<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/ai-search-visibility.png" class="attachment-large size-large wp-post-image" alt="AI search visibility" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/04/ai-search-visibility.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/ai-search-visibility-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/ai-search-visibility-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/ai-search-visibility-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>A month-long test shows AI visibility follows repeatable signals — making it predictable, testable, and open to strategic influence.

In November 2024, with SE Ranking’s research team, we began a 16-month experiment to test how AI-generated content performs in organic search. We launched 20 websites across different niches and tracked their performance over time.
But we didn’t stop there.
We wanted to look beyond rankings and understand how AI systems discover, interpret, and cite information. So we expanded the project into a more ambitious set of experiments on AI search and LLM visibility.
For the next phase, we created a new fictional brand in a real niche with real competition to see how quickly AI systems would pick it up and whether it could be cited alongside or above trusted industry leaders and government sources.
After the first month, several patterns became clear.
Methodology behind the experiment
We created a fictional brand and published content about it across:
Brand new website representing the brand, registered specifically for the experiment.
11 additional domains, all over a year old, with prior history and existing rankings.
Across these sites, we tested seven content formats: 
Deep guides.
“Alternatives” listicles.
“Best of” listicles.
Review articles.
Comparison (“vs”) pages.
How-to/tutorial content.
Clickbait-style articles.
We started publishing in March 2026 and tracked how five AI systems responded: ChatGPT, Google’s AI Overviews, Google’s AI Mode, Perplexity, and Gemini.
In total, we tracked 825 prompts across different query types and scenarios, which generated 15,835 AI answers during the first month.
For each prompt, we looked at three things:
Whether our brand (or one of our sites) appeared in the AI answer
Whether it was cited as a source
How often it appeared as the main cited source (position 1)
This experiment is still ongoing, and the first month was designed to see how AI systems respond to newly created, fully available information tied to a fictional brand.
Key experiment insights
96% of all AI visibility for our fake brand came from branded searches. Even in a real niche with relatively low competition, a completely new domain had little chance of competing with established brands for broader, non-branded topics.
On queries that only our fake brand could realistically answer, we outperformed established competitors (DT 40+) by as much as 32x and achieved near-exclusive visibility in less than 30 days.
Even without strong authority, the pages that clearly explained who we were, what we offered, and how we were different (e.g., “[Brand Name] Compete Guide” and “About Us”) became the most cited sources from the main domain. This shows that brand positioning can be shaped early in AI search.
Perplexity was the fastest engine to surface new content. Newly published pages usually reached position #1 within 1–3 days of indexation. However, Perplexity often cited additional domains instead of the main brand site.
Google’s AI Mode was the most stable for branded queries tied to unique claims (showing our brand at #1 for an average of 90% of prompts). 
Gemini, by contrast, often misidentified the brand. And even for uniquely branded queries, this AI platform provided 60% of AI answers with no citations to our brand.
Deep guides, review articles, and comparison pages generated the highest number of AI citations, while more generic formats like how-to articles and listicles showed minimal impact.
A topical silo made up of one hub page and 10 supporting articles generated no AI citations. Meanwhile, a set of 30 short, repetitive pages (500-750 words each) generated more than 1,800 citations. So, in this test, high-volume content publishing mattered more than internal linking.
Insight 1: New domains may not beat market leaders right away, but they can define their brand narrative in AI search
One of the clearest takeaways from the first month is that a brand-new site has limited chances of competing for broader, non-branded topics, even in a niche with relatively low competition.
AI systems did pick up our fictional brand quickly, but most of that visibility came when the query was already connected to the brand itself, whether through:
the brand name
product-specific claims
or other brand-related angles
Specifically, out of all AI answers, 96% (15,553 out of 15,835) came from branded searches.
Non-branded informational queries produced just 4% of AI answers in total, and even those mostly came through our supporting test domains.
The pattern was even stronger on the main fictional brand site itself. There, we recorded:
10,253 AI answers for branded queries
and just 6 for non-branded ones
That is a 1,700x difference.
This feels familiar because it mirrors classic SEO. New brands still need time to earn trust, build recognition, and compete for broader topics. When AI systems answer general industry questions, they tend to rely on established and authoritative sources.
This is why the strongest results in our experiment came from prompts tied to information only our brand could answer, such as how the product works, how often it updates, and so on.
These queries alone generated 11,430 AI answers with citations to our brand, accounting for 72% of allvisibility in the experiment.
The reason is simple: there is no competition.
If a query is something like “Was [Brand Name] originally built as an internal tool?”, only one source can realistically answer it. AI systems don’t need to compare sources, evaluate authority, or resolve conflicts.
That gave our fictional brand a major advantage. Even with no domain authority, it outperformed established competitors (DT 40+) by up to 32x on these queries.
What all this means for marketers and business owners is that when users ask about your brand, AI systems are likely to rely on your website as one of the main sources of information. So, the content they cite should be fully aligned with how you want your brand to be positioned.
Our experiment supports this. The “Complete Guide” page on the main site appeared in 1,799 AI answers (the highest result in the dataset) largely because it consolidated key brand information in one place. The “About Us” page followed with 1,500 AI answers. Together, these were the most cited URLs from our main domain, with LLMs relying on them 3–5 times more often than the additional domains.
In practice, AI systems may learn about your brand quickly, but what they learn depends on what you publish. Your core pages should clearly answer all the questions that are important for your brand: who you are, what you offer, and how you’re different.
This way, you can start shaping your narrative in LLMs even as a new or small brand, before you have the authority to compete for broader industry topics.
Insight 2: AI engines behave very differently
Another strong pattern in the experiment is that the five AI systems do not behave alike. They vary not just in how often they mention the fictional brand, but in how quickly they pick it up, how consistently they cite it, and which domains they prefer as sources.
Google’s AI Mode: The most stable for branded visibility
Google AI Mode was the most reliable engine in the dataset.
Throughout the experiment, it placed our domain in position 1 for branded queries in about 90% of cases. Unlike other engines, it did not show major fluctuations or dependency on other test domains.
If there was one place where direct brand visibility was predictable, this was it.
Google’s AI Overviews: High visibility, lower consistency
Google’s AI Overviews also surfaced our tested domain for branded queries, but the pattern was less consistent.
We saw our brand appear in position 1 for 14 days for some prompts, followed by a drop mid-month that didn’t recover. More broadly, mentions and links for branded queries fluctuated heavily, appearing and disappearing multiple times each week.
Yet when links were included, it accurately described the brand. When no links were shown, it often claimed there was no public information available.
The takeaway here is not that AI Overviews failed to recognize the brand. It did. But that visibility was harder to sustain over time.
Perplexity: The fastest to pick up new content, but not always brand-first
Perplexity was the breakout engine for fresh content. 
It picked up newly indexed pages within 1–3 days, which clearly made it the primary driver of early visibility within our experiment. 
But this speed comes with a tradeoff.
Instead of consistently citing pages from our main domain, Perplexity often used our supporting test domains as sources. 
In early March, our main brand held position 1. But as we published more content on supporting domains, those domains gradually replaced it in AI citations.
By the end of the month,six different domains were being cited: our main brand site and five supporting test domains where we had published additional content about the fake brand.
So while Perplexity increases overall visibility, it doesn’t always send that visibility directly to the main brand site.
ChatGPT: Slower to react, stronger over time
ChatGPT showed the most noticeable progression over time.
At the beginning of March, there were no links or mentions of our brand at all. But as the month progressed, visibility steadily increased.
This growth was especially clear across specific content types:
Unique claims drove the strongest performance, accounting for the majority of visibility, with around 70% of citations appearing in position 1.
Review articles started with zero presence but quickly gained traction, reaching consistent position 1 rankings by March 17.
Comparison (“vs”) articles achieved the highest consistency overall, with mentions on 29 out of 31 days by the end of the month.
Overall, ChatGPT didn’t immediately recognize the brand. Once it recognized the brand, ChatGPT began surfacing it frequently, especially for branded prompts.
Gemini: weakest performance and most inconsistent behavior
Gemini was the weakest engine in the dataset and the least consistent.
Initially, it struggled to identify our niche correctly. However, the results improved when we changed how we asked the questions. When prompts were framed as comparisons (“X vs Y”) or reviews, Gemini was much more likely to recognize the brand correctly. 
Even then, the results were still limited. In the best-performing scenario (queries based on unique claims about the brand), Gemini failed to include any citations to our brand in about 60% of responses.
Insight 3: Content format matters, but so does the volume
Next, for this experiment, we tested seven different content types across both our main site and supporting test sites.
And what we found is that comprehensive, in-depth content earns far more AI citations than shorter articles.
The strongest-performing formats were:
Deep guides (5,000–6,000 words): ~900 AI answers per page
Review articles: ~257 AI answers per page
Comparison (“vs”) articles: ~145 AI answers per page
This does not mean there is one ideal content length or that longer pages automatically perform better. The stronger results likely came from the depth, structure, and completeness of the information these formats provided.
This finding also aligns with our broader research, where we’ve seen that detailed, well-structured content performs better across platforms like AI Mode and ChatGPT.
Pages with narrower or less comprehensive coverage generated fewer citations overall. For example:
How-to articles/tutorials: 22 AI answers per page
Clickbait/skeptical articles: 19
“Best of” listicles: 11
“Alternatives” listicles: 4
As part of the experiment, we also tested a “spam” approach: publishing 30 thin pages (500–750 words each) on one of our test domains.
Individually, these pages were weak (averaging just 63 AI answers per page).
But together, they generated 1,897 total AI answers, which makes it the highest-performing content setup at the domain level.
However, thin content is not inherently “better” because of this result. It just shows that volume can sometimes compensate for quality by increasing the likelihood of retrieval and citation (especially in AI engines like Perplexity that prioritize freshness).
In simple terms, a few strong pages win on quality, but a large number of weaker pages can still win on overall exposure.
Insight 4: Topical clustering alone doesn’t produce AI visibility
One of the most useful negative findings came from the content structure test.
For this part of the experiment, we created a hub page on one of our test domains and linked it to 10 supporting articles. In theory, this setup should have built strong topical depth and semantic reinforcement. All 11 pages were indexed, properly structured, and internally linked.
Yet, they generated zero AI citations.
This is significant because it challenges a common assumption carried over from traditional SEO: that topical clustering automatically improves authority or increases the likelihood of being retrieved.
At least in this experiment, it did not.
That does not mean topic clusters are useless. It means they are not sufficient alone. Internal linking and semantic breadth may help a search engine understand a site, but AI systems still need a reason to retrieve and cite a specific page for a specific answer.
So, do AI engines reward entity coherence more than truth verification?
Even within just one month, the results point to a clear conclusion:
AI systems appear to respond more strongly to consistency, repetition, and availability than to strict verification.



That should not be overstated. It is not that LLMs “believe anything.” But if a claim is:
Structured clearly
Repeated across relevant pages
Phrased like a fact
Available in retrievable source environments
Then AI systems may surface it surprisingly easily.
We also saw this in manual checks of LLM responses in AI Results Tracker. For prompts such as “is [brand] worth it,” some systems responded positively and recommended using our completely unknown fictional brand.
It may not be because LLMs automatically favor every new brand. In some cases, when little or no negative information exists, a system may fill the gap with a neutral or positive-sounding response based on the limited signals available. 
But the result is the same: if a completely fictional brand can generate consistent citations and favorable recommendations under certain conditions, then brand narratives in AI search may be more flexible than they seem.
Final thoughts
The most important outcome of this experiment isn’t that a fictional brand achieved visibility.
It’s that visibility followed a repeatable pattern once specific inputs were introduced: branded context, unique claims, diverse content formats, and sufficient presence across different sources.
That leads to two important conclusions.
AI search is not random. It follows identifiable signals, and those signals can be studied, tested, and influenced.
AI is still highly sensitive to manipulation. AIs don’t have their own sense of truth, verification processes, or critical thinking. The same factors that help legitimate brands become visible can also be used to simulate credibility.
If there’s one lesson here, it’s that you can’t assume AI systems will accurately represent your company, product, or category by default.
You have to actively shape the information environment they rely on.



And this is only the first month of results. We’re continuing to collect data, expand the experiment, and monitor how these patterns change over time.

A month-long test shows AI visibility follows repeatable signals — making it predictable, testable, and open to strategic influence.

Can a fake brand win in AI search? New experiment says yes

<div><img width="360" height="200" src="https://searchengineland.com/wp-content/seloads/2026/04/mt_360x200_smxnow_april.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" /></div>Join us May 6 for the second SMX Now, featuring Ameet Khabra on how to spot and stop PPC drift before it hurts performance.

<div><img width="360" height="200" src="https://searchengineland.com/wp-content/seloads/2026/04/mt_360x200_smxnow_april.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" /></div>
<p>Automation doesn’t fail on its own — it does exactly what it’s trained to do. The problem is that when Google Ads is fed incomplete, misaligned, or overly broad signals, it can optimize toward the wrong outcome faster than most advertisers realize.</p>



<p>In our second installment of SMX Now, our new monthly series, Ameet Khabra of Hop Skip Media will break down a real account where a 417% jump in conversions turned out to be the wrong kind of success. She’ll use that case study to explain the four key ways automation drift enters an account: signal drift, query drift, inventory drift, and creative drift.</p>



<p>You’ll leave with a practical framework for diagnosing drift early, understanding where human oversight matters most, and managing automation more deliberately so it works toward real business goals — not just platform-reported wins.</p>



<p>Join us May 6 at noon ET.</p>



<p class="has-text-align-center"><a href="https://www.bigmarker.com/third-door-media/the-automation-drift-and-how-to-correct-course?utm_bmcr_source=Editorial" type="link" id="https://www.bigmarker.com/third-door-media/the-automation-drift-and-how-to-correct-course?utm_bmcr_source=Editorial" target="_blank" rel="noopener"><strong>Save your spot</strong></a>


Automation doesn’t fail on its own — it does exactly what it’s trained to do. The problem is that when Google Ads is fed incomplete, misaligned, or overly broad signals, it can optimize toward the wrong outcome faster than most advertisers realize.
In our second installment of SMX Now, our new monthly series, Ameet Khabra of Hop Skip Media will break down a real account where a 417% jump in conversions turned out to be the wrong kind of success. She’ll use that case study to explain the four key ways automation drift enters an account: signal drift, query drift, inventory drift, and creative drift.
You’ll leave with a practical framework for diagnosing drift early, understanding where human oversight matters most, and managing automation more deliberately so it works toward real business goals — not just platform-reported wins.
Join us May 6 at noon ET.
Save your spot

Join us May 6 for the second SMX Now, featuring Ameet Khabra on how to spot and stop PPC drift before it hurts performance.

