AI Overviews? ](#what-are-google-ai-overviews) - [02 How Do AI Overviews Work? ](#how-ai-overviews-work) - [03 When Do AI Overviews Trigger? ](#when-ai-overviews-trigger) - [04 Strategic Implications: What AI Overviews Change for SEO ](#strategic-implications-for-seo) - [05 How to Optimize Content for AI Overviews ](#how-to-optimize-for-ai-overviews) - [06 Measuring AI Overview Performance ](#measuring-ai-overviews) - [07 The Alice Labs LLMO Citation Benchmark ](#alice-labs-llmo-citation-benchmark) Part of [AI Search Optimization: The Complete Guide for 2026](/en/insights/ai-search-optimization-guide) 01 / 07 Chapter ## What Are Google AI Overviews? Google AI Overviews are AI-generated answer boxes that appear above traditional 'blue link' results on a subset of Google searches. They launched broadly in May 2024 as the rebranded successor to Search Generative Experience (SGE), which was first announced at Google I/O 2023. Google AI Overviews are synthesised answer blocks that appear at the top of certain Google search results. They summarise information from multiple web sources and link to each one with numbered citations. Their public history runs in two stages. The system was first unveiled as Search Generative Experience (SGE) by Sundar Pichai at Google I/O on May 10, 2023, initially as an opt-in Search Labs experiment. One year later, at Google I/O 2024 on May 14, 2024, Google rebranded the feature to "AI Overviews" and began the broad US rollout. Expansion to the UK and additional markets followed through 2024 and into 2025. Visually, an AI Overview looks like an extended Featured Snippet. Functionally it is different — it can synthesise from multiple sources, answer multi-part questions, and adapt to follow-up interactions inside the same SERP. AI Overviews always appear above the traditional ten blue links when triggered. They do not replace organic results — they sit on top of them, pushing classic listings further down the page. That layout change is why organic-only strategies now underperform, and why brands bring in an [AI search optimization consultant](/en/ai-search) to reclaim visibility inside the Overview itself — or engage [our ai seo services](/en/ai-seo) to run it end-to-end. From SGE to AI Overviews Search Generative Experience (SGE) and AI Overviews are the same product line. SGE was the May 2023 Google I/O announcement and Search Labs experiment. 'AI Overviews' is the May 14, 2024 rebrand and broad rollout. If you read older content referencing SGE, it is describing the same system. blog.google 02 / 07 Chapter ## How Do AI Overviews Work? In short AI Overviews are powered by Google's Gemini family of large language models. Google has stated the system uses standard organic ranking signals plus additional grounding logic to verify claims and select source pages for citation. AI Overviews are generated by the Gemini family of large language models, Google's flagship LLM line. The model produces a synthesised answer for the query, then grounds the answer against web content. Per Google's public statements, the underlying retrieval still relies on standard organic ranking signals. Pages that rank highly for related queries are far more likely to be retrieved as grounding sources. Layered on top is additional grounding logic. The system selects a small set of source pages to cite, attributes specific claims to them, and renders numbered links inline with the generated text. This is the practical implication for content owners. The same organic ranking work that earns a top-ten position is the foundation for AI Overview citation. Schema, freshness, and entity clarity all still matter. What is added is a citation layer. The model needs verifiable, extractable, citation-friendly statements — short factual claims with clear sourcing — to anchor its synthesised answer. AI Overviews experienced public scrutiny over hallucination issues during the early rollout in May and June 2024. Google has since tightened guardrails and reduced the frequency of low-confidence answers, but the directional principle holds: stronger grounding signals reduce hallucination risk and increase citation odds. Rank first, then earn the citation AI Overviews lean heavily on standard organic ranking signals. If you do not rank in the top results for a query, you are unlikely to be retrieved as a grounding source. Classic SEO is the price of entry — citation optimization is the next layer on top. 03 / 07 Chapter ## When Do AI Overviews Trigger? In short AI Overviews trigger on a subset of Google queries — Google has confirmed they do not appear on every search. They are more common on informational and exploratory queries and less common on transactional or commercial ones, though Google has not published a precise trigger rate. Google has been explicit that AI Overviews do not trigger on every search. They appear on a subset