Knowledge base v2.13.0 · Community edition · data through 2 October 2026
Topic · AI and Search
A community speaker argued that rankings and clicks no longer equal business outcomes: visibility can rise while clicks fall, and traffic that follows an AI recommendation may arrive direct, through paid search or through another channel. In one controlled test with no other marketing changes, work on a brand's AI visibility coincided with more inquiries and a 36% rise in paid search click-through rate, figures from one unpublished test of one unnamed brand. The talk leaned on third-party studies (Similarweb on AI-recommended brands, a 2018 eye-tracking study on familiar brands) that Google has not published; Google's own position is that total organic clicks are relatively stable and that sites should judge visits by conversions and value, not clicks alone. This matches Google's Day 1 advice to measure success through metrics that matter to the business, and Google's AI features guide suggests tracking conversions and time on site to value traffic from AI features, because Search Console's generative AI report shows impressions but no clicks. Day 1's first lightning session, captured by a second recording, added community methods. One speaker proposed measuring AI search as a five-stage funnel (know, association, search, retrieval, selection) with metrics such as an AI bot allow rate, a brand association rate asked with web browsing switched off, and a brand co-occurrence rate in fan-out queries, arguing that most tracking covers only selection and copies rank tracking with invented prompts of unknown search volume. Another speaker said a study linking JSON-LD to AI visibility was followed by a controlled study that found no benefit, the markup being a sign of teams doing everything else right, that listicles earn more AI mentions because LLMs check that a thing exists rather than whether the content is honest, and cited a study (heard as Peec AI's) in which 43% of prompts produced fan-out queries in another language. An agency built its own AI visibility tracker to control the method, and a community speaker reported that on client sites AI systems cited information near the end of the content. In the Q&A Google said AI query data is hard to provide because AI questions do not map back to keywords and the data would have to be grouped to protect privacy while staying useful. Author’s view: in Peec AI's published study the 43% is the share of fan-out searches for non-English prompts that ran in English, not a share of prompts. Google's AI optimisation guide says structured data is not required for generative AI search, so use markup for rich results and clear data, not as an AI-visibility lever.
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An agency built its own AI visibility tracking tool so it could control the methodology: the team wrote pages of flowchart-style prompts (if this happens, do that) and tested manually, and AI only generated the code.
Speaker James PowleyIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker said a third-party study from early 2026 found that websites with JSON-LD were shown much more often, and that the industry reacted by adding schema markup everywhere.
Speaker Thiago PojdaIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker said a later controlled study, which added schema markup to pages that lacked it and kept everything else unchanged, found only a negative correlation: the markup had been a sign of teams doing everything else right, not a cause.
Speaker Thiago PojdaIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker said listicles do earn more mentions in AI answers, not because they are trusted more but because LLMs mainly check that the thing being talked about exists, not whether the content is honest.
Speaker Thiago PojdaIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker suggested that LLMs pick up some of Google's years of work on judging honesty and trust (E-E-A-T) by proxy, because they send many queries (probably fan-out queries) to search engines.
Speaker Thiago PojdaIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker warned that the SEO industry is overusing listicles for AI visibility the way it once overused links.
Speaker Thiago PojdaIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker cited a third-party study (heard as Peec AI's) finding that 43% of prompts produced fan-out queries in a language other than the one the user searched in.
Speaker Thiago PojdaIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker compared traditional search to walking into a store and searching the shelves yourself, and AI search to asking a sales assistant who searches, selects, summarises and presents the answer for you.
Speaker not identifiedIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker said most AI-visibility tracking copies rank tracking and is worse than it, because it tracks invented prompts with no known search volume.
Speaker not identifiedIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker proposed measuring AI search as a five-stage funnel (know, association, search, retrieval, selection) to learn whether the AI understands, trusts and finally chooses a brand, measuring the earlier stages rather than only the last.
Speaker not identifiedIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker said most AI-visibility measurement covers only the selection stage, whether a brand is mentioned or selected, without showing why or why not.
Speaker not identifiedIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker named two signals that in their view strongly influence whether an AI selects a brand: the bias already in the model's memory before it searches, and the brand's presence in the search results the model is grounded on.
Speaker not identifiedIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker proposed an 'AI bot allow rate' for the know stage: the share of AI training bots a site allows (three of four is 75%), with 100% as the target.
Speaker not identifiedIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker described the association stage as whether an AI already connects a brand with the topics that matter to it before running any search, and said problems there are mostly brand problems.
Speaker not identifiedIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker's 'brand association rate' asks a model, with web browsing switched off so that only its memory answers, to name ten topics it associates with the brand, or the other way round, starting from a topic.
Speaker not identifiedIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker advised repeating brand-association prompts over time: association in every run is good, in about half the runs shows some association with work to do, and little or none shows a clear gap.
