Strengthen technical SEO, publish original evidence and first-hand expertise, and ignore AI-search shortcuts that add work without improving visibility.

Marketers trying to improve visibility in Google’s AI search should focus on strong content, technical SEO, first-hand expertise, supporting evidence, images and video, accurate structured data, and measurement. They can ignore many of the new GEO tactics built around special AI files, artificial content structures, and other supposed shortcuts.

That distinction has become more important as AI search reaches a much larger audience.

On May 15, 2026, Google published a detailed guide to improving visibility in generative search features such as AI Overviews and AI Mode. The document addresses content quality, technical SEO, images, ecommerce data, measurement, and several popular tactics that websites can safely ignore.

The guidance arrived as these features moved well beyond the experimental stage. AI Overviews now has more than 2.5 billion monthly active users, while AI Mode has crossed one billion monthly users. Queries in AI Mode have more than doubled every quarter since its launch.

At the same time, organic search is becoming less predictable.

A December 2025 analysis of 300,000 keywords found that AI Overviews were associated with a 58% lower average click-through rate for the top-ranking result. The dataset compared 150,000 keywords with AI Overviews against 150,000 informational keywords without them.

The challenge for marketers is therefore broader than winning the first organic position. Content also needs to be useful enough to support an AI-generated answer and persuasive enough to earn a click when the searcher already has a summary in front of them.

So, what deserves attention, and which AI search tactics are creating unnecessary work?

The guidance in one view

A quick summary of the practices worth investing in and the AI search tactics that can be deprioritized.

What to prioritize for Google AI Search

Area	Worth investing in	Safe to deprioritize
Content	Original research, expert insight, first-hand experience, and complete topic coverage	Generic summaries and separate pages for every keyword variation
Technical SEO	Crawlability, indexing, internal links, correct canonicals, and a strong page experience	A separate technical infrastructure created only for AI search
Formatting	Clear sections, useful headings, readable paragraphs, and relevant visuals	Breaking every article into tiny “AI-friendly” chunks
AI-assisted writing	Research support, structure, editing, and content maintenance with human review	Publishing large volumes of lightly reviewed pages
Structured data	Valid markup for established search features and accurate commercial information	Invented “AI schema” or excessive markup added only for AI Overviews
Brand authority	Genuine expertise, customer evidence, and authentic third-party discussion	Purchased mentions designed to influence AI citations
Measurement	AI impressions, qualified traffic, conversions, and branded demand	Treating citation counts or rankings as the only success metric

The rest of the article explains why these priorities are taking shape and how marketing teams can apply them.

How Does Google AI Search Work?

Google AI Search works by understanding the meaning and intent behind a query, retrieving relevant information from multiple sources, and using AI to combine those findings into a direct answer. A page still needs to be discovered, crawled, and indexed before it can appear as a supporting source in Google’s AI search features.

How Does AI Search Understand a Query?

AI Search understands a query by analyzing its meaning, context, and relationships rather than relying only on exact keyword matches.

• Natural language processing: AI search analyzes the complete question to understand what the user means, including context and relationships between different parts of the query.
• Semantic matching: It can connect a query with relevant information based on meaning, even when the page does not contain the searcher’s exact words.
• Vector embeddings: Many AI retrieval systems represent words, passages, and queries numerically, helping identify information that is semantically related rather than simply keyword-matched.

How Does AI Search Retrieve Information?

AI Search retrieves information by finding relevant external sources that can help answer the user’s question. Retrieval-Augmented Generation (RAG) uses this information to ground the generated answer rather than relying only on information learned during model training.

Google AI Search can also use query fan-out. A complex question can be broken into subtopics, with multiple related searches performed simultaneously to gather information that addresses different parts of the question.

For example, a search for “Which CRM is suitable for a 50-person SaaS company with a small sales team?” could expand into questions about:

• pricing and implementation time;
• integrations and data migration;
• reporting and sales automation; and
• scalability and customer support.

