Tag: AI Overviews

What UK Scrutiny of Google Search Could Mean for Businesses
UK scrutiny of Google Search is moving beyond complaints about individual ranking changes. As reported by CrushPress.AI, the Competition and Markets Authority (CMA) is pressing Google on three connected issues: how organic results are ranked, how publishers can respond to AI Overviews, and whether users can transfer their search data to authorized services.

Taken together, the reported requirements point toward a broader form of accountability. The central question is not simply whether Google may update Search, but whether affected businesses receive understandable rules, meaningful notice and workable ways to challenge decisions.

Key takeaways
- The CMA reportedly wants Google to apply objective, non-discriminatory criteria to organic results, including AI Overviews but excluding sponsored placements.
- Businesses would gain clearer explanations of ranking practices, advance notice of significant changes and a defined process for raising concerns.
- Site owners would be offered a way to opt out of AI Overviews, according to the supplied report.
- A separate data-portability requirement would let users transfer search data to authorized third parties.
- The difficult boundary will be providing useful transparency without exposing ranking systems to manipulation.
The CMA is treating ranking governance as a business issue

According to CrushPress.AI, UK businesses told the CMA that Google’s ranking practices lack fairness and transparency. Their concerns reportedly include changes being introduced without enough notice and inadequate channels through which affected companies can question those changes.

The CMA’s reported response addresses both the substance of ranking and the process surrounding it. Google would be expected to use objective and non-discriminatory criteria for organic results, explain more about how ranking works, warn businesses before significant changes and establish procedures for receiving and addressing complaints. The report gives Google six months to implement the ranking-related measures.

This distinction matters. A business can lose visibility even when a search system is operating according to its stated goals. Procedural safeguards would not guarantee a particular position, but they could help businesses distinguish an ordinary competitive loss from a technical problem, an unexplained policy shift or a decision worth challenging.

AI Overviews expand the transparency question

The supplied report says the organic-results requirements include AI Overviews while excluding sponsored results. It also says Google must provide site owners with a way to opt out of AI Overviews. That combination places AI-generated answers within the same policy discussion as conventional search visibility, rather than treating them as an entirely separate product issue.

For publishers, an opt-out mechanism introduces a consequential choice. Participation may offer exposure inside an AI-generated search feature, while opting out may provide greater control over how material is used or presented. The source does not specify the mechanism’s design or its effect on ordinary search listings, so businesses should not assume what opting out would do until operational details are available.

The inclusion of AI Overviews also raises the standard for useful explanations. Traditional ranking transparency concerns which pages appear and in what order. AI-generated results add questions about which sources contribute to a synthesized answer and how prominently those sources are represented. The reported CMA measures establish a direction for oversight, but the supplied account does not describe the level of AI-specific disclosure Google would have to provide.

Data portability targets a different source of market power

Ranking rules govern how businesses reach search users; data portability concerns what users can do with the information generated through their own search activity. CrushPress.AI reports that the CMA wants Google to let users transfer search data to authorized third parties within three months.

The examples in the report include rewards platforms and businesses offering personalized deals or discount codes. It also suggests that access could support tailored travel recommendations and more relevant shopping offers. These are possible uses rather than confirmed services or outcomes.

Conceptually, portability can reduce the advantage created when useful history remains inside one platform. Its practical effect, however, will depend on details not provided in the source: what information is transferable, how authorization works and what safeguards accompany access. The ranking and portability measures therefore address different relationships with Google Search, but both attempt to give outside parties more agency.

Useful disclosure does not require publishing the algorithm

The supplied article is skeptical that Google will comply readily, arguing that extensive disclosure could expose a valuable ranking system to competitors or make manipulation easier. That concern identifies the central implementation tension, but it does not necessarily make meaningful transparency impossible.

There is a difference between revealing a complete ranking formula and explaining the governance around it. Clear policy criteria, notice of consequential changes, documented complaint routes and reasoned responses can improve accountability without publishing every signal or its weighting. The value of the CMA’s reported intervention will therefore depend less on the volume of information released than on whether businesses can use it to understand and contest material decisions.

Businesses should watch for the eventual scope of the AI Overview opt-out, the specificity of ranking-change notices and the independence and responsiveness of the complaint process. Those implementation details will determine whether the measures alter day-to-day dealings with Google or remain largely procedural.

The next phase will test whether the CMA’s reported deadlines produce workable controls while preserving the integrity of search results. For publishers and other search-dependent businesses, the most important development will be whether formal scrutiny becomes practical leverage when visibility changes.

References
- CrushPress.AI — How UK Authorities Are Challenging Google’s Search Practices
June 17, 2026
AI Platforms Face Publisher Accountability on Two Fronts
Publisher accountability disputes are converging on two different stages of the AI supply chain: how platforms acquire protected material and what they say after processing it. One dispute challenges the collection and distribution of publisher content through Common Crawl; another treats false statements in Google’s AI Overviews as content for which Google may be directly responsible.

Together, the reports suggest that platforms may find it harder to rely on a single intermediary defense. Publishers are pressing for control before their work enters AI systems and for meaningful remedies when those systems generate unsupported claims.

Key takeaways
- AI accountability is developing at both the input layer, where publisher content is collected, and the output layer, where generated answers can affect publishers.
- Digital Content Next argues that copyright requires permission rather than a publisher opt-out, while Common Crawl disputes allegations that it bypasses paywalls or misleads publishers.
- The reported Munich ruling treated disputed AI Overview statements as Google’s own content because they presented standalone claims rather than merely directing users to sources.
- Links and removal procedures do not resolve the same problem: attribution cannot correct an unsupported generated accusation, while output accuracy does not answer whether source material was authorized.
One accountability debate begins before generation

The Common Crawl dispute concerns the material available to AI developers before a model produces any answer. According to the source report, Digital Content Next sent the Common Crawl Foundation a cease-and-desist letter demanding that it stop collecting and distributing protected content belonging to its members. The organization also sought removal of member content already present in datasets, including paywalled and subscriber-only articles.

