A traffic decline is a warning, not a diagnosis. Before rewriting an underperforming page, teams need to determine whether the page lost rankings, stopped matching search intent, encountered more zero-click results, or serves a topic with shrinking demand.
This framework uses Search Console trends and a live results-page review to connect each pattern with an appropriate response.
Start with the decision, not the refresh
Content decay is a sustained reduction in organic performance, rather than an isolated weekly fluctuation. Search Engine Land identifies four distinct causes. Only ranking decay is consistently suited to a conventional content refresh.
A wide spreadsheet compares example.com Blog and News URLs from Dec-25 to May-26, alongside impressions, positions, click losses, decay percentages, CTR, and trends.
Ranking decay: The page has lost visibility to competitors, become outdated, lost links, or begun competing with another page on the same site.
Zero-click capture: The page remains visible, but an AI Overview, featured snippet, or another search feature satisfies more users without a visit.
Intent drift: Google now favors a different type of result, such as a video, product page, forum discussion, or comparison table.
Demand decay: The page still performs competitively, but fewer people search for its subject.
A fifth pattern sits outside this framework: a date-aligned, site-wide decline may indicate an algorithm-related issue requiring broader investigation.
Read the combined Search Console signals
Clicks reveal that performance changed, but impressions and average position help explain why. The source recommends reviewing six months of monthly clicks for the trend, then comparing three months year over year for clicks, impressions, and position. This reduces short-term noise while accounting for seasonality.
Search results for "how to lock your bike" feature an AI Overview with locking advice and bike diagrams, alongside video suggestions and a Reddit tips thread.
Clicks
Impressions
Average position
Likely diagnosis
Down
Down
Worse
Ranking decay
Down
Flat or up
Stable or better
Zero-click capture
Down
Down
Stable or better
Demand decay
Down
Varies
Holding, but results changed
Intent drift
Intent drift cannot be confirmed from exported metrics alone. Search the main queries manually and inspect which formats, features, and source types now occupy prominent positions.
Match the intervention to the cause
Rebuild pages with genuine ranking losses
A ranking-decay refresh should add substance rather than merely change the publication date. Useful interventions include original testing, proprietary information, missing answers, stronger internal links, and consolidation of competing URLs. Competitor analysis should also consider whether Google now prefers a different source type.
A search results page displays discussions and forums about how to lock a bike, followed by four short video previews demonstrating bike-locking techniques.
Create value that a search summary cannot replace
Zero-click capture calls for assets that reward a visit: calculators, tools, original data, or a defensible perspective. Clear organization and unique evidence may also make a page more suitable for citation by answer systems. If the query no longer produces meaningful visits, resources may be better directed toward comparison, service, or other conversion-oriented pages.
Change the format or retire the topic
For intent drift, preserve the established URL when practical but reshape the experience around the format users now receive. For confirmed demand decay, first check whether attention moved to forums, video, social search, or AI assistants. If the audience has genuinely disappeared, consolidation, redirection, or pruning is more rational than rewriting.
Organic visibility, paid media and artificial intelligence merge into one connected search ecosystem, where vivid data streams link a creative website with a powerful analytics dashboard.
Rule out measurement and editing problems
Before assigning any decay type, compare the decline date with the page’s revision history. If performance fell immediately after an edit, restoring the previous version provides a cleaner test than adding another rewrite.
Historical impression data also needs caution. Search Engine Land reports that Google’s removal of the num=100 parameter in September 2025 reduced bot-inflated counts. The source also notes that Google disclosed a logging error that had inflated impressions from May 2025 and corrected it without repairing the historical figures; clicks were reportedly unaffected. A pattern that resembles lost demand should therefore be checked against the live results page, especially when position is stable or improving.
Turn diagnosis into a quarterly operating habit
A practical review sorts declining pages by decay type, recoverable traffic, and business value. GA4 conversion or revenue data can improve prioritization, while rank-tracking and search-feature data can help evaluate zero-click exposure at scale.
Key takeaways
Use clicks, impressions, and average position together before deciding to refresh.
Confirm intent changes and ambiguous zero-click patterns on the live results page.
Reserve rewrites for pages where better content can address the diagnosed cause.
Redirect effort when demand has disappeared or the search result no longer produces valuable visits.
The strongest content-maintenance program is selective. Its advantage comes from recognizing when editing can recover value and when another decision will produce a better return.
Visual discovery is becoming a journey rather than a single search feature. People can encounter an idea in an image gallery, inspect it through a social video, refine it with a multimodal query and, in some cases, ask an AI search experience to generate a new visual without visiting a publisher.
For search teams, the practical challenge is therefore larger than image optimization. Multimodal SEO must make pages, media, structured data and distributed brand profiles easy for machines to interpret and useful enough for people to continue exploring.
Visual discovery is moving ahead of the conventional query
Two reported Google changes illustrate how the opening stage of search may be changing. The Google Images redesign article describes a personalized, browseable homepage built around an immersive gallery rather than the service’s historically dominant search box. Search by text, voice or image reportedly remains available, but browsing, saving and returning to visual collections become more prominent parts of the experience.
That distinction matters because a gallery can create demand before a person has formulated a precise query. Instead of asking for a known object, destination or style, a user can move among related images and gradually clarify an interest. Saved collections can also extend that process across sessions. In this environment, relevance is not limited to matching a typed phrase; an asset must also be suitable for recommendation, visual comparison and thematic grouping.
The travel SEO source reports a parallel pattern in a commercially important category. It describes search results in which hotel tools, prices, maps, advertisements, directory modules and social videos can appear before a conventional organic listing. For discovery-oriented travel searches, it also reports short-form material from TikTok, Instagram and YouTube appearing within Google’s results. The Images report focuses on Google’s own gallery, while the travel analysis focuses on blended search surfaces, but together they point to the same strategic shift: discovery can happen through a sequence of visual modules without beginning or ending on a brand website.
This does not make the website irrelevant. It changes its role. A site becomes one authoritative node in a larger system that may include image results, business listings, social profiles, video platforms, structured feeds and AI-generated answers.
Multimodal visibility depends on interpretable page structure
Image quality alone cannot explain how a machine should understand a visually complex page. The visual-semantics source argues that document meaning is communicated through layout, hierarchy and function as well as text. Cards, calculators, comparison modules, tables, filters and buttons establish relationships that may not be expressed in an ordinary paragraph. A price beside one hotel image, for example, must not be confused with the price attached to an adjacent property.