Different pages can contribute useful information to different parts of the final answer. This means a page does not necessarily have to answer the entire original query. A strong section, comparison, statistic, or explanation can provide relevant information for one of its supporting questions.

How Does AI Search Generate Answers?

AI Search generates answers by combining the relevant information it retrieves into a coherent, conversational response.

• Information synthesis: The model combines relevant information retrieved from multiple sources around the user’s question.
• Response generation: It turns those findings into a direct, conversational answer rather than simply presenting a list of search results.
• Source links: Supporting links allow users to explore the webpages behind parts of the generated response and verify or learn more about the information presented.

For marketers, this changes how topic coverage and keywords should be approached. A page can become relevant because one well-developed section answers a supporting question, even when it does not use the exact phrase entered by the searcher.

The opportunity is to build content around real questions, related decisions, entities, evidence, and complete topic coverage rather than creating multiple near-identical pages for slight keyword variations.

How Is AI Changing Google Search?

AI is changing Google Search by making it more conversational, interactive, multimodal, and capable of answering complex questions directly. Users can ask follow-up questions, search with text, images, or voice, evaluate sources, and continue researching without repeatedly returning to a traditional results page.

The change also affects how users interact with Search:

• Multimodal inputs expand discovery: Users can search using text, voice, images, or combinations of these inputs.
• Context carries across follow-ups: Users can refine or narrow a question without explaining the entire request again.
• Complex intent can appear in one query: Requirements such as budget, location, features, preferences, and constraints can be included together.

How Do AI Answers Affect Search Clicks?

AI answers can reduce the need for some search clicks by giving users explanations, comparisons, recommendations, and supporting information directly within Search. Users may no longer need to open several pages simply to assemble a basic answer.

As a result, the value of a click is also changing:

• Basic information may no longer earn the click: Definitions and simple summaries can often be satisfied within the AI response.
• Deeper information creates a stronger reason to visit: Original research, detailed comparisons, tools, first-hand evidence, and proprietary data can extend what the AI answer provides.
• Visibility can happen without a website visit: A brand or publisher can appear as a supporting source even when the user continues researching within Search.

What Can Google AI Mode Do?

Google AI Mode can help users research complex questions, compare options, refine their requirements, and work through multi-step tasks within Search. With Gemini 3.5 Flash as its default model, AI Mode is designed to handle detailed questions while producing responses with less delay.

This expands the types of tasks users can complete through Search:

• Research can develop over several stages: A broad question can progress into comparisons, evaluation, and more specific follow-ups.
• Search can assist with decisions: Users can provide multiple requirements and ask Search to evaluate suitable options.
• The interaction becomes iterative: Each response can provide the context for the user’s next question.

How Do Citations Work in Google AI Search?

Citations in Google AI Search connect generated information with supporting web sources, giving users a direct route to the pages behind individual claims. Inline citations and desktop hover previews can also provide information about a source before the user decides whether to visit it.

That creates several additional considerations for publishers:

• Page titles influence source selection: A specific title can help users understand what additional information they will find after clicking.
• Brand recognition can influence trust: Familiar publishers may be easier for users to identify among several cited sources.
• Citation placement creates new visibility: A source can appear beside the exact fact, explanation, or recommendation for which it was selected.

How Does Google AI Search Surface First-Hand Perspectives?

Google AI Search can surface first-hand perspectives from forums, reviews, creator communities, social platforms, and other sources alongside synthesized information. Features such as Expert Advice can give practical experiences greater visibility within the search journey.

These sources can contribute information that traditional articles may not always provide:

• Product experience adds practical context: Users can see how products or services perform in real situations.
• Community discussions reveal different viewpoints: Forums and social platforms can surface questions, problems, and experiences from multiple users.
• Creators can contribute specialized knowledge: People with direct expertise can provide details that generic summaries may overlook.

How Do Follow-Up Questions Work in AI Search?

Follow-up questions in AI Search allow users to continue researching a topic without starting a new search from scratch. Explore More, related questions, and personalized recommendations can move users from an initial question into progressively more specific areas.