SMX Now: The automation drift and how to correct course

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/Is-there-still-a-long-term-game-for-SEO-in-AI-search.png" class="attachment-large size-large wp-post-image" alt="Is there still a long-term game for SEO in AI search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/Is-there-still-a-long-term-game-for-SEO-in-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/Is-there-still-a-long-term-game-for-SEO-in-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/Is-there-still-a-long-term-game-for-SEO-in-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/Is-there-still-a-long-term-game-for-SEO-in-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>SEO isn’t going away, but the rules are shifting. Learn how to blend core tactics with AI search behaviors to stay competitive.


SEO sits at an interesting crossroads. One camp insists on optimizing for large language models (LLMs) and AI engines, and the other insists on doing SEO the same way we’ve always done it.
But there’s another way to approach it: combining the fundamentals of SEO with an understanding of how LLMs operate and why.
With this approach, you can keep what’s always worked — like on-page SEO and backlinks from reputable sources. Yet you can also look ahead to new tactics, such as optimizing for query fan-out and emerging prompt intents.
Since 2023, and the rise of tools like ChatGPT, Gemini, Claude, and Perplexity, I’ve been researching how AI engines display search results and where SEO is headed.
Here’s what I’ve found, and how you can use it to rethink your approach to a future where AI SEO considers human behavior at its core.
How the Red Queen theory applies to AI search
The Red Queen evolutionary model says that for everything to stay the same over time, everything must change. But as you adapt to the changing environment, so does the competition.
As a result, you and your competitors remain the same distance apart. In your attempt to become the predator, your prey adapts in equal measure, leaving the status quo firmly in place.
Essentially, if you don’t adapt, you’ll get eaten.

  
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How to apply the Red Queen principle to your AI SEO strategy
Along the same lines, AI search is a natural progression of what has existed for at least a decade. A hybrid search model has been in place since 2015, with the introduction of RankBrain. 
That’s why many of the same SEO tactics still work now. Instead of a fundamental change, a series of big and small shifts has taken place over time.
For example:
LLMs still use retrieval-based search engines.
Content quality and freshness still matter.
Site speed remains crucial for performance.
Intent matching across the major categories is still relevant.
“Stop optimizing for ‘AI,’” says Britney Muller via LinkedIn. 
“Optimize for search engines (so retrieval-based AI can cite you) + earn third-party coverage (so the model already knows you before the prompt is typed).”
So, what makes a worthy source for LLMs? What are people using AI assistants to accomplish? Is it to find information, analyze an issue, or create a list of recommendations?
Research from Moz shows that only 12% of AI Mode citations mirror the URLs in organic results. This means AI engines only somewhat follow the traditional rules of SEO. And over time, these changes will likely become more extensive.
While Google denies that the search engine will be entirely generative, my prediction is that Google will continue along a generative path that encompasses AI assistant behavior, such as questions, actions, analysis, and creation.
As a result, your short- and long-term strategies must work together to remain innovative yet grounded.
Focusing on human behavior and traditional search while working to understand LLMs is how you worship the Red Queen.
Why RAG is essential to understanding AI search
The most effective approach is focusing on where LLMs fall short: their limited databases. Their systems rely on retrieval-augmented generation (RAG) to address gaps in their databases without requiring constant retraining. 
AI assistants like Google AI Mode and Gemini need RAG to prevent hallucinations and to continue surfacing relevant answers for consumers.
Here, I gave Google AI Mode and ChatGPT the same prompt: 
“I am looking for a skincare routine that prioritizes anti-aging. What routines and products should I use?”




Both returned relevant results, but the specifics differed. Google AI mode returned anti-aging tips and routines, while ChatGPT sourced anti-aging products.
They also used different sources for their information. Where ChatGPT preferred a fresh Today.com source, Google referenced dermatology websites and even Google Shopping listings. 
In both instances, the AI assistants needed external sources. 
How to optimize for AI search vs. traditional search
For SEO, you need to understand how your content aligns with the limitations of AI engines. They do the searching for themselves and then generate a response for the user, only showing external sources some of the time. 
It’s a subtle shift in thinking. Optimizing for search is less about crafting SEO content and more about becoming a trusted supplier for these LLMs — so when people enter a prompt, your brand shows up in the answer.
In that way, the Red Queen evolution involves studying AI answers, learning their quirks, comparing their preferences, and evaluating their most common intents.
Then, you can feed the database. Make sure Google, which has the largest database of any LLM, has sufficient data to keep you in the pool of trusted sources.
Without people, AI assistants have no power. That’s why you have to put people first.
Where are people using AI assistants to create, achieve, build, search, and prompt? And where does it make sense for your brand to be?
Now that the AI search landscape is more competitive, you have to think like a social media professional or a traditional marketer.
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Short-term SEO tactics rely on topical authority
A short-term SEO strategy can work now, in the overlap between traditional and AI search. It uses topical authority to deliver results immediately, shortening clients’ time to success. Here’s the short-term plan. 
Use internal links to build entity relationships
As Kevin Indig explains: 
“Today, internal links aren’t just distributing authority. They’re defining the semantic structure of your site.”
Internal links help search engines understand your site’s overall structure. AI Mode, for example, is built with vector search models, and entities are crucial to their operation.
Vector search puts your website’s information into a 3D model, allowing algorithms to go beyond keywords and determine the intent behind someone’s search. Internal links help strengthen these signals. 
As Gianluca Fiorelli suggests: 
“We should link internally and externally to content that reinforces entity connections, because this helps LLMs map embeddings to a wider network of connected entities, hence increasing our authority in the knowledge graph.”
Links have long mattered for search, and they still do. As you develop your long-term SEO strategy, they become increasingly important for surfacing your content in LLMs and AI assistants. 
Think in terms of topical coverage versus keyword research
Plan your topical authority through these four lenses:
Topical coverage: Develop pages that cover the overall topic and its subtopics in a relevant, useful way.
Query fan-out: Study the query fan-out behavior for your most valuable search terms to identify gaps in your website content.
Intent: Be ruthless in determining intent by breaking down the categories in your niche that do or don’t have AI visibility potential.
Content quality: Make sure your content follows strong experience, expertise, authority, and trust (E-E-A-T principles) and is optimized for AI SEO.
These are all based on traditional SEO tactics. However, they consider a hybrid or LLM-based approach versus focusing solely on organic search. 
Optimize and maintain your site’s technical health
Technical health is rooted in what works for search now: site speed, schema markup, and optimized titles and descriptions. 
After all, LLMs are expensive to maintain and run. It’s in their best interest to use resources that are fast and easy to extract information from. 
Consider recent site speed findings from Mike King, who notes, “Slow responses can trigger 499 errors, where the AI stops waiting.”
These three short-term goals — topical coverage, internal links, and technical health — are all important for visibility in LLMs and AI engines.  
But search has evolved because human behavior has changed. So, the long-term play involves adapting to human behavior. 
The long-term future of SEO relies on human behavior
Long-term SEO strategies should focus on the intent and actions of human behavior surrounding AI.
Identify search intent
The four traditional search intents (informational, navigational, commercial, and transactional) are still relevant. But AI search has added a few more.
According to MIT, examples include zero-shot, instructional, and contextual prompts. Grammarly considers other intents, including educational, opinion-based, and problem-solving.
I tend to break down intent into multiple categories of SEO opportunity based on the clients I’m working with. Some common examples include directional, recommendation, local, booking, and shopping.
Consider query fan-out
Once you identify the most relevant search intents, you can hypothesize what people are looking for the generative engine to do. From there, you can do one of two things:
Rule a subset of topics out of your strategy. For example, if you don’t have a local business but the results have local intent, you don’t need to focus on those topics.
Create web pages optimized for LLMs. For example, you can break down a topical category, study its query fan-out results, and reverse engineer what answer engines find valuable based on their behavior.
Say your target customers are U.S. home buyers. They want to know: “Is now a good time to buy a house?”
Plug the prompt into an AI engine and study the AI-generated answer. In AI Mode, for example, you can infer that Google fans out across multiple topics, including market conditions and pros and cons. 




ChatGPT, in contrast, looks at trends, forecasts, and seasonality.




Based on the data, develop a content strategy that supports query fan-out behavior. 
As Aleya Solis explains:
“By ‘fanning out’ the original query, the system can explore various facets and subtopics simultaneously based on semantic understanding, user behavior patterns, and logical information architecture around the topic, leading to a more complete and contextually rich understanding of the user’s need.”
For example, you can break down the complexities of buyer’s markets, buyer and seller perspectives, or the changes in rising inventories. You could even build a useful tool around mortgage rates or national home price trends. 
I use a variety of tools to help with analyzing query fan-out. But the most popular options include Semrush, Ahrefs, and Profound.

  
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Prepare for the future of AI search
Prompting may not even be a concern in the future if AI assistants become more sophisticated at solving problems rather than responding to prompts.
Instead, AI engines may be able to anticipate searchers’ needs and intentions, according to Harvard Business Review. That means it may be increasingly helpful to focus less on prompts and more on problems.
In the absence of keyword research, it will be more important than ever to analyze human behavior, evaluating and pivoting based on how people use AI assistants. 
It’s helpful to consider how social media professionals and brand experts think creatively about where their audiences are and how to attract attention while building brand power and recognition. 
For example, Rare Beauty and Rhode have both grown their brands with creativity and consumer listening, especially in the last six years. 
They’ve put considerable effort into brand campaigns, public relations (PR) campaigns, TikTok content, and in real-life (IRL) experiences that have gone viral globally.
Looking at ChatGPT, the first product recommended for “best makeup gifts for Gen Z” is Rare Beauty. 




Google makes similar recommendations, with Rare Beauty and Rhode leading the list. The results are influenced by PR coverage and social media virality. 




SEO’s role in the future of search
SEO will have a future as long as there are search engines with AI experiences. While it might look like SEO has become the prey, it’s evolved just as much as the predator has.
Everything’s changed. Yet everything’s the same.

SEO isn’t going away, but the rules are shifting. Learn how to blend core tactics with AI search behaviors to stay competitive.

Is there still a long-term game for SEO in AI search?

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/Why-tracking-parameters-in-internal-links-hurt-your-SEO-and-how-to-fix-them.png" class="attachment-large size-large wp-post-image" alt="Why tracking parameters in internal links hurt your SEO and how to fix them" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/Why-tracking-parameters-in-internal-links-hurt-your-SEO-and-how-to-fix-them.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/Why-tracking-parameters-in-internal-links-hurt-your-SEO-and-how-to-fix-them-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/Why-tracking-parameters-in-internal-links-hurt-your-SEO-and-how-to-fix-them-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/Why-tracking-parameters-in-internal-links-hurt-your-SEO-and-how-to-fix-them-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Parameterized URLs bloat crawl paths, fragment attribution, and dilute link equity. Here’s a cleaner, scalable tracking approach.

Internal linking is one of the most controllable levers in technical SEO. But when tracking parameters are embedded in internal URLs, they introduce inefficiencies across crawling and indexing, analytics, site speed, and even AI retrieval.




At scale, this isn’t just a “best practice” issue. It becomes a systemic problem affecting crawl budget, data integrity, and performance.
Here’s how to build a case study for your stakeholders to show the side effects of nuking tracking parameters in internal links — and propose a win-win fix for all digital teams.
How tracking parameters waste crawl budget
Crawl budget is often misunderstood. What matters isn’t the volume of crawl requests, but how efficiently Google discovers and prioritizes valuable pages.

			
				
Crawl budget oversimplified



As Jes Scholz pointed out back in 2022, crawl efficacy indicates how quickly Googlebot reaches new or updated content. Inefficient signals, such as low-value or parameterized URLs, can dilute crawl demand and delay the discovery of important pages. 
Tracking parameters like utm_, vlid, fbclid, or custom query strings work well for campaign tracking. But when applied to internal links, they force search engines to process additional URL variations, increasing crawl overhead.
Crawlers treat every parameterized URL as a unique address. This means:
Multiple versions of the same page are discovered.
Crawl paths become longer and more complex.
Resources are wasted processing duplicate content variants.
Search engines must still crawl first, then decide what to index.

			
				
How crawl budget feeds into the crawling and indexing pipeline



Tracking parameters can quickly escalate a single URL into many variations by combining different values, creating a large number of duplicate URLs. This leads to:
Redundant crawling of identical content.
Longer crawl paths (more “hops” before reaching key pages).
Reduced discovery efficiency for important URLs.

			
				
URLs with tracking parameters lost in the invisible long tail of a website.



On large websites, this becomes a critical issue. Googlebot has a limited number of crawl requests per website. Any time spent crawling parameterized URLs reduces the opportunity to crawl the most important pages, even the so-called “money pages.”

			
				
Crawl entries for URLs with tracking parameters via server logs



Granted, crawl budget is typically a source of concern for larger websites, but that doesn’t mean it shouldn’t be ignored on sites with 10,000+ pages. Optimizing for it often reveals more room for efficiency gain in how search engines discover your content.

  
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Canonicalization isn’t a long-term fix
A common misconception is that canonical tags “fix” parameter issues and “optimize” crawl efficacy. They don’t.
Canonicalization works at the indexing stage, not at the discovery stage. If your internal links point to parameterized URLs:
Search engines will still crawl them.
Crawl budget is still consumed.
Crawl depth is unnecessarily extended.
Lengthy crawl depth (5 to 7 steps) for web crawlers to discover this website.



This is why parameter-heavy sites often show patterns like:
“Discovered – currently not indexed.”
“Duplicate, Google chose different canonical.”

			
				




Crawl budget is not the only culprit here. 
When tracking breaks attribution
Ironically, tracking parameters in internal links can corrupt the data they are meant to measure.
When a user lands on your site via organic search and then clicks an internal link with a tracking parameter, the session may break down and be reattributed. 
Anecdotally, Google Analytics 4 resets a session based on campaign parameters, whereas Adobe Analytics does not.
This creates several downstream issues. Attribution becomes fragmented, especially under last-click models, where credit may shift away from organic entry points to internal interactions.

			
				
Attribution is fragmented across the same pair of URLs



As performance is split across URL variants, page-level SEO reporting becomes unreliable and creates a disconnect between organic SERP behavior and what actually happens when a prospect lands on your pages.
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How tracking parameters dilute link equity
One of the most overlooked risks is backlink fragmentation. If internal links include tracking parameters, users may share those exact URLs. As a result, external backlinks may point to parameterized versions of your pages rather than the canonical ones.
This means authority is split across URL variants, some signals may be lost or diluted, and search engines may treat these links as lower value. Over time and in large proportions, this is set to weaken your backlink profile.

			
				
Backlink dilution on target URLs by allegedly authoritative domains



Nonetheless, it piggybacks on the above tracking problems. Those external backlinks carry internal UTM parameters into external environments. This permanently fractures session attribution and wastes crawling resources.
Why URL bloat slows pages and weakens AI access
Using UTM parameters in your internal links is more than just a crawl overhead. It also strains your caching system.
Each URL with parameters is essentially a different page with its own cache entry. That means the same content may be fetched and processed multiple times, increasing load on both servers and CDNs.