The source connects this problem to research and patents involving vision-based page segmentation, HTML-aware processing, structured information cards and layout-aware document understanding. These materials do not establish that every described method is a current ranking system. They do, however, illustrate the underlying retrieval problem: a search engine needs boundaries that reveal which labels, values, images and actions belong together.
This makes multimodal SEO partly an information-architecture discipline. Semantic HTML, coherent component boundaries, descriptive headings and clear associations among captions, controls and media help define the meaning of a region. The objective is not decorative polish for its own sake. It is a page whose visible and structural hierarchies agree about the primary purpose.
The same source discusses Google’s concept of a “centerpiece annotation” as a way of identifying primary content. It also reports a large programmatic case study in which a calculator was moved from the bottom of a page to the top and made visually prominent as part of 19 changes. Across more than 100,000 pages, the source reported clicks rising from 3.47 million to 4.53 million and impressions from 84.1 million to 167 million after the broader update. The author explicitly cautioned that the effect of the calculator could not be isolated perfectly, so the result should be treated as directional evidence rather than a controlled proof.
The more transferable lesson is that a page’s principal utility should be easy to locate and extract. The travel analysis reaches a compatible conclusion from a different angle: concise entries, interactive maps and clearly separated itinerary, cost and timing information can serve fragmented user needs more directly than a long, undifferentiated guide. Both sources support designing content in meaningful modules, although neither justifies fragmenting a page merely to manufacture more components.
Search assets now extend beyond images and webpages
A multimodal strategy has to distinguish between assets a brand controls and experiences a platform assembles. On the controlled side are original images, page modules, video, structured data, inventory feeds and profile information. On the assembled side are galleries, carousels, maps, AI summaries and other interfaces that decide how those inputs are combined.
The travel source makes this distinction concrete. It recommends treating real-time accommodation prices, availability, inventory, taxes and fees in Google Hotel Center as essential search infrastructure. It likewise emphasizes accurate Google Business Profile categories, amenities, location information and other attributes. Its argument is that visibility for a filtered request can depend on structured facts, review sentiment and geographic information, not persuasive destination copy alone.
The same analysis treats social profiles as distributed landing pages because travelers may use public videos and posts for reassurance without reaching the primary domain. That approach implies consistent branding and factual context across each asset: the subject should be recognizable, the location should be unambiguous and the account should connect visibly to the business or entity it represents. The source also reports that Google Search Console introduced social and video content analytics, reinforcing the need to evaluate search exposure beyond conventional webpage clicks.
Google’s reported addition of text-to-image generation inside AI Overviews introduces a different kind of competition. According to the source, the feature uses Google’s Nano Banana model to create a custom image from a prompt and was announced for English-language rollout in regions supporting image creation in AI Mode. Because the source describes an announced rollout rather than a mature outcome study, its traffic implications remain uncertain.
Even so, the strategic tension is clear. A gallery can recommend an existing publisher image, while a generative interface can satisfy some visual needs by producing a new one. Publishers therefore cannot rely solely on being the nearest aesthetic match to a prompt. Assets gain defensibility when they carry information or evidence that generation cannot simply substitute: an original product view, a documented location, a useful comparison, a demonstration, a current inventory state or a recognizable brand perspective.
A practical model for multimodal SEO
A useful audit can examine four connected properties: findability, interpretability, usefulness and continuity. Findability asks whether important media and data are available to search systems through crawlable pages, supported feeds and public profiles. Interpretability asks whether the entity, subject, location and relationships among page elements are clear. Usefulness asks whether the asset helps someone compare, decide or act. Continuity asks whether the same facts and identity remain consistent as the journey moves between the website, image search, maps, social platforms and AI interfaces.
At the page level, the audit should begin with the centerpiece. The principal image, tool or answer should align with the page title and visible heading, while unrelated navigation and promotional elements should not interrupt its meaning. Each repeated card or listing needs a stable internal structure so that its name, image, attributes, price and action remain associated. Mobile presentation deserves particular attention because a component that appears coherent on a wide screen can become ambiguous when its elements stack.
At the asset level, optimization should preserve factual context rather than reducing every image to a keyword target. Descriptive surrounding copy, captions where they help readers, meaningful file handling and accessible alternatives all contribute to understanding. Originality should also have a purpose: a distinctive visual is more valuable when it demonstrates something, documents something or makes a decision easier.
At the ecosystem level, the canonical business facts should agree across the site, feeds, profiles and public media. Measurement should then separate exposure from destination traffic. Search impressions and clicks remain useful, but they do not capture every discovery touchpoint described in the sources. Teams also need to watch the visibility of visual assets, engagement with off-site content, feed accuracy and the actions users take after arriving. Because the reported interfaces can satisfy needs within Google, a fall in click-through rate does not automatically reveal whether visibility, demand or commercial outcomes have weakened.
Key takeaways
Visual discovery can begin with browsing and recommendation before a user enters a fully formed query.
Multimodal SEO includes layout, component boundaries and structured relationships, not just image files and alternative text.
Feeds, business profiles and social accounts can function as search assets alongside the primary website.
Generative images may reduce some visits for generic visual needs, increasing the value of original, factual and decision-supporting media.
Performance measurement should connect cross-surface exposure with user actions and business outcomes instead of relying on webpage clicks alone.
The next advantage will come from connecting disciplines that are often managed separately: technical SEO, visual production, interface design, structured data, social distribution and analytics. As search becomes more capable of browsing, interpreting and generating visuals, the strongest assets will be those that retain clear meaning wherever the journey encounters them.
At Resolve, we define link building for legitimacy as earning authoritative backlinks, brand mentions, and media coverage that demonstrate trust, expertise, and credibility to search engines and AI systems. Instead of chasing link volume, we use digital PR, original research, thought leadership, and journalist relationships to earn genuine editorial citations. These are the authority signals behind Google’s E-E-A-T framework, and they can help us appear in AI Overviews, earn citations from large language models, and build visibility that survives ranking swings.
We believe a little competition is healthy. It challenges us, sharpens our thinking, and pushes us to pursue bigger and better results.
However, today’s search environment is changing faster than ever. Large language models, AI-generated answers, and frequent algorithm updates are reshaping how people find information, making it increasingly difficult for us to rely on yesterday’s playbook.