The journey can become more individualized as it continues:

• Related prompts introduce adjacent questions: Users may discover considerations they did not include in their original search.
• Personal context can shape recommendations: AI experiences can use available context and preferences to make results more relevant.
• Preferred sources can receive greater prominence: Publications a user already follows or subscribes to may be highlighted more visibly.

12 Practices to Improve Visibility in Google AI Search

A practical breakdown of the content, technical, and measurement improvements that increase a page’s chances of being discovered and referenced.

1. Get the Technical SEO Foundations Right

Get the technical SEO foundations right by making important pages crawlable, indexable, internally connected, and eligible to appear in Search. AI Overviews and AI Mode still depend on the infrastructure Google uses to discover and process web pages.

Before building a separate AI search program, review whether important pages can already be found and processed correctly.

A practical technical review should cover whether:

• Important URLs are indexable
• Canonical tags point to the preferred page
• Internal links connect related articles and commercial pages
• JavaScript prevents essential content from loading
• Images and videos can be accessed
• The page is eligible to appear with a search snippet
• Duplicate or outdated URLs are consuming crawl resources

These checks rarely make headlines, but they often determine whether a page is eligible to appear at all.

Use relevant structured data where it supports established Search features. Article, Product, Organization, LocalBusiness, and other applicable markup can help Search understand page information and entities, but the markup should accurately represent visible content. There is no separate “AI schema” required for AI Overviews or AI Mode.

Technical clarity also helps teams diagnose performance. When a well-researched page fails to appear, the team can separate access and indexing problems from issues related to content quality or competition.

2. Publish Original Research

Publish original research that competitors cannot easily reproduce. Internal research, customer evidence, product testing, expert analysis, implementation experience, and proprietary data give a page of information with a clear origin. Here’s something to know:

• Commodity content is interchangeable. It may be accurate and well written, yet it adds little beyond what a searcher can already find across dozens of websites. Generic definitions, surface-level checklists, and articles assembled from the current top-ranking results usually fall into this category.
• Non-commodity content introduces information with a clear origin. It may come from internal research, customer interviews, product testing, expert analysis, implementation experience, or a strong point of view developed through years of work.

Consider two articles about improving a SaaS pricing page.

• The first collects seven common conversion tips: simplify the layout, add social proof, clarify the plans, and test the call to action. The advice is familiar and easy to reproduce.
• The second article explains how a company redesigned its pricing structure, which customer objections shaped the new page, why two experiments failed, and what happened to trial conversion after the change. That article contributes evidence that did not exist before the company published it.

Originality does not always require a large research budget. A content team can create distinctive material through:

• Interviews with customers or internal specialists
• Examples from completed projects
• Screenshots of real workflows
• An analysis of public company data
• A comparison based on hands-on testing
• Lessons from a failed launch or implementation
• Benchmarks drawn from anonymized internal data

The aim is to give the page a reason to exist beyond targeting a keyword.

3. Demonstrate First-Hand Experience and Expertise

Demonstrate first-hand experience and expertise by showing how real knowledge shaped the article’s analysis, examples, and recommendations. Author biographies, credentials, expert reviewers, and dedicated author pages can support trust, but the article itself should show evidence of experience.

A warehouse operations guide written by someone who has worked inside distribution environments should contain details about labor constraints, replenishment delays, slotting decisions, and implementation trade-offs. A software comparison should reflect actual product use. A marketing teardown should explain the choices behind a company’s growth system rather than restating its website.

Experience often appears through small details:

• The assumptions that shaped a decision
• The constraint that delayed implementation
• The feature that performed differently in practice
• The customer objection that changed the strategy
• The metric that looked positive but hid a deeper problem
• The compromise the team accepted to keep the project moving

These details make an article more useful because they prepare the reader for real decisions. They also separate expert-led content from summaries that can be produced without first-hand knowledge.

This broader shift is also visible across search results. Community discussions, product reviews, and expert conversations have become more prominent because they often contain practical insights that are difficult to reproduce through generic content.