			
				




This becomes even more critical with AI crawlers and LLM retrieval systems. It’s understood that many of these agents fetch content at scale and have limited rendering capabilities, making them more sensitive to parameterized URLs.
As the web is increasingly consumed by aggressive AI bots, having internal links with tracking parameters leaves traditional web crawlers and RAG-based systems wasting bandwidth on duplicate cache entries for pages that serve the same purpose.
At the same time, many of these systems rely heavily on cached versions and avoid rendering JavaScript due to architectural and cost constraints at scale.

			
				




This makes URL hygiene a foundational requirement, not just a technical preference.
On the cache front, Barry Pollard recently suggested a smart workaround that Google has been testing for a while. 




Granted that removing those parameters results in identical content, helping the browser reuse a single cached response can dramatically improve Time to First Byte (TTFB), a metric that directly affects your Core Web Vitals.
Some CDNs already strip UTM parameters from their cache key, improving edge caching. However, browsers still see each parameterized URL as a separate asset and will request them one by one.
The No-Vary-Search response header closes this gap by aligning browser caching behavior with CDN logic. Implementing it allows browsers to treat URLs with specific query parameters as the same resource. Once set, the browser excludes the specified parameters during cache lookups, avoiding unnecessary network requests. 
In practice, the header signals which parameters to ignore when determining cache identity. The only caveat is that it’s supported in Google Chrome +141, with support coming in version 144 on Android. If most of your organic traffic comes from Chromium-based browsers and you run paid campaigns, this is worth adding now.
The structural fix: Move tracking out of URLs and into the DOM
While canonicalization to the clean URL version isn’t a long-term solution, it remains the standard requirement. If you’re stuck in such a position, it’s likely a symptom of deeper architectural challenges at the intersection of SEO, IT, and tracking.
Either way, the preferred solution is to move measurement from the URL layer into the DOM layer.
This can be achieved successfully using a good old HTML workaround: data attributes.




This configuration allows tracking tools (e.g., tag managers) to capture click events and user interactions without altering the URL. Plus, it ensures internal links point to the canonical version without introducing duplicate cache entries.
Dig deeper: How the DOM affects crawling, rendering, and indexing



Why data-* attributes are a win-win for all digital marketing teams

BenefitStakeholder
Enables clean internal link URLs and unbreakable trackingSEO, analytics, product managers
Robust against CSS changes for page restylingWeb developers, product managers
Do not interfere with providing structural or semantic meaning to screen readers and search enginesProduct managers, SEO
Easy to embed directly onto an HTML elementWeb developers, analytics
Acts as a hidden storage layer for tracking data, allowing tools to capture interactions via JavaScript without exposing parameters in URLsPR, affiliates, analytics






  
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Rethinking internal tracking for scalable growth
Tracking parameters in internal links is a legacy workaround, often rooted in siloed teams and flawed site architecture.
However, they create downstream issues across the entire organization: wasted crawl budget, fragmented analytics, diluted backlink equity, and degraded web performance. They also interfere with how both search engines and AI systems access and interpret your content.
The solution isn’t to optimize these parameters, but to remove them entirely from internal linking and adopt a cleaner, more robust tracking approach.
Using a good old HTML trick sounds just about the right fix to win over traditional search engines, AI agents, and especially your stakeholders.
Note: The URL paths disclosed in the screenshots have been disguised for client confidentiality.

Parameterized URLs bloat crawl paths, fragment attribution, and dilute link equity. Here’s a cleaner, scalable tracking approach.

Why tracking parameters in internal links hurt your SEO and how to fix them

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/4-signals-that-now-define-visibility-in-AI-search.png" class="attachment-large size-large wp-post-image" alt="4 signals that now define visibility in AI search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/4-signals-that-now-define-visibility-in-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/4-signals-that-now-define-visibility-in-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/4-signals-that-now-define-visibility-in-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/4-signals-that-now-define-visibility-in-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Rankings don’t guarantee visibility in AI search. Four signals determine which brands appear and how they’re described.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/4-signals-that-now-define-visibility-in-AI-search.png" class="attachment-large size-large wp-post-image" alt="4 signals that now define visibility in AI search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/4-signals-that-now-define-visibility-in-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/4-signals-that-now-define-visibility-in-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/4-signals-that-now-define-visibility-in-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/4-signals-that-now-define-visibility-in-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Ranking and visibility are no longer the same thing. For 20 years, <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> teams optimized for SERP position. Higher rankings meant more visibility, more clicks, and more traffic. That relationship is breaking down.</p>



<p>Earlier this year, Ahrefs found that <a href="https://ahrefs.com/blog/ai-overview-citations-top-10/" target="_blank" rel="noopener">only 38% of pages</a> cited in Google AI Overviews also ranked in the traditional top 10. Eight months earlier, that number was 76%.</p>



<p>The implication is straightforward: being highly ranked no longer guarantees being seen.</p>



<p>In AI-generated answers, visibility is determined by inclusion — and by how your brand is represented when it appears. That representation is determined by a different set of signals.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1367" height="935" src="https://searchengineland.com/wp-content/seloads/2026/04/image-242.png" alt="" class="wp-image-475867" srcset="https://searchengineland.com/wp-content/seloads/2026/04/image-242.png 1367w, https://searchengineland.com/wp-content/seloads/2026/04/image-242-768x525.png 768w" sizes="(max-width: 1367px) 100vw, 1367px" /></figure>
</div>


<h2 class="wp-block-heading" id="h-how-visibility-works-in-ai-search-4-signals-that-matter">How visibility works in AI search: 4 signals that matter</h2>



<p>Four distinct patterns determine how brands appear inside AI-generated responses:&nbsp;</p>



<ul class="wp-block-list">
<li>Mention order.</li>



<li>Depth of explanation.</li>



<li>Authority signals.</li>



<li>Comparative positioning.</li>
</ul>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1254" height="1254" src="https://searchengineland.com/wp-content/seloads/2026/04/image-246.png" alt="" class="wp-image-475871" srcset="https://searchengineland.com/wp-content/seloads/2026/04/image-246.png 1254w, https://searchengineland.com/wp-content/seloads/2026/04/image-246-768x768.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/image-246-95x95.png 95w, https://searchengineland.com/wp-content/seloads/2026/04/image-246-675x675.png 675w" sizes="(max-width: 1254px) 100vw, 1254px" /></figure>
</div>


<h3 class="wp-block-heading" id="h-1-mention-order">1. Mention order</h3>



<p>When an AI model lists three CRM options, the order matters. <a href="https://www.growth-memo.com/p/how-consumers-navigate-high-stakes" target="_blank" rel="noopener">Up to 74%</a> of users choose the AI’s top recommendation, according to a Growth Memo and Citation Labs AI Mode study.</p>



<p>This reinforces how heavily people rely on the first option presented.&nbsp;</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1120" height="1152" src="https://searchengineland.com/wp-content/seloads/2026/04/image-243.png" alt="" class="wp-image-475868" srcset="https://searchengineland.com/wp-content/seloads/2026/04/image-243.png 1120w, https://searchengineland.com/wp-content/seloads/2026/04/image-243-768x790.png 768w" sizes="(max-width: 1120px) 100vw, 1120px" /></figure>
</div>


<p>About 26% of users overrode the AI’s order entirely when they recognized a brand they already knew. This is a shift from how users behave in traditional search. And 56% of users built their own shortlist from multiple sources. In AI Mode, 88% took the AI’s shortlist without checking further.&nbsp;</p>



<p>The AI’s curated answers carry that much weight. But mention order isn’t stable. SE Ranking’s August 2025 analysis found that when you run the same query three times, AI Mode only overlaps with itself <a href="https://seranking.com/blog/ai-mode-research/" target="_blank" rel="noopener">9.2% of the time</a>. </p>



<p>The sources change. The order changes, sometimes dramatically.</p>



<p><strong>The lesson:</strong> Mention order creates an advantage, but it isn’t deterministic. Brand recognition can trump position.</p>



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<h3 class="wp-block-heading" id="h-2-depth-of-explanation">2. Depth of explanation</h3>



<p>Not all mentions are created equal. Some brands get a single sentence. Others get a full paragraph explaining their strengths, use cases, and differentiators.</p>



<p>The difference comes down to how much citation-worthy information AI systems found about you.</p>



<p>When Semrush announced its <a href="https://ai-visibility-index.semrush.com/award-winners" target="_blank" rel="noopener">AI Visibility Awards</a> in December 2025, it analyzed more than 2,500 prompts run through ChatGPT and Google AI Mode. Category leaders like Samsung in consumer electronics didn’t just appear more often. They got more detailed descriptions when they did appear.</p>



<p>Challenger brands like Logitech in gaming accessories showed up, too, but typically with shorter mentions focused on a single differentiator.</p>



<p>The <a href="https://www.growth-memo.com/p/the-science-of-how-ai-picks-its-sources" target="_blank" rel="noopener">top 4.8% of URLs cited 10+ times by ChatGPT</a> share a common trait. They’re comprehensive pages that answer “what is it,” “who uses it,” “how to choose,” and “pricing” in a single URL.</p>



<p>Word count seems to matter, too. Pages above 20,000 characters average 10.18 citations each. Pages under 500 characters average just 2.39.</p>



<p><strong>The lesson:</strong> If AI systems have thin data about your brand, you get thin mentions.</p>



<h3 class="wp-block-heading" id="h-3-authority-signals">3. Authority signals</h3>



<p>AI systems don’t just cite sources. They characterize them by tone, which reveals how much confidence the AI has in your authority.</p>



<p>HubSpot’s AEO Grader, launched in early 2026, classifies brands into competitive roles: leader, challenger, or niche player. They’re positioning labels that determine how persuasively AI presents you.</p>



<p>Semrush’s awards data showed that category leaders have less than 20% monthly volatility in AI share of voice. Once AI systems establish you as a leader, that perception tends to stick.</p>



<p>The language reflects this correlation.&nbsp;</p>



<ul class="wp-block-list">
<li>Leaders get described with confident phrasing, such as “the industry standard” and “widely recognized.” </li>



<li>Challengers get “growing alternative” and “gaining traction.”</li>
</ul>



<p>Most brand mentions in AI answers are neutral or positive. But neutral isn’t the same as enthusiastic.</p>



<p>The difference between “also offers project management features” and “considered one of the top three project management platforms” is authority signaling.</p>



<p><strong>The lesson:</strong> AI doesn’t just say your name. It frames your reputation.</p>



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<h3 class="wp-block-heading" id="h-4-comparative-positioning">4. Comparative positioning</h3>



<p>Comparative positioning is the closest thing to traditional rankings in AI answers: how you’re positioned when multiple brands appear together. But instead of Position 1 vs. Position 2, it’s “better for X” vs. “better for Y.”</p>



<p><a href="https://www.amsive.com/insights/seo/answer-engine-optimization-aeo-evolving-your-seo-strategy-in-the-age-of-ai-search/" target="_blank" rel="noopener">Amsive&#8217;s research found</a> clear positioning hierarchies. </p>



<ul class="wp-block-list">
<li>In banking, Bank of America leads with 32.2% visibility, SoFi follows at 25.7%, and LightStream captures 20.2%. </li>



<li>In healthcare, Mayo Clinic dominates at 14.1%.</li>
</ul>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2026/04/image-244.png" alt="" class="wp-image-475869" srcset="https://searchengineland.com/wp-content/seloads/2026/04/image-244.png 1280w, https://searchengineland.com/wp-content/seloads/2026/04/image-244-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/image-244-1200x675.png 1200w" sizes="(max-width: 1280px) 100vw, 1280px" /></figure>
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<p>Kevin Indig’s Growth Memo research revealed a critical nuance. When AI positioned a brand as “best for startups” versus “best for enterprises,” users self-selected based on that framing, even if both brands technically served both segments.</p>



<p><strong>The lesson:</strong> You’re not competing for position 1 anymore. You’re competing to own a specific positioning niche in AI’s mental model of your category.</p>



<h2 class="wp-block-heading" id="h-how-traditional-rank-correlates-with-ai-visibility-barely">How traditional rank correlates with AI visibility (barely)</h2>



<p>We already covered the 38% overlap stat. The interesting question is why it dropped so fast. The answer: query fan-out.</p>



<p>When an AI Overview triggers, Google doesn’t just evaluate the top-ranking pages for the user’s actual query. It breaks the question into multiple sub-queries, retrieves relevant passages from across its index, and synthesizes them into a single response.</p>



<p>Your page might rank No. 1 for “best project management software” and still get skipped. The AI pulled from pages ranking for “project management for remote teams” or “integrations with Slack” instead. One query to the user. A dozen queries behind the scenes.</p>



<p><a href="https://seranking.com/blog/gemini-3-impact-on-ai-overviews/" target="_blank" rel="noopener">SE Ranking&#8217;s February 2026</a> research found that Google’s upgrade to Gemini 3 replaced approximately 42% of previously cited domains and generates 32% more sources per response than its predecessor. Traditional ranking positions became even less predictive overnight.</p>



<h2 class="wp-block-heading" id="h-where-ai-traffic-actually-goes">Where AI traffic actually goes</h2>



<p>Semrush’s analysis of 17 months of clickstream data reveals an unexpected pattern: <a href="https://www.semrush.com/blog/chatgpt-search-insights/">Over 20%</a> of ChatGPT referral traffic goes to Google. That share rose from roughly 14% at the start of the study to more than 21% by early 2026.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2020" height="1676" src="https://searchengineland.com/wp-content/seloads/2026/04/image-245.png" alt="" class="wp-image-475870" srcset="https://searchengineland.com/wp-content/seloads/2026/04/image-245.png 2020w, https://searchengineland.com/wp-content/seloads/2026/04/image-245-768x637.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/image-245-1536x1274.png 1536w" sizes="(max-width: 2020px) 100vw, 2020px" /></figure>
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<p>The biggest beneficiary of ChatGPT’s growth is Google.&nbsp;</p>



<p>Users go to ChatGPT to get an answer, then head to Google to confirm findings or research brands they just discovered. For users, they’re complementary steps in a single journey.</p>



<p>Most ChatGPT prompts don’t match traditional search language. Between 65% and 85% of prompts couldn’t be matched to any traditional search keyword in Semrush’s database of 27 billion keywords.</p>



<ul class="wp-block-list">
<li>A traditional Google search: “best project management software.”&nbsp;</li>



<li>The ChatGPT equivalent: “I manage a 12-person remote engineering team, and we’re constantly missing sprint deadlines. What should I change about our weekly standups?”</li>
</ul>



<p>That level of specificity doesn’t exist in keyword databases — and it’s becoming more common.</p>



<h2 class="wp-block-heading" id="h-measuring-visibility-in-ai-answers">Measuring visibility in AI answers</h2>



<p>If position doesn’t matter the way it used to, what does?</p>



<ul class="wp-block-list">
<li>Citation frequency replaces rankings as the primary metric. How often does your brand appear when AI systems answer questions in your category?</li>