The metrics we once used to keep brands afloat — traffic, domain authority increases, and keyword rankings — no longer define SEO success on their own. We can reach the top of a search results page and still see very few conversions.
If we continue chasing those numbers in isolation, we risk being left behind. We have to adapt.
We now widen our view to the outcomes that matter most: trust and brand authority. Unlike a temporary ranking or traffic spike, trust and authority are not earned quickly or easily.
We need time to spread the word about our brand, and we need even more time to prove that people can rely on us. Once we establish that trust, however, it becomes much harder to dislodge.
An algorithm update can cut our traffic overnight. It cannot erase genuine trust overnight.
Our challenge is learning how to build trust and strengthen our brand while every competitor is trying to do the same. We also need meaningful ways to measure concepts that can initially seem difficult to quantify.
We have found that the answers involve some nuance, but they are simpler than they appear. The process begins with a shift in perspective.
Why we see link building as more than rankings
For years, we treated link building like a popularity contest. The site that collected the most votes, in the form of backlinks, was often rewarded with a prominent position in the search results.
Relevance and trust beat volume: 86% of journalists use at least some PR-pitched stories, yet 54% seldom respond, while 61% of Gen Z turn to generative AI instead of Google.
Over time, Google and other search engines updated their algorithms to improve the search experience. With each change, Google cracked down on more sites that tried to manipulate the system with backlink volume instead of earning links with real editorial and audience value. Countless sites lost traffic, and many still feel the effects.
Today, we see Google place more emphasis on relevance, industry trust, and authority. That helps explain why a familiar brand can attract more searchers than a smaller competitor even when both publish similar content and target similar keywords.
Large language models and Google’s AI Overviews have widened this divide. These systems can use retrieval-augmented generation, or RAG, to retrieve relevant sources, often favoring authoritative publications and proprietary information. If we merely repeat a statistic already cited by a top-tier publication, an AI system may choose the better-known source to reduce the risk of spreading inaccurate information.
We also see younger searchers moving toward AI tools. In a 2025 Resolve study, 61% of Gen Z respondents said they used generative AI instead of Google.
None of this means every form of link building looks like spam to Google or an LLM. It means we need backlinks to work alongside a broader set of authority signals.
When publications and journalists cite our brand, they signal authority. When we publish original content and proprietary data, we signal authority. When we create useful graphics and informative videos, we signal authority again.
Once Google and AI systems recognize these signals, the backlinks supporting them become meaningful votes of confidence. Our site may then be more likely to rank prominently, appear in AI Overviews, and receive citations in LLM-generated answers.
How we use E-E-A-T in a competitive search environment
Experience: We demonstrate that an author has personally engaged with the subject. Examples include a forum where people describe testing a product or a gardener documenting firsthand pest-prevention trials.
Expertise: We show that the author has relevant knowledge, qualifications, or credentials supporting the information and advice.
Authoritativeness: We earn recognition from credible sources and industry voices that cite or link to our work, helping establish us as a respected participant in the field.
Trustworthiness: We remain transparent, accurate, and honest. We avoid deceiving readers or using manipulative link-building practices.
We apply E-E-A-T both on and off the page. Author biographies can demonstrate expertise, while accurate sourcing can demonstrate trustworthiness. Off the page, we strengthen E-E-A-T signals through the quality of the sites that link to us and the journalists who rely on us as a source. Both dimensions influence how search engines assess whether our information is useful, accurate, and credible.
If we consistently earn backlinks from dozens of irrelevant websites, that pattern can look like a low-quality or manufactured signal. If several respected journalists mention our brand because we published a valuable study, those mentions are much more likely to function as genuine votes of confidence.
Move beyond fragile SEO numbers. This side-by-side graphic shows how earned media, branded searches, industry citations and conversions build authority that can survive algorithm updates.
For us, link quantity is no longer a reliable proxy for legitimacy. We look for backlinks that demonstrate real relevance and value.
We cannot earn those links half-heartedly. We need a coordinated strategy that strengthens credibility both on and off our site.
The off-page SEO tactics we use to demonstrate value
When we ask how to earn links that search engines and LLMs will treat as signs of trust, we do not look for a single outreach tactic. Strong links usually emerge from several activities that we sustain over time.
We create genuinely linkable assets
To prove that people genuinely want to reference our site, we first create content worth referencing. If we are accustomed to quick and easy links, this may require a larger investment in content than we have made before. A routine how-to article or listicle is rarely enough by itself.
We define linkable content as something journalists, publishers, and readers find distinctive and useful — something they have not already encountered dozens of times. We often draw from the following content formats.
Original data and proprietary research: We publish information people cannot find elsewhere. In a crowded search environment, that means conducting original research rather than recycling familiar statistics. When a journalist needs a statistic and our site is the primary source, we can earn a natural backlink.
Thought leadership and expert commentary: We share an original perspective from a credible expert within our organization, giving publishers a useful idea or quotation they may cite in future coverage.
Authoritative long-form guides: We answer the main question thoroughly and anticipate the follow-up questions a reader is likely to ask. This depth can help us earn links as audiences move further into their research.
Engaging visuals and infographics: We invest in visual assets that make complex information easier to understand and share. Ahrefs found that YouTube mentions strongly correlated with inclusion in AI Overviews. Videos can be especially valuable, but informative infographics also give publishers a useful visual for their own audiences.
These formats demand more time, effort, and money, but we have found that they are often more sustainable than disposable content. They help us earn credible editorial citations and build industry authority that is more resilient to algorithm updates.
We connect link building with digital PR
We place digital PR at the center of authority building because it connects brand development with link acquisition. It helps us earn coverage, attract links, and introduce our organization to new audiences through credible journalists. Those are precisely the kinds of signals search engines can consider when assessing legitimacy.
Unlike traditional PR, our digital PR work focuses on online coverage and backlinks from news organizations and media outlets. We create useful assets or proprietary data, identify the journalists most likely to care, and pitch stories that fit their established beats.
Many of these publications carry significant influence and reach large audiences that can introduce our brand to more people. When a highly authoritative outlet covers our story, other journalists may discover and cite it organically. Syndication can amplify the effect further when a media group republishes an article across its network, potentially producing many relevant links from one story.
Our strongest digital PR campaigns typically use one or more of the following approaches.
Build lasting brand authority in five steps: target trusted publications, create citation-worthy assets, launch digital PR, nurture journalist relationships, then measure and refine your approach.