The report identifies Digital Content Next as representing publishers including the Associated Press, The New York Times, NBC Universal, Bloomberg, NPR and Fox. Its position is that copyright is not an opt-out regime and that making protected material available for AI development without authorization or compensation constitutes infringement. These remain claims advanced by the publisher group, not findings reported as having been resolved by a court.

Common Crawl presents a different account. Executive Director Rich Skrenta denied bypassing paywalls or misleading publishers and said the foundation responds to requests to remove previously collected material within the constraints of its dataset architecture. The source also notes that Common Crawl maintains a registry of sites that have opted out, while Digital Content Next questions whether the organization’s stated compliance has been adequate.

The practical importance extends beyond one crawler. The report describes Common Crawl, established in 2008, as a repository containing billions of webpages and as an important source of AI training material. It also relays two indicators of that role: The New York Times’ 2023 lawsuit against OpenAI reportedly said Common Crawl supplied 60% of GPT-3’s training data, and a 2024 Mozilla Foundation paper reportedly concluded that generative AI would scarcely exist in its current form without the repository. Those figures and characterizations are source-reported rather than independently verified here.

A second debate begins when an AI answer causes harm

$Readers face information tiles projected by an AI terminal while one warped tile casts a fractured shadow on a publisher's desk.$

The reported German ruling addresses a later stage: responsibility for claims generated after information has been collected and processed. The Regional Court of Munich reportedly considered false AI Overview statements that connected two Munich publishers with scams and questionable practices even though the linked pages did not support those allegations.

According to the account, the misinformation resulted from the system conflating information about other entities with information about the publishers. That detail matters because the disputed allegations apparently could not be traced to the cited pages. If Google were treated only as a conduit, the affected publishers would have no obvious third-party author to pursue for the newly assembled claim.

The court reportedly rejected that characterization. It viewed AI Overviews as processing material and presenting it in a distinct form, not simply listing third-party pages. Because the accusations appeared as complete answers and were created through a feature and algorithms controlled by Google, the court treated them as Google’s own content. Traditional protections for search engines acting as indirect intermediaries therefore did not apply in the same way.

The presence of links did not shift the burden back to users. The ruling account says the court rejected the argument that readers could verify the claims by opening the cited pages, reasoning that the Overview presented assertions that stood on their own. The resulting injunction required Google to refrain from repeating the disputed allegations. The court also reportedly considered comparison against primary sources technically possible, at least in analogous circumstances.

Permission, provenance and accuracy require separate controls

The two disputes are related, but they should not be collapsed into a single copyright or misinformation issue. The Common Crawl conflict asks whether material may be copied, retained and redistributed for AI development. The Munich case asks who owns the consequences when a platform transforms information into a new, unsupported statement. A platform could improve its answer verification without resolving a publisher’s rights objection, just as it could license every source and still generate a false claim.

Provenance also has different functions at each stage. During collection, it can identify where material came from, what access conditions applied and whether a removal request covers stored copies. At the answer stage, citations can help users inspect supporting material, but they do not establish that the generated wording is supported. The Munich report illustrates the gap: the pages were linked, yet the allegations attributed to them were reportedly absent.

This distinction changes what meaningful platform accountability looks like. Input governance concerns authorization, access controls, opt-out or consent signals, retention and downstream distribution. Output governance concerns entity matching, faithful synthesis, verification against cited material, correction and prevention of repeated harmful claims. Treating either set of controls as a substitute for the other leaves publishers exposed at a different point in the system.

What publishers can learn from the two disputes

For publishers, evidence should be organized around the stage at which the alleged failure occurred. A collection dispute depends on records such as ownership, access conditions, crawler instructions, removal correspondence and the continued presence or distribution of material. A generated-answer dispute instead depends on preserving the exact output, its citations, the underlying pages and the differences between what those pages say and what the platform asserted.

The reported cases also make platform promises worth examining at an operational level. A stated opt-out policy is not the same as confirmed removal from existing datasets. A cited answer is not necessarily a supported answer. A correction mechanism is not necessarily protection against repetition. Publishers evaluating an AI platform’s accountability can therefore ask whether its controls cover historical data as well as future collection, and whether answer citations are checked for actual support rather than merely attached.

Legal conclusions will depend on jurisdiction and the facts of each dispute, so the German ruling should not be treated as a universal rule and Digital Content Next’s allegations should not be treated as adjudicated findings. Their combined significance is narrower but still substantial: AI systems are prompting separate challenges to assumptions that web access implies permission and that automated synthesis remains neutral intermediation.

If consent requirements become stronger, the Common Crawl report suggests that licensed sources could gain importance relative to broadly collected web content. If courts continue to distinguish generated answers from conventional search results, platforms may also need more rigorous source validation and remedies at publication time. The durable accountability model will have to govern both directions of the exchange: what AI platforms take from publishers and what they publish about them.

References
- CrushPress.AI – Google Held Accountable for False AI Claims in Germany
- CrushPress.AI – Publishers Demand Halt in AI Data Collection by Common Crawl
June 10, 2026
Google AI Search Ads: Access, Control and Measurement
Google’s AI-enhanced search experiences are changing more than ad placement. They are separating campaign management into three distinct questions: how advertisers gain access to AI Search inventory, how they guide automated decisions, and how they measure results when reporting remains incomplete.

The supplied CrushPress.AI report, based on a discussion involving Google Ads Liaison Ginny Marvin and the PPC Chat community, offers useful answers across those questions. Viewed together, its details point to a system in which participation remains relatively open, but effective optimization increasingly depends on strong data and carefully defined instructions.

