Generative AI reports in Search Console: Google follows the path opened by Bing

The arrival of the new generative AI performance reports in Google Search Console marks an important moment for every professional who closely follows the evolution of organic search. For years, SEO analysis has been strongly focused on relatively stable metrics: impressions, clicks, average position, landing pages, queries, countries and devices. However, the introduction of artificial intelligence-generated answers in search results has changed the way users discover information, assess sources and reach websites.

Google has now announced new reports dedicated to performance in generative AI experiences across Search and Discover. These reports were designed to help website owners understand how their pages appear in features such as AI Overviews, AI Mode and other generative formats integrated into the search experience. This information will continue to be part of the overall performance view, but there is now a specific area to analyse the visibility obtained within these new formats.

This step is relevant, but it does not come out of nowhere. Bing Webmaster Tools had already moved in this direction when it introduced the AI Performance report, which is still in beta. Microsoft’s decision opened a door that many SEO professionals also expected to see within the Google ecosystem: the separation, even if still partial, between traditional visibility in search engines and visibility earned inside AI-generated answers.

At BYDAS, this topic has been followed very closely. The agency has been particularly attentive to the changes that generative AI is bringing to organic search, not only at a conceptual level, but also at an operational level. The use of Bing’s AI Performance in SEO reports for clients has already made it possible to start analysing exposure patterns in AI-assisted search experiences, even at a stage where the metrics are still evolving.

From classic search to AI-assisted search

For a long time, the logic of organic search was relatively direct: a user entered a query, the search engine displayed a list of results, each page competed for visibility and, ideally, the click took the user to the website. SEO work aimed to improve the technical, semantic and editorial relevance of pages so that they could appear in more favourable positions.

With generative AI, this sequence becomes more complex. The search engine no longer presents only a list of links and instead builds a synthesised answer supported by multiple sources. A website’s content can help ground an answer, be cited, appear as a reference or influence the composition of an overview without necessarily generating a direct visit.

This change creates a new layer of visibility. A brand can be present in an AI-generated answer without that presence immediately translating into a click. It can gain authority, reinforce recognition and occupy informational space at an early stage of the user journey. At the same time, it can lose traffic in searches where the generated answer resolves the user’s need without a website visit.

This is why dedicated generative AI reports are so important. Without separate data, it becomes harder to understand whether a rise or fall in organic impressions is due to changes in classic ranking, new search features, AI tests, shifts in user behaviour or a combination of all these factors.

What Google now shows in Search Console

According to the information made available, the new generative AI performance reports in Search Console will show specific data about the presence of URLs in generative features across Google Search and Discover. The rollout begins with a subset of websites, with the goal of testing the reports, collecting feedback and preparing broader availability.

Among the announced metrics and dimensions, the most important are impressions, pages, countries, devices and dates. Impressions indicate how often URLs from a website appeared in generative AI features. The pages dimension allows website owners to identify which URLs gained visibility in these areas. The countries dimension helps understand in which markets visibility is stronger. The devices dimension, available for search results, makes it possible to distinguish usage contexts. Date analysis offers a time-based view with hourly, daily, weekly and monthly granularity.

This information represents an important step forward for marketing teams, content managers, technical teams and e-commerce decision-makers. When a page appears in a generative answer, that event becomes more clearly measurable. Even if the report does not answer every question, it already makes it possible to start crossing AI presence with traffic changes, business goals, brand awareness campaigns and editorial initiatives.

There is, however, one limitation that deserves attention: the announced report does not include click data. In other words, Google now shows impressions and visibility context in generative AI features, but at this stage it does not reveal how many users click from those answers to websites. This absence is not entirely surprising, but it limits the ability to measure direct traffic impact.

Why the absence of clicks matters so much

The difference between an impression and a click is central to any SEO analysis. An impression indicates that a page was visible. A click indicates that this visibility generated a visit. In traditional environments, the relationship between these two metrics allows teams to calculate click-through rate and assess whether the title, description, position, search intent and value proposition are aligned.