Data-led PR campaigns: We begin with what journalists and their readers care about, not simply what we find interesting. We review local news, Google News, and current coverage to understand which subjects are gaining attention. By considering journalist intent from the start, we improve our chances of receiving responses and earning placements.
Newsjacking or reactive PR: When we can move quickly, we contribute expert opinions, data, or commentary to breaking stories that relate to our brand. This gives journalists material they can use while the topic is still timely.
Proactive PR: We anticipate trends before they break and prepare insights around recurring news cycles, holidays, and other relevant media moments.
Contributed content and guest features: We place useful content written by our experts in relevant publications, allowing us to speak directly to established audiences and earn recognition.
When we combine these tactics effectively, we can elevate our brand to a level that competitors cannot reproduce with a batch of low-value links.
We build relationships with journalists and publishers
We know that even fascinating proprietary data, packaged in an expertly designed analysis, can fail if our journalist outreach is poorly targeted.
Journalists receive an enormous number of PR pitches, and those messages can either support or obstruct their work. According to a 2026 Muck Rack study, nearly nine in 10 journalists said at least some of their stories originated with PR pitches.
The same survey found that 54% of journalists seldom or never responded to most pitches. Relevance was a central problem: nearly half said a genuinely relevant pitch was rare.
If we send a journalist at an economics publication a pitch about music-listening habits, we should expect a rejection because the subject may matter to only a small part of that publication’s audience. We do not take that response personally. Journalists build their careers around particular topics and beats, and our job is to support that work rather than distract from it.
We therefore approach outreach as relationship building: a two-way exchange that should benefit everyone involved. Above all, we remember that there is a real person on the other side of every email.
We personalize our emails and explain why a story fits the journalist’s audience.
We respond graciously when a journalist says no because our next idea may be a better fit.
We share relevant work from journalists and publications through social media.
We contribute thoughtful comments when we have something useful to add.
We cite journalists’ reporting in future content when it genuinely supports our work.
As we strengthen these relationships, journalists become more likely to consider future opportunities. A thoughtful follow-up or second pitch can receive a warmer response when a reporter already knows that we provide reliable data and useful commentary.
PR relationships grow over time. Even when our first pitch does not fit a journalist’s beat, we remain willing to return with a better story or a new set of relevant data.
How we measure real brand authority
We recognize that authority, trust, and legitimacy feel less concrete than traffic or keyword position. Yet they have become more important. A traffic surge may look encouraging while reflecting temporary attention, weak intent, or an advantage that disappears after an algorithm update.
Authority and legitimacy are more durable. We can also measure the impact of credibility-focused work through several meaningful indicators.
EZ Contacts’ six-month digital PR campaign delivered 1,000+ media placements, raised Domain Rating from 40 to 43, and doubled visibility across ChatGPT and Google AI Overviews.
Earned media placements: We track the publications that cover our brand, including coverage containing an unlinked mention. These placements help us assess brand credibility.
Branded search volume: We monitor whether more people search for our company or products after discovering us through media coverage.
Industry coverage: We look for the point at which publications we have not contacted begin citing our work. That organic pickup is a valuable sign that our authority is spreading.
Conversions: We measure whether greater credibility leads more people to trust our organization, products, or services and ultimately take meaningful action.
Organic ranking improvements for target keywords: We still review rankings, but we treat them as one indicator within a broader picture. Sustained movement can show that search engines increasingly view us as a credible result relative to competing pages.
We do not expect these indicators to appear overnight.
We invest real effort in creating proprietary data.
We build trust with journalists through repeated, useful interactions.
We grow authority through sustained work over time.
Our advice is simple: we stay patient, keep improving, and allow credible results to compound.
How we build a credibility-focused link strategy
Knowing the principles of SEO authority is one thing; building an entire campaign around them is another. We use the following five-step process to turn those principles into consistent action.
Step 1 — We define our target publications: We identify five to 10 publications that our audience trusts and that search engines are likely to recognize as authoritative within our field. These become our priority coverage targets.
Step 2 — We develop linkable assets: We create at least two content or media assets designed around the interests of those publications. We may use original survey data, visual guides, proprietary analysis, or expert thought leadership.
Step 3 — We launch a digital PR campaign: We proactively pitch our assets to relevant publications. We can also use platforms such as Connectively or Muck Rack to identify ongoing opportunities with writers covering subjects related to our research.
Step 4 — We nurture relationships: We treat every positive media interaction as the beginning of a longer relationship. We follow up with useful information, engage with published coverage, and build the kind of rapport a journalist can rely on.
Step 5 — We measure and iterate: We review our authority indicators each quarter, learn from the response to our campaigns, and adjust our content and outreach accordingly.
We know this process can consume a team’s time, particularly when resources or specialized expertise are limited.
In those situations, we may benefit from working with a link-building and digital PR specialist who can expand our capacity and keep pace with search changes. The right support can help us establish sustainable visibility without allowing every minor ranking fluctuation to pull us off course.
How we build authority that lasts at Resolve
We know that quality usually stands the test of time better than quantity. The difficult part is maintaining that focus when competitors appear to be winning with sudden traffic spikes or eye-catching vanity metrics.
We do not let temporary numbers distract us from the larger goal. We focus on lasting authority and legitimacy earned through sustained content creation, thoughtful PR outreach, and genuine relationship building.
When an internal team lacks the time or patience required to maintain that effort, we can step in.
At Resolve, we work with brands to build credibility-focused SEO campaigns through linkable content, data-led digital PR, and hands-on link building. Our goal is sustainable organic growth, not a burst of visibility that disappears after the next algorithm update.
We have seen this approach pay off. In a recent data-led campaign for EZ Contacts, we earned more than 1,000 placements in outlets including the New York Post and Yahoo. As the coverage grew, the brand’s visibility in ChatGPT and Google’s AI Overviews doubled. That is the kind of durable growth we want to build — growth that extends beyond the next algorithm update.
A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.
When we are ready to build links that last, we can visit growresolve.com to learn more.
Our answers to common link-building questions
How do we distinguish link building from digital PR?
We see considerable overlap between link building and digital PR, but we do not treat them as identical. Link building is the broader practice of acquiring backlinks from other websites to improve search authority. Digital PR is a particular approach within that practice, focused on earning links through media coverage, journalist relationships, and placements in credible publications rather than relying on directory submissions, guest-post exchanges, or other lower-authority tactics.