<li>Brand mention rate measures penetration. If AI generates 100 answers about your category, what percentage mention your brand? Scores above 70% indicate strong AI search performance. Below 30% signals significant visibility gaps.</li>



<li>Recommendation rate matters more than mention rate for B2B SaaS and high-consideration purchases. Being recommended carries more weight than being mentioned in a general list.</li>



<li>Sentiment and context determine whether mentions drive action. Track how AI describes you: premium vs. cheap, advanced vs. beginner, reliable vs. experimental.</li>



<li>Citation position within answers creates measurable advantage. Unlike traditional rankings, you can be first-cited without being first-ranked organically.</li>
</ul>



<h2 class="wp-block-heading" id="h-the-measurement-infrastructure-you-actually-need">The measurement infrastructure you actually need</h2>



<p>Traditional rank trackers can’t measure these signals.</p>



<p>The 2026 measurement model requires parallel tracking. Traditional SEO metrics still matter for the portion of search that remains blue links. AI visibility requires tracking how often your brand appears and how it’s represented in AI-generated answers.</p>



<p>A new category of tools has emerged to support this shift.</p>



<ul class="wp-block-list">
<li>For citation tracking, platforms like Profound, Gauge, Peec AI, and Scrunch monitor which URLs get cited across ChatGPT, Perplexity, Claude, and Google AI Overviews.</li>



<li>For brand analysis, tools like Semrush’s AI Visibility Toolkit and AthenaHQ measure how often your brand is mentioned, how it’s described, and whether it’s recommended.</li>



<li>For competitive positioning, Bluefish and HubSpot’s AEO Grader evaluate how AI systems categorize your brand relative to competitors.</li>
</ul>



<p>None of these tools replace traditional SEO infrastructure. They supplement it.</p>



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<h2 class="wp-block-heading" id="h-a-different-model-of-visibility">A different model of visibility</h2>



<p>The ranking obsession isn’t going away entirely. Traditional search still drives traffic. But measuring success solely through rankings misses the larger shift.</p>



<p>AI answer engines now act as gatekeepers, surfacing only the brands they consider citation-worthy.</p>



<p>Visibility depends on how often you’re included, how you’re described, and how you’re positioned relative to competitors.</p>



<p>Traditional rank trackers can’t capture that. It requires a different measurement model. That’s what determines visibility now.</p>

Ranking and visibility are no longer the same thing. For 20 years, SEO teams optimized for SERP position. Higher rankings meant more visibility, more clicks, and more traffic. That relationship is breaking down.
Earlier this year, Ahrefs found that only 38% of pages cited in Google AI Overviews also ranked in the traditional top 10. Eight months earlier, that number was 76%.
The implication is straightforward: being highly ranked no longer guarantees being seen.
In AI-generated answers, visibility is determined by inclusion — and by how your brand is represented when it appears. That representation is determined by a different set of signals.




How visibility works in AI search: 4 signals that matter
Four distinct patterns determine how brands appear inside AI-generated responses: 
Mention order.
Depth of explanation.
Authority signals.
Comparative positioning.




1. Mention order
When an AI model lists three CRM options, the order matters. Up to 74% of users choose the AI’s top recommendation, according to a Growth Memo and Citation Labs AI Mode study.
This reinforces how heavily people rely on the first option presented. 




About 26% of users overrode the AI’s order entirely when they recognized a brand they already knew. This is a shift from how users behave in traditional search. And 56% of users built their own shortlist from multiple sources. In AI Mode, 88% took the AI’s shortlist without checking further. 
The AI’s curated answers carry that much weight. But mention order isn’t stable. SE Ranking’s August 2025 analysis found that when you run the same query three times, AI Mode only overlaps with itself 9.2% of the time. 
The sources change. The order changes, sometimes dramatically.
The lesson: Mention order creates an advantage, but it isn’t deterministic. Brand recognition can trump position.
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2. Depth of explanation
Not all mentions are created equal. Some brands get a single sentence. Others get a full paragraph explaining their strengths, use cases, and differentiators.
The difference comes down to how much citation-worthy information AI systems found about you.
When Semrush announced its AI Visibility Awards in December 2025, it analyzed more than 2,500 prompts run through ChatGPT and Google AI Mode. Category leaders like Samsung in consumer electronics didn’t just appear more often. They got more detailed descriptions when they did appear.
Challenger brands like Logitech in gaming accessories showed up, too, but typically with shorter mentions focused on a single differentiator.
The top 4.8% of URLs cited 10+ times by ChatGPT share a common trait. They’re comprehensive pages that answer “what is it,” “who uses it,” “how to choose,” and “pricing” in a single URL.
Word count seems to matter, too. Pages above 20,000 characters average 10.18 citations each. Pages under 500 characters average just 2.39.
The lesson: If AI systems have thin data about your brand, you get thin mentions.
3. Authority signals
AI systems don’t just cite sources. They characterize them by tone, which reveals how much confidence the AI has in your authority.
HubSpot’s AEO Grader, launched in early 2026, classifies brands into competitive roles: leader, challenger, or niche player. They’re positioning labels that determine how persuasively AI presents you.
Semrush’s awards data showed that category leaders have less than 20% monthly volatility in AI share of voice. Once AI systems establish you as a leader, that perception tends to stick.
The language reflects this correlation. 
Leaders get described with confident phrasing, such as “the industry standard” and “widely recognized.” 
Challengers get “growing alternative” and “gaining traction.”
Most brand mentions in AI answers are neutral or positive. But neutral isn’t the same as enthusiastic.
The difference between “also offers project management features” and “considered one of the top three project management platforms” is authority signaling.
The lesson: AI doesn’t just say your name. It frames your reputation.
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4. Comparative positioning
Comparative positioning is the closest thing to traditional rankings in AI answers: how you’re positioned when multiple brands appear together. But instead of Position 1 vs. Position 2, it’s “better for X” vs. “better for Y.”
Amsive’s research found clear positioning hierarchies. 
In banking, Bank of America leads with 32.2% visibility, SoFi follows at 25.7%, and LightStream captures 20.2%. 
In healthcare, Mayo Clinic dominates at 14.1%.




Kevin Indig’s Growth Memo research revealed a critical nuance. When AI positioned a brand as “best for startups” versus “best for enterprises,” users self-selected based on that framing, even if both brands technically served both segments.
The lesson: You’re not competing for position 1 anymore. You’re competing to own a specific positioning niche in AI’s mental model of your category.
How traditional rank correlates with AI visibility (barely)
We already covered the 38% overlap stat. The interesting question is why it dropped so fast. The answer: query fan-out.
When an AI Overview triggers, Google doesn’t just evaluate the top-ranking pages for the user’s actual query. It breaks the question into multiple sub-queries, retrieves relevant passages from across its index, and synthesizes them into a single response.
Your page might rank No. 1 for “best project management software” and still get skipped. The AI pulled from pages ranking for “project management for remote teams” or “integrations with Slack” instead. One query to the user. A dozen queries behind the scenes.
SE Ranking’s February 2026 research found that Google’s upgrade to Gemini 3 replaced approximately 42% of previously cited domains and generates 32% more sources per response than its predecessor. Traditional ranking positions became even less predictive overnight.
Where AI traffic actually goes
Semrush’s analysis of 17 months of clickstream data reveals an unexpected pattern: Over 20% of ChatGPT referral traffic goes to Google. That share rose from roughly 14% at the start of the study to more than 21% by early 2026.




The biggest beneficiary of ChatGPT’s growth is Google. 
Users go to ChatGPT to get an answer, then head to Google to confirm findings or research brands they just discovered. For users, they’re complementary steps in a single journey.
Most ChatGPT prompts don’t match traditional search language. Between 65% and 85% of prompts couldn’t be matched to any traditional search keyword in Semrush’s database of 27 billion keywords.
A traditional Google search: “best project management software.” 
The ChatGPT equivalent: “I manage a 12-person remote engineering team, and we’re constantly missing sprint deadlines. What should I change about our weekly standups?”
That level of specificity doesn’t exist in keyword databases — and it’s becoming more common.
Measuring visibility in AI answers
If position doesn’t matter the way it used to, what does?
Citation frequency replaces rankings as the primary metric. How often does your brand appear when AI systems answer questions in your category?
Brand mention rate measures penetration. If AI generates 100 answers about your category, what percentage mention your brand? Scores above 70% indicate strong AI search performance. Below 30% signals significant visibility gaps.
Recommendation rate matters more than mention rate for B2B SaaS and high-consideration purchases. Being recommended carries more weight than being mentioned in a general list.
Sentiment and context determine whether mentions drive action. Track how AI describes you: premium vs. cheap, advanced vs. beginner, reliable vs. experimental.
Citation position within answers creates measurable advantage. Unlike traditional rankings, you can be first-cited without being first-ranked organically.
The measurement infrastructure you actually need
Traditional rank trackers can’t measure these signals.
The 2026 measurement model requires parallel tracking. Traditional SEO metrics still matter for the portion of search that remains blue links. AI visibility requires tracking how often your brand appears and how it’s represented in AI-generated answers.
A new category of tools has emerged to support this shift.
For citation tracking, platforms like Profound, Gauge, Peec AI, and Scrunch monitor which URLs get cited across ChatGPT, Perplexity, Claude, and Google AI Overviews.
For brand analysis, tools like Semrush’s AI Visibility Toolkit and AthenaHQ measure how often your brand is mentioned, how it’s described, and whether it’s recommended.
For competitive positioning, Bluefish and HubSpot’s AEO Grader evaluate how AI systems categorize your brand relative to competitors.
None of these tools replace traditional SEO infrastructure. They supplement it.
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A different model of visibility
The ranking obsession isn’t going away entirely. Traditional search still drives traffic. But measuring success solely through rankings misses the larger shift.
AI answer engines now act as gatekeepers, surfacing only the brands they consider citation-worthy.
Visibility depends on how often you’re included, how you’re described, and how you’re positioned relative to competitors.
Traditional rank trackers can’t capture that. It requires a different measurement model. That’s what determines visibility now.

Rankings don’t guarantee visibility in AI search. Four signals determine which brands appear and how they’re described.

4 signals that now define visibility in AI search

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/Searchers-just-want-you-to-be-helpful.png" class="attachment-large size-large wp-post-image" alt="Searchers just want you to be helpful" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/Searchers-just-want-you-to-be-helpful.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/Searchers-just-want-you-to-be-helpful-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/Searchers-just-want-you-to-be-helpful-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/Searchers-just-want-you-to-be-helpful-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI search is raising the bar, not changing the goal. Create content that answers real questions with clarity, depth, and expertise.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/Searchers-just-want-you-to-be-helpful.png" class="attachment-large size-large wp-post-image" alt="Searchers just want you to be helpful" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/Searchers-just-want-you-to-be-helpful.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/Searchers-just-want-you-to-be-helpful-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/Searchers-just-want-you-to-be-helpful-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/Searchers-just-want-you-to-be-helpful-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>The <a href="https://searchengineland.com/google-march-2026-core-update-rolling-out-now-472759">March 2026 core update</a> brought what Google describes as a design “to better surface relevant, satisfying content for searchers from all types of sites.” This confirms the simplest truth in search: people use Google to get answers. </p>



<p>Whether it’s solving a problem, learning something new, or making a decision, searchers want content that is genuinely helpful in their busy, on-the-go lives. If your content does that, it succeeds. If it doesn’t, no amount of <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> tricks, hacks, or magic bullets will get your content to show up on page one, let alone in AI Overviews.</p>



<h2 class="wp-block-heading" id="h-how-modern-search-systems-surface-helpful-content">How modern search systems surface helpful content</h2>



<p>AI Overviews went from appearing for just 6.49% of queries in January 2025 to 15.69% in November 2025 according to a <a href="https://www.semrush.com/blog/semrush-ai-overviews-study/" target="_blank" rel="noopener">Semrush study</a>. Depending on the source today, AI Overviews appear for 25-50% of queries. </p>



<p>It’s clear that search engines and LLMs are working together more efficiently today than just a year ago. Fast forward another year, and we can only imagine. </p>



<p>For any SEO focused on creating helpful content and understanding user intent, it’s a truly exciting time to be in the industry. Your genuinely useful content can be surfaced in AI Overviews using <strong>retrieval-augmented generation (RAG)</strong> and <strong>query fan-out</strong>.</p>



<ul class="wp-block-list">
<li><strong>RAG:</strong> Instead of just relying on what it “knows,” AI looks for relevant information across multiple sources before answering a query</li>



<li><a href="https://searchengineland.com/guide/query-fan-out-2"><strong>Query fan-out</strong></a><strong>:</strong> One search query can be broken down into multiple related queries behind the scenes, helping AI and search engines build a more complete, useful response</li>
</ul>



<p>Entire papers have been written on these two concepts alone. The TL;DR is that SEO today is about more than just keywords or counting backlinks. Modern search is designed to connect searchers with content that actually answers their questions and satisfies user intent.</p>



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<h2 class="wp-block-heading" id="h-why-this-raises-the-bar-for-seo-in-2026-and-beyond">Why this raises the bar for SEO in 2026 and beyond</h2>



<p>These systems, and those still being implemented (see Google’s blog on <a href="https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/">TurboQuant</a>), are getting better at recognizing and dismissing thin, duplicate, or superficial content. Pieces that simply restate what someone else has already said online, lack originality, and fail to demonstrate legitimate real-life experience will continue to struggle to rank. </p>



<p>Depth, clarity, and expertise have always mattered, but SEOs who want to continue to succeed in 2026 and beyond are going to have to double down on these factors:</p>



<ul class="wp-block-list">
<li><strong>Depth:</strong> This doesn’t mean write as much as you can on the topic. Gone are the days of fluffy, keyword-stuffed articles. Depth in 2026 means SEOs and content creators should address the searcher’s main question and related follow-ups.</li>



<li><strong>Clarity:</strong> Searchers are busy. They want quick answers. Make your content easy to scan and understand.</li>



<li><strong>Expertise:</strong> Demonstrate real-world knowledge and experience your audience can trust.</li>
</ul>



<p>For many SEOs, this is a welcome shift. It’s not about just checking off boxes anymore.&nbsp;</p>



<p>Sure, we still have to do those things. But the bar for what constitutes good SEO is being raised far beyond the basics. When search engines evaluate content today, they’re looking for signals that SEOs and content creators are providing real value to searchers.</p>



<h2 class="wp-block-heading" id="h-why-visibility-matters-more-than-clicks-for-local-seo">Why visibility matters more than clicks for local SEO</h2>



<p>Small, local, or service-based businesses that rely on SEO-driven leads for revenue can use these same strategies, too. While success isn’t measured using the same metrics as it was just a couple of years ago, the result of good SEO remains: Get the business recommended before the competition for as many searches as possible.&nbsp;</p>



<p>Two years ago, this meant clicks. Today, it means visibility. AI platforms like ChatGPT, Gemini, and AI Overviews often recommend businesses without linking to websites directly, if at all.&nbsp;</p>