Key takeaways
- AI Max is not reported to be a prerequisite for ads to appear in AI Overviews or AI Mode.
- Broad match can provide a route into AI Search inventory, while AI Max can extend matching beyond the advertiser’s explicit keyword set.
- AI Search ads do not yet have a distinct reporting breakdown, limiting advertisers’ ability to isolate their contribution.
- Google’s direction combines automated matching with advertiser guidance, first-party data and measurement designed for longer conversion cycles.
AI Search eligibility is broader than AI Max

One of the most consequential distinctions in the report is between eligibility and expansion. According to CrushPress.AI’s account of Marvin’s comments, advertisers do not need to enable AI Max merely to participate in Google’s AI-driven search experiences. Search campaigns using broad match keywords can still be eligible for AI Overviews and AI Mode.

AI Max instead appears to widen the matching opportunity. The report says it can apply broad-match behavior to phrase and exact match keywords while also enabling keywordless matching. That makes AI Max less of an admission ticket and more of an additional discovery mechanism.

This distinction should shape campaign decisions. An advertiser can evaluate AI Search exposure separately from the decision to grant Google more latitude in matching queries. The relevant question is therefore not simply whether to adopt AI Max, but whether its broader reach fits the campaign’s economics, message constraints and tolerance for automation.

Reporting has not caught up with the new inventory

Access to AI Search inventory does not currently come with equivalent visibility. The source reports that ads appearing in AI Overviews and AI Mode are recorded like other top-of-page ads, without a separate reporting breakdown. It also says Google was still determining what dedicated reporting should eventually look like.

That creates an important analytical limitation. Advertisers may participate in AI Search without being able to isolate its traffic, conversion performance or incremental value from standard search placements. A campaign-level improvement cannot automatically be attributed to AI inventory, while a weak result does not reveal whether the problem arose from an AI placement, another top-of-page impression or a broader campaign setting.

Until reporting becomes more granular, AI Search should be treated as part of the campaign’s combined delivery environment. Conclusions about its standalone effectiveness would go beyond the evidence available in the reported interface.

AI Brief and first-party data serve different roles

The report describes AI Brief as a forthcoming control layer for AI Max. Advertisers are expected to be able to supply guidance covering matters such as target audiences, preferred message themes, priority search intents and prohibitions including instructions not to mention prices. CrushPress.AI says the rollout was planned to begin with English-language Search campaigns before extending to Performance Max and Shopping campaigns.

Those instructions and an advertiser’s data are complementary rather than interchangeable. AI Brief can communicate strategic boundaries: whom a campaign should address, which ideas it should emphasize and what it should avoid. First-party data provides signals about actual customer and conversion outcomes.

CrushPress.AI reports that Google emphasized data quality through a concept called Data Strength and pointed to Enhanced Conversions and Google Tag Gateway as important tools. The broader implication is that automation does not eliminate foundational measurement work. If the underlying signals are incomplete or unreliable, more sophisticated matching and creative guidance cannot supply the missing business evidence.

Measurement is moving toward longer customer journeys

Qualified Future Conversions, or QFC, represents another part of Google’s reported direction. The source describes it as a metric that estimates potential conversions occurring within 180 days after an ad interaction. It was reportedly being tested with selected advertisers and was presented as especially relevant to B2B and lead-generation businesses with lengthy sales cycles.

QFC addresses a different measurement problem from the missing AI Search breakdown. Dedicated placement reporting would help advertisers understand where an interaction occurred; a future-conversion estimate is intended to help evaluate what that interaction may eventually produce. Neither capability substitutes for the other.

The report also identifies new AI Search ad formats, QFC and YouTube Creator Partnerships as three areas Marvin highlighted after Google Marketing Live. Together, those priorities suggest attention to discovery, delayed outcomes and creator-led reach. For search advertisers, the immediate challenge is to prepare reliable inputs and explicit campaign guidance while avoiding stronger claims about AI-specific performance than the available reporting can support.

What advertisers should prepare next

The most durable preparation is not adoption of every automation feature. It is a campaign structure that can accommodate wider matching without losing strategic intent, supported by dependable conversion signals and documented messaging boundaries. As AI-specific controls and reporting develop, advertisers with those foundations will be better positioned to test new inventory and interpret the results responsibly.

References
- CrushPress.AI — Unlocking AI Search and Ads: Insights from Ginny Marvin
June 10, 2026
AI Search Visibility When Clicks No Longer Tell the Story
AI search visibility is changing what it means for a brand to succeed in search. A result can influence awareness, consideration, or a future branded query without producing an immediate website visit, while an AI-generated answer may describe or recommend a business before the user encounters its pages directly.

The practical response is not to abandon SEO, but to connect search performance with brand representation. The available reporting points to two linked priorities: remaining visible as clicks become less common and giving AI systems enough clear, credible, accessible information to represent the brand accurately.

Key takeaways
- Zero-click growth makes traffic an incomplete measure of search influence, although it remains important for commercial outcomes.
- AI visibility depends on whether systems can understand the brand, find evidence supporting its claims, and retrieve that evidence when answering relevant questions.
- SEO retains particular value for branded, local, and high-intent transactional searches, according to the zero-click study coverage.
- A durable strategy combines owned content with reviews, third-party mentions, case studies, credentials, and consistent business information.
- Measurement should distinguish presence, representation, engagement, and business outcomes instead of treating all search activity as a traffic-acquisition funnel.
Visibility is becoming an answer-layer problem

The reported zero-click trend establishes the scale of the change. The first source, summarizing a SparkToro study based on Similarweb clickstream data, reported that 68.01% of Google searches from January through April 2026 ended without a click. It placed the comparable 2024 share at 60.45%, while cautioning that changes in data sources make long-term comparisons imperfect.

