<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/How-to-measure-lead-quality-in-Google-Ads.png" class="attachment-large size-large wp-post-image" alt="How to measure lead quality in Google Ads" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/08/How-to-measure-lead-quality-in-Google-Ads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/How-to-measure-lead-quality-in-Google-Ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/How-to-measure-lead-quality-in-Google-Ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/How-to-measure-lead-quality-in-Google-Ads-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Connect your CRM to Google Ads, define qualified leads, and optimize campaigns around the leads that actually drive business value.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/How-to-measure-lead-quality-in-Google-Ads.png" class="attachment-large size-large wp-post-image" alt="How to measure lead quality in Google Ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/How-to-measure-lead-quality-in-Google-Ads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/How-to-measure-lead-quality-in-Google-Ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/How-to-measure-lead-quality-in-Google-Ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/How-to-measure-lead-quality-in-Google-Ads-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">After years of <a href="https://searchengineland.com/auditing-optimizing-google-ads-limited-data-461528">auditing Google Ads</a> accounts, one thing continues to surprise me. So many customer relationship management (CRM) tools still aren&#8217;t talking to Google Ads.</p>



<p class="wp-block-paragraph">Somewhere between the form submission and the sales pipeline, <a href="https://searchengineland.com/improve-ppc-lead-quality-431009">lead quality</a> data just disappears. Yet that data is one of the most valuable signals Google Ads could learn from.</p>



<p class="wp-block-paragraph">But what happens to those leads once they reach the CRM is also, at least partly, our responsibility as Google Ads managers. In addition to generating leads, we need to measure lead quality and track whether they turn into business.</p>



<h2 id="h-why-lead-volume-isn-t-enough-anymore" class="wp-block-heading">Why lead volume isn&#8217;t enough anymore</h2>



<p class="wp-block-paragraph">For years, getting more leads has been treated as a sign of good campaign performance. It’s an easy trap to fall into, especially when you start measuring your own performance by more conversions, graphs moving in the right direction, and even that reassuring Excellent ad strength rating from Google Ads.</p>



<p class="wp-block-paragraph">But a lead isn’t a sale. And two leads can be worth wildly different amounts to a business.</p>



<p class="wp-block-paragraph">This is why we should prioritize qualified leads. But how do we define that?</p>



<p class="wp-block-paragraph">More budget? The right location? A specific service? Someone ready to buy now? It can mean completely different things from one business to another.</p>



<p class="wp-block-paragraph">If you can&#8217;t define and measure what a qualified lead is, you can&#8217;t optimize for it. And if Google Ads only sees the form submission, it’ll optimize for more of the same.</p>



<p class="wp-block-paragraph">Instead, Google Ads needs to know what happens afterward.</p>


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<h2 id="h-how-to-measure-lead-quality-for-google-ads" class="wp-block-heading">How to measure lead quality for Google Ads</h2>



<p class="wp-block-paragraph">In three steps, you can connect your data, define what a qualified lead means, and optimize Google Ads for lead value.</p>



<h3 id="h-1-connect-your-crm-back-to-google-ads" class="wp-block-heading">1. Connect your CRM back to Google Ads</h3>



<p class="wp-block-paragraph">The first step is connecting your CRM data to Google Ads. This tells Google which leads became qualified, which turned into customers, and which went nowhere.</p>



<ul class="wp-block-list">
<li><strong>Capture the lead:</strong> When someone submits a form, capture the information Google will need to identify that lead later, such as the GCLID, email, or phone number.</li>



<li><strong>Track what happens next:</strong> Monitor how the lead moves through your CRM from new lead to qualified lead to customer.</li>



<li><strong>Confirm the outcome:</strong> Tell Google Ads whether that lead became qualified, turned into a customer, or went nowhere, so it can connect that outcome with the original ad click.</li>
</ul>



<p class="wp-block-paragraph">This is where offline conversions and enhanced conversions for leads come in:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1242" src="https://searchengineland.com/wp-content/seloads/2026/08/Connect-your-CRM-back-to-Google-Ads.jpeg" alt="Connect your CRM back to Google Ads" class="wp-image-485455" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Connect-your-CRM-back-to-Google-Ads.jpeg 2048w, https://searchengineland.com/wp-content/seloads/2026/08/Connect-your-CRM-back-to-Google-Ads-768x466.jpeg 768w, https://searchengineland.com/wp-content/seloads/2026/08/Connect-your-CRM-back-to-Google-Ads-1536x932.jpeg 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p class="wp-block-paragraph">Google recommends uploading offline conversion data regularly, ideally daily.</p>



<p class="wp-block-paragraph">There are also time limits. GCLID-based offline conversions generally need to be uploaded within 90 days of the ad click, while enhanced conversions for leads using first-party data have a 63-day window.</p>



<p class="wp-block-paragraph">If your data doesn’t match correctly, check the basics first:</p>



<ul class="wp-block-list">
<li>Is the GCLID being stored?</li>



<li>Is the first-party data being captured correctly?</li>



<li>Are any parameters getting lost between the ad, form, and CRM?</li>
</ul>



<p class="wp-block-paragraph">Enhanced conversions for leads can also improve campaign outcomes. <a href="https://support.google.com/google-ads/answer/2998031?hl=en" target="_blank" rel="noopener">According to Google</a>, advertisers using it see a median 10% increase in conversions over those with standard offline conversion imports.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/how-to-reduce-low-quality-leads-from-performance-max-campaigns-468687">Google Ads launches built-in lead management dashboard</a></em></strong>



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<h3 id="h-2-define-what-a-qualified-lead-is" class="wp-block-heading">2. Define what a qualified lead is</h3>



<p class="wp-block-paragraph">Once your CRM and Google Ads are talking to each other, you need to decide what counts as a qualified lead. Before you can teach Google Ads what a good lead looks like, you need to define it yourself.</p>



<p class="wp-block-paragraph">Talk to the sales team about what makes a lead worth pursuing. For a real estate business, for example, you might look at:</p>



<ul class="wp-block-list">
<li><strong>Budget:</strong> Can they realistically afford the properties you&#8217;re selling?</li>



<li><strong>Location:</strong> Are they looking in an area where you have properties available?</li>



<li><strong>Timeline:</strong> Are they looking to buy soon or just exploring?</li>
</ul>



<p class="wp-block-paragraph">I would keep the scoring as simple as possible to start. You don&#8217;t necessarily need a complicated model. For example:</p>



<ul class="wp-block-list">
<li><strong>0 – Not qualified:</strong> Doesn&#8217;t meet the basic criteria.</li>



<li><strong>1 – Qualified lead:</strong> Meets your criteria and is worth pursuing.</li>



<li><strong>2 – Customer:</strong> The lead has turned into business.</li>
</ul>



<p class="wp-block-paragraph">Your criteria will look different depending on the type of business you’re representing.</p>



<p class="wp-block-paragraph">You can always make the scoring more sophisticated later. What matters is that marketing and sales agree on what each stage means and use the scoring consistently.</p>



<h3 id="h-3-optimize-google-ads-for-lead-value" class="wp-block-heading">3. Optimize Google Ads for lead value</h3>



<p class="wp-block-paragraph">Once Google Ads knows which leads are qualified, you can go one step further and tell the platform that not every conversion has the same value.</p>



<p class="wp-block-paragraph">In a real estate campaign, for example, a qualified lead could be assigned a value of $50. And a customer who buys a property could be assigned the value generated for the business.</p>



<p class="wp-block-paragraph">The numbers will be different for every business. The point is to give Google Ads another signal about which outcomes matter most.</p>



<p class="wp-block-paragraph">Once you’re consistently sending conversion values back to Google Ads, you can use value-based bidding, such as Maximize conversion value or Target return on ad spend (ROAS).</p>



<p class="wp-block-paragraph">But first, you need sufficient data.</p>



<p class="wp-block-paragraph">A customer is more valuable than a qualified lead. But if the business only closes a few sales each month, Google may not have enough sales data to optimize effectively. In that case, optimizing toward qualified leads may make more sense.</p>



<p class="wp-block-paragraph">The goal isn&#8217;t necessarily to optimize for the deepest possible conversion. It&#8217;s to find the most meaningful conversion that gives Google enough data to learn from.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/how-to-reduce-low-quality-leads-from-performance-max-campaigns-468687">How to reduce low-quality leads from Performance Max campaigns</a></em></strong>



<h2 id="h-from-measuring-leads-to-reporting-on-real-business-value" class="wp-block-heading">From measuring leads to reporting on real business value</h2>



<p class="wp-block-paragraph">With this system in place, you can provide more in-depth Google Ads reporting. Instead of reporting on leads, you can share the number of qualified leads and the value of the pipeline they generated.</p>



<p class="wp-block-paragraph">That gives you a much clearer picture of what your campaigns are doing for the business.</p>



<p class="wp-block-paragraph">It also gives you more confidence when making recommendations, asking for additional budget, or preparing your next proposal. And for a consultant or agency, demonstrating that value can make conversations around renewals, expanding campaigns, or even upselling additional services much more straightforward.</p>



<p class="wp-block-paragraph">Lead quality isn&#8217;t clear at the moment of the click. It&#8217;s determined by everything that happens afterward. And that&#8217;s exactly the information you should be feeding back into Google Ads.</p>

After years of auditing Google Ads accounts, one thing continues to surprise me. So many customer relationship management (CRM) tools still aren’t talking to Google Ads.
Somewhere between the form submission and the sales pipeline, lead quality data just disappears. Yet that data is one of the most valuable signals Google Ads could learn from.
But what happens to those leads once they reach the CRM is also, at least partly, our responsibility as Google Ads managers. In addition to generating leads, we need to measure lead quality and track whether they turn into business.
Why lead volume isn’t enough anymore
For years, getting more leads has been treated as a sign of good campaign performance. It’s an easy trap to fall into, especially when you start measuring your own performance by more conversions, graphs moving in the right direction, and even that reassuring Excellent ad strength rating from Google Ads.
But a lead isn’t a sale. And two leads can be worth wildly different amounts to a business.
This is why we should prioritize qualified leads. But how do we define that?
More budget? The right location? A specific service? Someone ready to buy now? It can mean completely different things from one business to another.
If you can’t define and measure what a qualified lead is, you can’t optimize for it. And if Google Ads only sees the form submission, it’ll optimize for more of the same.
Instead, Google Ads needs to know what happens afterward.
your competitors win.
      