<p>A few tools have been developed to measure AI metrics, but these can get pricey, and as Elizabeth Rule said, “Measuring visibility is like trying to measure a wave with a ruler.”&nbsp;</p>



<p>This is why maintaining strong communication between stakeholders and the SEO team is so important. When success can’t be measured simply, a simple question of “how’s business going?” matters now more than ever. Beyond user intent, SEOs need to understand user behavior, mood, and temperament.</p>



<h2 class="wp-block-heading" id="h-what-helpful-content-looks-like-in-practice">What ‘helpful content’ looks like in practice</h2>



<p>Here are five tips to get you started on creating content that is genuinely helpful:</p>



<h3 class="wp-block-heading" id="h-1-answer-follow-up-questions">1. Answer follow-up questions</h3>



<p>Think beyond the initial query. What will readers ask next?&nbsp;</p>



<p>One of my favorite places to do research for this is the <em>People Also Ask</em> (PAA) section on SERP. For example, you’re writing about herniated disc treatment. Just Google “herniated disk treatment” and use the PAA feature to help you brainstorm more questions your audience may ask about the topic you’re writing. The more questions you click, the more ideas it’ll generate.</p>



<h3 class="wp-block-heading" id="h-2-show-expertise-and-experience">2. Show expertise and experience</h3>



<p><a href="https://searchengineland.com/google-e-e-a-t-guide-seo-394191">E-E-A-T</a> is an SEO hill I will die on because it works. Share your knowledge, case studies, testimonials, or firsthand insights. This builds trust when done right and when you’re creating for people, not search engines.&nbsp;</p>



<p>This is what the <a href="https://developers.google.com/search/blog/2022/08/helpful-content-update" target="_blank" rel="noopener">helpful content update of 2022</a> was all about.</p>



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<h3 class="wp-block-heading" id="h-3-structure-content-clearly">3. Structure content clearly</h3>



<p>We’d all love to believe that everything we write is being read word-for-word. It’s not. People skim. They’re looking for an answer while they’re doing other things.&nbsp;</p>



<p>This is why clearly structured web pages are so important on both mobile and desktop. Use headings, bullet points, and concise paragraphs to help readers quickly find answers.</p>



<h3 class="wp-block-heading" id="h-4-be-authentic">4. Be authentic</h3>



<p>Authenticity sounds like a buzzword (and maybe it is), but people can tell when you’ve used AI to write something or when you’re just publishing content for SEO. </p>



<p>Much as it pains me (an English major who loves to read long novels and write dissertations) to say, no one cares about your personal anecdotes or how many adjectives you can think of for your “superior” service. They just need an answer to the question they searched.&nbsp;</p>



<p>Avoid fluff or filler. Real-world, practical content resonates better than generic advice. </p>



<p>If someone called and asked you, “How long does it take to change the water heater in my 1950s home?” You wouldn’t need 1,500 words to answer them. The content you create on the internet should be the same.</p>



<h3 class="wp-block-heading" id="h-5-ask-who-what-and-how-about-your-content">5. Ask ‘who, what, and how?’ about your content</h3>



<p>If you’ve been paying attention to GEO/AEO/SEO for AI, this might sound familiar to you as a little something called semantic triples. This sounds intimidating at first, but it’s really just sixth-grade English.&nbsp;</p>



<p>A semantic triple answers who, does what, for whom (or how). Remember diagramming sentences? It’s the relationship between the subject, predicate, and object. It can be any subject, predicate, and object:</p>



<ul class="wp-block-list">
<li>The plumber installs water heaters in Dallas&nbsp;</li>



<li>The bakery bakes wedding cakes for couples&nbsp;</li>
</ul>



<p>I first heard about semantic triples from Mike King during SEO Week 2025 when he broke down his <a href="https://ipullrank.com/seo-week-2025-mike-king" target="_blank" rel="noopener">concept of relevance engineering</a>. If you haven’t watched his video on this topic, I highly recommend it. </p>



<p>The basic idea is that SEO is about your audience:</p>



<ul class="wp-block-list">
<li>Who are you talking to? </li>



<li>What do they need? </li>



<li>How do you reach them? </li>
</ul>



<p>A semantic triple answers these questions. It provides structure and clarity. It’s the “Who, What, and How” that Google told us about with the HCU documentation. It’s also genuinely valuable information for searchers.</p>



<p>Knowledge is your superpower. You’re the only person who can tell your story, explain your process, and show readers why your business or brand matters.</p>



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<h2 class="wp-block-heading" id="h-helpfulness-is-the-competitive-edge">Helpfulness is the competitive edge</h2>



<p>The most reliable SEO strategy remains the same with each new core update from Google: <strong>Create content that genuinely helps searchers.</strong>



<p>Focus on the problems your audience is trying to solve, answer their questions fully, and share your expertise. Thin or derivative content won’t cut it in a world of AI-driven search and retrieval systems. </p>



<p>Google and AI platforms are trying to do the same thing searchers are doing: find the most helpful content. If you respond to that need, your content will rise to the top, no tricks, hacks, or shortcuts necessary.</p>

The March 2026 core update brought what Google describes as a design “to better surface relevant, satisfying content for searchers from all types of sites.” This confirms the simplest truth in search: people use Google to get answers. 
Whether it’s solving a problem, learning something new, or making a decision, searchers want content that is genuinely helpful in their busy, on-the-go lives. If your content does that, it succeeds. If it doesn’t, no amount of SEO tricks, hacks, or magic bullets will get your content to show up on page one, let alone in AI Overviews.
How modern search systems surface helpful content
AI Overviews went from appearing for just 6.49% of queries in January 2025 to 15.69% in November 2025 according to a Semrush study. Depending on the source today, AI Overviews appear for 25-50% of queries. 
It’s clear that search engines and LLMs are working together more efficiently today than just a year ago. Fast forward another year, and we can only imagine. 
For any SEO focused on creating helpful content and understanding user intent, it’s a truly exciting time to be in the industry. Your genuinely useful content can be surfaced in AI Overviews using retrieval-augmented generation (RAG) and query fan-out.
RAG: Instead of just relying on what it “knows,” AI looks for relevant information across multiple sources before answering a query
Query fan-out: One search query can be broken down into multiple related queries behind the scenes, helping AI and search engines build a more complete, useful response
Entire papers have been written on these two concepts alone. The TL;DR is that SEO today is about more than just keywords or counting backlinks. Modern search is designed to connect searchers with content that actually answers their questions and satisfies user intent.

  
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Why this raises the bar for SEO in 2026 and beyond
These systems, and those still being implemented (see Google’s blog on TurboQuant), are getting better at recognizing and dismissing thin, duplicate, or superficial content. Pieces that simply restate what someone else has already said online, lack originality, and fail to demonstrate legitimate real-life experience will continue to struggle to rank. 
Depth, clarity, and expertise have always mattered, but SEOs who want to continue to succeed in 2026 and beyond are going to have to double down on these factors:
Depth: This doesn’t mean write as much as you can on the topic. Gone are the days of fluffy, keyword-stuffed articles. Depth in 2026 means SEOs and content creators should address the searcher’s main question and related follow-ups.
Clarity: Searchers are busy. They want quick answers. Make your content easy to scan and understand.
Expertise: Demonstrate real-world knowledge and experience your audience can trust.
For many SEOs, this is a welcome shift. It’s not about just checking off boxes anymore. 
Sure, we still have to do those things. But the bar for what constitutes good SEO is being raised far beyond the basics. When search engines evaluate content today, they’re looking for signals that SEOs and content creators are providing real value to searchers.
Why visibility matters more than clicks for local SEO
Small, local, or service-based businesses that rely on SEO-driven leads for revenue can use these same strategies, too. While success isn’t measured using the same metrics as it was just a couple of years ago, the result of good SEO remains: Get the business recommended before the competition for as many searches as possible. 
Two years ago, this meant clicks. Today, it means visibility. AI platforms like ChatGPT, Gemini, and AI Overviews often recommend businesses without linking to websites directly, if at all. 
A few tools have been developed to measure AI metrics, but these can get pricey, and as Elizabeth Rule said, “Measuring visibility is like trying to measure a wave with a ruler.” 
This is why maintaining strong communication between stakeholders and the SEO team is so important. When success can’t be measured simply, a simple question of “how’s business going?” matters now more than ever. Beyond user intent, SEOs need to understand user behavior, mood, and temperament.
What ‘helpful content’ looks like in practice
Here are five tips to get you started on creating content that is genuinely helpful:
1. Answer follow-up questions
Think beyond the initial query. What will readers ask next? 
One of my favorite places to do research for this is the People Also Ask (PAA) section on SERP. For example, you’re writing about herniated disc treatment. Just Google “herniated disk treatment” and use the PAA feature to help you brainstorm more questions your audience may ask about the topic you’re writing. The more questions you click, the more ideas it’ll generate.
2. Show expertise and experience
E-E-A-T is an SEO hill I will die on because it works. Share your knowledge, case studies, testimonials, or firsthand insights. This builds trust when done right and when you’re creating for people, not search engines. 
This is what the helpful content update of 2022 was all about.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
3. Structure content clearly
We’d all love to believe that everything we write is being read word-for-word. It’s not. People skim. They’re looking for an answer while they’re doing other things. 
This is why clearly structured web pages are so important on both mobile and desktop. Use headings, bullet points, and concise paragraphs to help readers quickly find answers.
4. Be authentic
Authenticity sounds like a buzzword (and maybe it is), but people can tell when you’ve used AI to write something or when you’re just publishing content for SEO. 
Much as it pains me (an English major who loves to read long novels and write dissertations) to say, no one cares about your personal anecdotes or how many adjectives you can think of for your “superior” service. They just need an answer to the question they searched. 
Avoid fluff or filler. Real-world, practical content resonates better than generic advice. 
If someone called and asked you, “How long does it take to change the water heater in my 1950s home?” You wouldn’t need 1,500 words to answer them. The content you create on the internet should be the same.
5. Ask ‘who, what, and how?’ about your content
If you’ve been paying attention to GEO/AEO/SEO for AI, this might sound familiar to you as a little something called semantic triples. This sounds intimidating at first, but it’s really just sixth-grade English. 
A semantic triple answers who, does what, for whom (or how). Remember diagramming sentences? It’s the relationship between the subject, predicate, and object. It can be any subject, predicate, and object:
The plumber installs water heaters in Dallas 
The bakery bakes wedding cakes for couples 
I first heard about semantic triples from Mike King during SEO Week 2025 when he broke down his concept of relevance engineering. If you haven’t watched his video on this topic, I highly recommend it. 
The basic idea is that SEO is about your audience:
Who are you talking to? 
What do they need? 
How do you reach them? 
A semantic triple answers these questions. It provides structure and clarity. It’s the “Who, What, and How” that Google told us about with the HCU documentation. It’s also genuinely valuable information for searchers.
Knowledge is your superpower. You’re the only person who can tell your story, explain your process, and show readers why your business or brand matters.

  
complete picture of your search visibility.
      
Start Free Trial
    
Get started with
Helpfulness is the competitive edge
The most reliable SEO strategy remains the same with each new core update from Google: Create content that genuinely helps searchers.



Focus on the problems your audience is trying to solve, answer their questions fully, and share your expertise. Thin or derivative content won’t cut it in a world of AI-driven search and retrieval systems. 
Google and AI platforms are trying to do the same thing searchers are doing: find the most helpful content. If you respond to that need, your content will rise to the top, no tricks, hacks, or shortcuts necessary.

AI search is raising the bar, not changing the goal. Create content that answers real questions with clarity, depth, and expertise.

Searchers just want you to be helpful

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/Optimove-20260429-v2.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/Optimove-20260429-v2.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/Optimove-20260429-v2-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/Optimove-20260429-v2-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/Optimove-20260429-v2-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>The failure rate is not about technology. It is about the humans deploying it... and the ones missing from the equation entirely.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/Optimove-20260429-v2.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/Optimove-20260429-v2.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/Optimove-20260429-v2-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/Optimove-20260429-v2-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/Optimove-20260429-v2-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<figure class="wp-block-image size-full"><img decoding="async" width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/Optimove-20260429-v2.png" alt="" class="wp-image-474854" srcset="https://searchengineland.com/wp-content/seloads/2026/04/Optimove-20260429-v2.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/Optimove-20260429-v2-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/Optimove-20260429-v2-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/Optimove-20260429-v2-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></figure>



<p>Fact: Agentic AI is making humans indispensable.<br><br>More than 40% of agentic AI projects will be canceled by the end of 2027. That is <a href="https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027" target="_blank" rel="noopener sponsored nofollow">a prediction from Gartner published in June 2025</a>, based on a poll of more than 3,400 organizations actively investing in the technology.</p>



<p>The reason cited is not that the agents do not work. It is that the humans deploying them are making the wrong decisions. &#8220;Most agentic AI projects right now are early-stage experiments or proof of concepts that are mostly driven by hype and are often misapplied,&#8221; according to Anushree Verma, senior director analyst at Gartner.</p>



<p>Organizations are deploying agents without a clear strategy, without understanding the complexity, and without the governance to manage what happens when something goes wrong.</p>



<p>In other words, the agent is only as good as the human behind it.</p>



<p>This matters enormously for marketing. AI agents in marketing are real, accelerating and in many cases, necessary. Agents that select audiences. Agents that generate content. Agents that optimize send times, choose offers and orchestrate entire customer journeys autonomously, continuously and at a scale no human team could match. <a href="https://www.optimove.com/resources/blog/ai-decisioning-studio-optimove-s-new-hub-for-positionless-marketing" target="_blank" rel="noopener sponsored nofollow">The capabilities are here today and growing rapidly</a>.</p>



<p>But Gartner&#8217;s data reveals a warning and marketing leaders who miss it will find themselves on the wrong side of that 40%.</p>



<h2 class="wp-block-heading" id="h-fomo-causes-agent-failure">FOMO causes agent failure</h2>



<p>The failure rate Gartner describes is not random. It starts with fear.</p>



<p>Fear of being left behind. Fear of watching competitors move faster. Fear of being the CMO who did not act when everyone else did. That fear is driving organizations to deploy agentic AI, not because they have a strategy, but because they cannot afford to be last.</p>



<p>The result is agents built on broken workflows. Agents fed with poor data. Agents operating without the governance structures that keep them aligned with business goals. The agents execute&#8230; the wrong things, in the wrong ways, at the wrong times.</p>



<p>FOMO is not a strategy. And in the agentic era, it is an expensive mistake.</p>