Speaker not identifiedIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker proposed a 'brand co-occurrence rate' for the search stage: the share of an AI's fan-out queries that contain the brand (one in three is 33%), which also hints at the associations the model already has; problems there are mostly relevance problems.
Speaker not identifiedIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker said Microsoft is ahead of Google in giving site owners the grounding queries its AI uses.
Speaker not identifiedIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker described the retrieval stage as whether an AI requests a site's pages when grounding its answer; if it does not, the cause may be a crawling or an indexing issue.
Speaker not identifiedIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker described the selection stage as whether a brand is chosen in the answer, measured with mentions and similar metrics; failure there is mostly a trust or relevance issue.
Speaker not identifiedIn Day 1, 13:10 · Lightning session A: Automation and AIEvidence transcript
A community speaker advised placing the most important information at the beginning or the end of a piece of content, because information in the middle is less likely to be cited by AI systems.
“It's relevant to put your most important information at the beginning or at the end.”
Speaker Jovana AvramovicIn Day 1, 16:20 · Lightning session C: CrawlingEvidence transcript
A community speaker reported that on client sites information towards the end of the content got cited, and invited others to check their own examples.
Speaker Jovana AvramovicIn Day 1, 16:20 · Lightning session C: CrawlingEvidence transcript
An audience member asked for data on AI features as a feedback channel for improving content, as an alternative to prompt-tracking tools.
From the audienceIn Day 1, 16:35 · Q&AEvidence transcript
Query data for AI features is hard to provide because people ask AI very different kinds of questions that do not map back to keywords as in classic search, and the data would have to be grouped to protect privacy while staying useful.
Speaker not identifiedIn Day 1, 16:35 · Q&AEvidence transcript
Google's AI optimisation guide says structured data is not required for generative AI search and needs no special schema.org markup, and on Day 2 Google said raw schema.org is generally not put into model context (D2-C477); use markup for rich-result eligibility and clear data, not as an AI-visibility lever.
Author Ibrahim AnjroAnnotates Day 1, 13:10 · Lightning session A: Automation and AI
The study cited on stage matches Peec AI's analysis (reported in February 2026) of over 10 million ChatGPT prompts and 20 million fan-outs: 43% of the fan-out searches for non-English prompts ran in English, and nearly 78% of non-English prompt runs had at least one English fan-out (from 66% for Spanish to 94% for Turkish), so the 43% is a share of fan-out searches, not of prompts.
Author Ibrahim AnjroAnnotates Day 1, 13:10 · Lightning session A: Automation and AI
A community speaker described a business whose visibility and average rankings for many important, high-value commercial keywords improved over a year while its clicks and impressions did not rise with them.
Speaker not identifiedIn Day 3, 14:20 · Lightning session L: Understanding SERPs and your usersEvidence transcript
A community speaker cited a recent Similarweb study as showing that being recommended in AI-driven services multiplies a brand's chance of getting traffic downstream; a Similarweb study reported in June 2026, probably the one meant, found brands recommended by ChatGPT 2.5 times more likely to get a site visit within 7 days (US desktop data, finance, travel and beauty).
Speaker not identifiedIn Day 3, 14:20 · Lightning session L: Understanding SERPs and your usersEvidence transcript
Traffic that follows an AI recommendation need not arrive through organic search but may come direct, through paid search or through another channel, a community speaker said, predicting the effect will show in data in the coming months and years.
Speaker not identifiedIn Day 3, 14:20 · Lightning session L: Understanding SERPs and your usersEvidence transcript
Used byglossary term Downstream impact
A community speaker ran a controlled test with a brand that had seen organic clicks drop: for a set period the only activity was work to raise the brand's AI visibility, with no external campaigns and no budget changes.
Speaker not identifiedIn Day 3, 14:20 · Lightning session L: Understanding SERPs and your usersEvidence transcript
A community speaker reported that a higher rate of recommendation in AI answers brought one brand a really significant uplift in inquiries during a controlled test, without giving a figure.
Speaker not identifiedIn Day 3, 14:20 · Lightning session L: Understanding SERPs and your usersEvidence transcript
During a community speaker's controlled test, the brand's paid search click-through rate rose by 36% while the only activity was work to raise its AI visibility.
Speaker not identifiedIn Day 3, 14:20 · Lightning session L: Understanding SERPs and your usersEvidence transcript
In a community speaker's controlled test the brand's AI visibility was tracked across the main AI engines and was described as really strong early in the experiment (whether as a starting level or a gain was not said).
Speaker not identifiedIn Day 3, 14:20 · Lightning session L: Understanding SERPs and your usersEvidence transcript
A community speaker cited a study from about 10 years ago, repeated since, as finding that 82% of people clicked on a brand they already knew regardless of its position; the matching source is Red C's eye-tracking study of shopping-type searches, reported by Econsultancy in October 2018 (about eight years before the event).