In generative AI answers, this reading becomes more delicate. A page can be used as a source, appear as a reference or contribute to an answer without the user feeling the need to open the result. The website participates in the search experience, but the generated value may not arrive in the form of direct traffic. It may arrive as perceived authority, brand familiarity or influence over a future decision.

Without click data, SEO teams will need to work with broader analysis models. It will be necessary to cross AI impressions with overall organic traffic, behaviour on strategic pages, evolution of branded searches, assisted conversions, CRM data and qualitative signals. Measurement stops being only a reading of website entry and starts including a reading of presence within the search ecosystem.

This is one of the reasons why Google’s additional transparency should be valued, even if it is still incomplete. By separating visibility in generative features, Search Console makes it easier to understand the origin of certain fluctuations. For those who monitor websites rigorously, this separation reduces grey areas and provides a stronger basis for explaining results to clients.

Bing had already opened the way with AI Performance

Before this move by Google, Bing Webmaster Tools had already taken a similar step with AI Performance. Still in beta, this report brought a perspective to SEO analysis that quickly became highly relevant: understanding how website content appears in AI search experiences within the Bing ecosystem.

The relevance of this detail should not be underestimated. Although Google continues to hold a dominant position in search across many markets, Bing took a pioneering role in the visible integration of generative AI into the search experience. The availability of specific reports allowed many teams to start creating an analysis methodology before Google presented a similar area.

For BYDAS, Bing’s AI Performance has been a useful tool in client SEO reports. Even in beta, it makes it possible to observe trends, identify pages with presence in AI experiences and discuss the impact of this new search layer on organic results. More than a technical curiosity, this metric has become part of a strategic conversation about digital visibility.

The fact that Google is now moving forward with its own reports confirms that measuring presence in generative AI is no longer peripheral. It is no longer just about tracking classic organic positions. It is about understanding the contexts in which a brand is used, cited, suggested or associated with deeper answers in search engines.

More transparency in traffic attribution

One of the biggest challenges created by generative AI is attribution. When the user sees a complete answer inside the search engine itself, the role of the website becomes harder to measure. The page may have contributed to the answer, but direct traffic may not reflect that contribution. Without specific data, analysis runs the risk of attributing increases or decreases to the wrong causes.

Google’s new reports help reduce this problem. By showing impressions in generative features, involved pages, countries and devices, Search Console starts offering a more detailed view of organic exposure. This view makes it possible to relate AI presence with changes in traditional visibility, market variations and different behaviours between desktop and mobile.

For companies, this means greater interpretation capacity. A page that maintains stable traffic but gains impressions in AI may be strengthening authority at early stages of the journey. A page that loses clicks but grows in generative exposure may require a more refined analysis: is AI satisfying the user’s intent before the click? Is the content still being valued, but in a way that is less visible in traditional data? The answer will depend on each case.

This evolution also helps communicate SEO results more effectively. Many decision-makers still look at organic traffic as the main metric. However, modern search can no longer be reduced to sessions. Visibility, presence in answers, semantic relevance and brand association with specific topics now carry weight in performance evaluation.

The impact on on-site SEO initiatives

Generative AI reports are not only useful for observing data. They are also useful for improving on-site decisions. If a specific page frequently appears in AI features, that may indicate that the content responds well to informational intents, has a clear structure, covers the topic in depth and offers useful trust signals.

On the other hand, if strategic pages do not appear, the team can review editorial quality, semantic organisation, content depth, heading structure, structured data, clarity of answers and demonstrated authority. Generative AI tends to favour content that can be interpreted, summarised and contextualised easily.

This reality reinforces the importance of on-site work. Technical optimisation remains essential, but it must be associated with content that demonstrates experience, clarity, specificity and real usefulness. Superficial, generic or excessively commercial pages may find it harder to serve as a basis for generative answers, especially in topics where accuracy is decisive.

For e-commerce teams, this point is particularly sensitive. Generative search can influence product comparisons, recommendations, pre-purchase questions and discovery searches. A website with poor descriptions, weak category content or insufficient support content may lose space to competitors that offer more complete and structured information.