of queries where the system judges generative synthesis genuinely helps the user. Google has not published a precise percentage of searches that show AI Overviews, and public estimates from third parties vary substantially by vertical, country, and snapshot date. Treat any single round number with caution. What Google and broad industry research consistently agree on is the qualitative pattern. AI Overviews are more common on informational, multi-part, and exploratory queries — the kind a user would historically have followed up across two or three articles. They are less common on transactional and clearly commercial queries. Searches with strong purchase intent, brand specificity, or local geographic signals tend to retain traditional SERPs more often. They are also less common on highly sensitive query categories. Google has signalled extra caution on health, finance, and elections-related searches. For content strategy this asymmetry matters. Top-of-funnel informational content (the historical TOFU SEO play) is the most exposed to AI Overview displacement. Bottom-of-funnel commercial queries are more insulated. Do not trust specific 'X% of searches show AI Overviews' claims Google has not publicly released a precise trigger rate, and third-party estimates vary widely by methodology, vertical, country, and snapshot date. Use the qualitative direction (more common on informational queries) and audit your own query mix instead of citing viral round numbers. ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. [Book a Discovery Call](#contact) 04 / 07 Chapter ## Strategic Implications: What AI Overviews Change for SEO In short AI Overviews shift the strategic objective from earning clicks to earning citations on informational queries. Click-through rates on TOFU content come under pressure, while citation share inside AI Overviews becomes the new visibility primitive. For two decades the SEO success metric was simple. Rank, click, convert. Click was the load-bearing word. AI Overviews break that chain on informational queries. A user can read the AI Overview, see your numbered citation, and never click through. You earned visibility but not a session. Public estimates of AI Overview CTR loss vary substantially across studies, verticals, and snapshot dates. The qualitative direction is unambiguous — informational click rates are under pressure — but the specific number depends on your query mix. What is unambiguous is the strategic implication. If your TOFU content is being summarised inside an AI Overview, you need to be one of the cited sources. If you are not cited, you are invisible on that query. The strategic question becomes: in queries that no longer convert to clicks, what is the next-best visibility primitive? The answer most Alice Labs clients have settled on is the citation. A citation in an AI Overview does three things even when no click follows. It establishes brand authority, it surfaces your name to a high-intent user, and it influences downstream brand consideration and brand-search lift. This is not a recommendation to abandon click optimization. Bottom-of-funnel commercial queries still drive revenue and still convert at click. The shift is about adding a citation strategy on top of click strategy, not replacing it. The dual mandate Run click optimization on commercial / BOFU queries where AI Overviews are less common and clicks still drive revenue. Run citation optimization on informational / TOFU queries where AI Overviews dominate and the goal becomes being the cited source. ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png) Alice Labs practitioner team ## Want to know if your brand is cited inside Google AI Overviews? We run a 30-prompt LLMO Citation Benchmark across Google AI Overviews, ChatGPT, Perplexity, Claude, and Gemini to show exactly where your brand is — and isn't — getting cited today. [Request LLMO Citation Benchmark](#contact) 05 / 07 Chapter ## How to Optimize Content for AI Overviews In short Optimizing for AI Overviews combines classic SEO fundamentals with citation-friendly content design. The biggest levers are E-E-A-T signals, Schema.org structured data, citation-rich claims, and self-contained FAQ-style answers — all of which align with Google's stated quality framework and the Aggarwal et al. (2024) GEO research. AI Overviews reward the same content fundamentals as classic organic search, with a tighter emphasis on extractability and verifiability. The optimization work splits into four levers. **1\. E-E-A-T signals.** Google's Experience, Expertise, Authoritativeness, and Trustworthiness framework is the published quality lens that sits behind AI Overview source selection. Author bios, reviewer attribution, transparent methodology, and original first-party data all strengthen E-E-A-T. **2\. Schema.org structured data.