Speaker not identifiedIn Day 3, 14:20 · Lightning session L: Understanding SERPs and your usersEvidence transcript
A community speaker suggested that people who meet a brand at several touchpoints, including in AI services, become familiar with it and are then more likely to choose it in a generic search, which can lift other metrics.
Speaker not identifiedIn Day 3, 14:20 · Lightning session L: Understanding SERPs and your usersEvidence transcript
A community speaker said brand familiarity should be built through AI-driven services and earned media as a whole, by improving content and visibility across services ('earned media' is an uncertain reading of the recording).
Speaker not identifiedIn Day 3, 14:20 · Lightning session L: Understanding SERPs and your usersEvidence transcript
A community speaker's simplified journey: an AI recommendation makes a visit more likely; the person may check the brand on social media or see it while scrolling, search the brand name, and later click it in a generic search because they know it.
Speaker not identifiedIn Day 3, 14:20 · Lightning session L: Understanding SERPs and your usersEvidence transcript
A community speaker recommended picking areas of a business where testing is possible and measuring how AI visibility affects conversion rates and revenue there.
Speaker not identifiedIn Day 3, 14:20 · Lightning session L: Understanding SERPs and your usersEvidence transcript
In an August 2025 post, Google's Head of Search wrote that total organic click volume from Google Search to websites had been relatively stable year-over-year and that Google was sending slightly more quality clicks (clicks where users do not quickly click back) than a year earlier; the post gave no figures.
Publisher Google blog (6 August 2025)Annotates Day 3, 14:20 · Lightning session L: Understanding SERPs and your users
Beyond Search Console, Google's AI features guide suggests tracking conversions and time spent on the site in tools such as Google Analytics to understand the value of traffic from AI features.
Publisher Google Search CentralAnnotates Day 3, 14:20 · Lightning session L: Understanding SERPs and your users
A May 2025 Search Central blog post advised site owners to look at the overall value of visits from Search rather than focusing too much on clicks, using indicators of conversion such as sales, sign-ups, a more engaged audience or information lookups about the business.
Publisher Search Central blog (21 May 2025)Annotates Day 3, 14:20 · Lightning session L: Understanding SERPs and your users
The community speaker's figures (paid search click-through rate up 36%, organic sessions halved, a record month) come from one unpublished test of one unnamed brand with no stated period or baseline; use them as an illustration, not as a benchmark.
Author Ibrahim AnjroAnnotates Day 3, 14:20 · Lightning session L: Understanding SERPs and your users
For a site's own test of AI visibility, write down the period, the baseline and every other marketing change before starting, and compare a test area with a similar control area; paid click-through rate and direct and branded traffic are the signals the community speaker watched.
Author Ibrahim AnjroAnnotates Day 3, 14:20 · Lightning session L: Understanding SERPs and your users
The two studies cited on stage are third-party and narrow (Similarweb, 2026: ChatGPT only, US desktop, three industries; Red C, 2018: eye-tracking on shopping-type searches), so quote them with that scope; Similarweb found that 55.9% of the resulting site traffic came from branded searches, the step after an AI recommendation in the speaker's customer journey.
Author Ibrahim AnjroAnnotates Day 3, 14:20 · Lightning session L: Understanding SERPs and your users
Google's AI optimisation guide says structured data is not required for generative AI search and needs no special schema.org markup, and on Day 2 Google said raw schema.org is generally not put into model context (D2-C477); use markup for rich-result eligibility and clear data, not as an AI-visibility lever.
A community speaker said schema markup helps AI agents interpret a page: on a product page, marking up which number is the price saves the agent from guessing.
The study cited on stage matches Peec AI's analysis (reported in February 2026) of over 10 million ChatGPT prompts and 20 million fan-outs: 43% of the fan-out searches for non-English prompts ran in English, and nearly 78% of non-English prompt runs had at least one English fan-out (from 66% for Spanish to 94% for Turkish), so the 43% is a share of fan-out searches, not of prompts.
A community speaker cited a third-party study (heard as Peec AI's) finding that 43% of prompts produced fan-out queries in a language other than the one the user searched in.
Beyond Search Console, Google's AI features guide suggests tracking conversions and time spent on the site in tools such as Google Analytics to understand the value of traffic from AI features.
Google's guide for generative AI features recommends the Generative AI performance report in Search Console for measuring how content performs in generative AI features on Google Search and Discover.
A May 2025 Search Central blog post advised site owners to look at the overall value of visits from Search rather than focusing too much on clicks, using indicators of conversion such as sales, sign-ups, a more engaged audience or information lookups about the business.
Myth: the old metrics don't work in the AI era. Google's answer: measure success through metrics that matter to your business.