The same coverage reported that the share of searches producing at least one click fell by 9.51 percentage points between 2024 and 2026. That measure included organic results, advertisements, and Google-owned destinations such as Maps and YouTube, but excluded follow-up searches within Google. Meanwhile, the share leading to another Google search reportedly increased by 7.2 percentage points. Together, those findings suggest that search journeys are increasingly being continued or resolved inside the results environment.

AI-generated results may reinforce that pattern, but the source does not establish a single cause. It reported that AI Overviews appeared in more than 20% of Google searches and were associated with a nearly 60% reduction in click-through rates when present. SparkToro suggested that the feature could be contributing to zero-click growth, but the study did not isolate how much of the increase it caused.

AI Mode was a comparatively small part of the observed journey during the study period: only 0.34% of searches reportedly transitioned into it. The article also cited Google’s I/O 2026 announcement that AI Mode had more than 1 billion monthly users and that its query volume was more than doubling each quarter. Those figures describe different dimensions, so they should not be treated as contradictory: one concerns transitions recorded in a particular clickstream study, while the other concerns Google’s reported product usage.

Brand presence depends on what machines can establish

Lower click-through rates create a distribution challenge, but the second source identifies a representation challenge as well. AI systems form a picture of a business from the information available across its digital footprint. Websites, content, reviews, testimonials, credentials, case studies, and external mentions may each supply only part of that picture. Valuable expertise embedded in sales conversations, customer support, project delivery, and other daily operations may remain invisible unless it is documented and published.

Understandability

The source’s understandability test asks whether an AI system can determine who the organization is, what it does, and whom it serves. About pages, product or service pages, and structured data contribute to that understanding. They become more useful when names, offerings, audiences, locations, and differentiators are expressed consistently rather than scattered across ambiguous pages.

Credibility

Understandable claims still require support. The source frames credibility through notability, experience, expertise, authoritativeness, trustworthiness, and transparency. In operational terms, that means connecting assertions to visible evidence such as case studies, credentials, customer testimony, responsible authorship, and clear information about the business. Independent reviews and mentions can complement owned claims because they show how other parties describe the brand.

Deliverability

Evidence has limited value if relevant systems cannot retrieve it in the context of a user’s question. The source associates deliverability with topical content, marketing activity, and authority material. This connects conventional SEO with AI visibility: useful pages still need clear subject focus, accessible presentation, internal relationships, and distribution beyond the company website.

A practical operating model joins SEO and brand evidence

The synthesis of the two sources is a shift from optimizing isolated pages to managing a verifiable body of brand knowledge. A business can begin by creating a maintained source of truth for its identity, offerings, audiences, locations, expertise, policies, and substantiated differentiators. This is not necessarily a single public page; it is an internal reference that helps teams publish consistent information across appropriate channels.

Operational knowledge should then be converted into suitable public evidence. Repeated customer questions can inform explanatory content. Demonstrable results can become case studies when permissions and context allow. Staff expertise can be attached to identifiable authors or subject-matter contributors. Credentials, review patterns, and relevant third-party recognition can be made easier to verify. The goal is not to manufacture signals, but to expose knowledge and proof that already exist inside the organization.

Distribution matters because an AI-generated answer may assemble its view from more than the brand’s preferred landing page. Core facts should remain consistent across the website, business profiles, relevant platforms, earned coverage, and other legitimate sources. Each channel has a different role: owned pages provide depth and control, customer feedback supplies experience-based evidence, and independent references can reinforce recognition and authority.

This model also clarifies where traditional SEO remains essential. The zero-click coverage cited SparkToro co-founder Rand Fishkin’s view that SEO continues to matter for branded searches, local business inquiries, and high-intent transactional searches. These are contexts in which accurate pages and direct visits can still connect discovery to action. Broader audience development should also occur on the platforms where prospective customers already spend time, even when that activity does not immediately produce referral traffic.

Measurement must separate exposure from acquisition

A traffic-only dashboard cannot show whether a brand appeared inside an answer, was represented accurately, or influenced a later decision. Measurement should therefore follow several layers. Presence concerns whether the brand appears for relevant questions. Representation evaluates whether the answer describes its identity, services, audience, and differentiators correctly. Engagement covers visits, branded searches, profile interactions, and other observable responses. Outcomes connect those interactions to inquiries, qualified demand, sales, retention, or another business objective.

These layers should not be collapsed into a single visibility score. A mention can be prominent but inaccurate; an accurate citation can produce no click; and a decline in noncommercial traffic can coexist with strong performance on branded or high-intent searches. Separating the layers makes diagnosis more useful: unclear representation points toward content and entity consistency, weak credibility points toward missing evidence, and limited reach points toward discoverability or distribution.

The reported study also sets an important analytical boundary. Its dataset covered U.S. Google desktop and mobile web searches, estimated that two-thirds of searches occurred on mobile devices, and excluded searches inside Google’s mobile search app, where the source said zero-click behavior might be higher. Results should therefore be treated as directional evidence from a defined sample rather than a universal benchmark for every audience, market, or search environment.

As answer interfaces expand, the strongest search programs will be built around both retrieval and reputation. Brands that keep their knowledge current, support claims with accessible evidence, and evaluate how they are represented will be better prepared for a search journey in which influence often begins before any click occurs.

References
- CrushPress.AI — Google’s Zero-Click Searches Surpass 68%: A 2026 Study Insight
- CrushPress.AI — How AI Shapes Your Brand’s Digital Presence
June 9, 2026
Navigate AI-Driven Searches with Engaging Reading Strategies

I’ve realized that AI Overviews are fundamentally changing how users interact with search results. Gone are the days of simple, task-oriented searches. Today, AI Overviews encourage users to dive into comprehensive reading sessions right on the search engine results pages (SERPs).