Analyze your competitors
    
How to measure lead quality for Google Ads
In three steps, you can connect your data, define what a qualified lead means, and optimize Google Ads for lead value.
1. Connect your CRM back to Google Ads
The first step is connecting your CRM data to Google Ads. This tells Google which leads became qualified, which turned into customers, and which went nowhere.
Capture the lead: When someone submits a form, capture the information Google will need to identify that lead later, such as the GCLID, email, or phone number.
Track what happens next: Monitor how the lead moves through your CRM from new lead to qualified lead to customer.
Confirm the outcome: Tell Google Ads whether that lead became qualified, turned into a customer, or went nowhere, so it can connect that outcome with the original ad click.
This is where offline conversions and enhanced conversions for leads come in:




Google recommends uploading offline conversion data regularly, ideally daily.
There are also time limits. GCLID-based offline conversions generally need to be uploaded within 90 days of the ad click, while enhanced conversions for leads using first-party data have a 63-day window.
If your data doesn’t match correctly, check the basics first:
Is the GCLID being stored?
Is the first-party data being captured correctly?
Are any parameters getting lost between the ad, form, and CRM?
Enhanced conversions for leads can also improve campaign outcomes. According to Google, advertisers using it see a median 10% increase in conversions over those with standard offline conversion imports.
Dig deeper: Google Ads launches built-in lead management dashboard




	
	
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
2. Define what a qualified lead is
Once your CRM and Google Ads are talking to each other, you need to decide what counts as a qualified lead. Before you can teach Google Ads what a good lead looks like, you need to define it yourself.
Talk to the sales team about what makes a lead worth pursuing. For a real estate business, for example, you might look at:
Budget: Can they realistically afford the properties you’re selling?
Location: Are they looking in an area where you have properties available?
Timeline: Are they looking to buy soon or just exploring?
I would keep the scoring as simple as possible to start. You don’t necessarily need a complicated model. For example:
0 – Not qualified: Doesn’t meet the basic criteria.
1 – Qualified lead: Meets your criteria and is worth pursuing.
2 – Customer: The lead has turned into business.
Your criteria will look different depending on the type of business you’re representing.
You can always make the scoring more sophisticated later. What matters is that marketing and sales agree on what each stage means and use the scoring consistently.
3. Optimize Google Ads for lead value
Once Google Ads knows which leads are qualified, you can go one step further and tell the platform that not every conversion has the same value.
In a real estate campaign, for example, a qualified lead could be assigned a value of $50. And a customer who buys a property could be assigned the value generated for the business.
The numbers will be different for every business. The point is to give Google Ads another signal about which outcomes matter most.
Once you’re consistently sending conversion values back to Google Ads, you can use value-based bidding, such as Maximize conversion value or Target return on ad spend (ROAS).
But first, you need sufficient data.
A customer is more valuable than a qualified lead. But if the business only closes a few sales each month, Google may not have enough sales data to optimize effectively. In that case, optimizing toward qualified leads may make more sense.
The goal isn’t necessarily to optimize for the deepest possible conversion. It’s to find the most meaningful conversion that gives Google enough data to learn from.
Dig deeper: How to reduce low-quality leads from Performance Max campaigns



From measuring leads to reporting on real business value
With this system in place, you can provide more in-depth Google Ads reporting. Instead of reporting on leads, you can share the number of qualified leads and the value of the pipeline they generated.
That gives you a much clearer picture of what your campaigns are doing for the business.
It also gives you more confidence when making recommendations, asking for additional budget, or preparing your next proposal. And for a consultant or agency, demonstrating that value can make conversations around renewals, expanding campaigns, or even upselling additional services much more straightforward.
Lead quality isn’t clear at the moment of the click. It’s determined by everything that happens afterward. And that’s exactly the information you should be feeding back into Google Ads.

Connect your CRM to Google Ads, define qualified leads, and optimize campaigns around the leads that actually drive business value.

How to improve Google Ads lead quality with CRM data

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/3-phases-to-build-authority-and-earn-AI-citations.png" class="attachment-large size-large wp-post-image" alt="3 phases to build authority and earn AI citations" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/3-phases-to-build-authority-and-earn-AI-citations.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/3-phases-to-build-authority-and-earn-AI-citations-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/3-phases-to-build-authority-and-earn-AI-citations-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/3-phases-to-build-authority-and-earn-AI-citations-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI visibility compounds when technical accessibility, useful content, and third-party authority work together.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/3-phases-to-build-authority-and-earn-AI-citations.png" class="attachment-large size-large wp-post-image" alt="3 phases to build authority and earn AI citations" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/3-phases-to-build-authority-and-earn-AI-citations.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/3-phases-to-build-authority-and-earn-AI-citations-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/3-phases-to-build-authority-and-earn-AI-citations-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/3-phases-to-build-authority-and-earn-AI-citations-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">There’s plenty of noise in the search industry about what does and doesn’t move the needle for AI visibility. Questions about FAQ schema, content volume, and llms.txt are important foundational considerations. </p>



<p class="wp-block-paragraph">That said, the real goal is to drive actionable next steps from consumers — something that’s harder when AI increasingly acts as a billboard rather than a traditional conversion driver.</p>



<p class="wp-block-paragraph">Brands need to understand what gets them mentioned in AI responses and, more importantly, what gets them cited — with a link back to the source, ideally an owned property. <a href="https://www.airops.com/blog/page-elements-increase-likelihood-llm-recognition" target="_blank" rel="noopener">Up to 85%</a> of top-of-funnel B2B brand mentions in AI search come from third-party content, AirOps research found.</p>



<p class="wp-block-paragraph">To be found in AI, your products and messaging need to be understood by both potential customers and LLMs. To be chosen, you need to build trust and authority.</p>



<p class="wp-block-paragraph">Here’s a three-phase, compounding process we use at my agency that combines AEO and SEO across owned and earned media to build trust and authority.</p>



<h2 id="h-phase-1-make-your-pages-easily-accessible-to-llms" class="wp-block-heading">Phase 1: Make your pages easily accessible to LLMs</h2>



<p class="wp-block-paragraph">This is where much of conventional SEO comes into play, along with classic UX. </p>



<p class="wp-block-paragraph">Proper, logical navigation, machine-readable page structure, SEO and accessibility-focused tagging, updates to existing website content — including product detail pages (PDPs), blog and editorial content, and company profiles — and your social content can all improve initial accessibility and understanding.</p>



<p class="wp-block-paragraph">This is when initial page structure optimizations should begin. Experiment with adding key takeaways to popular editorial pieces. Make sure all PDPs follow a logical path (product-focused H1s and H2s, segmented feature bullets, descriptions, comparisons, and &#8220;Complete the look&#8221; for apparel brands) — all this feeds into retail results in AI.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/technical-seo-geo-460898">A technical SEO blueprint for GEO: Optimize for AI-powered search</a></em></strong>


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<h2 id="h-phase-2-create-fresh-content-new-and-repurposed" class="wp-block-heading">Phase 2: Create fresh content — new and repurposed</h2>



<p class="wp-block-paragraph">Google Search loves <a href="https://searchengineland.com/content-fresh-ai-470005">fresh content</a>. LLMs love fresh takes. Brands will always say positive things about the products or services provided — that’s a given. </p>



<p class="wp-block-paragraph">Both humans and LLMs know this. You need owned content to contain brand-informed messaging. It’s where the source of truth sits and allows LLMs to understand your brand.</p>



<p class="wp-block-paragraph">Your website and owned social profiles must be optimized from a technical perspective and to control baseline messaging around who you are and what you offer. This will help with consistency when communicating value propositions to AI, along with Trustpilot, Reddit, TikTok, and so on.</p>



<p class="wp-block-paragraph">It’s not always easy to scale this, especially outside a website update or blog post, let alone video creation. Luckily, this phase can be broken down into two parts: fresh and refreshed.</p>



<p class="wp-block-paragraph">For fresh content, it’s important to target your gaps. I recently worked with an insurance provider to guide a 12-month organic content roadmap, based on 3+ years of keyword research, SEO insights from QBRs, and an analysis of assets that were being cited by AI.&nbsp;</p>



<p class="wp-block-paragraph">Along with that roadmap came a list of seasonal editorial pieces for both on-site use and newsletter distribution, complete with first-party, proprietary, data-backed studies.</p>



<p class="wp-block-paragraph">Next, the approach I use to supplement fresh content (the refresh) is simple repurposing. Have a successful informational blog post (or at least one that was successful before AI search)? Build that out as an infographic.</p>



<p class="wp-block-paragraph">Is there a topic you’ve written about on-site but need to share on socials? What about those newsletter topics I mentioned earlier? Create a hub-and-spoke approach with granular breakdowns of that topic for organic social and post on Instagram or TikTok in a short-form video.</p>



<p class="wp-block-paragraph">This is where growth continues to compound. Now you have an owned asset plan, a slew of topics, and identified subject matter experts that will serve you in the next step.</p>



<p class="wp-block-paragraph">This will help you become one of the brands or products mentioned — called out — in AI responses. It may not help you get cited — linked back to your brand as a reference that builds up your authority.&nbsp;</p>



<p class="wp-block-paragraph">Just because an AI understands what you offer doesn’t mean it will decide to cite you in its results. That’s where external users, partnerships, and third-party sources come in.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/revise-old-content-ai-search-optimization-470434">How to revise your old content for AI search optimization</a></em></strong>



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<h2 id="h-phase-3-dig-into-outreach-and-digital-pr" class="wp-block-heading">Phase 3: Dig into outreach and digital PR</h2>



<p class="wp-block-paragraph">Organic strategy is incredibly important in the AI space, especially as major LLMs still grapple with how they interpret paid media. With that, SEO is having the time of its life — because you need those classic technical and backend optimizations to build a solid foundation for your content.</p>