We often use digital PR to pursue the strongest editorial backlinks because reputable outlets have real audiences and established review standards. At the same time, this work builds brand visibility and consumer trust in ways that many conventional link-building methods do not.
How long do we wait for meaningful results?
We do not expect authoritative backlinks or earned media coverage to produce results overnight. That is an honest trade-off when we choose a credibility-focused approach instead of more aggressive tactics. Most brands can begin seeing meaningful domain-authority gains and early ranking movement after three to six months of consistent execution, while highly competitive keywords and top-tier placements may require more time.
The advantage is that our results can compound. Links from credible publications tend to endure, strong journalist relationships can create repeat opportunities, and the authority generated through consistent coverage can keep delivering value long after the initial campaign.
What do we mean by an authority backlink?
We define an authority backlink as a link from a source that search engines and its audience regard as credible and trustworthy. These sources typically have genuine readers, clear editorial processes, established authority, and topical relevance to our industry.
A regular backlink can come from any website willing to link to us, regardless of its relevance, quality, or editorial standards. That distinction matters because search engines do not evaluate every link equally. One editorial link from a respected industry publication can be more valuable than dozens of links from low-authority sites, while also supporting the kind of E-E-A-T credibility that bulk link acquisition cannot reproduce.
Do we value brand mentions without hyperlinks?
Yes. We recognize that Google can associate brand mentions with entities even when a publication does not include a hyperlink. Relevant, unlinked mentions in credible coverage can still contribute to the wider authority signals surrounding our brand.
That is why we consider digital PR valuable even when every placement does not produce a direct link. A credibility-focused off-page strategy should not be reduced to backlink acquisition alone. Our larger objective is to build a brand that respected publications genuinely want to mention, cite, and cover.
What risks do we face with outdated link-building tactics?
We see risks ranging from wasted effort to serious search penalties. Link buying, reciprocal-link schemes, private blog networks, and manipulative anchor-text optimization can violate Google’s spam policies. These tactics may trigger manual actions or algorithmic suppression that substantially reduces our search visibility.
Even when outdated tactics do not produce an immediate penalty, they can lose their value as search systems become better at identifying manufactured signals. Recovering from a link-related penalty can be slow and expensive. We would rather invest in credible link building from the beginning than repair the damage caused by shortcuts later.
SMX Next returns online Nov. 18, and I’m excited to help shape a program focused on today’s complex search landscape and the tactics that will define success in 2027 and beyond.
Search marketing isn’t just changing. From my perspective, it has become an entirely new kind of challenge, and that is exactly why fresh voices and practical expertise matter so much right now.
In SEO, I’m seeing the field shift toward AI Overviews, search everywhere optimization, and the rise of autonomous AI agents that browse on behalf of users. Trustworthiness, digital authority, and precise alignment with user intent are no longer nice-to-have ideas. They are becoming essential.
On the PPC side, generative AI and deep automation are creating new levels of personalization. At the same time, they are raising urgent questions for marketers: How do we keep strategic control, protect data privacy, and avoid wasted spend?
If you’re an enthusiastic search marketer with a passion for sharing what you know, I hope you’ll consider submitting a session pitch for SMX Next. I’m looking for subject matter experts who can share insights, strategies, and tactics that help SEO and PPC marketers thrive in 2027.
Whether you’ve been speaking for years or you’re a practitioner ready to share something new you’ve developed, I want to hear from you. I’m especially interested in new speakers with diverse points of view and real-world experience.
The deadline for SMX Next pitches is Aug. 7.
When I review session proposals, I’m looking for ideas that feel original, specific, and useful. Advanced, forward-thinking topics or unique frameworks that aren’t already common at other search events will stand out.
I also want to see actionability. Be clear about what attendees will be able to do better, faster, or differently after your session.
Bring the data whenever you can. A case study, concrete example, or tested approach makes your pitch stronger, especially when you explain how the lesson can scale across different types of organizations.
Keep the scope focused. A 30-minute session works best when it goes deep on a narrow or specialized topic instead of trying to cover too much at once.
Most importantly, give attendees something tangible to take with them. I’m looking for sessions that leave people with a clear action plan, framework, or process they can put to work right away.
Visit this page for more details on how to submit a session idea, or go directly to this page to create your profile and submit your pitch.
If you have questions, feel free to contact me directly at kathy.bushman@semrush.com. I’m looking forward to reading your proposals!
I see TikTok becoming harder to ignore in SEO because discovery no longer happens in one clean path. Someone might find a restaurant on TikTok, verify it through Google Reviews, check Reddit for honest opinions, scan the menu on the business website, and then book a table. Someone else might take those same steps in a completely different order.
Nearly half of U.S. consumers used TikTok as a search engine in 2026, up from 41% in 2024, according to Adobe survey data. What stands out to me is why people search there: short-form video, storytelling, interactivity, tutorials, product reviews, personal stories, and influencer recommendations all make the platform feel more immediate than a traditional results page.
I also think TikTok recent updates show how seriously the platform wants to be part of the search journey. Many purchase decisions are visual, social, emotional, and trust-driven, which is exactly where TikTok has strength. With Local Feed, AI summaries, creator reviews, and shopping features, TikTok is trying to meet people at the moment they are exploring, comparing, and deciding.
So instead of asking whether TikTok is a traditional search engine, I ask a more useful question: how do I make sure people can find, understand, trust, and choose a brand wherever their search journey begins? More often than many marketers want to admit, that starting point may be TikTok.
TikTok SEO Is More Than Hashtags Now
I think of TikTok SEO much like traditional SEO: it is the work of making a business, place, product, service, or experience easier to discover. As TikTok has evolved, the discovery surfaces have expanded far beyond captions and hashtags.
In the past, I mostly associated TikTok optimization with captions, hashtags, trending sounds, posting times, and the hope that a video would land on the For You feed. Those pieces still matter, but they are no longer the full picture.
Today, I have to think about TikTok Search, recommendations, Local Feed, Places, reviews, comments, creator content, visual cues, product signals, and AI-assisted discovery. A stronger TikTok SEO strategy now includes search query relevance, spoken topic clarity, on-screen text, captions, hashtags, location context, creator reviews, comments, product visuals, and the searches people make after seeing a video.
TikTok documentation says search results can be shaped by how well content matches a query, along with hashtags, sounds, user interactions, language, and location. The For You feed also weighs user interactions, content information, user information, and watch behavior, which means usefulness and engagement both matter.