Rather than competing with those sources, publishers should learn from what makes them valuable: first-hand knowledge, transparent opinions, and evidence from real-world experience.

4. Cover the Topic and Search Intent Completely

Cover the topic and search intent completely by answering the related questions that a reader needs to make a decision. Query fan-out can turn one question into several related searches, creating more opportunities for a comprehensive page to provide useful information.

Suppose the primary topic is choosing CRM software for a growing company. The reader may begin with a comparison, but the decision quickly expands into implementation effort, pricing, integrations, data migration, reporting, and adoption.

A strong guide could therefore move through the decision in a natural sequence:

Define specialized terminology when it is necessary to understand the subject. Clear definitions establish what an entity, process, or concept means before the article builds more complex explanations around it.

Each section supports the same reader and the same decision. The article remains focused even though it covers several related searches.

By comparison, a cluster of thin pages often repeats the same introduction, examples, and product claims. Readers move between pages without gaining a coherent view, while the site creates unnecessary overlap.

Comprehensive does not mean adding every fact available on the subject. It means answering the questions that naturally arise before the reader can make a decision.

5. Structure Content for Readers and Search

Structure content for readers and Search by making answers easy to find while keeping related ideas logically connected. Use clear headings, answer-first openings, full paragraphs for explanations, bullets for grouped information, tables for comparisons, and visuals where they improve understanding.

Some points to consider:

• Very long paragraphs can make a page difficult to scan. A page made entirely from one-sentence paragraphs creates a different problem: every thought appears to carry the same weight, and the reader loses the relationship between ideas.
• Headings should help readers understand where the discussion is going. They should describe the subject of the section rather than force every idea into a question.
• Transitions also deserve attention. A section about original content can lead naturally into first-hand experience because experience is one source of originality. A section about complete topic coverage can then lead into structure because deeper content needs a clear route through it.
• Every section should naturally lead to the next, so the article reads like one continuous argument rather than 12 disconnected tips.

6. Optimize Images and Videos for AI Search

Optimize images and videos for AI Search by using visuals that explain, demonstrate, compare, or provide evidence rather than simply decorate the page. Relevant visual content can also appear alongside web links in AI-powered search experiences.

Useful visuals include:

• A comparison table showing meaningful differences
• A process diagram explaining how a system works
• A product screenshot annotated with important details
• A chart built from original or properly sourced data
• A before-and-after example
• A video demonstrating a complex workflow
• A decision tree that guides the reader towards an option

A technical article about website migration could include a redirect map. A warehouse article could show how inventory travels through a facility. A software comparison could display how the same task is completed in each product.

Captions and surrounding text should explain the relevance of the visual. Descriptive filenames and accurate alt text also provide useful context, while keyword-stuffed alt text creates a poorer experience.

The simplest editorial test is to remove the image temporarily. When the reader loses an explanation, example, or piece of evidence, the visual is doing useful work.

7. Make Important Pages Crawlable and Indexable

Make important pages crawlable, indexable, and eligible to appear with a Search snippet. These conditions are necessary before a page can support an AI Overview or AI Mode response, although eligibility does not guarantee inclusion.

Content teams should work with technical teams to review pages that contribute directly to revenue or authority. These may include product pages, comparison pages, service pages, original research, and high-value educational resources.

Pay particular attention to:

• Pages blocked by robots.txt
• Accidental noindex directives
• Canonicals pointing towards outdated URLs
• Important content available only after login
• CDN or firewall rules that block crawlers
• Orphan pages with no internal links
• Snippet controls that limit how content can appear
• JavaScript that fails to render key information

For larger websites, server log analysis can show whether search crawlers are actually reaching important URLs, how frequently they visit them, and whether crawl resources are being spent on duplicate, outdated, or low-value pages.

Search Console now also includes a setting for inclusion in generative search experiences. Inclusion is the default, while exclusion removes a property’s links and content from supported generative features.

Most commercial websites will want to remain included. Publishers with licensing or content-control concerns may choose to assess the setting more carefully.