<p class="wp-block-paragraph">Finally, the real teamwork comes into play. Keyword research is still vital, but that must be combined with understanding the types of prompts users are asking. From there, you can build a query fan-out list to really dive into the topics and solutions your potential customers are trying to achieve.</p>



<p class="wp-block-paragraph">With that list of prompts, the keywords you seeded it with, and the understanding gained from analyzing gaps in both traditional and AI results, you can see who is cited and what is shown in AI results.&nbsp;</p>



<p class="wp-block-paragraph">The who and what are where digital PR comes into play. Connecting with authoritative third-party sources, including publications, podcasts, and Substacks, drives valuable trust from sources not directly invested in a brand, making citations from them as valuable as a Google or Yelp review.</p>



<p class="wp-block-paragraph">“OK, Eric, I get what you’re saying, but I’m just a small brand with a strong, friendly customer base — how can I get third-party placement or work with influencers?”</p>



<p class="wp-block-paragraph">Not everyone needs constant Tier 1 placements to win the AI results race. Targeting highly authoritative, niche blogs and editorial sites can have a lasting impact on AI citations because industry experts in your brand’s field are going to be the main storytellers for LLMs.</p>



<p class="wp-block-paragraph">Most organizations already have internal members on hand who would be more than happy to produce shareable, citable insights for journalists to use when writing about your industry. These are <strong>your</strong> subject matter experts.</p>



<p class="wp-block-paragraph">There are additional, specific strategies for microinfluencer outreach that mimic this approach, too. Again, working with industry-specific influencers makes more sense to human users, so it will also make sense to LLMs.</p>



<p class="wp-block-paragraph">In addition, reach out to fans who have a strong, established following. Brands can also work with an agency to identify highly specific microinfluencers.</p>



<p class="wp-block-paragraph">Consistency is key when gaining that trust. This means making sure brand messaging remains consistent across owned and earned media, while sticking with publications or influencers that represent what a brand stands for.&nbsp;</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/7-digital-pr-secrets-behind-strong-seo-performance-468159">7 digital PR secrets behind strong SEO performance</a></em></strong>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0102ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
  <div>
    
    <div>
      
      <div id="semrush-one-headline" class="headline-responsive">
        If AI can’t find you, <span>customers won’t either</span>.
      </div>
      <p id="semrush-one-subhead">
       Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
      </p>
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    <div>
      <span id="semrush-one-cta">See your AI visibility</span>
    </div>
    </div>
    </a>




<h2 id="h-the-compounding-effect" class="wp-block-heading">The compounding effect</h2>



<p class="wp-block-paragraph">These three phases don’t work in isolation. They’re a sequence, with each one strengthening the next. Technical accessibility gets you found. Fresh, brand-informed content gets you understood. Earned coverage and digital PR get you chosen because that’s the trust no brand can manufacture about itself.</p>



<p class="wp-block-paragraph">Skip any one phase, and the other two lose their leverage — a well-optimized site with no fresh content has nothing to cite, while great content with no third-party validation gets understood but may never be trusted enough to get chosen.</p>



<p class="wp-block-paragraph">Run all three together, and the effect compounds. Each citation reinforces the last. Every piece of earned coverage makes your owned content more credible. Authority in AI results is built the same way it always has been in marketing: with consistency across every channel.</p>



<p class="wp-block-paragraph"><strong>Key takeaways:</strong>



<ul class="wp-block-list">
<li>Combining owned and earned media strategies helps drive improved mentions and citations in AI responses.</li>



<li>It’s important for messaging to remain consistent across first- and third-party platforms, so LLMs can find, understand, and connect your brand proposition across owned and earned assets.</li>



<li>Outlining a combined organic content marketing plan, with optimizations to owned properties and guidance for earned partners, can improve sentiment in LLM responses, increasing citation rates.</li>
</ul>



<p class="wp-block-paragraph">

There’s plenty of noise in the search industry about what does and doesn’t move the needle for AI visibility. Questions about FAQ schema, content volume, and llms.txt are important foundational considerations. 
That said, the real goal is to drive actionable next steps from consumers — something that’s harder when AI increasingly acts as a billboard rather than a traditional conversion driver.
Brands need to understand what gets them mentioned in AI responses and, more importantly, what gets them cited — with a link back to the source, ideally an owned property. Up to 85% of top-of-funnel B2B brand mentions in AI search come from third-party content, AirOps research found.
To be found in AI, your products and messaging need to be understood by both potential customers and LLMs. To be chosen, you need to build trust and authority.
Here’s a three-phase, compounding process we use at my agency that combines AEO and SEO across owned and earned media to build trust and authority.
Phase 1: Make your pages easily accessible to LLMs
This is where much of conventional SEO comes into play, along with classic UX. 
Proper, logical navigation, machine-readable page structure, SEO and accessibility-focused tagging, updates to existing website content — including product detail pages (PDPs), blog and editorial content, and company profiles — and your social content can all improve initial accessibility and understanding.
This is when initial page structure optimizations should begin. Experiment with adding key takeaways to popular editorial pieces. Make sure all PDPs follow a logical path (product-focused H1s and H2s, segmented feature bullets, descriptions, comparisons, and “Complete the look” for apparel brands) — all this feeds into retail results in AI.
Dig deeper: A technical SEO blueprint for GEO: Optimize for AI-powered search



  
AI recommends.
      
See your AI visibility
    
Phase 2: Create fresh content — new and repurposed
Google Search loves fresh content. LLMs love fresh takes. Brands will always say positive things about the products or services provided — that’s a given. 
Both humans and LLMs know this. You need owned content to contain brand-informed messaging. It’s where the source of truth sits and allows LLMs to understand your brand.
Your website and owned social profiles must be optimized from a technical perspective and to control baseline messaging around who you are and what you offer. This will help with consistency when communicating value propositions to AI, along with Trustpilot, Reddit, TikTok, and so on.
It’s not always easy to scale this, especially outside a website update or blog post, let alone video creation. Luckily, this phase can be broken down into two parts: fresh and refreshed.
For fresh content, it’s important to target your gaps. I recently worked with an insurance provider to guide a 12-month organic content roadmap, based on 3+ years of keyword research, SEO insights from QBRs, and an analysis of assets that were being cited by AI. 
Along with that roadmap came a list of seasonal editorial pieces for both on-site use and newsletter distribution, complete with first-party, proprietary, data-backed studies.
Next, the approach I use to supplement fresh content (the refresh) is simple repurposing. Have a successful informational blog post (or at least one that was successful before AI search)? Build that out as an infographic.
Is there a topic you’ve written about on-site but need to share on socials? What about those newsletter topics I mentioned earlier? Create a hub-and-spoke approach with granular breakdowns of that topic for organic social and post on Instagram or TikTok in a short-form video.
This is where growth continues to compound. Now you have an owned asset plan, a slew of topics, and identified subject matter experts that will serve you in the next step.
This will help you become one of the brands or products mentioned — called out — in AI responses. It may not help you get cited — linked back to your brand as a reference that builds up your authority. 
Just because an AI understands what you offer doesn’t mean it will decide to cite you in its results. That’s where external users, partnerships, and third-party sources come in.
Dig deeper: How to revise your old content for AI search optimization




	
	
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
Phase 3: Dig into outreach and digital PR
Organic strategy is incredibly important in the AI space, especially as major LLMs still grapple with how they interpret paid media. With that, SEO is having the time of its life — because you need those classic technical and backend optimizations to build a solid foundation for your content.
Finally, the real teamwork comes into play. Keyword research is still vital, but that must be combined with understanding the types of prompts users are asking. From there, you can build a query fan-out list to really dive into the topics and solutions your potential customers are trying to achieve.
With that list of prompts, the keywords you seeded it with, and the understanding gained from analyzing gaps in both traditional and AI results, you can see who is cited and what is shown in AI results. 
The who and what are where digital PR comes into play. Connecting with authoritative third-party sources, including publications, podcasts, and Substacks, drives valuable trust from sources not directly invested in a brand, making citations from them as valuable as a Google or Yelp review.
“OK, Eric, I get what you’re saying, but I’m just a small brand with a strong, friendly customer base — how can I get third-party placement or work with influencers?”
Not everyone needs constant Tier 1 placements to win the AI results race. Targeting highly authoritative, niche blogs and editorial sites can have a lasting impact on AI citations because industry experts in your brand’s field are going to be the main storytellers for LLMs.
Most organizations already have internal members on hand who would be more than happy to produce shareable, citable insights for journalists to use when writing about your industry. These are your subject matter experts.
There are additional, specific strategies for microinfluencer outreach that mimic this approach, too. Again, working with industry-specific influencers makes more sense to human users, so it will also make sense to LLMs.
In addition, reach out to fans who have a strong, established following. Brands can also work with an agency to identify highly specific microinfluencers.
Consistency is key when gaining that trust. This means making sure brand messaging remains consistent across owned and earned media, while sticking with publications or influencers that represent what a brand stands for. 
Dig deeper: 7 digital PR secrets behind strong SEO performance



  
customers won’t either.
      
See your AI visibility
    
The compounding effect
These three phases don’t work in isolation. They’re a sequence, with each one strengthening the next. Technical accessibility gets you found. Fresh, brand-informed content gets you understood. Earned coverage and digital PR get you chosen because that’s the trust no brand can manufacture about itself.
Skip any one phase, and the other two lose their leverage — a well-optimized site with no fresh content has nothing to cite, while great content with no third-party validation gets understood but may never be trusted enough to get chosen.
Run all three together, and the effect compounds. Each citation reinforces the last. Every piece of earned coverage makes your owned content more credible. Authority in AI results is built the same way it always has been in marketing: with consistency across every channel.
Key takeaways:




Combining owned and earned media strategies helps drive improved mentions and citations in AI responses.
It’s important for messaging to remain consistent across first- and third-party platforms, so LLMs can find, understand, and connect your brand proposition across owned and earned assets.
Outlining a combined organic content marketing plan, with optimizations to owned properties and guidance for earned partners, can improve sentiment in LLM responses, increasing citation rates.

AI visibility compounds when technical accessibility, useful content, and third-party authority work together.