Local Feed Creates a New Discovery Surface
TikTok launched Local Feed in the U.S. on Feb. 11 as a home-screen tab for nearby content related to travel, events, restaurants, shopping, small businesses, and local creators. TikTok says posts can appear based on location, topic, and when the content was published.
I see Local Feed as another organic discovery touchpoint, especially for local businesses. A restaurant can appear while someone is deciding where to eat nearby. A wellness club can show up when someone is looking for weekend plans. A venue can answer practical before-you-go questions before a guest ever reaches the box office.
There are limits I would keep in mind. TikTok precise location setting is optional, off by default, available only for users 18 and older, and still rolling out across the U.S. TikTok also says private accounts, accounts for users under 18, and posts limited to Friends or Only You will not appear in Local Feed.
Local Explorer Shows TikTok Is Investing in Places
TikTok Local Explorer Program is one of the clearest signs I have seen that the platform wants to build stronger place-based discovery. The program encourages people to submit location-based reviews and rewards participation with experience points, levels, badges, community access, and other perks.
I would not assume every market has the same access or level of activity, because availability has been limited and uneven by region. Still, the direction matters: TikTok is building more ways for users to evaluate places inside the app.
I have also seen TikTok incentivize reviews for places that do not already have TikTok reviews. In one example, a coffee shop had no TikTok reviews, and I was offered a $1 Promote coupon to leave one.
When a place does not have native TikTok reviews, I have seen TikTok pull reviews from TripAdvisor and, in some cases, Google. That makes the Places tab a useful comparison surface where people can evaluate reviews, videos, and comments before deciding whether to visit a local business.
Visual Search Links Matter More Than Exact Keywords
TikTok increasingly adds automated search links and related query prompts beneath videos. I pay attention to these because they show how TikTok can connect a video to a broader topic, place, or product discovery path.
For example, a video about a place like Glen Ivy may show a search bar at the bottom that lets users explore more related content. Those search bars can appear even when a creator has not overloaded the description with exact-match keywords, which tells me TikTok is reading more than just captions.
TikTok Shop Turns Discovery Into Buying
With TikTok Shop, someone can see a product in a video, search for it, compare it through comments and creator content, and buy it without leaving the app. That makes TikTok more than a discovery channel for ecommerce brands; it can become part of the full purchase path.
I would optimize TikTok Shop content around the information TikTok needs to understand a product. Search relies heavily on how well a shopper query matches product information such as titles, categories, attributes, and content context.
TikTok Shop has also released Shoppable Photos in beta for select sellers. Eligible sellers can create image-based posts, include multiple photos, and tag products directly in the post. These posts may appear in the For You feed, Search, and the Shop tab, giving sellers a simpler way to showcase inventory without producing a full video.
AI Is Becoming Part of TikTok Discovery
I am also watching TikTok AI-assisted discovery features closely, even though availability varies by market, account, and test. Features such as Tako, AI Overviews, Quick Highlights, AI summaries, and Content Studio all point in the same direction: TikTok wants to help users search, summarize, and create faster.
Tako is TikTok chatbot, and it lets users search in a way that feels similar to using the app search bar. It can surface relevant TikTok videos and external sources, including articles.
TikTok also now offers AI Overviews for some searches. When users search a topic, they may see an AI-generated summary of the results. If they click a visual search bar, they may also see Quick Highlights that summarize that search experience.
The Places tab includes AI summaries too, and users can see how many posts were used to generate a place summary. For local businesses, that makes the quality and clarity of creator posts, customer videos, and reviews even more important.
On the creator and seller side, TikTok AI tools can help generate captions, hashtags, and even videos. I would treat these tools as helpful support, not a substitute for real strategy, because features like Content Studio are still not available to everyone and remain in testing.
How I Would Improve Visibility on TikTok
On TikTok, visibility comes from what people search for, what TikTok can understand, and what the camera actually shows. That means I would focus less on cleverness and more on showing people what they need to see before they choose a business, product, or place.
For restaurants, I would show menu items, exterior signage, the dining room, takeout packaging, seasonal dishes, and neighborhood cues. Those visuals help both users and TikTok understand what the place offers and where it fits.
For retail, I would show product displays, packaging, try-ons, shelf layout, gift ideas, and the storefront. The more clearly a video communicates what is available, who it is for, and where someone can get it, the stronger the discovery signal becomes.
I would also build simple habits into every TikTok content workflow: use location context naturally, show products clearly, show the storefront or interior when relevant, mention the city or neighborhood when it helps, create timely content around local moments, tag the physical location when appropriate, and work with creators who already understand discovery-driven content.
Keyword Research
I would start TikTok keyword research inside the app because that is where the search behavior is happening. Seed topics might include best brunch, World Cup outfits, things to do in [location], wedding inspiration, or gluten-free bakery.
From there, I would search each phrase on TikTok, document autocomplete suggestions, review suggested filters, look for Others searched for prompts, study top videos, and pay close attention to comment themes. I would also test city and neighborhood modifiers, then compare TikTok findings with Google Search Console, Google autocomplete, Reddit, YouTube, and site search data.
TikTok Creator Search Insights can add another useful layer by showing personalized information about search topics, content gaps, and how content tied to searched topics is performing.
Keyword Placement
I would place the core topic where TikTok and viewers can recognize it quickly: in the first few seconds of the video, the first text overlay, the opening of the caption, relevant hashtags, location tags, pinned comments, reply videos, the profile bio, playlist names, and creator briefs.
Comments and Reviews
I would treat comments and reviews as visibility assets, not afterthoughts. That means pinning genuinely helpful comments, replying to repeated questions with videos, correcting misinformation when trust is at stake, watching for recurring objections, and turning repeated questions into FAQs, landing page content, Google Business Profile posts, and future videos.
A creator saying that a bakery is the best gluten-free option in Portland because it takes cross-contamination seriously may be more useful than a generic five-star review. That kind of specific language can shape website copy, FAQ strategy, and customer messaging.
Referral Traffic and Branded Search
I would track TikTok referral traffic and monitor branded searches over time. When a TikTok post performs well, I would annotate it and compare branded search trends against a baseline.
I would look for directional movement in branded clicks, branded impressions, TikTok referral traffic, Google Business Profile actions, and engagement on related pages. At the same time, I would avoid giving TikTok credit for every increase without considering PR, paid campaigns, email, promotions, seasonality, and other marketing activity.