Let’s talk about some critical insights. AI Overviews merge multiple search intents into a single reading session, disrupting the traditional understanding of search behavior. Winning what I call the ‘second impression’ is crucial for different types of web pages.

Recently, I teamed up with Eric Van Buskirk from Clickstream Solutions to analyze vast amounts of anonymized clickstream data. We discovered that time-on-SERP is no longer solely dependent on search intent when AI Overviews are in play.

Historically, search intent—navigational, informational, etc.—predicted user behavior. But with AI Overviews, now users spend similar amounts of time regardless of their initial intent.

These insights are crucial. Consider Google’s change in approach: it’s less about presenting links and more about providing exact answers. This requires us to think differently about how we engage users.

For operators like me, understanding the significance of the ‘second impression’ helps us adapt our strategy for product, category, and blog pages.

In product detail pages (PDPs), it’s important to manage schemas and compare competitors’ offerings. On category detail pages (CDPs), having visible filters and vast product arrays can make all the difference.

$```json { "alt": "Visual guide outlining three playbooks for PDP, CDP, and Blog content focusing on trust and relevance signals.", "caption": "Discover strategic playbooks for product detail, category detail pages, and blogs to boost trust and relevance. Enhance online visibility with targeted schema and content strategies.", "description": "This image presents a structured guide titled 'THE_SECOND_IMPRESSION_HAS_3_PLAYBOOKS', focusing on enhancing online trust and relevance through three types of content: Product Detail Pages (PDP), Category Detail Pages (CDP), and Blogs. It details strategies like using product schemas, comparison review counts, and exposing filter facets for better Google sitelinks. The guide emphasizes the importance of visible publication dates and article schemas. Ideal for SEO and content strategists aiming to enhance SERP visibility. Source: Growth Memo." } ```$

As for blog content, I’m focusing on credibility signals like publication dates and author names within schema markup to gain trust and validation clicks.

Instead of predicting user behavior as before, the new focus is on optimizing my content’s visibility and trustworthiness in an AI-influenced SERP landscape. This shift doesn’t change our core content strategy but adds new layers of intricacy to how we optimize for SERP.

Inspired by this post on Search Engine Land.

June 3, 2026
Unveiling Google Search Console’s AI Controls and Reports

As someone who eagerly follows Google’s updates, I was thrilled to learn about the latest developments in Google Search Console. Recently, Google has started to roll out new Search Generative AI performance reports. These reports, along with a feature to block your content in AI responses, are designed to give website owners more control.

Currently, these features are being introduced to a select group of website owners in the UK, but there are plans to expand access in the near future. This gradual rollout allows us to get accustomed to these changes before they become widely available.

Exploring the Search Generative AI Performance Report

The new AI performance report in Google Search Console is something I’ve been anticipating. Although it doesn’t cover everything, it does provide some important insights into how our content is performing within AI responses, AI Mode, and AI Overviews on Google Search. The report includes data on impressions, pages, countries, devices, and dates. However, a notable omission is click data, so we’re left guessing about the exact number of searchers clicking through to our sites from AI responses.

Google stated:

– We’re rolling out new insights for website owners regarding their pages’ appearances in generative AI Search features. These insights include impressions metrics and information on which pages appear in AI responses and in which countries. We’re working closely with website owners to determine what insights would be most helpful and will expand the metrics available over time.

Additionally, Google shared more details about the metrics we can expect:

– Impressions: Frequency of your site’s URLs appearing in generative AI features in Search and Discover.

– Pages: Identifying URLs that appeared within AI features.

– Countries: Understanding visibility on a country basis.

– Devices: Identifying the devices used to view your website. Available for Search results.

– Dates: Monitoring performance with hourly, daily, weekly, and monthly granularity.

I inquired about click data from a Google representative, who mentioned that they are exploring additional metrics that will help inform our strategies in the future.

Initially, this report is available to a subset of users in the UK, with plans to expand globally in the future.

If you want to explore more about this report, I recommend checking out the Google help center document.

Introducing AI Blocking Controls

Another exciting feature Google introduced is the ability to block your content from appearing in AI search features like AI Overviews, AI Mode, or AI Discover. Google described this as a “new toggle” within Google Search Console, allowing us to decide whether or not our site should be part of these AI search features.

Google notes that opting out will prevent your site from receiving traffic or impressions from these features. Importantly, this control won’t affect your ranking in standard search results outside of generative AI Search features, so there’s no risk of negatively impacting core web search visibility.

Again, like the performance report, this toggle is currently available to a subset of UK website owners, with plans to widen access as they complete further testing. Google had promised these controls after facing some backlash from the EU, and it’s promising to see them starting to roll out now.

One study even showed that 1/3rd of SEOs are willing to block Google from showcasing their content in AI search features.

Why It Matters

As site owners and publishers, many of us have been asking for control over how and if our content appears in Google’s AI features. Now, we have just that. Although it’s initially limited, I’m hopeful these features will eventually be available to all.

Moreover, we’ve been requesting AI Search reporting from Google from day one. With Google’s announcement following Bing’s release of its own AI performance report, we’re taking a significant step forward. While Google’s report currently targets UK site owners and lacks click data, it holds promise for a global rollout soon.

Inspired by this post on Search Engine Land.

June 3, 2026

How to Measure AI Search Visibility, Traffic, and Value

You can see organic impressions rising, spot visits from an AI assistant, and still have no defensible answer when someone asks whether AI search is helping the business. The problem is rarely missing data. It is treating visibility, visits, and outcomes as if they were the same thing.