New questions for SEO reports

The inclusion of generative AI data forces SEO reports to evolve. It is no longer enough to present traffic, clicks, impressions and average position. It will become increasingly important to answer questions such as: which pages appear in AI experiences? In which countries? On which devices? With what time-based evolution? Does AI presence coincide with traffic increases, click reductions or changes in conversions?

These questions help transform reports into decision-making tools. A page that appears in generative answers may deserve editorial reinforcement, data updates, improved calls to action or the creation of complementary content. An absent page may require structural revision. A market with strong AI exposure may justify greater local investment. A device with distinct patterns may indicate behavioural differences.

In practice, analysis no longer looks only at the classic search funnel. It begins to consider an informational exposure phase that may precede the click or replace part of it. For brands, this means that the value of SEO can appear at less linear points in the journey, which requires richer reports and more careful interpretation.

BYDAS has incorporated this perspective into the way it supports clients. The reading of Bing’s AI Performance already provides useful signals, and the broader future availability of Google’s reports should enable more complete analyses. The goal is not to replace traditional indicators, but to add context and avoid simplistic conclusions about traffic gains or losses.

Controlling presence in AI features

Another relevant point in the announcement is the reference to a new control within Search Console, described as a toggle, which will allow some website owners to prevent their content from appearing in AI search features such as AI Overviews, AI Mode or generative experiences in Discover. This feature is also starting with a subset of websites, with expansion expected after testing.

This control responds to a legitimate concern shared by many publishers and site managers: to what extent should content feed AI answers when that may reduce visits? Google indicates that sites choosing to opt out of these features will not receive traffic or impressions from generative experiences. At the same time, it states that this option should not work as a ranking signal for classic organic search.

The existence of this control opens a strategic debate. Blocking presence in AI may protect part of the value of content in some scenarios, but it may also remove visibility from an area that is likely to become increasingly important. For many brands, the decision will not be obvious. It will be necessary to assess content type, business model, dependence on organic traffic, the value of awareness and the ability to convert users who arrive at more advanced stages of the journey.

In sectors where visibility and authority are essential, staying out of generative answers may mean losing influence. In editorial models that depend on revenue per visit, the equation may be different. What matters is that the decision is based on data, not only on fear or enthusiasm.

Generative AI does not eliminate SEO, it makes it more demanding

Whenever a major change appears in search engines, the idea that SEO will lose relevance reappears. History shows the opposite. Algorithm updates, mobile search, rich results, local search, voice search and structured data did not eliminate SEO. They made it more technical, more integrated and more strategic.

Generative AI follows the same logic. Content still needs to be found, understood, assessed and presented. The difference lies in the presentation format and in the way the user interacts with information. A page no longer competes only for a blue link position in the results. It competes for relevance within a composed answer, for topical authority and for usefulness in a synthesis generated by the search engine.

This demands higher editorial quality. Vague, repetitive content created only to fill keywords will have less ability to stand out. By contrast, content with clear answers, verifiable information, logical organisation, adequate depth and alignment with real search intents is likely to gain importance.

It also demands greater integration between SEO, content, web development, data and business. The technical team must ensure that the website is crawlable, fast and well structured. The content team must create useful and distinctive pages. The marketing team must understand how visibility relates to commercial goals. Analysis must bring all these dimensions together.

How brands should prepare

The first recommendation is not to treat generative AI reports as a curiosity. This data should progressively enter dashboards, monthly reports and strategic discussions. Even if the metrics are still limited, their evolution may reveal relevant patterns before they become obvious in traffic.

The second recommendation is to review strategic content. Pages that answer frequently asked questions, guides, comparisons, decision-support content and category pages should be analysed in light of generative search. The question is no longer only whether the page ranks. It is also whether the page offers information that is clear enough to be integrated into an AI answer.

The third recommendation is to map search intents. Generative experiences are particularly relevant in informational, exploratory and comparative searches. Brands that understand these intents can create content that accompanies the user before the direct conversion stage. This reinforces authority and increases the probability of future contact.