Attribution may never be perfect, but imperfect measurement does not make TikTok influence meaningless. I would rather measure directional impact than ignore a channel that is clearly shaping discovery behavior.
I Would Explore TikTok Instead of Ignoring It
Someone may find a business on TikTok before they ever search for its name on Google or ChatGPT. Someone else may turn to TikTok midway through the journey to decide whether the business is worth the trip, the purchase, or the recommendation.
Either way, I believe TikTok has earned a meaningful role in modern SEO strategy. Between Local Feed, Places, Tako, AI summaries, creator reviews, and TikTok Shop, the platform keeps adding new ways for businesses to be discovered, and many of those opportunities are still underused.
AI brand discovery is not one visibility problem. It is a sequence: a system must find and understand a brand, select its material as evidence, include the brand in an answer, and sometimes recommend it strongly enough to influence what the buyer does next.
The source material reveals why conventional search reporting captures only part of that sequence. Organic rankings can coexist with weak AI citations, while an AI recommendation can influence a later search visit without receiving credit in referral analytics. Brands therefore need a measurement and content strategy that follows the full path from discoverability to commercial action.
AI visibility is a chain, not a single ranking
The sources describe different stages of the same process. The B2B benchmark reported by Search Engine Land examines whether brands ranking in Google are cited in AI Overviews. HiGoodie’s guidance concentrates on making content clear, credible, and understandable to answer engines. A separate Search Engine Land report covers what users did after ChatGPT recommended a brand. Its assistive-agent framework then extends the journey from recommendation toward transactions completed by software.
Combined, these perspectives suggest four distinct visibility questions. Can an AI system discover the relevant material? Can it interpret and trust that material as evidence? Does the resulting answer cite or recommend the brand? Does that exposure influence a visit, comparison, or purchase? Success at one stage does not establish success at the next.
This distinction matters because citations and recommendations serve different functions. A citation identifies a source used in an answer. A recommendation places a brand into the buyer’s consideration set. Either can create value, but the downstream effect of a recommendation may be easier to see in buyer behavior than in a referral report.
Strong organic reach can conceal an AI citation deficit
The clearest evidence of a broken handoff comes from Walker Sands’ B2B AI Search Visibility Benchmark, as reported by Search Engine Land. The analysis covered more than 45 million March search queries associated with 828 enterprise B2B companies in 14 industries. It reported that the median company ranked for about 9,700 queries and encountered AI Overviews on 48.8% of its relevant ranking keywords, yet appeared as a citation in only 3% of those AI Overviews.
The benchmark also reported that 4.6% of the companies received no AI Overview citations for any relevant keywords. Even its top quartile reached a citation inclusion rate of only 4.5%, compared with 1.7% for the bottom quartile. These findings do not show that organic search has stopped mattering. They show that ranking coverage and selection as evidence are separate outcomes.
Category exposure also varied. According to the report, AI Overviews appeared in a median 59.9% of cybersecurity searches, where brands achieved the study’s highest median citation rate of 4.2%. Distribution and logistics had the lowest reported AI Overview incidence, at 29.6%, while both that category and professional services recorded median citation rates of 2.1%. A visibility target should therefore reflect how often AI answers appear in the category as well as how frequently the brand enters them.
The benchmark associates stronger citation performance with topical depth, direct explanations, structured information, and consistent coverage across related pages. HiGoodie’s article arrives at a compatible editorial prescription: organize content around real questions, connect related topics, and support claims with credibility signals. Together, the sources favor focused subject-matter coverage over simply publishing more pages for more keywords.
Recommendations can create demand that attribution misses
Citation inclusion is an intermediate metric; buyer response is closer to the business result. Search Engine Land’s account of a Similarweb study reported that U.S. desktop users who received a specific ChatGPT brand recommendation were, on average, 2.5 times more likely to visit the recommended brand than a direct competitor within seven days. The study followed activity from July through December 2025 across selected finance, travel, and beauty brand pairs. It excluded users who had recently visited the brand or explicitly named it in their prompt.
The reported pattern appeared in all three sectors, although its size differed by brand pair. After a Capital One recommendation, for example, 14.2% of users visited Capital One and 3.8% visited American Express. After a Kayak recommendation, 12% visited Kayak and 3.4% visited Skyscanner. These are reported observations from an opted-in desktop panel, not proof that every recommendation will produce the same effect in other audiences or categories.
The more consequential measurement finding is where those visits appeared. Similarweb reportedly attributed 55.9% of AI-influenced visits to search, versus 40.4% of non-AI-influenced visits. Direct traffic accounted for 19.9% of AI-influenced visits and 38.8% of standard visits. If a user learns about a brand in ChatGPT and later searches for it, a conventional last-touch view can credit search while overlooking the conversation that formed the preference.
The study also reported deeper activity among AI-influenced visitors: averages of 12 pages and 11.8 minutes on site, compared with 6.5 pages and 5.6 minutes for other visitors. That pattern is consistent with users reaching the website after narrowing their options, although it does not by itself establish why they engaged more deeply.
A practical operating model joins content, evidence, and measurement
A useful program begins by separating opportunity from performance. Organic keyword coverage shows where a brand is discoverable. AI Overview incidence shows where generated answers can mediate that discovery. Citation inclusion shows whether the brand’s material is selected. Recommendation monitoring asks whether the brand enters consideration. Branded search, site engagement, qualified actions, and sales outcomes then help reveal downstream demand.
Build the evidence layer before chasing mentions
The shared foundation across the sources is content that both people and machines can interpret. Pages should answer a defined buyer question promptly, explain relevant concepts precisely, and make important claims easy to evaluate. Related pages should collectively demonstrate depth rather than repeat a shallow definition. Earned media and corroborating information can complement first-party material by strengthening the wider evidence available about the brand.
The assistive-agent framework reported by Search Engine Land places this work above, rather than in place of, SEO. In that model, search supplies crawled and indexed information, assistive systems add language-model reasoning and corroboration, and agents can eventually interact with business systems. This is a conceptual framework, not a measured result, but it clarifies why technical accessibility, entity understanding, and accurate business data belong in the same plan as editorial quality.