** Article, FAQPage, HowTo, and Organization schema all help Google parse your content into discrete extractable units. Google has confirmed structured data is a supported signal for AI Overviews. **3\. Citation-rich content.** The Aggarwal et al. (2024) GEO paper tested nine content modifications across generative engines. Adding inline citations, specific statistics, and authoritative quotation produced up to 40% visibility lift (arXiv:2311.09735). **4\. Self-contained answers.** Write each paragraph and FAQ block so it can stand alone. The model extracts at the snippet level, not the article level — content that requires reading three sections to make sense will lose to content that answers crisply in one. The tactical playbook follows from these four levers. Add entity definitions at the top of every page. Use FAQPage schema on every article. Cite primary sources inline. Publish an llms.txt file (Answer.AI standard, September 2024). Maintain author and reviewer attribution. One Alice Labs media client recently saw a +2,092% click increase after a focused GEO optimization sprint covering schema, citation insertion, and entity clarification. The lift was driven by citation share growing — which then converted into a meaningful click tail on queries where AI Overviews still link out. GEO research finding (Aggarwal et al. 2024) Adding inline citations, specific statistics, and quotation from authoritative sources lifted source visibility in generative engines by up to 40%. The losers — fluency tweaks, jargon stuffing, unique-word injection — did not move the needle (arXiv:2311.09735). arxiv.org/abs/2311.09735 ### Want to discuss how this applies to your organization? Book a free 30-minute strategy call with our AI team. [Book a call](/en/ai-consulting-services#contact-form) 06 / 07 Chapter ## Measuring AI Overview Performance In short Measuring AI Overview performance combines Google Search Console data, citation tracking across AI assistants, branded search lift, and self-reported source attribution. No single tool captures the full picture — a layered measurement stack is required. Search Console alone will under-report your AI Overview visibility. It does not break out which impressions came from an AI Overview citation, and it does not show your citation share inside other AI assistants like ChatGPT, Perplexity, or Claude. Closing that measurement gap is now a core LLMO discipline. The stack we use with Alice Labs clients combines four layered data sources. 1. **Search Console with the AI dimension.** Google has added AI-feature breakouts to Search Console performance reports. Use them to filter clicks and impressions tied to AI Overview surfaces and benchmark CTR shifts on TOFU queries. 2. **Citation tracking.** A fixed prompt set run weekly across Google AI Overviews, ChatGPT, Perplexity, Claude, and Gemini. Tools like Otterly.ai, Profound, and Semrush AI Overview tracking automate this. 3. **Branded search lift.** Brand-name search volume in Google Search Console and Google Trends. A rising baseline with flat marketing spend usually indicates AI-citation-driven awareness compounding. 4. **Self-reported attribution.** Add ChatGPT, Perplexity, and "Google AI Overview" as explicit options on your "How did you hear about us?" lead form field. This surfaces AI-driven pipeline that no analytics platform currently captures end-to-end. None of these replace organic clicks as a metric. They sit alongside it, plugging the visibility gap that Search Console cannot fill in a zero-click, AI-mediated world. Add AI assistants to your lead form Add ChatGPT, Perplexity, Claude, and 'Google AI Overview' as explicit options on your 'How did you hear about us?' field. This single change surfaces AI-driven pipeline that no analytics platform currently captures. ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. [Book a Discovery Call](#contact) 07 / 07 Chapter ## The Alice Labs LLMO Citation Benchmark In short The Alice Labs LLMO Citation Benchmark tracks 100 SaaS brands quarterly across Google AI Overviews, ChatGPT, Perplexity, Claude, and Gemini. It measures citation share — the closest equivalent to organic share-of-voice in an AI-mediated search world. We built the LLMO Citation Benchmark because no public dataset answered the simplest LLMO question. For a given brand, on a given set of category-defining prompts, what is your citation share across Google AI Overviews and the major AI assistants? The benchmark covers 100 SaaS brands across HR tech, fintech, martech, devtools, and analytics. Each brand is evaluated against a consistent prompt set per vertical. Citations are logged across Google AI Overviews, ChatGPT, Perplexity, Claude, and Gemini. The scoring is straightforward. We measure three things per brand, per prompt, per platform. - **Cited / not cited.** Binary — does the brand or domain appear as a numbered source in the AI Overview or assistant answer? - **Citation position.