8. Add Crawlable Text to Interactive Content

Add crawlable supporting text to calculators, dashboards, videos, animations, and interactive product experiences so their essential information can be understood without relying entirely on the interaction.

Take a pricing calculator. A search system may struggle to understand the page when the pricing assumptions, available options, and interpretation of the output are never explained in text. Visitors may face the same difficulty before they begin entering information.

A supporting explanation should cover:

• What the tool calculates
• Which inputs does it use
• How the result should be interpreted
• Who the tool is designed for
• Which assumptions affect the estimate
• Where the calculation has limitations

The same approach applies to videos and product demonstrations. A written summary helps readers understand what the visual covers and gives search systems enough context to retrieve the page for relevant questions.

Interactive features work best as part of a complete page. They should deepen the explanation rather than carry the entire burden of communicating it.

9. Improve Page Experience After the Click

Improve page experience after the click by giving visitors fast access to useful information that goes deeper than the AI-generated answer they have already seen. As AI responses satisfy more preliminary information needs, the visitors who do click need a clear reason to stay.

A useful landing experience includes:

• Fast and reliable loading
• A clear relationship between the search question and the opening
• Navigation that helps readers reach relevant sections
• Limited interruption from pop-ups and advertisements
• A visible next step
• Mobile formatting that preserves tables and visuals
• Current information, rather than an article left untouched for years

Content duplication also affects the experience. Five overlapping articles can confuse readers about which page is current and force search systems to choose between several weak candidates.

Consolidating related pages often creates a stronger result. The best material can be combined, outdated claims can be removed, and internal links can point towards one authoritative resource.

10. Use AI-Generated Content With Human Review

Use AI-generated content with human review so automation supports research and production without replacing editorial judgment. Search policies focus on the value and quality of the finished page, while large-scale production without meaningful additions can fall under scaled content abuse.

Used carefully, AI can help with:

• Organizing research notes
• Transcribing interviews
• Identifying gaps in an outline
• Creating an initial structure
• Comparing claims across sources
• Updating repeated product information
• Checking consistency across a content library

Editorial responsibility remains with the publisher.

Statistics need verification. Product claims need current evidence. Quotes need accurate attribution. Examples need context. Recommendations should reflect someone who understands the subject and can recognize when an output sounds plausible but is wrong.

Disclosure can also be useful when automation played a substantial role, particularly when explaining the process helps readers understand how the work was produced.

The production method should support the quality of the article. It should never become the reason the page exists.

11. Keep Product and Local Business Information Accurate

Keep product and local business information accurate because AI-powered Search can surface prices, availability, locations, business details, and other commercial information directly during discovery.

For ecommerce and location-based businesses, content alone cannot carry the entire search strategy. Merchant Center feeds and Business Profiles need regular maintenance so the information appearing across search experiences remains accurate.

Review:

• Prices and promotional offers
• Inventory availability
• Store locations
• Opening hours
• Contact details
• Product images
• Delivery terms
• Return information
• Service areas

A useful article may introduce a customer to the brand. Incorrect pricing, availability, or opening hours can end the journey before a purchase or visit takes place.

Hence, commercial data should lie inside the search workflow rather than being treated as a separate administrative task.

12. Measure AI Search Visibility and Business Impact

Measure AI Search visibility against engagement and business outcomes rather than relying on impressions or citations alone. Visibility shows where a brand appears, but clicks, qualified visits, leads, conversions, and revenue show whether that visibility creates value.

A useful measurement framework should combine three levels:

How to measure AI search performance

Measurement level	Questions to answer	Example metrics
Visibility	Where is the brand appearing?	AI impressions, visible pages, countries, and devices
Engagement	What happens after discovery?	Organic clicks, engaged sessions, returning visitors, and branded searches
Commercial impact	Does the visibility contribute to growth?	Leads, demos, trials, subscriptions, assisted conversions, and revenue

A page may gain many AI impressions and very few direct clicks. Another may receive less visibility but attract visitors who are closer to a purchase.