The fourth recommendation is to avoid hasty decisions about AI blocking. The new control announced by Google may be useful in some contexts, but it should be evaluated case by case. Before opting out, it is important to understand which pages appear, which markets are involved and what relationship exists between impressions, traffic and business results.

The fifth recommendation is to improve data quality. Attribution in the age of AI requires cross-analysis between sources: Search Console, Bing Webmaster Tools, analytics tools, CRM platforms, sales data and brand indicators. The better the measurement architecture, the more reliable decisions will be.

An opportunity for technical SEO and quality content

Google’s new reports also reinforce the importance of a solid technical foundation. If the search engine cannot crawl, interpret or contextualise a page correctly, the likelihood of that page being used in advanced experiences decreases. HTML structure, structured data, information architecture, performance, indexation and semantic consistency remain essential pillars.

At the same time, content quality becomes more central. Generative AI needs sources that can support useful answers. This favours pages that present well-organised concepts, complete explanations, practical examples, updated data, direct answers and clear signals of authorship or expertise. Content should be created for people, but structured so that systems can understand it.

This balance between technical work and content is where many companies still fail. Some websites have good infrastructure but poor content. Others have good texts but crawlability and performance problems. Generative search penalises this fragmentation because it requires coherence between what the page says, how it is organised and what signals it transmits.

For brands with digital ambition, this is the right time to review priorities. The question is no longer only how to climb positions. It is how to build presence, authority and trust in a more complex search ecosystem, where the answer may be generated before the user visits any website.

What this step from Google changes for clients

For clients investing in SEO, the main change is transparency. Until now, part of the presence in AI experiences could remain mixed with general metrics or simply invisible. With dedicated reports, it becomes possible to explain certain changes more clearly, identify pages with generative exposure and make decisions based on more concrete signals.

This also makes it possible to value on-site initiatives that do not always have an immediate impact on traffic. Improving a guide, restructuring a category page, creating more complete answers or semantically optimising content can increase AI presence before generating measurable clicks. Without specific reports, that value would be harder to demonstrate.

For an agency such as BYDAS, this type of data helps enrich the relationship with clients. Reports no longer show only what entered the website; they also show where the website gained exposure. This distinction is essential for brands that want to grow in search environments increasingly mediated by AI.

Google’s move brings the market closer to a more realistic reading of modern SEO. Organic search is no longer only a source of traffic. It is also a space of influence, authority and validation. Measuring this presence in greater detail will be decisive to justify investment, correct strategies and protect the digital relevance of brands.

A new phase for measuring organic search

Generative AI reports in Search Console do not solve every challenge. The absence of click data leaves an important gap. Availability limited to a subset of websites means that many teams will still have to wait before accessing this information. The very definition of value in generative experiences is still under construction.

Even so, the announcement represents a significant step forward. Google acknowledges that presence in generative AI needs to be measured separately. Bing had already taken that step with AI Performance, still in beta, and Google’s response confirms that this area will become increasingly central to SEO analysis.

For digital marketing professionals, the message is clear: SEO reports will have to evolve. Measurement should consider classic visibility, AI presence, traffic quality, search intent, commercial impact and brand authority. Teams that start building this discipline now will have an advantage when the data becomes more complete.

For companies, the priority should be twofold: improve the quality of the website and interpret data with maturity. Generative AI does not make content less important. On the contrary, it makes the difference between superficial content and truly useful content more evident. It also does not make technical work less relevant. Fast, well-structured and semantically clear websites will continue to have an advantage.

Google’s evolution is therefore more than a new feature in Search Console. It is a sign of change in the way organic search should be understood, measured and managed. Visibility is taking new forms. Attribution is becoming more complex. SEO is becoming more strategic.

At BYDAS, we closely follow this transformation and already integrate generative AI signals, such as Bing’s AI Performance, into our clients’ SEO reports. With the arrival of Google’s new data, we reinforce our approach to organic traffic optimisation, with greater transparency, technical analysis and a focus on results.