** First-cited, mid-cited, last-cited. Earlier citations attract more user attention and click probability. - **Co-citation set.** Which competitors are cited alongside? This reveals the assistant's mental model of the category — and where you sit in it. The benchmark is run quarterly and informs the Alice Labs Implementation Index 2026. Together they give Nordic enterprise buyers a defensible view of LLMO maturity in their category. We do not publish the full ranked dataset publicly. Vertical-level findings are shared with clients during onboarding and form the baseline against which we measure 90-day citation lift. For a curated shortlist of vendors that operationalize these patterns, see our [ai content optimization tools](/en/insights/ai-content-optimization-tools) listicle. Methodology disclosure The Alice Labs LLMO Citation Benchmark uses fixed, vertical-specific prompt sets and queries each AI surface (Google AI Overviews and the major AI assistants) via its public consumer interface. Snapshots are taken at consistent dates each quarter to neutralise model-update drift. Google AI Overviews timeline — verified milestones from official Google announcements Date Milestone What changed Source May 10, 2023 Search Generative Experience (SGE) announced at Google I/O 2023 Sundar Pichai unveils generative AI in Search as opt-in Search Labs experiment Google 2023-2024 SGE Search Labs expansion Opt-in availability extended across additional markets and query types Google May 14, 2024 AI Overviews — broad US launch (Google I/O 2024) SGE rebranded to AI Overviews; broad rollout begins for US users Google May-June 2024 Public scrutiny over hallucination issues Early rollout produced widely-shared low-confidence answers; Google tightens guardrails Google / press 2024-2025 Expansion to UK and additional markets AI Overviews extended beyond US; coverage broadened across query categories Google 2024-2026 Continued integration with Search Console Google adds AI-feature dimensions to Search Console performance reports Google Source: Compiled from official Google announcements (blog.google, Google I/O keynotes 2023 & 2024) ## About the Authors & Reviewers Published May 6, 2026 · Updated September 16, 2026 Written by ![Linus Ingemarsson - CEO & Co-Founder, Alice Labs at Alice Labs](/images/linus-ingemarsson.png) [Linus Ingemarsson](https://www.linkedin.com/in/linus-ingemarsson/) CEO & Co-Founder, Alice Labs CEO & Co-Founder at Alice Labs. Author of 7 research reports on AI adoption, governance and labor markets cited across EU, OECD and US benchmarks. - 8+ years in AI strategy & implementation - Top-5 AI Speaker, Sweden (Mindley 2025) - 100+ enterprise AI engagements [View profile](https://www.linkedin.com/in/linus-ingemarsson/) [](https://www.linkedin.com/in/linus-ingemarsson/)[](mailto:linus@alicelabs.ai) Reviewed by September 16, 2026 ![Eric Lundberg - Co-Founder, Alice Labs at Alice Labs](/images/eric-lundberg.png) [Eric Lundberg](https://www.linkedin.com/in/eric-lundberg-3530451bb/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Builds AI automation, agent workflows and integration systems that hold up in real business operations. - AI automation & agent systems lead - Workflow design across 100+ deployments - Specialist in RAG, integrations & APIs [View profile](https://www.linkedin.com/in/eric-lundberg-3530451bb/) [](https://www.linkedin.com/in/eric-lundberg-3530451bb/)[](mailto:eric@alicelabs.ai) Published May 6, 2026 · Updated September 16, 2026 Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### When did Google AI Overviews launch? ▾ Google AI Overviews were originally announced as Search Generative Experience (SGE) by Sundar Pichai at Google I/O on May 10, 2023, initially as an opt-in Search Labs experiment. They were rebranded to 'AI Overviews' and broadly rolled out at Google I/O 2024 on May 14, 2024, starting in the US and expanding to the UK and other markets through 2024 and 2025. ### Do AI Overviews appear on every Google search? ▾ No. Google has explicitly stated AI Overviews trigger on a subset of queries, not every search. They are more common on informational and exploratory queries — the kind that historically required reading multiple articles — and less common on transactional, clearly commercial, or local queries. Google has not published a precise trigger rate, and third-party estimates vary by methodology, vertical, country, and snapshot date. ### Do AI Overview citations actually drive clicks? ▾ Sometimes, but less often than a top-ten organic listing did pre-AI Overviews. AI Overviews include numbered source citations linking to the underlying pages, and a portion of users do click through. However, the strategic value of an AI Overview citation extends beyond the immediate click — it establishes brand authority, surfaces your name to a high-intent user, and