Both cases need analysis at the website and conversion level. Citation or impression counts cannot explain the quality of demand on their own.

7 tactics marketers can safely ignore

The rapid growth of AI search has created demand for fast answers. That environment naturally attracts tactics that promise a simple route to generating results.

The new documentation is useful because it removes several of those distractions.

1. You Do Not Need to Rebuild SEO Around GEO or AEO

You do not need to rebuild your SEO strategy around GEO or AEO to appear in Google’s AI search features. AI Overviews and AI Mode continue to rely on Google’s broader Search infrastructure and systems.

GEO and AEO can be helpful labels when teams discuss AI visibility, citation monitoring, or the way generated answers represent a brand.

They do not require a separate foundation.

AI Overviews and AI Mode continue to use the main Search index, ranking systems, and quality systems. Technical SEO, original content, crawlability, and authority remain central to visibility.

A company can add AI-specific reporting and research to its existing search program. Rebranding the whole discipline does not create an advantage by itself.

2. You Do Not Need llms.txt for Google Search

You do not need an llms.txt file to improve visibility in Google Search because Google does not use it for AI Overviews, AI Mode, or traditional Search.

Other services may choose to support it, so companies can still maintain one for those systems. It should remain well below crawlability, indexing, and content quality on the priority list.

3. You Do Not Need Separate AI Versions of Every Page

You do not need separate Markdown files, simplified AI pages, or machine-readable copies of every page to appear in Google’s generative search features. A standard crawlable and indexable page is sufficient.

Duplicate versions increase maintenance work and create another place where claims, links, or product information can become outdated. They can also complicate canonicalization when several versions cover the same material.

One complete, current, and well-structured page is easier to maintain than several versions created for speculative technical reasons.

4. You Do Not Need to Break Every Article Into Tiny Chunks

You do not need to break every article into tiny chunks for AI Search. Section length should follow the complexity of the subject and what the reader needs to understand it.

Some sections may need a direct 50-word explanation. Others may require examples, evidence, and a table. Forcing every idea into two sentences usually weakens the transitions and makes the article feel fragmented.

Good structure creates clear sections without destroying the relationship between ideas.

5. You Do Not Need a Special AI-Search Writing Style

You do not need a special AI-search writing style to make content understandable to Google. Robotic phrasing, excessive keyword repetition, and question headings above every paragraph are unnecessary.

Modern search systems can understand synonyms, related concepts, and pages that do not repeat the exact wording of every possible query.

Write in the language your audience uses. Define specialist terms when needed. Use direct answers where they help the reader, and expand the explanation when the subject requires context.

A page should sound like a knowledgeable person explaining the topic clearly.

6. There Is No Ideal Word Count for AI Search

There is no ideal word count for AI Search because the appropriate length depends on the question, evidence, and complexity of the reader’s decision.

A narrow question may be fully answered in 600 words. A technical guide or research-heavy teardown may need several thousand. The subject, evidence, and decision complexity should determine the length.

Longer articles become weaker when additional words repeat the same idea. Very short articles fail when they leave out the information a reader needs to act.

Length is an editorial decision, not a ranking formula.

7. Do Not Buy Artificial Mentions or Invent AI Schema

Do not buy artificial mentions or add invented AI schema in an attempt to influence Google’s AI-generated results. Manufactured references provide little durable value, and there is no special schema.org markup that unlocks AI Overviews or AI Mode.

Structured data should continue to support established search features and remain consistent with the visible page.

Authentic reputation develops through useful products, credible research, satisfied customers, and worthwhile coverage. It cannot be replaced by a markup field or a purchased forum comment.

What Remains Uncertain About Google AI Search?

Several parts of Google AI Search remain uncertain, including why individual sources are selected, which fan-out queries run behind a response, how AI visibility affects traffic quality, and how quickly AI Overview coverage will change.

Why Does Google AI Search Cite Some Pages and Not Others?