Audit the questions closest to a decision
Broad awareness coverage can reveal demand, but recommendation visibility becomes especially important when buyers compare providers, test suitability, or seek a shortlist. An audit should examine what an AI answer says, which sources it cites, whether the brand appears, how it is characterized, and which competitors receive stronger treatment. Because AI answers may vary, repeated observation is more informative than treating one response as a permanent ranking.
Measure influence without forcing false precision
AI referral traffic remains useful, but it should not be treated as the full contribution of AI discovery. Teams can examine changes in branded search, direct visits, engaged sessions, assisted conversions, and customer-reported discovery alongside citation and recommendation monitoring. None is a perfect substitute for controlled attribution; together, they can expose demand that a referral-only dashboard would miss.
Key takeaways
Organic rankings create discoverability, but they do not guarantee inclusion in an AI-generated answer.
AI citations, brand recommendations, website visits, and transactions are different stages and require different measures.
Clear answers, topical depth, structured information, and corroborating authority form the content foundation described across the sources.
AI-influenced demand may later appear as search traffic, so referral analytics alone can understate AI’s role.
Category-level AI exposure should shape priorities because the incidence of generated answers and citation rates can differ substantially.
As more discovery and evaluation move into generated answers, the defensible advantage will come from connecting machine-readable evidence with trustworthy buyer experiences. The next step is not merely to seek more AI mentions, but to learn which questions create recommendations and whether the business is prepared to convert the demand they produce.
Brand visibility in Google AI results is no longer a simple matter of ranking or being cited. A company can make its content available, have that content used as evidence, and still watch Google recommend a competitor.
The two source reports expose different sides of that problem: one examines controls over participation in Google’s AI experiences, while the other shows why participation alone does not secure an endorsement. Together, they suggest a more useful framework for managing AI visibility.
AI visibility now passes through three separate gates
Google AI visibility can be understood as three related but distinct outcomes: eligibility, citation and recommendation. Treating them as interchangeable can produce misleading reports and poor strategic decisions.
Eligibility: Whether a publisher permits its content to appear in an AI-powered search experience.
Citation: Whether Google uses a page as supporting material in an AI-generated response.
Recommendation: Whether the response presents the brand itself as an option a user should consider.
The article about Google’s reported AI opt-out controls concentrates on the first gate. It says site owners are being given a way to exclude content from experiences such as AI Overviews and AI Mode, alongside early-stage AI reporting in Google Search Console. The article about self-promotional software listicles concentrates on the second and third gates, reporting that Google may cite a company’s page without recommending that company.
This distinction changes the central business question. Being available does not guarantee selection, but becoming unavailable removes even the opportunity to supply evidence, earn a mention or influence the comparison.
Why a citation can create visibility for a competitor
The clearest warning comes from the analysis attributed to Lily Ray in the source about "best" software listicles. According to that report, Ray examined 100 B2B software queries across three collection dates: April 15, May 15 and June 8. Eighty of those queries produced an AI Overview.
The source reports that self-serving listicles appeared among the citations 323 times, but that the publishing brands were not recommended in 224 of those instances. It also reports that such listicles were cited in 69% of the B2B software queries studied. Those figures come from a limited query set and should not be generalized to every market, but they illustrate an important failure mode: content visibility and commercial visibility can move in different directions.
In one example described by the source, an Oasis LMS page was cited for a query about the best learning management system for selling courses, while Kajabi and other competitors appeared among the recommended options. The owned page may therefore have helped Google construct an answer without persuading the system to favor its publisher.
The same report says third-party sources including Reddit, Forbes and YouTube were becoming more prominent in citations for these queries. That observation supports a broader interpretation: a brand’s claim about itself is only one input, while external discussion may help determine whether the brand is treated as a credible recommendation. The sources do not establish a precise causal formula, so this should be treated as a strategic hypothesis rather than a confirmed ranking rule.
Opting out changes brand eligibility, not user demand
The opt-out source argues that withdrawing content does not stop people from using AI Overviews or AI Mode. Instead, it changes which brands and sources remain eligible to appear. Under that interpretation, an absent publisher leaves Google to assemble its response from participating competitors and third parties.
That does not make participation an automatic choice for every organization. Publishers may have legitimate concerns about content rights, representation, traffic substitution or the commercial value exchanged when their work supports an AI answer. The key is to evaluate those concerns against the actual effect of the control. An opt-out is a content-distribution decision, not a mechanism for reversing user adoption of AI search.
The listicle findings make the trade-off more complicated. Remaining eligible can create an opportunity to be cited, but citation may still transfer attention to another brand. The strategic task is therefore not merely to stay present. It is to improve the probability that Google’s answer connects the evidence supplied by a company with a favorable, accurate representation of that company.
A measurement model for meaningful AI visibility
The opt-out article calls for reporting that extends beyond conventional SEO traffic and includes brand mentions, citation frequency and representation across AI platforms. The listicle analysis demonstrates why those dimensions must be separated rather than collapsed into a single visibility score.
A practical monitoring program can classify each important query using the following fields:
AI result presence: Whether the query triggers an AI-generated result.
Source inclusion: Whether the company’s domain is cited or otherwise used.
Brand inclusion: Whether the company is named in the generated answer.
Recommendation status: Whether the brand is presented as a preferred or relevant option.
Competitor benefit: Which rival brands are recommended when the company’s content is cited.
Representation quality: Whether the description of the brand, product and limitations is accurate.
This structure makes several otherwise hidden outcomes visible. A page can win a citation while the brand loses the recommendation. A brand can be mentioned without receiving a link. A competitor can gain the commercial benefit from evidence published by someone else.
Content reviews should follow the same separation. Self-authored comparison pages need a transparent method, supportable claims and meaningful treatment of alternatives; simply declaring the publisher’s product the best may not influence the recommendation as intended. Because the reported study also observed more third-party citations, teams should assess how the brand is described outside its own domain instead of treating owned content as the whole AI visibility strategy.
Key takeaways
Eligibility, citation and recommendation are separate stages of Google AI visibility.
According to the reported B2B software analysis, Google often cited self-promotional listicles without recommending their publishers.
Opting out may remove a brand’s content from consideration, but it does not remove the user’s underlying AI search activity.
Reporting should identify who supplies the evidence, who receives the mention and who ultimately earns the recommendation.
As Google’s controls and reporting mature, the strongest strategy will be based on observable outcomes rather than a binary debate over participation. Brands that distinguish being used as a source from being selected as an answer will be better equipped to protect and improve their visibility.
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.
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
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.
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.