3 phases to build authority and earn AI citations

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-AI-Max-experiments.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-AI-Max-experiments.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Google-AI-Max-experiments-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Google-AI-Max-experiments-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Google-AI-Max-experiments-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google is expanding AI Max with new experiments and planning tools to help advertisers test budgets, ROI targets and controls.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-AI-Max-experiments.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-AI-Max-experiments.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Google-AI-Max-experiments-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Google-AI-Max-experiments-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Google-AI-Max-experiments-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google is expanding AI Max for Search campaigns with new testing and planning capabilities designed to help advertisers understand the impact of changing budgets, ROI targets and campaign settings before committing to them.</p>



<p class="wp-block-paragraph">The updates include multi-campaign A/B testing, experiments that support brand and location controls, and new Performance Planner capabilities.</p>



<p class="wp-block-paragraph"><strong>What’s new.</strong> Starting in September, advertisers will be able to test different budgets and ROI targets across multiple Search campaigns in a single A/B test.</p>



<p class="wp-block-paragraph">The feature builds on <a href="https://searchengineland.com/ai-max-experiments-arrive-in-google-ads-heres-how-they-work-459935">Google&#8217;s existing one-click AI Max experiments</a> and is intended to give advertisers a clearer view of what happens to overall business performance when they scale campaigns.</p>



<p class="wp-block-paragraph"><strong>Testing gets more flexible.</strong> Google is also removing a potential obstacle for advertisers that depend on tighter campaign controls.</p>



<p class="wp-block-paragraph">AI Max experiments will support tests with brand and location controls enabled, allowing advertisers to measure AI Max&#8217;s impact without having to remove those guardrails for the experiment.</p>



<p class="wp-block-paragraph">That could make AI Max testing more practical for businesses with strict requirements around which brands or geographic areas their campaigns can target.</p>



<p class="wp-block-paragraph"><strong>Performance Planner expands.</strong> Google is also adding more functionality to Performance Planner, allowing advertisers to forecast how changes such as bidding or budget targets could affect existing campaign performance.</p>



<p class="wp-block-paragraph">Advertisers can then apply Google&#8217;s suggested changes directly to their campaigns with one click, shortening the path between forecasting a change and implementing it.</p>



<p class="wp-block-paragraph"><strong>Why we care.</strong> Multi-campaign experiments could be particularly useful for understanding the incremental impact of increasing budgets or changing ROI targets at a broader account level. Support for brand and location controls also means more advertisers can test AI Max while maintaining some of the restrictions their businesses require.</p>



<p class="wp-block-paragraph"><strong>Bottom line.</strong> As Google pushes AI Max further into Search campaign management, it&#8217;s also building out the <a href="https://blog.google/products/ads-commerce/ai-max-testing-planning-tools">experimentation and forecasting tools</a> advertisers need to evaluate its impact before making larger changes.</p>


Google is expanding AI Max for Search campaigns with new testing and planning capabilities designed to help advertisers understand the impact of changing budgets, ROI targets and campaign settings before committing to them.
The updates include multi-campaign A/B testing, experiments that support brand and location controls, and new Performance Planner capabilities.
What’s new. Starting in September, advertisers will be able to test different budgets and ROI targets across multiple Search campaigns in a single A/B test.
The feature builds on Google’s existing one-click AI Max experiments and is intended to give advertisers a clearer view of what happens to overall business performance when they scale campaigns.
Testing gets more flexible. Google is also removing a potential obstacle for advertisers that depend on tighter campaign controls.
AI Max experiments will support tests with brand and location controls enabled, allowing advertisers to measure AI Max’s impact without having to remove those guardrails for the experiment.
That could make AI Max testing more practical for businesses with strict requirements around which brands or geographic areas their campaigns can target.
Performance Planner expands. Google is also adding more functionality to Performance Planner, allowing advertisers to forecast how changes such as bidding or budget targets could affect existing campaign performance.
Advertisers can then apply Google’s suggested changes directly to their campaigns with one click, shortening the path between forecasting a change and implementing it.
Why we care. Multi-campaign experiments could be particularly useful for understanding the incremental impact of increasing budgets or changing ROI targets at a broader account level. Support for brand and location controls also means more advertisers can test AI Max while maintaining some of the restrictions their businesses require.
Bottom line. As Google pushes AI Max further into Search campaign management, it’s also building out the experimentation and forecasting tools advertisers need to evaluate its impact before making larger changes.

Google is expanding AI Max with new experiments and planning tools to help advertisers test budgets, ROI targets and controls.

Google adds new AI Max testing and planning tools

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/SEO-priorities-for-2027-Your-guide-to-search-success.png" class="attachment-large size-large wp-post-image" alt="SEO priorities for 2027- Your guide to search success" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/SEO-priorities-for-2027-Your-guide-to-search-success.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/SEO-priorities-for-2027-Your-guide-to-search-success-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/SEO-priorities-for-2027-Your-guide-to-search-success-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/SEO-priorities-for-2027-Your-guide-to-search-success-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Being found is only part of the challenge. See what it takes to make your brand understood, trusted, and cited across search in 2027.

Being found is only part of the challenge. See what it takes to make your brand understood, trusted, and cited across search in 2027.

SEO priorities for 2027: Your guide to search success

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/The-next-AI-money-grab-Charging-for-editorial-corrections.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/The-next-AI-money-grab-Charging-for-editorial-corrections.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/The-next-AI-money-grab-Charging-for-editorial-corrections-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/The-next-AI-money-grab-Charging-for-editorial-corrections-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/The-next-AI-money-grab-Charging-for-editorial-corrections-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Publishers already monetized links, placements, and removals. Correcting the information shaping AI answers could be their next money grab.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/The-next-AI-money-grab-Charging-for-editorial-corrections.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/The-next-AI-money-grab-Charging-for-editorial-corrections.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/The-next-AI-money-grab-Charging-for-editorial-corrections-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/The-next-AI-money-grab-Charging-for-editorial-corrections-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/The-next-AI-money-grab-Charging-for-editorial-corrections-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">A company contacts a publisher to correct an outdated author byline, job title, company description, or slogan.</p>



<p class="wp-block-paragraph">The publisher agrees. Kind of.</p>



<p class="wp-block-paragraph">They’re happy to make the change. But only after the company pays an “editorial processing fee.”</p>



<p class="wp-block-paragraph">That <strong>should</strong> be a red flag for all of us. Unfortunately, I can see a not-so-distant future in which “edits for AI” becomes the next publisher money grab.</p>



<p class="wp-block-paragraph">And we’ve seen this before.</p>



<p class="wp-block-paragraph">After Google began cracking down on manipulative links, penalized websites were expected to show they had made a “good faith effort” to remove them before submitting a disavow file. Publishers quickly realized they had leverage.</p>



<p class="wp-block-paragraph">“Want us to remove the garbage link we <s>charged you for</s> published? That’ll be $100!”</p>



<p class="wp-block-paragraph">This time, companies won’t be paying publishers to remove links. They’ll be paying them to correct the information influencing how AI describes people, products, and businesses.</p>



<p><a href="https://www.seoforlunch.com/embed" rel="noopener noreferrer">View embedded content</a>



<h2 id="h-ai-builds-its-answer-from-everyone-else" class="wp-block-heading">AI builds its answer from everyone else</h2>



<p class="wp-block-paragraph">I have little doubt this will eventually happen if publishers aren’t already doing it. But to understand the “why,” you first have to understand why companies would care enough to hand over their credit card.</p>



<p class="wp-block-paragraph">Businesses can update their websites, schema, LinkedIn profiles, and other community properties they control. What they can’t directly update are the years of articles, interviews, directories, author bios, and company profiles.</p>



<p class="wp-block-paragraph">AI systems don’t simply accept a company’s preferred version of itself. They consume all the information they can find, retrieve, and somewhat verify.</p>



<p class="wp-block-paragraph">When the same description appears repeatedly across the web, even outdated information can begin to look current or authoritative (the verification process).</p>



<p class="wp-block-paragraph">A company may have repositioned its services three years ago, but if dozens of third-party websites continue using its old description, AI systems may keep repeating it. (Fun fact: many AI results still refer to this newsletter as a weekly snapshot of search updates because that’s what it <strong>was</strong> for 6-7 years before shifting to what we see today.)</p>



<p class="wp-block-paragraph">Your website tells AI what you want to be known for. The rest of the web tells AI whether it should believe you.</p>



<h2 id="h-the-next-shakedown-pay-to-update" class="wp-block-heading">The next shakedown: Pay to update</h2>



<p class="wp-block-paragraph">As companies invest more and more to be visible in AI search engines, they’ll start auditing the third-party sources that shape how they’re represented online and, by extension, in AI answers.</p>



<p class="wp-block-paragraph"><strong>That will uncover all kinds of outdated information:</strong>



<ul class="wp-block-list">
<li>Executive and author bios.</li>



<li>Former job titles.</li>



<li>Company categories and descriptions.</li>



<li>Old slogans and positioning.</li>



<li>Products and capabilities.</li>



<li>Acquisitions, VC funding, ownership, and partnership details.</li>



<li>Statistics, quotes, links, and citations.</li>
</ul>



<p class="wp-block-paragraph">Not all companies will necessarily be attempting to manipulate AI answers. It may simply want an author bio or company profile presented as current, rather than describing a business that no longer exists in that form.</p>



<p class="wp-block-paragraph">Let’s be real, though: Without the AI benefit, 99% of leadership wouldn’t give a sh*t about outdated information buried somewhere online.</p>



<p class="wp-block-paragraph">But once publishers realize these seemingly minor corrections can influence visibility (and potentially revenue) some will monetize the leverage they hold.</p>



<ul class="wp-block-list">
<li>A $100 author-bio update.</li>



<li>A $250 company-description correction.</li>



<li>A $500 description tweak with a conveniently inserted link.</li>
</ul>



<p class="wp-block-paragraph">Maybe they’ll even offer an annual “profile maintenance” package to keep every reference current. (If that didn’t send a shiver down your spine, you may have just discovered your next revenue stream.)</p>



<p class="wp-block-paragraph">I’m not suggesting every publisher will do this. But I expect these “free” updates will happen about as frequently as publishers add links to the existing brand mentions you email them about today.</p>