You need an evidence chain. Search Console shows where discovery may be changing. GA4 shows what identifiable visitors do. Google Tag Manager can add section-level context. Used together, they turn an ambiguous channel into something you can manage.

Key takeaways

Measure AI visibility, traffic, engagement, and business outcomes separately.
Use Search Console for query and page trends, but do not label every organic change as an AI effect.
Use GA4 to evaluate identifiable AI referrals, Google organic landings, engagement, and key events.
Use GTM text-fragment tracking as supporting evidence that visitors are arriving at specific passages, not as proof of an AI citation.

Start with the questions your data can answer

A useful measurement plan starts with business questions, not a dashboard labeled “AI traffic.” The practical shift is to make AI search part of your broader search program because it can change how people discover and evaluate answers, even when the eventual visit resembles ordinary organic traffic.

Question	Signal to inspect	Primary tool	Decision it supports
Are relevant pages becoming easier to discover?	Impressions and clicks for stable query groups and landing pages	Google Search Console	Whether to strengthen topic coverage, answer clarity, or search-result appeal
Are identifiable AI services sending visits?	Sessions grouped by referral source and landing page	GA4	Which sources and pages deserve closer attention
Do those visits show useful engagement?	Engagement and navigation after the landing page	GA4	Whether the page satisfies the apparent intent and offers a sensible next step
Are visitors being sent to a particular passage?	A text-fragment landing event tied to a stable section label	GTM and GA4	Which answer blocks should be maintained, expanded, or connected to deeper content
Does the activity create business value?	Relevant key events or conversions by source and landing page	GA4	Whether visibility is contributing to a meaningful outcome

Keep these signals in separate columns. Search Console clicks and GA4 sessions come from different measurement systems, so forcing them to reconcile can create false confidence. Their job is to corroborate a pattern, not produce an identical total.

There is another important boundary: an AI-generated answer can expose your brand without producing a click. A traffic-only report misses that possibility. A visibility-only report, meanwhile, cannot tell you whether the exposure helped the business. Your dashboard needs both, with the limitation stated plainly.

Configure Search Console, GA4, and GTM as one evidence stack

Three connected measurement instruments represent search discovery, visitor journeys, and section-level event tracking.

Use Search Console to establish the discovery baseline

Begin with query-and-page pairs rather than sitewide totals. Group queries by intent, such as branded questions, informational problems, comparisons, and decision-stage searches. Keep each group’s definition stable so a later movement reflects the data rather than a changing filter.

For every group, retain impressions, clicks, click-through rate, average position, and the landing pages receiving visibility. Add an annotation whenever you materially revise an answer, heading, structured content block, title, or internal link. Compare the same group across consistent reporting windows and check whether the affected pages moved in the expected direction.

This is evidence of changing search performance, not automatic proof that an AI Overview caused the change. Search Console query analysis can help you investigate the impact of AI-driven discovery, but you still need landing-page and engagement evidence before making a stronger attribution claim.

Use GA4 to separate arrival from value

Create a reporting view for recognizable AI-assistant referrals. Maintain the source rule explicitly and record when you change it; otherwise, a larger referral list can masquerade as traffic growth. Report the original source alongside landing page, engagement, useful downstream navigation, and the key event that represents value for your site.

Keep Google organic traffic in its own segment. A visit that began around an AI feature on a Google results page may still appear as Google organic rather than carry a clean feature label. That makes the landing page, associated Search Console query trend, and on-page behavior more useful than the channel name alone.

Choose outcomes that match the page’s purpose. A documentation page may be expected to lead to another help resource. A commercial page may be expected to produce a qualified inquiry or purchase-related action. If you apply the same conversion expectation to every content type, useful informational visits can look like failures and weak commercial visits can look healthier than they are.

Add section-level context with text fragments

Text fragments can open a page at a specific passage. GTM can detect that kind of landing and send a custom event to GA4. Use a clear event name, attach the page path and a stable section identifier, and classify the referrer when it is available.

Do not send the literal highlighted text as an analytics parameter. It can create noisy, high-cardinality data and may capture words you do not want stored. Map the arrival to a controlled label such as the section’s internal identifier instead.

Test the trigger in GTM preview mode, confirm the event in GA4’s debugging view, and then verify that the live event carries the expected page and section labels. A text-fragment event only tells you that a targeted passage was opened. Treat it as corroborating evidence when it aligns with query visibility, a plausible referrer, and meaningful behavior.

Read patterns without claiming more than the data proves

Visibility rises while clicks stay flat

Your page may be appearing for more searches without giving people a reason to continue. It may also be losing clicks for reasons unrelated to AI. Inspect the affected queries and search results before changing the page. If the page already answers the immediate question, make the next value clear: a decision framework, working example, template, calculator, or deeper explanation. Do not weaken the answer merely to manufacture a click.

Traffic rises while useful outcomes stay flat

Check whether the landing page matches the intent implied by its query or referral context. Then inspect the path after arrival. A strong answer with no relevant next step can earn attention without moving the visitor forward. Add a specific internal link or call to action beside the passage that resolves the initial question, and measure that action separately from generic page engagement.

Text-fragment arrivals concentrate on one section

Treat that section as a content asset. Give it a descriptive heading, keep its central answer self-contained, remove references that make no sense out of context, and place the most relevant deeper resource nearby. Watch whether later edits preserve fragment arrivals and downstream behavior. The event is a prioritization signal, not proof that every visit came from an AI answer.

AI referrals appear without a matching Search Console change

The visits may originate outside Google, or your referral grouping may be too broad. Validate the source values and landing pages before connecting the movement to search visibility. If the visits are legitimate, evaluate their behavior on their own terms rather than expecting Search Console to explain a different discovery surface.