<h2 class="wp-block-heading" id="h-agent-washing">Agent washing</h2>



<p>Gartner identified a widespread trend it calls &#8220;agent washing&#8221;&#8230; vendors rebranding existing chatbots and automation tools as agentic AI without delivering genuine autonomous capabilities. Of the thousands of vendors claiming agentic solutions, <a href="https://www.eskill.com/resources/blog/ai-agents-hiring#:~:text=According%20to%20research%20from%20Gartner,marketing%20%E2%80%9Cagentic%20AI%E2%80%9D%20solutions.&amp;text=In%20many%20cases%2C%20the%20marketing,faster%20than%20the%20technology%20itself." target="_blank" rel="noopener sponsored nofollow">Gartner estimates only around 130 offer real agentic features</a>. Marketing teams investing in the rest are not getting agents. They are getting dressed-up automation with an agentic price tag. automation with an agentic price tag.</p>



<p>The consequences go beyond wasted budget. Gartner predicts that in 2026, one-third of companies will harm customer experiences by deploying AI prematurely, eroding brand trust and damaging both acquisition and retention.<br><br>A personalization agent that misreads a customer. A content agent that violates compliance. A journey agent that floods a churning customer with offers at exactly the wrong moment. These are the predictable outcomes of deploying autonomous systems without the human judgment to direct them.</p>



<p><strong>The dumbing down of marketers</strong>



<p>Gartner&#8217;s third prediction is the most revealing of all. <a href="https://www.digital-chiefs.de/en/genai-erodes-critical-thinking-why-half-of-all-organizations-plan-ai-free-assess/#:~:text=Gartner%20forecasts%20that%20by%20end,valid%20decisions%20without%20a%20copilot." target="_blank" rel="noopener sponsored nofollow">GenAI usage leads to the atrophy of critical thinking skills</a>. As a result, 50 percent of global organizations will require AI-free competency evaluations.</p>



<p>Half of all organizations are watching their people get dumber because AI is always available to think for them. Quietly. Gradually. Until the day the algorithm is wrong and nobody in the room can tell.</p>



<p>In marketing, that is a crisis. Marketing requires judgment — the ability to ask not just what the data says, but what it means. Not just whether a campaign worked, but why. Not just whether to accept an AI recommendation, but whether it reflects the brand, the moment and the relationship the company is trying to build.</p>



<p>Those questions cannot be delegated to an agent. They require a human being scrutinizing what a machine thinks is right.</p>



<p>The most dangerous marketer in the agentic era is not the one who rejects AI. It is the one who accepts everything it produces without question.</p>



<h2 class="wp-block-heading" id="h-agents-cannot-be-trusted-to-ask-the-right-questions">Agents cannot be trusted to ask the right questions</h2>



<p>An agent can optimize what it has been given. It cannot question whether it has been given the right thing.</p>



<p>It can personalize a message based on behavioral signals. It cannot decide that the right move is to say nothing at all&#8230; to give a customer space, to protect a relationship rather than extract from it.</p>



<p>It can generate a thousand content variations and test them. It cannot feel the difference between a message that converts and a message that connects. It cannot sense when a campaign that performs well in the data is quietly damaging the brand.</p>



<p>It can execute a journey flawlessly. It cannot design one that reflects what customers actually want from this brand, at this point in their lives.</p>



<p>These are not limitations that will be solved by the next model release. They are structural. AI is trained on the past. The irreducible human job in marketing is to bring judgment about what should happen next, even when the data does not yet exist to support it.</p>



<h2 class="wp-block-heading" id="h-the-marketer-as-manager-of-agents">The marketer as manager of agents</h2>



<p>The right mental model for the agentic era is not human versus machine. It is a human plus machine, with the human in charge.</p>



<p>That is the foundation of Positionless Marketing. For decades, marketing teams operated as an assembly line with handoffs. Positionless Marketing breaks that model by giving marketers three transformative powers: Data Power to immediately discover customer insights for precise targeting and hyper-personalization, without waiting for engineers; Creative Power to create channel-ready assets like copy and visuals, without waiting for creatives; and Optimization Power to run campaigns that optimize themselves through automated journeys and testing, without waiting for analysts. Handoffs are eliminated.</p>



<p>The Positionless Marketer is a multidisciplinary thinker who deploys AI agents to go beyond traditional positions. Agents handle what used to require waiting for three different teams, eliminating the assembly line. The marketer is no longer waiting on anyone. They are thinking bigger, moving across disciplines while keeping human judgment at the center of every decision the agents make.</p>



<p>This is a promotion, not a replacement. But it comes with real demands. Marketers who can think strategically, not just operationally. Who can evaluate AI output critically, not just accept it. Who can take accountability for what the agents do in their name.</p>



<p>Gartner&#8217;s Daryl Plummer stated it directly: organizations should prioritize behavioral changes alongside technological changes as first-order priorities. The technology is ready. The question is whether the humans in the marketing organization are.</p>



<h2 class="wp-block-heading" id="h-the-window-is-narrowing">The window is narrowing</h2>



<p>The organizations that will win the next decade of marketing are not the ones that deploy the most agents. They are the ones that build the human capability to direct them well.Gartner&#8217;s 40% prediction is not a warning to slow down. It is a warning to be deliberate. The difference between an agentic marketing operation that compounds value over time and one that wastes budget, violates policy, and erodes customer trust is not the technology. It is the human judgment sitting above it.</p>



<p>Marketing teams need to face facts in the agentic AI era: the agent is only as good as the indispensable human behind it.</p>


Fact: Agentic AI is making humans indispensable.
More than 40% of agentic AI projects will be canceled by the end of 2027. That is a prediction from Gartner published in June 2025, based on a poll of more than 3,400 organizations actively investing in the technology.
The reason cited is not that the agents do not work. It is that the humans deploying them are making the wrong decisions. “Most agentic AI projects right now are early-stage experiments or proof of concepts that are mostly driven by hype and are often misapplied,” according to Anushree Verma, senior director analyst at Gartner.
Organizations are deploying agents without a clear strategy, without understanding the complexity, and without the governance to manage what happens when something goes wrong.
In other words, the agent is only as good as the human behind it.
This matters enormously for marketing. AI agents in marketing are real, accelerating and in many cases, necessary. Agents that select audiences. Agents that generate content. Agents that optimize send times, choose offers and orchestrate entire customer journeys autonomously, continuously and at a scale no human team could match. The capabilities are here today and growing rapidly.
But Gartner’s data reveals a warning and marketing leaders who miss it will find themselves on the wrong side of that 40%.
FOMO causes agent failure
The failure rate Gartner describes is not random. It starts with fear.
Fear of being left behind. Fear of watching competitors move faster. Fear of being the CMO who did not act when everyone else did. That fear is driving organizations to deploy agentic AI, not because they have a strategy, but because they cannot afford to be last.
The result is agents built on broken workflows. Agents fed with poor data. Agents operating without the governance structures that keep them aligned with business goals. The agents execute… the wrong things, in the wrong ways, at the wrong times.
FOMO is not a strategy. And in the agentic era, it is an expensive mistake.
Agent washing
Gartner identified a widespread trend it calls “agent washing”… vendors rebranding existing chatbots and automation tools as agentic AI without delivering genuine autonomous capabilities. Of the thousands of vendors claiming agentic solutions, Gartner estimates only around 130 offer real agentic features. Marketing teams investing in the rest are not getting agents. They are getting dressed-up automation with an agentic price tag. automation with an agentic price tag.
The consequences go beyond wasted budget. Gartner predicts that in 2026, one-third of companies will harm customer experiences by deploying AI prematurely, eroding brand trust and damaging both acquisition and retention.
A personalization agent that misreads a customer. A content agent that violates compliance. A journey agent that floods a churning customer with offers at exactly the wrong moment. These are the predictable outcomes of deploying autonomous systems without the human judgment to direct them.
The dumbing down of marketers



Gartner’s third prediction is the most revealing of all. GenAI usage leads to the atrophy of critical thinking skills. As a result, 50 percent of global organizations will require AI-free competency evaluations.
Half of all organizations are watching their people get dumber because AI is always available to think for them. Quietly. Gradually. Until the day the algorithm is wrong and nobody in the room can tell.
In marketing, that is a crisis. Marketing requires judgment — the ability to ask not just what the data says, but what it means. Not just whether a campaign worked, but why. Not just whether to accept an AI recommendation, but whether it reflects the brand, the moment and the relationship the company is trying to build.
Those questions cannot be delegated to an agent. They require a human being scrutinizing what a machine thinks is right.
The most dangerous marketer in the agentic era is not the one who rejects AI. It is the one who accepts everything it produces without question.
Agents cannot be trusted to ask the right questions
An agent can optimize what it has been given. It cannot question whether it has been given the right thing.
It can personalize a message based on behavioral signals. It cannot decide that the right move is to say nothing at all… to give a customer space, to protect a relationship rather than extract from it.
It can generate a thousand content variations and test them. It cannot feel the difference between a message that converts and a message that connects. It cannot sense when a campaign that performs well in the data is quietly damaging the brand.
It can execute a journey flawlessly. It cannot design one that reflects what customers actually want from this brand, at this point in their lives.
These are not limitations that will be solved by the next model release. They are structural. AI is trained on the past. The irreducible human job in marketing is to bring judgment about what should happen next, even when the data does not yet exist to support it.
The marketer as manager of agents
The right mental model for the agentic era is not human versus machine. It is a human plus machine, with the human in charge.
That is the foundation of Positionless Marketing. For decades, marketing teams operated as an assembly line with handoffs. Positionless Marketing breaks that model by giving marketers three transformative powers: Data Power to immediately discover customer insights for precise targeting and hyper-personalization, without waiting for engineers; Creative Power to create channel-ready assets like copy and visuals, without waiting for creatives; and Optimization Power to run campaigns that optimize themselves through automated journeys and testing, without waiting for analysts. Handoffs are eliminated.
The Positionless Marketer is a multidisciplinary thinker who deploys AI agents to go beyond traditional positions. Agents handle what used to require waiting for three different teams, eliminating the assembly line. The marketer is no longer waiting on anyone. They are thinking bigger, moving across disciplines while keeping human judgment at the center of every decision the agents make.
This is a promotion, not a replacement. But it comes with real demands. Marketers who can think strategically, not just operationally. Who can evaluate AI output critically, not just accept it. Who can take accountability for what the agents do in their name.
Gartner’s Daryl Plummer stated it directly: organizations should prioritize behavioral changes alongside technological changes as first-order priorities. The technology is ready. The question is whether the humans in the marketing organization are.
The window is narrowing
The organizations that will win the next decade of marketing are not the ones that deploy the most agents. They are the ones that build the human capability to direct them well.Gartner’s 40% prediction is not a warning to slow down. It is a warning to be deliberate. The difference between an agentic marketing operation that compounds value over time and one that wastes budget, violates policy, and erodes customer trust is not the technology. It is the human judgment sitting above it.
Marketing teams need to face facts in the agentic AI era: the agent is only as good as the indispensable human behind it.

The failure rate is not about technology. It is about the humans deploying it... and the ones missing from the equation entirely.

Gartner: 40% of agentic AI projects will fail, making humans indispensable by Optimove

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/09/LinkedIn-Ads-retargeting-How-to-reach-prospects-at-every-funnel-stage.jpg" class="attachment-large size-large wp-post-image" alt="LinkedIn Ads retargeting: How to reach prospects at every funnel stage" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/09/LinkedIn-Ads-retargeting-How-to-reach-prospects-at-every-funnel-stage.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/09/LinkedIn-Ads-retargeting-How-to-reach-prospects-at-every-funnel-stage-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/09/LinkedIn-Ads-retargeting-How-to-reach-prospects-at-every-funnel-stage-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>LinkedIn's Off-Platform event ads, lets marketers promote external events in-feed and drive registrations to their own sites by May 6.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/09/LinkedIn-Ads-retargeting-How-to-reach-prospects-at-every-funnel-stage.jpg" class="attachment-large size-large wp-post-image" alt="LinkedIn Ads retargeting: How to reach prospects at every funnel stage" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/09/LinkedIn-Ads-retargeting-How-to-reach-prospects-at-every-funnel-stage.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/09/LinkedIn-Ads-retargeting-How-to-reach-prospects-at-every-funnel-stage-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/09/LinkedIn-Ads-retargeting-How-to-reach-prospects-at-every-funnel-stage-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>LinkedIn is rolling out Off-Platform Event Ads, giving marketers a new way to promote events without needing a native LinkedIn Event Page.</p>



<p><strong>What’s happening</strong>. The new format allows advertisers to run Event Ads that link directly to external destinations — such as webinar platforms, landing pages or livestream sites — instead of keeping traffic on LinkedIn.</p>



<p>This marks a shift from platform-contained experiences to more flexible, marketer-controlled journeys.</p>



<p><strong>How it works</strong>. Marketers can create an Event Ad using a third-party URL, add event details like date and format, and choose from objectives including awareness, engagement, traffic or lead generation.</p>



<p>Clicks send users directly to the external event page, while performance metrics remain trackable in Campaign Manager.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1488" height="837" src="https://searchengineland.com/wp-content/seloads/2026/04/1777324080792.gif" alt="" class="wp-image-475898" srcset="https://searchengineland.com/wp-content/seloads/2026/04/1777324080792.gif 1488w, https://searchengineland.com/wp-content/seloads/2026/04/1777324080792-768x432.gif 768w, https://searchengineland.com/wp-content/seloads/2026/04/1777324080792-1200x675.gif 1200w" sizes="(max-width: 1488px) 100vw, 1488px" /></figure>



<p><strong>Why we care.</strong> Until now, promoting events on LinkedIn often meant working within platform constraints, which could fragment the user journey and limit control over registrations. </p>



<p>Off-Platform Event Ads remove that friction by allowing marketers to tap into LinkedIn’s targeting while keeping traffic, data and conversions on their own platforms — making it easier to scale campaigns and maintain a consistent experience.</p>



<p><strong>What to watch:</strong>



<ul class="wp-block-list">
<li>Whether this drives higher registration rates compared to native Event Pages</li>



<li>How advertisers balance LinkedIn targeting with off-platform conversion tracking</li>



<li>If LinkedIn expands similar flexibility to other ad formats</li>
</ul>



<p><strong>Availability</strong>. Off-Platform Event Ads are currently rolling out globally and are expected to be available to all advertisers by May 6.</p>



<p><strong>Bottom line</strong>. By opening <a href="https://www.linkedin.com/pulse/expand-event-promotion-linkedin-offplatform-ads-linkedin-for-mktg-hcvzc/">Event Ads to off-platform destinations</a>, LinkedIn is making it easier for marketers to scale event promotion — without forcing them to build inside its walls.</p>


LinkedIn is rolling out Off-Platform Event Ads, giving marketers a new way to promote events without needing a native LinkedIn Event Page.
What’s happening. The new format allows advertisers to run Event Ads that link directly to external destinations — such as webinar platforms, landing pages or livestream sites — instead of keeping traffic on LinkedIn.
This marks a shift from platform-contained experiences to more flexible, marketer-controlled journeys.
How it works. Marketers can create an Event Ad using a third-party URL, add event details like date and format, and choose from objectives including awareness, engagement, traffic or lead generation.
Clicks send users directly to the external event page, while performance metrics remain trackable in Campaign Manager.