Google has not published a formula explaining why AI Search cites one eligible page and ignores another. Its documentation explains retrieval and query fan-out, but it does not reveal the weighting used to select individual sources.

A page may rank highly in traditional search and still fail to appear in an AI response. Another may support one part of the answer even when it does not hold a top organic position for the original query.

Marketers can improve eligibility through strong technical foundations, original information, and complete topic coverage. They cannot guarantee a citation for a specific prompt.

Can Higher-Quality Traffic Compensate for Fewer Clicks?

Higher-quality traffic could compensate for fewer clicks, but marketers need to test that through their own conversion and lead-quality data. AI-generated answers may reduce overall visits while sending some users to websites later in the decision journey.

Platform messaging has emphasized prominent links, website previews, and new link designs designed to encourage exploration.

Independent research continues to show significant pressure on organic click-through rates.

A website could receive fewer visitors while attracting a higher proportion of people with strong intent. That possibility has to be tested through each company’s analytics, lead quality, and conversion data.

Can You See Which Query Fan-Out Searches Google Uses?

No, marketers cannot see the exact query fan-out searches Google runs behind an individual AI response. Content teams can predict likely supporting questions, but the precise queries remain hidden.

Content teams can predict common follow-up questions through customer interviews, search data, sales conversations, and product research. They cannot see the precise set of searches generated for every AI response.

The best defense against that uncertainty is a content strategy built around real customer decisions rather than a list of prompt variations.

How Often Do AI Overviews Appear in Google Search?

AI Overview frequency changes significantly over time, so there is no stable percentage that marketers can apply across all Google searches.

In an analysis of more than 10 million keywords between January and November 2025. AI Overviews appeared for 6.49% of the tracked queries in January, climbed to 24.61% in July, and fell to 15.69% in November.

Likewise, an analysis of more than 600,000 commercially focused keywords found an average 71% increase in AI Overview appearances across ten industries over six months.

Forecasts based on one month, industry, or keyword set will age quickly. The direction is clear, AI answers are expanding across the search journey, while the exact frequency remains volatile.

How Do Google Ranking Updates Affect AI Search Visibility?

Google ranking updates can affect AI Search visibility by changing which pages and domains are available or competitive for retrieval. This makes it difficult to separate traffic changes caused by AI-generated answers from those caused by broader ranking changes.

A March 2026 core update was followed by another core update in May, while a global spam update rolled out in late June. These updates were not limited to AI Overviews, but they could still change which pages and domains are available for AI systems to retrieve.

This makes attribution difficult. A loss of traffic may come from an AI-generated answer reducing clicks, a ranking update changing organic visibility, or both happening at roughly the same time. Marketers should compare Search Console, analytics, ranking changes, and conversion data before connecting a performance shift to one cause.

Discover also received a dedicated core update in February 2026. The change affected how articles are surfaced in personalized feeds, reinforcing the need for publishers to produce original, relevant reporting rather than treating Discover as another headline-distribution channel.

What Should Marketers Do Next?

Marketers should start by improving the pages already connected to revenue, product adoption, or brand authority rather than launching a separate AI-search content program.

Confirm that they are crawlable and indexed. Review whether each page answers a complete customer problem. Replace generic claims with evidence, expert commentary, and examples from real work. Consolidate overlapping articles and strengthen the internal links between educational and commercial content.

Next, review the wider search experience. Product feeds, business details, visuals, page speed, and mobile usability all influence what a visitor sees before and after the click.

Finally, update the measurement. Rankings remain useful, though they no longer describe the entire search journey. AI impressions, branded demand, qualified visits, lead quality, and assisted conversions give a more complete view.

The search industry will continue producing new terms and shortcuts. The official guidance offers a more durable direction: make the website technically accessible, give every important page a clear purpose, and publish information that deserves to be retrieved.

AI has changed the shape of the results page. The standard for useful information remains demanding: original evidence, clear reasoning, genuine experience, and a page worth visiting after the summary ends.

AI search rewards what generic content cannot provide: original evidence, real experience, and information worth retrieving.