Turn the dashboard into an optimization workflow

An analyst reviews an abstract dashboard beside a circular sequence of investigation, optimization, testing, and measurement steps.

For each priority query group and landing-page family, record the visibility signal, arrival signal, engagement signal, business outcome, material content change, interpretation, confidence, and next action. This format forces you to distinguish an observation from an explanation.

A defensible note might say that impressions increased after an answer block was revised, while clicks and qualified actions did not move in the same direction. That supports further inspection of search-result appeal and the page’s next step. It does not support a claim that AI visibility generated revenue.

Use the weakest part of the chain to choose the work. Weak discovery calls for better intent coverage and clearer answer structure. Strong visibility with weak arrival calls for a more compelling continuation. Strong arrival with weak outcomes calls for closer intent alignment and a better next action. Concentrated fragment landings call for maintaining and extending the section people are being sent to.

Start with your highest-priority query cluster and its landing-page family. Establish the baseline, confirm the instrumentation, annotate the next meaningful change, and wait for the full evidence chain before declaring success. You will get a smaller headline than an all-purpose “AI traffic” number, but a far more useful decision.

References

May 28, 2026

Search Marketing in the AI Era: What Your Strategy Needs
Your rankings may look stable while fewer people visit your site. Paid campaigns may still meet their targets while giving you less control over how each bid is made. That does not mean search marketing is disappearing. It means the interface, measurement model, and division of labor are changing.

You need a strategy that works when a search engine answers the question itself, an AI assistant summarizes several options, or an automated system decides which ad to show. The practical response is to make your expertise easier to retrieve, measure outcomes beyond clicks, and reserve human attention for decisions machines cannot make well.

Treat AI search as another interface, not a separate market

Search has changed interfaces before. Voice queries became part of ordinary search behavior rather than a completely independent discipline. AI answers are following a similar pattern: people still want to learn, compare, decide, and act, but they may complete more of that journey without opening a traditional result.

This matters because AI Overviews can change publisher traffic and searcher behavior. A lower click-through rate does not automatically mean demand has fallen. Your answer may have been consumed before the visit, or your brand may have appeared during research without receiving the final click.

Organize your strategy around the user’s task, not the surface where the query appears. For each important topic, identify what someone needs while learning, what objections arise during comparison, and what evidence supports a decision. Then make sure the same facts remain consistent across your pages, structured data, product information, business profiles, and paid landing pages.

Do not create an isolated AI content program that competes with your SEO program. Give one owner responsibility for the accuracy of each core topic, then adapt that knowledge for conventional results, answer engines, assistants, and ads.

Build pages that can be understood before they are clicked

A page written only to win a blue-link click often delays the answer, repeats keywords, and hides important qualifications. That is weak service for a person and weak input for a system trying to extract a reliable response.

Make the answer easy to retrieve

State the main answer near the beginning of the relevant section. Use headings that reflect real questions or decisions. Keep definitions, requirements, exceptions, and next actions close to the claim they explain. If a reader must combine fragments from several pages to understand your position, an automated system faces the same unnecessary ambiguity.

Make the evidence easy to evaluate

Name the product, organization, method, or policy you are discussing. Show who the advice is for and when it does not apply. Support important claims with the best available evidence, and keep dates, author details, and update history visible where they affect trust. Useful specificity is more defensible than confident but generic copy.

Use technical clarity as reinforcement

Keep valuable pages crawlable, indexable, internally linked, and represented in your XML sitemap. Search Console grew from XML sitemap work into a broader way for site owners to understand search visibility, but its role is diagnostic rather than corrective: a submitted URL still needs a clear purpose and worthwhile content.

Add applicable schema markup that accurately describes what is already visible on the page. Connect entities consistently and validate the markup after publishing. Structured data is a clarity layer, not an admission ticket to an AI answer or enhanced result.

Let automation handle mechanics while people set direction

Paid search began changing fundamentally when Goto.com introduced a model in 1998 that gave clicks a direct monetary value. The work later expanded from occasional ad changes into complex campaign management, and automated bidding reduced some of the manual effort required to adjust auctions.

That history offers a useful rule for AI adoption: automate a repeatable mechanism, not the responsibility for the result. A bidding system can process auction signals faster than a person. It cannot decide whether your offer is credible, whether a promise fits the brand, or whether a technically efficient campaign is attracting the wrong customers.

Apply the same boundary to organic work. AI can cluster queries, propose outlines, reformat data, identify repeated language, and help inspect large sets of pages. A person should still approve the search intent, factual claims, distinctive point of view, examples, and publication decision. Structural assistance is valuable precisely because it frees experts to spend more time on judgment.

Before automating a task, write down its accepted input, expected output, review standard, and escalation condition. If you cannot describe what a correct result looks like, automation will increase volume without creating dependable quality.

Replace a rankings-only dashboard with an evidence chain

Search Console remains essential, but it does not provide separate, complete performance reporting for every appearance in Featured Snippets or AI Overviews. That creates a genuine blind spot. You cannot repair it by treating ordinary click data as a full record of AI visibility.

For each priority query group, record the user need, the search features present, whether your brand is visible, which page or entity appears to support that visibility, and the business outcome that follows. Use the same query groups when reviewing organic pages, AI answers, and paid campaigns. This gives you a coherent view of demand instead of three disconnected reports.

Pair platform data with first-party outcomes such as qualified enquiries, subscriptions, purchases, retained customers, or another result your organization already trusts. Add manual observations for AI surfaces that are not isolated in reporting. Label those observations clearly; they are snapshots, not precise impression counts.

When performance changes, diagnose the chain in order. Check whether demand changed, whether the results interface changed, whether your visibility changed, whether clicks shifted, and whether conversion quality moved. This prevents a traffic decline caused by an answer feature from being mistaken for a relevance problem, or a conversion problem from being blamed on rankings.