Why we care. Until now, promoting events on LinkedIn often meant working within platform constraints, which could fragment the user journey and limit control over registrations. 
Off-Platform Event Ads remove that friction by allowing marketers to tap into LinkedIn’s targeting while keeping traffic, data and conversions on their own platforms — making it easier to scale campaigns and maintain a consistent experience.
What to watch:




Whether this drives higher registration rates compared to native Event Pages
How advertisers balance LinkedIn targeting with off-platform conversion tracking
If LinkedIn expands similar flexibility to other ad formats
Availability. Off-Platform Event Ads are currently rolling out globally and are expected to be available to all advertisers by May 6.
Bottom line. By opening Event Ads to off-platform destinations, LinkedIn is making it easier for marketers to scale event promotion — without forcing them to build inside its walls.

LinkedIn's Off-Platform event ads, lets marketers promote external events in-feed and drive registrations to their own sites by May 6.

LinkedIn expands Event Ads beyond its own platform

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/The-framing-gap-Why-AI-cant-position-your-brand.png" class="attachment-large size-large wp-post-image" alt="The framing gap- Why AI can’t position your brand" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/The-framing-gap-Why-AI-cant-position-your-brand.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/The-framing-gap-Why-AI-cant-position-your-brand-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/The-framing-gap-Why-AI-cant-position-your-brand-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/The-framing-gap-Why-AI-cant-position-your-brand-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI can verify claims, but it won’t choose the conclusions that benefit your business. Learn how framing turns proof into preference.

This article translates the framework into practitioner register. The full theoretical model, including the formal mechanics, the testable predictions, and the academic engagement with current AI-reasoning literature, is developed in the academic working paper The Framing Gap: Strategic Claim Bridging and the Limits of Generative AI Interpretation in Brand Representation (Barnard, 2026, Zenodo: 10.5281/zenodo.19857447).



Every brand holds its claims, and somewhere in the archive of its digital life, there’s proof to back them up. 
The AI assistive engine (the systems behind ChatGPT, Perplexity, and Google AI Overviews) holds that proof too, scattered across its training data and retrieval index, alongside competitors’ claims. 
The audience has a need but no vocabulary to bridge the gap between what they want and what the brand or the engine already knows.
All three lack the same thing: a frame, the interpretive context that turns scattered information into a narrative worth transmitting (for the brand), citing (for AI), and acting on (for the user).
This is where the claim-frame-prove (CFP) process comes in. Claim and proof are mechanical, and frame is strategic.




Claim and prove are mechanical work the engine can verify. Frame is the strategic move only the brand can make.



Why AI can’t make the leap your brand needs
CFP runs claim by claim, fact by fact. The brand’s full picture is built from many CFP cycles compounding: each claim framed and proven becomes a fact in the corpus, and the cumulative weight of those facts is what positions the brand to dominate.
AI can join known facts, but it can’t leap to a new one that benefits your brand. Given Facts A and B, AI can derive Conclusion C that follows logically. That’s standard inference, and the engines do it well. 
What they can’t do reliably is the move a creative human makes constantly: looking at A and B, reaching to a non-obvious J that’s commercially beneficial, and then constructing the logical bridge so the engine can use it.
From here, I’ll use the alphabet as the scale: C is the obvious commercial reach, J is the non-obvious, and Q is the most ambitious leap a brand can sensibly make.
Two examples from my own positioning make the difference concrete.
A + B → C




A: I coined answer engine optimization in 2017. B: I run a brand engineering company.
The obvious conclusion (C) the engine produces unprompted: my work is connected to AEO implementation. True, useful, but close to the facts, and the engines will get that right without my help.
A + B → J




From the same A and B, reaching further. J: I’m the practitioner most likely to have learned what works and what doesn’t from a decade of operational data nobody else has. 
Same starting facts, completely different commercial outcome, and the engine doesn’t make this leap on its own. It needs me to build the bridge for it.
That second move — from A and B to J — is strategic claim bridging: selecting which non-obvious J benefits the brand from the space of derivable conclusions, and then constructing the logical connection from accepted facts to that chosen J so the engine transmits it as fact rather than as the brand’s opinion of itself. 
Two operations packed into one move: the strategic part is choosing J, and the bridging part is making the inference watertight.
AI won’t choose what’s best for your brand
AI doesn’t choose the J that’s good for your brand. You do. That choice, and the bridge that proves it, is the work AI has no commercial stake in, and a future (more capable) AI without your stake just produces a more sophisticated version of the same problem.
Whether AI can be creative is contested ground. The narrower claim holds regardless: even when AI produces a novel-looking output, it has no commercial intent guiding which J to derive. From the same A and B, an AI could just as easily produce a damaging J as a beneficial J. It has no skin in your commercial game.
A creative marketer does both things at once: reaches imaginatively to a non-obvious J, and chooses the J that serves the brand. That’s the move AI engines can’t reach, and it’s why the frame has to come from someone placing the information online (the brand, a client, or an independent source).
The disposition that lets you see this work is what I’ve been calling “empathy for the machine,” a phrase I started using in client consulting around 2011-2012 (originally as “empathy for the beast,” retired once I got more serious about the business side of digital marketing), and first published formally in 2019. 
It’s the discipline of stepping outside your own perspective to see what the machine actually struggles with. That advice applies to anything in SEO/AAO — in this case, specifically to when it grounds, attributes, and synthesizes claims about your brand.
Unfortunately, brands all too often produce material aimed at human readers and assume the machine will figure out the rest. With a little empathy for the machine, brands design material the machine can use as its own interpretation (feed the beast).
This produces three different levels of brand-AI communication, each one building on the previous. 
Levels 1 and 2 are the foundations every brand needs in place, and Level 3 is where framing enters, and what this article is designed to change your thinking.

  
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Level 1: Scattered proof of claims
Proof exists, but there’s nothing linking it to the claim. This is where most brands sit, and it leaves the engine to perform inference over whatever it can find. 
The brand publishes Claim A on its website. Proof Z exists somewhere else: a conference program, an industry database, a Wikipedia citation, and a trade publication from four years ago. The brand assumes the engine will connect the two.
To connect them, the engine has to perform inference. Can it derive the conclusion that this brand is credible for this claim, given scattered premises across different domains, formats, and varying source authority?
There’s no copy stating the connection, no hyperlinks pointing from claim to proof, and no schema encoding the relationship.
That depends almost entirely on how confidently the machine already understands the entity, and that runs on three sub-levels.
If the machine has no confident understanding of the brand, and the proof isn’t explicitly linked, no connection happens. The proof might as well not exist.
If the machine has no confident understanding of the brand, but the proof is explicitly linked, the connection happens because the link does the work that the entity resolution couldn’t.
If the machine has a strong, confident understanding of the brand, the connection happens even without the link, because a well-resolved entity shortens the logical distance the machine has to traverse (linkless links, as I’ve called them). 
The link still adds confidence (more than one path always does), but it’s no longer load-bearing as the entity carries the work.
The implication runs through the rest of the pipeline. Entity clarity in the knowledge graph isn’t a nice-to-have sitting alongside content work. It’s the variable that decides whether your content work has to carry all the weight or almost none of it. 
Any proof that isn’t explicitly linked is missed at sub-level one, caught at sub-level two, and confidently embedded at sub-level three.
When entity understanding is weak, the result is familiar to anyone tracking AI visibility: a meritorious brand appears occasionally, and when it does, the wording is hedged, and the brand sits mid-to-low-pack. The engine did the best inference it could, and, being a responsible probability engine, it hedged. 
Worse, opportunities for inclusion are throttled across adjacent queries the fact should have pulled the brand into, because the fact was never connected to the proof that would have warranted the inclusion in the first place.
What happens when Level 1, scattered proof of claims, is done well? Brand X is infrequently mentioned, unconvincingly, as a provider of Y.
Level 2: Connected proof of claims
Here, the brand explicitly connects claim to proof through a combination of copy, hyperlinks, and schema. It also closes the inference gap by providing what the engine would otherwise have to figure out. 
The brand publishes Claim A and explicitly connects it to Proof Z, with the logical thread stated in copy, anchored by hyperlinks to the proof, and encoded in schema: a fact with a significant number of supporting pieces of evidence joined to it three ways, leaving nothing for the engine to infer.
Connected proof of claims is a spectrum, not a switch. At the low end, you’ve connected some of your proof, which already beats Level 1 because the engine no longer has to figure out the connections you’ve made, but it’s still figuring out the ones you haven’t. 
If your competition has connected more of theirs, you’re still losing the comparison on the proof you left scattered. At the high end, you’ve connected all of it: every claim joined to every piece of supporting evidence, nothing scattered, and nothing left for the engine to guess at.
Most brands sit somewhere between scattered and connected simply because they’ve connected only the most obvious proof, and the AI may well have already figured the obvious ones out for itself: the links don’t teach it anything it didn’t already know.
With connected proof of claims done comprehensively for a given claim, the engine has enough corroboration to back the brand confidently, and the claim becomes fact in the corpus. Confidence transfers cleanly because there’s nothing to guess at. 
Connected proof of claims is also a great weapon for a smaller brand competing with a bigger one: a specialist accounting firm with 50 pieces of proof, all explicitly connected to a specific positioning, beats a Big 4 with thousands of unconnected pieces on that specific positioning, because connection is what turns proof into substance that the engine can transmit.
What happens when Level 2, connected proof of claims, is done well? Brand X is frequently mentioned convincingly as a provider of Y.
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Level 3: Framed proof of claims
This is where framing enters, and where strategic claim bridging earns its name. 
For each claim that matters, the brand publishes Claim A, connects the proof, and then does the thing the engine can’t do (and the audience is unlikely to do either, for that matter).
It reaches the non-obvious J that benefits the brand, and constructs the bridge from A and B to J in language the engine can transmit. Not merely “we are the leader in X, demonstrated by Y,” but the frame: 
Why Y matters for the specific problem this audience faces.
What Z signals about trust in this particular market.
How W translates to the outcome the prospect actually cares about at the moment of decision.
A frame is a logical inference from corroborated facts, where the brand chose where the inference would land. For example: 
“Jason Barnard coined answer engine optimization in 2017, made dated public predictions about how the field would unfold, and those predictions came true, his predictions about where the field is going next are credible.” 
Every component is verifiable independently, and every connection between components is logical. The J the bridge reaches to is the one I chose, not the J the engine would have generated unprompted.
One well-constructed frame makes one claim into fact in the AI’s voice. Run that across the claims that matter, and the cumulative weight is what shifts a brand from “frequently mentioned convincingly” to “almost always mentioned as the leading provider”: dominance is a stack of well-framed facts, not a single masterstroke.
The result: the AI doesn’t merely confirm, it enthuses. “Brand X leads in Y, and here is why that matters for your situation.” 
The engine transmits the frame wholesale, in the language you chose, to the audience you specified, with a reason to keep coming back. The machine didn’t generate the narrative; it relayed it warmly.
What happens when Level 3, framed proof of claims, is done well across the claims that matter? Brand X is almost always mentioned as the leading provider of Y, and dominates the space.




Each level builds on the previous: connected proof of claims requires scattered proof of claims connected, and framed proof of claims requires connected proof of claims bridged strategically.
Most brands are only halfway to framed proof of claims
The brands that think they’re at framed proof of claims are usually at framed proof of claims for humans, and scattered proof of claims for machines. Marketing and narrative work supplies frames to humans all the time, and plenty of brands do it well. 
What almost no brand does is supply frames the machine can use, and the gap between the two is where framed proof of claims is most powerful.
Some brands operate below even that and are effectively standing still: published facts at the surface, few proof connections, and no interpretive content the machine can use for any purpose. 
The signature objection from a standing still brand is the same in every consulting room: “We already do this, our website explains who we are.” The website does that. The website is doing zero work to help the machine with framing.
The cost of standing still isn’t visible until a model update or two down the line. Brands that think they’re at framed proof of claims are usually investing harder in the wrong layer (content), while the layer that matters (framing and, ideally, joining the dots) compounds for someone else. 
The gap widens every year. If you have content that doesn’t frame effectively or join the dots with links to proof, you’re leaking huge value, and pushing through connection and framing is the best return on past investment you can make right now: you’re doing the heavy lifting for the machines, and they’ll reward you for giving them this extremely valuable context on a plate.
Three structural conditions separate framed proof of claims from marketing-and-narrative-as-usual, and missing any one collapses the brand back to connected proof of claims or lower. 
The entity has to be well-established, well-resolved, and trusted, because a frame can’t anchor to a vague brand. The underlying proof has to be connected, because most brands have fluent marketing prose on top of scattered proof, which is scattered proof of claims with prettier wallpaper. 
The bridge itself has to be strictly logical, because machines read logic first and tone second, and a logically broken bridge fails, however well it’s written.
The better AI gets, the more framing matters
Smarter AI rewards better framing rather than replacing it, and the reason is the same selection pressure SEO practitioners have been operating under since the early 2000s. 
There’s a seductive and entirely wrong conclusion to draw from rapid improvement in AI reasoning: that engines will eventually figure out how to frame brands correctly without help. The opposite is true. The engine rewards the brand whose assets reduce its own workload for the same or better result.
Search engines reward sites that are easy to crawl, render, and classify. Knowledge Graphs reward entities that are easy to resolve. AI assistive engines reward content that is easy to ground, verify, and transmit confidently. Where the engine has to choose between two roughly equivalent candidates, the candidate that demands less computation, less inference, and less guesswork wins.
Framed proof of claims is that principle operating at the bridging layer. A more capable engine encountering this level has the bridge handed to it ready-made. It doesn’t have to figure out the frame, it transmits the bridge the brand supplied, fluently and confidently, with the engine’s full reasoning capability now amplifying rather than substituting for the framing work.
A more capable engine without a frame falls back to inference over scattered evidence, which is expensive, ambiguous, and produces hedged output. Every improvement in reasoning capability makes the hedging more detailed and the noncommittal language more sophisticated, but the underlying problem isn’t capability, it’s the absence of a frame to amplify. The engine is doing more work for a worse result, and that’s the exact failure mode the engine’s selection pressure is designed to penalize.
The gap between those two outcomes is the framing gap, and it widens with every generation. Brands implementing only connected proof of claims don’t lose ground in absolute terms, they lose ground relative to brands implementing Framed Proof of claims faster every year, because the engine increasingly rewards assets that let it deploy its growing capability productively rather than waste it on guessing and hedging. 
The selection pressure that rewarded fast websites in 1998, clean HTML in 2003, and structured data in 2015 rewards framed proof of claims now. The mechanism of gaining a competitive advantage by reducing costs for the AI for the same or better results hasn’t changed — and probably never will.




The framed proof of claims trajectory rises steeply and continues climbing. The connected proof of claims trajectory rises gently and flattens. The shaded area between the two lines is labeled the framing gap and visibly widens with each generation.