contributes to brand-search lift even when no click follows. ### Do AI Overviews kill SEO? ▾ No, but they change the success metric on informational queries. Classic SEO fundamentals — entity clarity, organic ranking signals, Schema.org structured data, freshness, and E-E-A-T — are still the foundation, because AI Overviews use standard organic ranking signals as the retrieval base. The shift is that on TOFU informational queries, citation share inside the AI Overview becomes as important as the click-through rate beneath it. ### How do I track AI Overview performance for my site? ▾ Combine four data sources. Use Google Search Console with the AI-feature dimension Google has added to performance reports. Run a fixed weekly prompt set across Google AI Overviews, ChatGPT, Perplexity, Claude, and Gemini (manually or via tools like Otterly.ai or Profound). Track branded search lift in Search Console and Google Trends. Add 'Google AI Overview' as an explicit option on your lead-form attribution field. ### What about AI Overview hallucination risks? ▾ AI Overviews experienced public scrutiny over hallucination issues during the early broad rollout in May and June 2024, with widely-shared examples of low-confidence answers. Google has since tightened guardrails and reduced the frequency of low-confidence outputs. From a content-owner perspective, the directional principle is unchanged — stronger grounding signals (citations, schema, primary sources, clear entity definitions) reduce the chance of being misrepresented inside an AI Overview. ### Does Schema.org structured data help with AI Overviews? ▾ Yes. Google has confirmed Schema.org structured data is a supported signal for AI Overviews. Article, FAQPage, HowTo, and Organization schema all help Google parse your content into discrete extractable units, which improves the odds of citation. Schema is one of the highest-leverage technical levers you can pull, alongside E-E-A-T signals and citation-rich content design. ### How are Google AI Overviews different from ChatGPT Search? ▾ Google AI Overviews are integrated above traditional Google search results on a subset of queries, powered by Google's Gemini family of models, and grounded against Google's organic web index using standard ranking signals. ChatGPT Search is a separate consumer product launched by OpenAI on October 31, 2024, integrated inside ChatGPT, that retrieves and cites live web content via a different model and retrieval stack. Both include numbered citations, but they are different products on different surfaces — and a comprehensive LLMO strategy optimises for both. [Previous in AI Search & LLMO ### Entity SEO for AI: Build Machine-Readable Authority in 2026 ](/en/insights/entity-seo-for-ai)[Next in AI Search & LLMO ### Schema.org for AI: Structured Data That LLMs Understand (2026) ](/en/insights/schema-org-for-ai) ## Further reading - [Google — Generative AI in Search (broad AI Overviews launch, May 2024)](https://blog.google/products/search/generative-ai-google-search-may-2024/)· blog.google - [GEO: Generative Engine Optimization (Aggarwal et al., 2024)](https://arxiv.org/abs/2311.09735)· arxiv.org - [SparkToro — 2024 zero-click search study (Rand Fishkin / Datos)](https://sparktoro.com/blog/in-2024-we-finally-satisfyingly-measured-how-much-of-google-search-traffic-goes-to-google-properties/)· sparktoro.com - [Schema.org — Article, FAQPage, HowTo documentation](https://schema.org/Article)· schema.org ## Related reading [glossary ### What Is LLMO? Large Language Model Optimization Explained Glossary definition of LLMO — the overarching discipline that includes AI Overview optimization. 7 min](/en/insights/what-is-llmo) [deepdive ### Zero-Click Search in the AI Era How AI Overviews and conversational AI assistants pushed zero-click past 60% — and how brands are responding. 12 min](/en/insights/zero-click-search-ai-era) [pillar ### AI Search Optimization: Complete Guide for 2026 Full playbook covering Google AI Overviews, ChatGPT, Perplexity, Claude, and Gemini. 14 min ](/en/insights/ai-search-optimization-guide) ## Sources 1. [Google — Generative AI in Search (broad AI Overviews launch, May 2024)](https://blog.google/products/search/generative-ai-google-search-may-2024/)(accessed 2026-05-06) 2. [Aggarwal et al. — GEO: Generative Engine Optimization (arXiv:2311.09735, 2024)](https://arxiv.org/abs/2311.09735)(accessed 2026-05-06) 3. [SparkToro / Datos — 2024 zero-click search analysis (Rand Fishkin)](https://sparktoro.com/blog/in-2024-we-finally-satisfyingly-measured-how-much-of-google-search-traffic-goes-to-google-properties/)(accessed 2026-05-06) 4. [Schema.org — Article, FAQPage, HowTo, Organization documentation](https://schema.org/Article)(accessed 2026-05-06) Next scheduled review: 2026-12-15 ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. 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