Key takeaways
- Plan around the user’s task across search results, AI answers, assistants, and ads instead of building a separate strategy for every interface.
- Publish direct answers with visible evidence, clear entities, useful qualifications, and accurate structured data.
- Use automation for repeatable mechanics, while people retain control of positioning, creative judgment, factual approval, and business tradeoffs.
- Measure visibility, engagement, and business outcomes as a chain; rankings and clicks alone no longer describe the whole journey.
- Document what good output means before scaling any AI-assisted workflow.
Start with one commercially important topic. Map its user decisions, strengthen the page that answers them, validate its technical signals, inspect how it appears across conventional and AI search, and connect that visibility to a real outcome. Once that evidence chain works, expand it topic by topic.

References
- Search Engine Land – Brad Geddes on 20 years of paid search evolution
- Search Engine Land – Vanessa Fox on the birth of Google Search Console
May 28, 2026
Discover How AI Transforms User Behavior in Search Results
I find it fascinating that users interact differently when faced with AI Overviews compared to AI Mode. New clickstream data reveals that AI Overviews significantly alter user behavior—from reverse scrolling to extended evaluation of search results across various intents.

Take Netflix, for example. The average user spends about 18 minutes just browsing. They skim through tiles, watch trailers, and often circle back. It turns out, searching isn’t much different these days, thanks to new insights.

This week, I’m diving into:
- Four notable behavioral shifts observed with AI Overviews, gathered from over 846,000 Google sessions.
- The evolving role of brand-name searches and why they no longer offer the same shortcuts.
- An insight that might change how you craft title tags and meta descriptions this quarter.
Eric Van Buskirk from Clickstream Solutions mined anonymized clickstream data supplied by Surfer SEO. The study analyzed around 846,000 U.S.-based Google searches from February and March of 2026.

This marks the fifth study on user behavior with Google’s AI features over the past year. Earlier, a UX study on 70 users in May 2025 utilized think-aloud and screen recording methods, while a study from October 2025 examined AI Mode specifically. This research trades depth for scale, uncovering patterns too subtle for smaller studies.

For a bit of context, previous SERP mouse-tracking studies involved only a handful of people—this one, however, evaluates queries from tens of thousands of users.

A fascinating contrast surfaces: User behavior in AI Overviews starkly opposes that in AI Mode, where AI Mode is akin to autoplay, while AI Overviews replicate the browsing experience.

This article outlines four major findings from this recent study and how they might influence your title tags and meta descriptions in 2026. Full methodology available here.

With groundbreaking insights, like how nearly half of AI Overview interactions involve reverse scrolling and how search types no longer reliably predict behavior, this data is invaluable. It challenges traditional assumptions and has meaningful implications for e-commerce and decision-heavy categories.

Surprising findings include brand searches losing their shortcut advantage, implying even users searching specifically for brands might pause to consider adjacent content on the SERP.

Read more intriguing insights on how the AI landscape shifts user engagement and strategy in SEO.

Inspired by this post on Search Engine Land.
May 27, 2026
Condé Nast Shifts Focus as Google Search Traffic Fades

Over the years, as Google continually tweaked its algorithms and transformed its search results pages, I’ve seen Condé Nast adjust its strategies considerably. Now, we’re designing our business around the notion that search traffic barely impacts us anymore.

In a recent conversation featured on TBPN—the tech media network that’s been likened to “technology’s daily show”—CEO Roger Lynch shared that we’ve stopped regarding Google search as a dependable traffic source.

Here’s what Lynch explained. While Google traffic isn’t expected to vanish completely, we’re intentionally planning as if it’s on the decline:

“Last year, I instructed our teams: plan as if there is no search—consider search as non-existent.”

“We’re not saying it will be gone entirely… but we anticipate it will comprise only single digits of our overall traffic—very minimal.”

The background. Throughout the past few years, Lynch has observed a recurring trend: Google’s adjustments consistently exceeded our expectations in reducing our visibility.

“For each of the last three years, we predicted some search traffic declines in our budgets, but it fell even more than anticipated,” he noted.

Why has our search traffic dwindled? Lynch attributes this decline not only to algorithm changes but also to AI Overviews and Google’s increasingly commercial-centric results.

“Seven or eight years ago, search results had a few ads, followed by ’10 blue links.’”

Currently, users first encounter AI Overviews, then a slew of commerce links, pushing organic results further down the page.

“It’s worked out well for Google,” Lynch commented.

A shifting landscape. The alterations made by Google have disrupted the model that other digital entities, like BuzzFeed, used to convert social media and search traffic into revenue.

“That era has ended,” he declared.

Lynch mentioned that brands in the intermediary stages are having the most trouble adapting to changes in AI and search frameworks.

“In today’s world, having a specified niche with a dedicated audience is crucial. Relying solely on advertising to support significant journalism investments is a challenging position,” he stated.

Shifting priorities at Condé Nast. We are now emphasizing brands that excel in these areas:

Dedicated direct audiences.

Potential for subscriptions.

Undeniable expertise in a given niche or category.

Lynch also hinted at a potential advantage for premium publishers against AI-generated content:

“Our audience expects and desires human-generated content. Creating AI-generated content doesn’t play to our strengths. Identifying and building on your competitive advantages is vital.”

Why this matters. Lynch emphasized that the practice of turning search and social media traffic into lucrative businesses is outdated. Publishers lacking a strong brand or dedicated readership might face challenges, as platforms can revise their methods at any moment.

The full interview. You can watch Lynch’s discussion, where he elaborates why human journalism remains crucial in the AI era, starting at 30:28 here.

Inspired by this post on Search Engine Land.

May 13, 2026