<p class="wp-block-paragraph"><strong>Hint:</strong> <a href="https://www.seoforlunch.com/p/hypocrisy" target="_blank" rel="noopener">They don’t</a>. Apparently, linking to the source they already referenced is “against editorial policy.”</p>



<p class="wp-block-paragraph">The publisher already controls the source. AI visibility simply gives that control new financial value.</p>



<h2 id="h-correction-or-reputation-laundering" class="wp-block-heading">Correction or reputation laundering?</h2>



<p class="wp-block-paragraph">There’s an important distinction between correcting inaccurate information and rewriting inconvenient history.</p>



<p class="wp-block-paragraph">An outdated job title, incorrect author bio, or evergreen company description presented as current should be updated. Publishers have an editorial responsibility to maintain accurate information.</p>



<p class="wp-block-paragraph">But outdated information isn’t automatically inaccurate.</p>



<p class="wp-block-paragraph">If a 2018 article accurately described what a company did in 2018, the publisher shouldn’t rewrite it simply because the business has since repositioned itself. The same applies to an embarrassing quote, failed product launch, critical review, or inconvenient piece of company history.</p>



<p class="wp-block-paragraph">Those things shouldn’t disappear simply because someone is willing to pay.</p>



<p class="wp-block-paragraph">That isn’t a correction. It’s reputation laundering — and companies already pay <strong>big</strong> money for it.</p>



<p class="wp-block-paragraph">Legitimate reputation management can help a company correct misinformation or rebuild trust. It shouldn’t buy the company a cleaner version of history.</p>



<p class="wp-block-paragraph">As I’ve written before, ethics have to play some role in SEO and AI. The standard should be factual accuracy, not whether the subject has enough money to influence the historical record.</p>



<p class="wp-block-paragraph">Publishers can charge for advertising, sponsored content, and other commercial opportunities. But charging someone to correct objectively false information crosses a line.</p>



<p class="wp-block-paragraph"><strong>Accuracy shouldn’t be an upsell.</strong>



<h2 id="h-when-accuracy-becomes-a-revenue-opportunity" class="wp-block-heading">When accuracy becomes a revenue opportunity</h2>



<p class="wp-block-paragraph">Shocker: People are greedy and mostly willing to do anything for a dollar.</p>



<p class="wp-block-paragraph">The web has monetized access, placement, links, reviews, removals, and reputation. <strong>Updates are next.</strong>



<p class="wp-block-paragraph">AI companies will undoubtedly get better at recognizing conflicting information and prioritizing authoritative sources. But publishers will still control much of the information shaping those answers. (You see where this is going…)</p>



<p class="wp-block-paragraph">Once enough businesses connect third-party sources to AI visibility — and AI visibility to revenue — publishers will recognize the money-grab opportunity.</p>



<p class="wp-block-paragraph">By the time companies realize they need the web corrected, someone will already have built a rate card for it.</p>



<p class="wp-block-paragraph">When correcting the web starts influencing revenue, correcting the web will stop being free.</p>



<p class="wp-block-paragraph"><em>This post first appeared on the author’s website and is republished here with permission.</em>


<div class="wp-block-image">
<figure class="aligncenter"><a href="https://seoforlunch.com/?utm_source=searchengineland.com&amp;utm_medium=social"><img decoding="async" width="1774" height="887" src="https://searchengineland.com/wp-content/seloads/2026/05/leroy2.jpg.webp" alt="Leroy2" class="wp-image-476987" srcset="https://searchengineland.com/wp-content/seloads/2026/05/leroy2.jpg 1774w, https://searchengineland.com/wp-content/seloads/2026/05/leroy2-768x384.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/leroy2-1536x768.jpg 1536w" sizes="(max-width: 1774px) 100vw, 1774px" /></a></figure>
</div>

A company contacts a publisher to correct an outdated author byline, job title, company description, or slogan.
The publisher agrees. Kind of.
They’re happy to make the change. But only after the company pays an “editorial processing fee.”
That should be a red flag for all of us. Unfortunately, I can see a not-so-distant future in which “edits for AI” becomes the next publisher money grab.
And we’ve seen this before.
After Google began cracking down on manipulative links, penalized websites were expected to show they had made a “good faith effort” to remove them before submitting a disavow file. Publishers quickly realized they had leverage.
“Want us to remove the garbage link we charged you for published? That’ll be $100!”
This time, companies won’t be paying publishers to remove links. They’ll be paying them to correct the information influencing how AI describes people, products, and businesses.
View embedded content



AI builds its answer from everyone else
I have little doubt this will eventually happen if publishers aren’t already doing it. But to understand the “why,” you first have to understand why companies would care enough to hand over their credit card.
Businesses can update their websites, schema, LinkedIn profiles, and other community properties they control. What they can’t directly update are the years of articles, interviews, directories, author bios, and company profiles.
AI systems don’t simply accept a company’s preferred version of itself. They consume all the information they can find, retrieve, and somewhat verify.
When the same description appears repeatedly across the web, even outdated information can begin to look current or authoritative (the verification process).
A company may have repositioned its services three years ago, but if dozens of third-party websites continue using its old description, AI systems may keep repeating it. (Fun fact: many AI results still refer to this newsletter as a weekly snapshot of search updates because that’s what it was for 6-7 years before shifting to what we see today.)
Your website tells AI what you want to be known for. The rest of the web tells AI whether it should believe you.
The next shakedown: Pay to update
As companies invest more and more to be visible in AI search engines, they’ll start auditing the third-party sources that shape how they’re represented online and, by extension, in AI answers.
That will uncover all kinds of outdated information:




Executive and author bios.
Former job titles.
Company categories and descriptions.
Old slogans and positioning.
Products and capabilities.
Acquisitions, VC funding, ownership, and partnership details.
Statistics, quotes, links, and citations.
Not all companies will necessarily be attempting to manipulate AI answers. It may simply want an author bio or company profile presented as current, rather than describing a business that no longer exists in that form.
Let’s be real, though: Without the AI benefit, 99% of leadership wouldn’t give a sh*t about outdated information buried somewhere online.
But once publishers realize these seemingly minor corrections can influence visibility (and potentially revenue) some will monetize the leverage they hold.
A $100 author-bio update.
A $250 company-description correction.
A $500 description tweak with a conveniently inserted link.
Maybe they’ll even offer an annual “profile maintenance” package to keep every reference current. (If that didn’t send a shiver down your spine, you may have just discovered your next revenue stream.)
I’m not suggesting every publisher will do this. But I expect these “free” updates will happen about as frequently as publishers add links to the existing brand mentions you email them about today.
Hint: They don’t. Apparently, linking to the source they already referenced is “against editorial policy.”
The publisher already controls the source. AI visibility simply gives that control new financial value.
Correction or reputation laundering?
There’s an important distinction between correcting inaccurate information and rewriting inconvenient history.
An outdated job title, incorrect author bio, or evergreen company description presented as current should be updated. Publishers have an editorial responsibility to maintain accurate information.
But outdated information isn’t automatically inaccurate.
If a 2018 article accurately described what a company did in 2018, the publisher shouldn’t rewrite it simply because the business has since repositioned itself. The same applies to an embarrassing quote, failed product launch, critical review, or inconvenient piece of company history.
Those things shouldn’t disappear simply because someone is willing to pay.
That isn’t a correction. It’s reputation laundering — and companies already pay big money for it.
Legitimate reputation management can help a company correct misinformation or rebuild trust. It shouldn’t buy the company a cleaner version of history.
As I’ve written before, ethics have to play some role in SEO and AI. The standard should be factual accuracy, not whether the subject has enough money to influence the historical record.
Publishers can charge for advertising, sponsored content, and other commercial opportunities. But charging someone to correct objectively false information crosses a line.
Accuracy shouldn’t be an upsell.



When accuracy becomes a revenue opportunity
Shocker: People are greedy and mostly willing to do anything for a dollar.
The web has monetized access, placement, links, reviews, removals, and reputation. Updates are next.



AI companies will undoubtedly get better at recognizing conflicting information and prioritizing authoritative sources. But publishers will still control much of the information shaping those answers. (You see where this is going…)
Once enough businesses connect third-party sources to AI visibility — and AI visibility to revenue — publishers will recognize the money-grab opportunity.
By the time companies realize they need the web corrected, someone will already have built a rate card for it.
When correcting the web starts influencing revenue, correcting the web will stop being free.
This post first appeared on the author’s website and is republished here with permission.

Publishers already monetized links, placements, and removals. Correcting the information shaping AI answers could be their next money grab.

The next AI money grab: Charging for editorial corrections

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google Ads API v25.1 adds new tools for lift measurement, benchmarking, brand sentiment, and loyalty reporting.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Google-Ads-API-v25.1-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google released Google Ads API v25.1, adding new capabilities for conversion and brand lift measurement, competitive benchmarking, brand sentiment analysis and loyalty program reporting.</p>



<p class="wp-block-paragraph">The Aug. 19 release is a drop-in upgrade for v25, but developers will need to update their client libraries and code to access the new features.</p>



<p class="wp-block-paragraph"><strong>What’s new.</strong> Google is expanding what advertisers and third-party platforms can retrieve and analyze through the API, with several of the biggest additions focused on measurement and deeper performance insights.</p>



<p class="wp-block-paragraph">The release includes comprehensive API support for lift studies, new benchmark metrics, brand sentiment data, loyalty program segmentation and a metric showing conversion value before Google&#8217;s value adjustments are applied.</p>



<p class="wp-block-paragraph"><strong>Lift measurement.</strong> Google has added read-only API resources for Conversion Lift and Brand Lift studies, allowing developers to inspect study configurations, flight dates, campaigns and conversion goals.</p>



<p class="wp-block-paragraph">The update also introduces 24 new Conversion Lift metrics, winner score metrics for statistical analysis and Brand Lift dimensions covering age range, campaign, device, gender and video.</p>



<p class="wp-block-paragraph">There&#8217;s an important limitation: Brand Lift and Conversion Lift API features are currently available only to allowlisted Google Ads accounts, with advertisers instructed to contact their Google representative for access.</p>