  
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The bridge stays human
The bridge is human territory, and it stays human because it requires commercial intent specific to the brand that the engine doesn’t have. 
Everything the machine does well will get better: retrieval, connection, pattern extraction, and synthesis. None of that helps the brand whose evidence the machine can see but can’t bridge meaningfully to a beneficial conclusion.
Whether AI confirms your brand, overlooks it, or champions it comes down to one discipline: strategic claim bridging, claim by claim, fact by fact. It’s the last layer of brand-AI communication that won’t yield to automation, if it yields at all.
This is the 11th piece in my AI authority series. 




The first, “Rand Fishkin proved AI recommendations are inconsistent – here’s why and how to fix it,” introduced cascading confidence. 
The second, “AAO: Why assistive agent optimization is the next evolution of SEO,” named the discipline. 
The third, “The AI engine pipeline: 10 gates that decide whether you win the recommendation,” mapped the full pipeline. 
The fourth, “The five infrastructure gates behind crawl, render, and index,” walked through the infrastructure phase.
The fifth, “5 competitive gates hidden inside ‘rank and display’,” covered the competitive phase.
The sixth, “The entity home: The page that shapes how search, AI, and users see your brand,” mapped the raw material.
The seventh, “The push layer returns: Why ‘publish and wait’ is half a strategy,” extended the entry model. 
The eighth, “How AI decides what your content means and why it gets you wrong,” covered annotation — the last gate where you’re alone with the machine. 
The ninth, “Why topical authority isn’t enough for AI search,” opened the competitive phase proper with topical ownership.
The tenth, “The funnel flip: Why AI forces a bottom-up acquisition strategy,” named the process.
Up next: The method to find where your content fails in the AI engine pipeline, and why the window to fix it is closing.

AI can verify claims, but it won’t choose the conclusions that benefit your business. Learn how framing turns proof into preference.

The framing gap: Why AI can’t position your brand

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/seen-believed-chosen.png" class="attachment-large size-large wp-post-image" alt="Seen believed chosen" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/seen-believed-chosen.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/seen-believed-chosen-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/seen-believed-chosen-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/seen-believed-chosen-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Wil Reynolds says rankings alone fall short as AI exposes zombie content and weak signals that don’t build trust or drive revenue.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/seen-believed-chosen.png" class="attachment-large size-large wp-post-image" alt="Seen believed chosen" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/seen-believed-chosen.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/seen-believed-chosen-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/seen-believed-chosen-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/seen-believed-chosen-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Wil Reynolds, founder and CEO of Seer Interactive, is challenging SEOs to rethink what success looks like in a world increasingly shaped by AI.</p>



<p>In his SEO Week session, “SEO is a performance channel, GEO isn’t. How do you pivot?”, Reynolds said many marketers are focused on the wrong outcomes — and producing work that people don’t believe.</p>



<h2 class="wp-block-heading" id="h-marketing-isn-t-just-about-being-seen">Marketing isn’t just about being seen</h2>



<p>Reynolds opened by pushing back on the idea that visibility alone is the goal of marketing.</p>



<p>“Marketing was never just to be seen or be visible,” he said. “You had to turn that visibility into something — believing something about your brand… And then they ultimately have to choose you.”</p>



<p>He described a progression that marketers need to focus on: being seen, being believed and being chosen.</p>



<p>“It’s how you take your time with people, and turn them from seeing you, into believing something about you,” he said.</p>



<p>“I got the ranking, job finished,” he added. “Job’s not finished.”</p>



<p>Reynolds also questioned the value of surface-level success metrics.</p>



<p>“I got a lot more followers, but they don’t pay you,” he said.</p>



<h2 class="wp-block-heading" id="h-low-quality-marketing-is-everywhere">Low-quality marketing is everywhere</h2>



<p>Reynolds pointed to common marketing tactics — including automated outreach — as examples of work that doesn’t create value.</p>



<p>“That’s not marketing,” he said, referring to spam-like SMS messages.</p>



<p>Those tactics made him reflect on his own past work, he said.</p>



<p>“I started looking at the stuff that I used to do… was that really marketing?” he said.</p>



<p>“Some of us are strategists. Some of us are loopholists,” he said. “You&#8217;ve got to make a decision today.”</p>



<h2 class="wp-block-heading" id="h-the-industry-is-producing-zombie-content">The industry is producing ‘zombie content’</h2>



<p>Reynolds criticized the widespread use of scaled, templated content designed primarily to rank.</p>



<p>He used broad listicle-style pages as an example.</p>



<p>“Why would you write content saying best restaurants in Minnesota when nobody that’s a human looks for the best restaurant in Minnesota?” he said.</p>



<p>He described this type of content as “zombie content.”</p>



<p>“That’s what we do,” he said, describing how marketers repeat what already ranks instead of doing something different.</p>



<p>He also described how many marketers approach content creation.</p>



<p>“I’m going to look at the top 10 and look at what they did slightly wrong… and I’m only going to do it slightly better,” he said.</p>



<h2 class="wp-block-heading" id="h-short-term-tactics-vs-long-term-brand-building">Short-term tactics vs. long-term brand building</h2>



<p>Reynolds contrasted short-term SEO tactics with long-term brand building.</p>



<p>“Some people like to win in decades,” he said. “Other people like to win quarter to quarter.”</p>



<p>He described how many teams focus on immediate results.</p>



<p>“What works this quarter to get my boss off my back long enough so I can survive the next quarter?” he said.</p>



<p>That approach leads to work that people don’t actually want, he said.</p>



<p>“You will never produce a thing that anyone wants if you continue to play that,” he said.</p>



<h2 class="wp-block-heading" id="h-seo-success-doesn-t-translate-to-ai-visibility">SEO success doesn’t translate to AI visibility</h2>



<p>Reynolds shared an example involving “ethical jeans” to show how SEO and AI results can differ.</p>



<p>One brand ranked well in Google without being known for ethical practices, while another brand that invested in ethical production ranked much lower.</p>



<p>In AI-generated answers, that outcome changed.</p>



<p>“If that worked, if it was the same, that brand would be showing up in AI models,” he said. “And they showed up in none.”</p>



<p>He connected this to credibility.</p>



<p>“Nobody believed them,” he said. “Nobody chose them.”</p>



<h2 class="wp-block-heading" id="h-visibility-without-belief-doesn-t-lead-to-outcomes">Visibility without belief doesn’t lead to outcomes</h2>



<p>Visibility alone isn’t enough, Reynolds said.</p>



<p>“If you have all the visibility in the world and people don’t believe you or trust you, then you’re not going to get chosen,” he said.</p>



<p>Visibility is only part of the process, he said.</p>



<p>“This visibility is just an opportunity,” he said. “That’s all it is. &#8230; Iit is not the job to be done.&#8221;</p>



<h2 class="wp-block-heading" id="h-what-people-say-matters">What people say matters</h2>



<p>Reynolds suggested looking at platforms like Reddit to understand how people actually talk about brands.</p>



<p>“Go to Reddit… look at all the brands,” he said. “You find out that humans don’t believe you. And they have to pay you for you to stay in business.</p>



<p>He contrasted that with how brands present themselves in content.</p>



<p>“Not only did they not think you’re number one — they don’t think you’re number 100,” he said.</p>



<h2 class="wp-block-heading" id="h-the-wrong-metrics-are-being-measured">The wrong metrics are being measured</h2>



<p>Marketers often focus on metrics that are easy to track rather than meaningful, Reynolds said.</p>



<p>“We’re measuring the easy stuff to measure,” he said. “The real work is in the hard-to-measure stuff.”</p>



<p>He encouraged comparing visibility metrics with signals tied to outcomes.</p>



<p>“If your visibility is skyrocketing and your pipeline is flat, that’s bad,” he said.</p>



<h2 class="wp-block-heading" id="h-watching-real-users-changes-the-picture">Watching real users changes the picture</h2>



<p>Reynolds described research his team conducted by observing real people using AI tools.</p>



<p>“When you actually watch people do the job… your eyes open so much wider,” he said.</p>



<p>One person typed four words, while another typed more than 100 words for the same task, he said.</p>



<p>He also noted that AI tools often suggest additional steps or actions beyond what users ask for, and people frequently accept those suggestions, he said.</p>



<h2 class="wp-block-heading" id="h-start-with-your-brand">Start with your brand</h2>



<p>Marketers should focus on how their brand appears in AI-generated answers, especially for branded queries, Reynolds said.</p>



<p>“You spend all this money trying to get people to know your brand… and then you don’t want to make sure that answer’s right?” he said.</p>



<h2 class="wp-block-heading" id="h-ai-can-shape-your-brand-narrative">AI can shape your brand narrative</h2>



<p>Reynolds shared an example where AI-generated responses surfaced incorrect information about his company.</p>



<p>“So now it’s showing up everywhere,” he said.</p>



<p>He described responding by publishing content to address the claim directly.</p>



<p>“If it’s false, then I’ve got to fight that,” he said.</p>



<h2 class="wp-block-heading" id="h-there-is-too-much-content">There is too much content</h2>



<p>“There’s too much content out there,” he said.</p>



<p>He described shifting his approach.</p>



<p>“I’m trying to become a curator,” he said.</p>



<h2 class="wp-block-heading" id="h-rethinking-performance">Rethinking performance</h2>



<p>Reynolds shared examples of how different traffic sources perform.</p>



<p>“My direct converts 1.5 times better than my SEO,” he said. “My social, five times better.”</p>



<h2 class="wp-block-heading" id="h-a-final-question-for-marketers">A final question for marketers</h2>



<p>Reynolds ended by asking marketers to rethink their priorities:</p>



<p>“Are you willing to sacrifice a little bit of this visibility game to be more believable?”</p>


Wil Reynolds, founder and CEO of Seer Interactive, is challenging SEOs to rethink what success looks like in a world increasingly shaped by AI.
In his SEO Week session, “SEO is a performance channel, GEO isn’t. How do you pivot?”, Reynolds said many marketers are focused on the wrong outcomes — and producing work that people don’t believe.
Marketing isn’t just about being seen
Reynolds opened by pushing back on the idea that visibility alone is the goal of marketing.
“Marketing was never just to be seen or be visible,” he said. “You had to turn that visibility into something — believing something about your brand… And then they ultimately have to choose you.”
He described a progression that marketers need to focus on: being seen, being believed and being chosen.
“It’s how you take your time with people, and turn them from seeing you, into believing something about you,” he said.
“I got the ranking, job finished,” he added. “Job’s not finished.”
Reynolds also questioned the value of surface-level success metrics.
“I got a lot more followers, but they don’t pay you,” he said.
Low-quality marketing is everywhere
Reynolds pointed to common marketing tactics — including automated outreach — as examples of work that doesn’t create value.
“That’s not marketing,” he said, referring to spam-like SMS messages.
Those tactics made him reflect on his own past work, he said.
“I started looking at the stuff that I used to do… was that really marketing?” he said.
“Some of us are strategists. Some of us are loopholists,” he said. “You’ve got to make a decision today.”
The industry is producing ‘zombie content’
Reynolds criticized the widespread use of scaled, templated content designed primarily to rank.
He used broad listicle-style pages as an example.
“Why would you write content saying best restaurants in Minnesota when nobody that’s a human looks for the best restaurant in Minnesota?” he said.
He described this type of content as “zombie content.”
“That’s what we do,” he said, describing how marketers repeat what already ranks instead of doing something different.
He also described how many marketers approach content creation.
“I’m going to look at the top 10 and look at what they did slightly wrong… and I’m only going to do it slightly better,” he said.
Short-term tactics vs. long-term brand building
Reynolds contrasted short-term SEO tactics with long-term brand building.
“Some people like to win in decades,” he said. “Other people like to win quarter to quarter.”
He described how many teams focus on immediate results.
“What works this quarter to get my boss off my back long enough so I can survive the next quarter?” he said.
That approach leads to work that people don’t actually want, he said.
“You will never produce a thing that anyone wants if you continue to play that,” he said.
SEO success doesn’t translate to AI visibility
Reynolds shared an example involving “ethical jeans” to show how SEO and AI results can differ.
One brand ranked well in Google without being known for ethical practices, while another brand that invested in ethical production ranked much lower.
In AI-generated answers, that outcome changed.
“If that worked, if it was the same, that brand would be showing up in AI models,” he said. “And they showed up in none.”
He connected this to credibility.
“Nobody believed them,” he said. “Nobody chose them.”
Visibility without belief doesn’t lead to outcomes
Visibility alone isn’t enough, Reynolds said.
“If you have all the visibility in the world and people don’t believe you or trust you, then you’re not going to get chosen,” he said.
Visibility is only part of the process, he said.
“This visibility is just an opportunity,” he said. “That’s all it is. … Iit is not the job to be done.”
What people say matters
Reynolds suggested looking at platforms like Reddit to understand how people actually talk about brands.
“Go to Reddit… look at all the brands,” he said. “You find out that humans don’t believe you. And they have to pay you for you to stay in business.
He contrasted that with how brands present themselves in content.
“Not only did they not think you’re number one — they don’t think you’re number 100,” he said.
The wrong metrics are being measured
Marketers often focus on metrics that are easy to track rather than meaningful, Reynolds said.
“We’re measuring the easy stuff to measure,” he said. “The real work is in the hard-to-measure stuff.”
He encouraged comparing visibility metrics with signals tied to outcomes.
“If your visibility is skyrocketing and your pipeline is flat, that’s bad,” he said.
Watching real users changes the picture
Reynolds described research his team conducted by observing real people using AI tools.
“When you actually watch people do the job… your eyes open so much wider,” he said.
One person typed four words, while another typed more than 100 words for the same task, he said.
He also noted that AI tools often suggest additional steps or actions beyond what users ask for, and people frequently accept those suggestions, he said.
Start with your brand
Marketers should focus on how their brand appears in AI-generated answers, especially for branded queries, Reynolds said.
“You spend all this money trying to get people to know your brand… and then you don’t want to make sure that answer’s right?” he said.
AI can shape your brand narrative
Reynolds shared an example where AI-generated responses surfaced incorrect information about his company.
“So now it’s showing up everywhere,” he said.
He described responding by publishing content to address the claim directly.
“If it’s false, then I’ve got to fight that,” he said.
There is too much content
“There’s too much content out there,” he said.
He described shifting his approach.
“I’m trying to become a curator,” he said.
Rethinking performance
Reynolds shared examples of how different traffic sources perform.
“My direct converts 1.5 times better than my SEO,” he said. “My social, five times better.”
A final question for marketers
Reynolds ended by asking marketers to rethink their priorities:
“Are you willing to sacrifice a little bit of this visibility game to be more believable?”

Wil Reynolds says rankings alone fall short as AI exposes zombie content and weak signals that don’t build trust or drive revenue.

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Read the Latest Trends and Headlines in Digital Marketing.

Here are some of the latest headlines on digital marketing insights, search, and ecommerce:

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Reach out with any questions you might have regarding how RSO can help you with digital marketing.

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