<p class="wp-block-paragraph"><strong>Benchmarking gets more granular.</strong> Google&#8217;s BenchmarksService can now compare performance against advertisers within specific Product &amp; Service Categories, rather than relying on broader benchmark groups.</p>



<p class="wp-block-paragraph">Google is also adding metrics including aggregate cost and views alongside share-based measurements such as share of voice, potentially giving advertisers and reporting platforms more context around competitive performance.</p>



<p class="wp-block-paragraph"><strong>Brand sentiment comes to the API.</strong> The ContentCreatorInsightsService now supports brand sentiment data. Developers can retrieve sentiment distributions and summaries for creators and brands, expanding the type of creator and brand intelligence that can be incorporated into advertising tools and reporting workflows.</p>



<p class="wp-block-paragraph"><strong>Loyalty reporting expands.</strong> Advertisers can now segment reporting metrics according to the loyalty program tier of users who converted. Google is also introducing loyalty membership as a primary dimension for conversion value rules, making it possible to identify when a loyalty membership condition was satisfied as part of a conversion value rule.</p>



<p class="wp-block-paragraph"><strong>Original conversion values.</strong> A new <code>original_conversion_value</code> metric provides the value of a biddable conversion before conversion value rules or customer lifecycle goal adjustments are applied.</p>



<p class="wp-block-paragraph">That could be particularly useful for advertisers and measurement platforms trying to understand how much Google&#8217;s value adjustments are changing the numbers ultimately used for reporting and optimization.</p>



<p class="wp-block-paragraph"><strong>Reach planning.</strong> Google is adding parental status targeting to ReachPlanService, allowing it to be used within targeting for reach forecasting and plannable product discovery.</p>



<p class="wp-block-paragraph"><strong>Why we care.</strong> The standout additions are around measurement and visibility. Lift study data, category-specific benchmarks and original conversion values give advertisers and third-party tools more ways to understand performance beyond standard campaign metrics. The ability to see conversion values before Google&#8217;s adjustments is particularly useful for teams that want to distinguish the underlying conversion data from values subsequently modified by rules or lifecycle goals.</p>



<p class="wp-block-paragraph"><strong>What developers need to do.</strong> Google says v25.1 is a drop-in upgrade for v25. Developers using Google&#8217;s client libraries will need to download the latest versions before they can access the new capabilities.</p>



<p class="wp-block-paragraph">Google has also scheduled a live walkthrough of the release for Aug. 20 at 10 a.m. ET, with the session available through its developer community and YouTube.</p>



<p class="wp-block-paragraph"><strong>Bottom line.</strong> <a href="https://ads-developers.googleblog.com/2026/08/announcing-v251-of-google-ads-api.html">Google Ads API v25.1</a> is largely a measurement and insights release, giving developers deeper access to lift studies, competitive benchmarks, brand sentiment, loyalty performance and the underlying conversion values behind Google&#8217;s optimization adjustments.</p>


Google released Google Ads API v25.1, adding new capabilities for conversion and brand lift measurement, competitive benchmarking, brand sentiment analysis and loyalty program reporting.
The Aug. 19 release is a drop-in upgrade for v25, but developers will need to update their client libraries and code to access the new features.
What’s new. Google is expanding what advertisers and third-party platforms can retrieve and analyze through the API, with several of the biggest additions focused on measurement and deeper performance insights.
The release includes comprehensive API support for lift studies, new benchmark metrics, brand sentiment data, loyalty program segmentation and a metric showing conversion value before Google’s value adjustments are applied.
Lift measurement. Google has added read-only API resources for Conversion Lift and Brand Lift studies, allowing developers to inspect study configurations, flight dates, campaigns and conversion goals.
The update also introduces 24 new Conversion Lift metrics, winner score metrics for statistical analysis and Brand Lift dimensions covering age range, campaign, device, gender and video.
There’s an important limitation: Brand Lift and Conversion Lift API features are currently available only to allowlisted Google Ads accounts, with advertisers instructed to contact their Google representative for access.
Benchmarking gets more granular. Google’s BenchmarksService can now compare performance against advertisers within specific Product & Service Categories, rather than relying on broader benchmark groups.
Google is also adding metrics including aggregate cost and views alongside share-based measurements such as share of voice, potentially giving advertisers and reporting platforms more context around competitive performance.
Brand sentiment comes to the API. The ContentCreatorInsightsService now supports brand sentiment data. Developers can retrieve sentiment distributions and summaries for creators and brands, expanding the type of creator and brand intelligence that can be incorporated into advertising tools and reporting workflows.
Loyalty reporting expands. Advertisers can now segment reporting metrics according to the loyalty program tier of users who converted. Google is also introducing loyalty membership as a primary dimension for conversion value rules, making it possible to identify when a loyalty membership condition was satisfied as part of a conversion value rule.
Original conversion values. A new original_conversion_value metric provides the value of a biddable conversion before conversion value rules or customer lifecycle goal adjustments are applied.
That could be particularly useful for advertisers and measurement platforms trying to understand how much Google’s value adjustments are changing the numbers ultimately used for reporting and optimization.
Reach planning. Google is adding parental status targeting to ReachPlanService, allowing it to be used within targeting for reach forecasting and plannable product discovery.
Why we care. The standout additions are around measurement and visibility. Lift study data, category-specific benchmarks and original conversion values give advertisers and third-party tools more ways to understand performance beyond standard campaign metrics. The ability to see conversion values before Google’s adjustments is particularly useful for teams that want to distinguish the underlying conversion data from values subsequently modified by rules or lifecycle goals.
What developers need to do. Google says v25.1 is a drop-in upgrade for v25. Developers using Google’s client libraries will need to download the latest versions before they can access the new capabilities.
Google has also scheduled a live walkthrough of the release for Aug. 20 at 10 a.m. ET, with the session available through its developer community and YouTube.
Bottom line. Google Ads API v25.1 is largely a measurement and insights release, giving developers deeper access to lift studies, competitive benchmarks, brand sentiment, loyalty performance and the underlying conversion values behind Google’s optimization adjustments.

Google Ads API v25.1 adds new tools for lift measurement, benchmarking, brand sentiment, and loyalty reporting.

Google Ads API v25.1 expands measurement and insights

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Microsoft is rolling out AI Max globally, bringing AI targeting, creative and landing page optimization to Search campaigns.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Ads-Ai-Max-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Microsoft Advertising has begun rolling out AI Max globally, bringing a new suite of AI-powered targeting, creative and landing page capabilities to Search campaigns.</p>



<p class="wp-block-paragraph">Microsoft Advertising Product Liaison Navah Hopkins said the rollout combines broader search matching, automated creative generation and dynamic landing page selection, while keeping AI Max <strong>opt-in</strong> for new and existing campaigns.</p>



<p class="wp-block-paragraph"><strong>What’s happening.</strong> AI Max is starting to roll out across all Microsoft Advertising accounts globally. The suite includes three core features: Search term matching, Text customization and Final URL expansion.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="800" height="538" src="https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Adv-AI-Max.jpeg" alt="" class="wp-image-485531" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Adv-AI-Max.jpeg 800w, https://searchengineland.com/wp-content/seloads/2026/08/Microsoft-Adv-AI-Max-768x516.jpeg 768w" sizes="(max-width: 800px) 100vw, 800px" /></figure>



<p class="wp-block-paragraph">Microsoft is positioning the features as complementary, encouraging advertisers to test all three together while using brand controls, URL rules and ad group-level settings to manage how the automation operates.</p>



<p class="wp-block-paragraph"><strong>Search term matching.</strong> The feature expands campaign reach beyond an advertiser’s existing keyword list by using keywords, ads, landing pages, user intent and contextual signals to identify additional relevant searches.</p>



<p class="wp-block-paragraph">Microsoft says this could be particularly useful for reaching more complex, conversational queries, including searches happening through AI-powered experiences on Bing and Copilot.</p>



<p class="wp-block-paragraph"><strong>Text customization.</strong> Microsoft AI can use an advertiser’s existing creative assets and website content to generate additional messaging variations.</p>



<p class="wp-block-paragraph">The system then selects what it considers the most appropriate creative combinations at auction time, with the aim of making ads more relevant to individual searches and AI-native experiences.</p>



<p class="wp-block-paragraph"><strong>Final URL expansion.</strong> Instead of directing every user to a predetermined landing page, AI Max can select a page from the advertiser’s website that it determines is more closely aligned with the user&#8217;s intent.</p>



<p class="wp-block-paragraph">Microsoft says this is designed to create greater consistency between the search query, ad creative and the page users ultimately visit.</p>



<p class="wp-block-paragraph"><strong>Advertiser controls.</strong> Microsoft is emphasizing that automation doesn&#8217;t mean advertisers have to surrender all control. AI Max launches with brand controls, including term exclusions for text asset generation, as well as reporting, URL rules and ad group-level settings.</p>



<p class="wp-block-paragraph">Advertisers can also use optimization experiments to test the entire AI Max suite or individual features before applying them more broadly.</p>



<p class="wp-block-paragraph"><strong>Existing automation moves into AI Max.</strong> Advertisers already using autogenerated text assets for Search campaigns or Predictive matching will see those capabilities moved under the AI Max umbrella.</p>



<p class="wp-block-paragraph">Those corresponding AI Max settings will be switched on in affected campaigns. However, Microsoft says it won&#8217;t automatically activate the other AI Max features, and AI Max will remain an opt-in setting for new and existing campaigns.</p>



<p class="wp-block-paragraph"><strong>What about Google Ads imports?</strong> AI Max settings will also carry across when advertisers import eligible Search campaigns into Microsoft Advertising.</p>



<p class="wp-block-paragraph">If an imported Search campaign already has AI Max features enabled, Microsoft will preserve those corresponding settings. There is one notable exception: AI Max Search campaigns that originated from upgraded Dynamic Search Ads will be converted back into DSA campaigns on Microsoft Advertising while the company develops additional functionality.</p>



<p class="wp-block-paragraph"><strong>Why we care.</strong> Microsoft is bringing more of the automation increasingly shaping paid search into a single Search campaign framework, while notably keeping AI Max optional and providing controls from launch. For advertisers already managing AI Max campaigns in Google Ads, the ability to carry settings across through imports could also reduce duplicated setup work, although differences between the two platforms mean imported campaigns will still need monitoring.</p>



<p class="wp-block-paragraph"><strong>Bottom line.</strong> <a href="https://www.linkedin.com/posts/navahhopkins_ai-max-is-here-microsoft-advertising-has-share-7495901209418088449-W0t-/">AI Max marks Microsoft&#8217;s latest push</a> to make Search campaigns work across both traditional search and emerging AI experiences, with broader query matching, generated creative and dynamic landing pages now rolling out globally.</p>


<a href="https://www.semrush.com/analytics/traffic/paid-search?utm_campaign=ic_sel_0102ppc&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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Microsoft Advertising has begun rolling out AI Max globally, bringing a new suite of AI-powered targeting, creative and landing page capabilities to Search campaigns.
Microsoft Advertising Product Liaison Navah Hopkins said the rollout combines broader search matching, automated creative generation and dynamic landing page selection, while keeping AI Max opt-in for new and existing campaigns.
What’s happening. AI Max is starting to roll out across all Microsoft Advertising accounts globally. The suite includes three core features: Search term matching, Text customization and Final URL expansion.




Microsoft is positioning the features as complementary, encouraging advertisers to test all three together while using brand controls, URL rules and ad group-level settings to manage how the automation operates.
Search term matching. The feature expands campaign reach beyond an advertiser’s existing keyword list by using keywords, ads, landing pages, user intent and contextual signals to identify additional relevant searches.
Microsoft says this could be particularly useful for reaching more complex, conversational queries, including searches happening through AI-powered experiences on Bing and Copilot.
Text customization. Microsoft AI can use an advertiser’s existing creative assets and website content to generate additional messaging variations.
The system then selects what it considers the most appropriate creative combinations at auction time, with the aim of making ads more relevant to individual searches and AI-native experiences.
Final URL expansion. Instead of directing every user to a predetermined landing page, AI Max can select a page from the advertiser’s website that it determines is more closely aligned with the user’s intent.
Microsoft says this is designed to create greater consistency between the search query, ad creative and the page users ultimately visit.
Advertiser controls. Microsoft is emphasizing that automation doesn’t mean advertisers have to surrender all control. AI Max launches with brand controls, including term exclusions for text asset generation, as well as reporting, URL rules and ad group-level settings.
Advertisers can also use optimization experiments to test the entire AI Max suite or individual features before applying them more broadly.
Existing automation moves into AI Max. Advertisers already using autogenerated text assets for Search campaigns or Predictive matching will see those capabilities moved under the AI Max umbrella.
Those corresponding AI Max settings will be switched on in affected campaigns. However, Microsoft says it won’t automatically activate the other AI Max features, and AI Max will remain an opt-in setting for new and existing campaigns.
What about Google Ads imports? AI Max settings will also carry across when advertisers import eligible Search campaigns into Microsoft Advertising.
If an imported Search campaign already has AI Max features enabled, Microsoft will preserve those corresponding settings. There is one notable exception: AI Max Search campaigns that originated from upgraded Dynamic Search Ads will be converted back into DSA campaigns on Microsoft Advertising while the company develops additional functionality.
Why we care. Microsoft is bringing more of the automation increasingly shaping paid search into a single Search campaign framework, while notably keeping AI Max optional and providing controls from launch. For advertisers already managing AI Max campaigns in Google Ads, the ability to carry settings across through imports could also reduce duplicated setup work, although differences between the two platforms mean imported campaigns will still need monitoring.
Bottom line. AI Max marks Microsoft’s latest push to make Search campaigns work across both traditional search and emerging AI experiences, with broader query matching, generated creative and dynamic landing pages now rolling out globally.
a customer you lose.
      
See who’s stealing your traffic

Microsoft is rolling out AI Max globally, bringing AI targeting, creative and landing page optimization to Search campaigns.

Microsoft Advertising rolls out AI Max globally for Search campaigns

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations.png" class="attachment-large size-large wp-post-image" alt="Google local AI search citations" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>A study of 14,472 AI citations found business websites led Gemini's local results, but repeated searches rarely returned the same sources.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations.png" class="attachment-large size-large wp-post-image" alt="Google local AI search citations" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/google-local-ai-search-citations-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Business websites accounted for nearly 60% of Gemini&#8217;s citations in local AI search, according to a new study. Steady Demand&#8217;s analysis of 14,472 citations from 1,487 local search queries across 50 U.S. metro areas also found that identical searches often produced different AI recommendations.</p>



<p class="wp-block-paragraph"><strong>Business websites beat directories. </strong>Nearly 60% of Gemini&#8217;s citations pointed directly to businesses&#8217; own websites. That was more than the combined total for directories, review platforms, and forums.</p>



<ul class="wp-block-list">
<li>Reddit ranked second, accounting for 13.7% of citations. It outperformed the entire local-service directory category, including Angi, Thumbtack, and HomeAdvisor. </li>
</ul>



<p class="wp-block-paragraph"><strong>AI answers weren&#8217;t repeatable</strong>. The report&#8217;s biggest finding was what Steady Demand co-founder Ben Fisher called &#8220;Grounding Drift.&#8221;</p>



<ul class="wp-block-list">
<li>Repeating the same Gemini query produced overlapping cited sources only about 40% of the time. </li>



<li>In a control test, Google&#8217;s local pack returned the same top listing about 90% of the time. </li>



<li>Gemini recommended the same top business only about 7% of the time.</li>



<li>Fisher said a single AI search should be treated as a snapshot, not a reliable measure of long-term visibility.</li>
</ul>



<p class="wp-block-paragraph"><strong>ChatGPT cited different sources</strong>. The same 1,487 local search queries were also run through ChatGPT: </p>



<ul class="wp-block-list">
<li>Both AI platforms cited many of the same domains only 8% of the time. They recommended the same top business just 4.2% of the time. </li>



<li>Gemini mainly cited business websites, while ChatGPT relied much more on Reddit and business directories.</li>
</ul>



<p class="wp-block-paragraph"><strong>Why we care</strong>. Never assume success in one AI engine will carry over to another. Gemini and ChatGPT rarely cited the same sources or recommended the same businesses. Monitoring only one platform could give you a misleading picture of your AI visibility.</p>



<p class="wp-block-paragraph"><strong>About the data</strong>. <a href="https://www.steadydemand.com/ai-citation-ledger/" target="_blank" rel="noopener">Steady Demand analyzed</a> 14,472 citations from 1,487 Gemini local search queries across 50 of the largest U.S. metro areas and 10 service categories. The researchers also repeated the same queries in ChatGPT to compare citation patterns across AI platforms.</p>


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Business websites accounted for nearly 60% of Gemini’s citations in local AI search, according to a new study. Steady Demand’s analysis of 14,472 citations from 1,487 local search queries across 50 U.S. metro areas also found that identical searches often produced different AI recommendations.
Business websites beat directories. Nearly 60% of Gemini’s citations pointed directly to businesses’ own websites. That was more than the combined total for directories, review platforms, and forums.
Reddit ranked second, accounting for 13.7% of citations. It outperformed the entire local-service directory category, including Angi, Thumbtack, and HomeAdvisor. 
AI answers weren’t repeatable. The report’s biggest finding was what Steady Demand co-founder Ben Fisher called “Grounding Drift.”
Repeating the same Gemini query produced overlapping cited sources only about 40% of the time. 
In a control test, Google’s local pack returned the same top listing about 90% of the time. 
Gemini recommended the same top business only about 7% of the time.
Fisher said a single AI search should be treated as a snapshot, not a reliable measure of long-term visibility.
ChatGPT cited different sources. The same 1,487 local search queries were also run through ChatGPT: 
Both AI platforms cited many of the same domains only 8% of the time. They recommended the same top business just 4.2% of the time. 
Gemini mainly cited business websites, while ChatGPT relied much more on Reddit and business directories.
Why we care. Never assume success in one AI engine will carry over to another. Gemini and ChatGPT rarely cited the same sources or recommended the same businesses. Monitoring only one platform could give you a misleading picture of your AI visibility.
About the data. Steady Demand analyzed 14,472 citations from 1,487 Gemini local search queries across 50 of the largest U.S. metro areas and 10 service categories. The researchers also repeated the same queries in ChatGPT to compare citation patterns across AI platforms.
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A study of 14,472 AI citations found business websites led Gemini's local results, but repeated searches rarely returned the same sources.

Business websites dominate Gemini local AI search citations: Study

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Enhanced Matching could help advertisers expand the reach of their first-party audiences using consented publisher signals.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match.png 1672w, https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/enhanced-customer-match-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google Ads appears to be giving advertisers another way to expand the reach of their first-party customer audiences by matching consented users across participating publishers.</p>



<p class="wp-block-paragraph"><strong>What&#8217;s happening</strong>. A new Enhanced matching option is appearing within Customer Match settings in some Google Ads accounts.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="800" height="272" src="https://searchengineland.com/wp-content/seloads/2026/08/Google-enhanced-match.jpeg" alt="" class="wp-image-485491" srcset="https://searchengineland.com/wp-content/seloads/2026/08/Google-enhanced-match.jpeg 800w, https://searchengineland.com/wp-content/seloads/2026/08/Google-enhanced-match-768x261.jpeg 768w" sizes="(max-width: 800px) 100vw, 800px" /></figure>



<p class="wp-block-paragraph">When enabled, Google says it can use connected customer lists to expand audience reach by matching consented users with consented users from publishers, where available.</p>



<p class="wp-block-paragraph">The setting appears under Account settings alongside Google&#8217;s existing Customer Match controls.</p>



<p class="wp-block-paragraph"><strong>The details</strong>. Enhanced matching joins several options advertisers can already use with Customer Match data, including:</p>



<ul class="wp-block-list">
<li>Smart Bidding.</li>



<li>Optimized Targeting.</li>



<li>Conversion-based customer lists.</li>
</ul>



<p class="wp-block-paragraph">The new option appears to be opt-in, with the setting shown unchecked in the interface spotted so far.</p>



<p class="wp-block-paragraph"><strong>Why we care</strong>. Customer Match traditionally lets advertisers use their own first-party customer data to reconnect with known audiences across Google properties. Enhanced matching could extend the usefulness of those lists beyond Google&#8217;s existing matching capabilities by incorporating consented publisher signals.</p>



<p class="wp-block-paragraph">That potentially gives advertisers greater audience reach from the same first-party data without requiring them to build additional customer lists.</p>



<p class="wp-block-paragraph"><strong>Between the lines</strong>. The language around consent is important. Google specifically describes the feature as matching consented users with other consented users from publishers, suggesting the expansion is designed to work within Google&#8217;s evolving privacy and data-sharing framework.</p>



<p class="wp-block-paragraph">It also reflects a broader shift toward advertising systems that combine advertiser first-party data with privacy-conscious signals from platforms and publishers.</p>



<p class="wp-block-paragraph"><strong>Yes, but</strong>. Google doesn&#8217;t appear to have publicly detailed exactly how much incremental reach Enhanced matching can provide, which publishers participate, or how the matching process differs from existing Customer Match functionality.</p>



<p class="wp-block-paragraph">Availability also appears limited for now, suggesting the feature may still be rolling out or undergoing testing.</p>



<p class="wp-block-paragraph"><strong>The bottom line</strong>. Enhanced matching could make Customer Match audiences more scalable by connecting advertisers&#8217; consented first-party data with additional consented publisher signals — potentially extending reach while keeping Customer Match at the center of the targeting strategy.</p>



<p class="wp-block-paragraph"><strong>First spotted</strong>. The new setting was noticed by Joey Bidner who shared the screenshot on <a href="https://www.linkedin.com/feed/update/urn:li:activity:7495132274326216705/">LinkedIn</a>. </p>


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Google Ads appears to be giving advertisers another way to expand the reach of their first-party customer audiences by matching consented users across participating publishers.
What’s happening. A new Enhanced matching option is appearing within Customer Match settings in some Google Ads accounts.




When enabled, Google says it can use connected customer lists to expand audience reach by matching consented users with consented users from publishers, where available.
The setting appears under Account settings alongside Google’s existing Customer Match controls.
The details. Enhanced matching joins several options advertisers can already use with Customer Match data, including:
Smart Bidding.
Optimized Targeting.
Conversion-based customer lists.
The new option appears to be opt-in, with the setting shown unchecked in the interface spotted so far.
Why we care. Customer Match traditionally lets advertisers use their own first-party customer data to reconnect with known audiences across Google properties. Enhanced matching could extend the usefulness of those lists beyond Google’s existing matching capabilities by incorporating consented publisher signals.
That potentially gives advertisers greater audience reach from the same first-party data without requiring them to build additional customer lists.
Between the lines. The language around consent is important. Google specifically describes the feature as matching consented users with other consented users from publishers, suggesting the expansion is designed to work within Google’s evolving privacy and data-sharing framework.
It also reflects a broader shift toward advertising systems that combine advertiser first-party data with privacy-conscious signals from platforms and publishers.
Yes, but. Google doesn’t appear to have publicly detailed exactly how much incremental reach Enhanced matching can provide, which publishers participate, or how the matching process differs from existing Customer Match functionality.
Availability also appears limited for now, suggesting the feature may still be rolling out or undergoing testing.
The bottom line. Enhanced matching could make Customer Match audiences more scalable by connecting advertisers’ consented first-party data with additional consented publisher signals — potentially extending reach while keeping Customer Match at the center of the targeting strategy.
First spotted. The new setting was noticed by Joey Bidner who shared the screenshot on LinkedIn. 
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Enhanced Matching could help advertisers expand the reach of their first-party audiences using consented publisher signals.

Google Ads tests Enhanced Matching for Customer Match

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/08/JavaScript-links-can-make-your-pages-invisible-to-AI-search.png" class="attachment-large size-large wp-post-image" alt="JavaScript links can make your pages invisible to AI search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/08/JavaScript-links-can-make-your-pages-invisible-to-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/08/JavaScript-links-can-make-your-pages-invisible-to-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/08/JavaScript-links-can-make-your-pages-invisible-to-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/08/JavaScript-links-can-make-your-pages-invisible-to-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>A 41-day experiment reveals how JavaScript-only navigation limits AI crawler discovery and why fixing it later can be harder.

“Google renders JavaScript” has been the standard answer to client-side rendering concerns for years, and it got easier to believe in 2019, when Googlebot went evergreen, continuously running on the latest version of Chromium.
The increasing flow of search traffic through AI assistants renders that wisdom incomplete because the crawlers for AI assistants behave nothing like Googlebot.
To get numbers rather than assumptions about what that means for JavaScript links, I ran a controlled experiment. I set up one site with half its internal links hard-coded in HTML and the other half injected by JavaScript (JS). I added a logging layer that recorded every bot request for 41 days.
The results were more lopsided than I expected. 
Google’s crawler stack was the only one that executed JavaScript and followed the injected links. 
Almost none of that work came from Googlebot, the crawler that actually builds the search index. 
Googlebot reached only 2% of the JavaScript-linked pages. 
Meanwhile, GPTBot, ClaudeBot, Bingbot, PerplexityBot, Meta’s and Amazon’s crawlers, and every other bot I tracked found exactly zero.
How the experiment worked
The test site is a Brazilian business classification directory with roughly 2,400 pages, a project I own and operate. The experiment ran on the site’s hierarchy: 21 top-level sections branching into divisions, groups, and classes, 1,062 pages in total. The remaining pages are flat leaf pages that sat outside the test.
I split the 21 sections into two groups, alternating to avoid clustering bias:
HTML group (11 sections): Every child link was hard-coded in the raw HTML so that any bot can parse and follow them.
JS group (10 sections): Every child link, at every level of the hierarchy, was injected by JavaScript after page load. The raw HTML contained an empty div, and a bot that doesn’t execute JavaScript would see no links at all.
To keep the test clean, I removed every alternative discovery path:
Sitemaps: Disabled so that all sitemap URLs returned 404.
Breadcrumbs: Removed so deep pages couldn’t leak links back up the hierarchy.
Internal hierarchy panels: Removed for the same reason.
The only way to reach a division page in the JS group was to render JavaScript on its parent section page.
Site hierarchy split into 11 HTML-linked sections and 10 JavaScript-linked sections, with all discovery paths below JS sections requiring JavaScript execution.
The site’s 21 sections alternated between hard-coded HTML links and JavaScript-injected links. Any crawl below the signal line in the JS group proves JavaScript execution.



Middleware logged every bot request to a database, including details on path, user agent, referrer, IP, and timestamp. Googlebot and Bingbot IPs were verified via reverse DNS to filter out spoofed user agents. Each request was tagged as belonging to the HTML group or the JS group based on its URL path.
One measurement detail matters. Section pages for both groups were always linked from the index in plain HTML. A bot hitting a JS-group section page proves nothing. 
The experiment signal starts one level deeper: A hit on a division page in the JS group is only possible if the bot rendered the parent page’s JavaScript and followed the injected link.
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Phase 1: Crawlers arrive as the test begins
Crawlers arrived fast and crawled hard for the first 72 hours.
GPTBot swept the site within hours of deployment and crawled all 759 pages of the HTML group on day one, all the way down to class level. In the JS group, it hit all 10 section pages and stopped. It never reached a single division page.
ClaudeBot followed the same pattern a day later: deep, systematic HTML crawling and a hard stop at the JavaScript boundary.
Google’s two crawlers behaved differently from each other. GoogleOther reached its first JS-injected division page 54 minutes after launch. Googlebot took 9.9 hours. Both worked progressively deeper into the JS hierarchy over the following days, which is only possible if they rendered the parent pages and followed the injected links. Google documents rendering for Googlebot. For GoogleOther, this is an observation from my logs, not documented behavior.
The crawl burst lasted three days. On day four, discovery dropped about 90% for both groups, and the coverage numbers froze.
Daily page discovery by crawler showing GoogleOther’s three-day burst, GPTBot’s single-day sweep, and Bingbot starting on day 10.
New unique pages discovered per day. GoogleOther’s burst lasted three days. GPTBot and ClaudeBot swept the HTML group in a single day each. Bingbot’s crawl burst began around day 10. Googlebot isn’t plotted because its volume, 42 pages across both groups, doesn’t register at this scale.



Only Google executed the JavaScript
The full scorecard at the end of Phase 1, after 27 days, displays counts covering hierarchy pages only (division level and deeper), where a JS-group hit is unambiguous proof of JavaScript execution. 
One sizing note: The HTML sections hold 748 hierarchy pages and the JS sections 293, so for Google, the table also shows coverage as a share of each group:

CrawlerRenders JS?HTML hierarchy pagesJS hierarchy pages

GooglebotYes35 (5%)7 (2%)
GoogleOtherYes495 (66%)142 (48%)
GPTBot (OpenAI)No7480
ClaudeBot (Anthropic)No7480
BingbotNo2823
OAI-SearchBot (ChatGPT search)No100
Meta-ExternalAgent (Meta AI)No5370
Amazonbot (Amazon)No7350
ChatGPT-User (live browsing)No460
PerplexityBotNo00






Even Google barely touched the JavaScript pages
The two Google rows are doing very different jobs, and the difference matters more than the raw counts. Google’s crawler stack, GoogleOther, passed the rendering test. Googlebot, the crawler that feeds Search, mostly sat it out. 
This means the 66% and 48% on the GoogleOther row say nothing about whether those pages can appear in Google. The row that speaks to search visibility is Googlebot: 5% of the HTML group and 2% of the JS group.
Coverage varied by agent



The two groups differ in size, so the fair comparison is the share of each group’s own pages that got crawled.
Googlebot reached 5% of the 748 HTML-linked hierarchy pages and 2% of the 293 JS-linked ones. Six of the 10 JS sections got no Googlebot hierarchy crawl at all in 27 days. GoogleOther is where the volume was: 66% of the HTML group and 48% of the JS group, with its JS coverage falling by depth, from 67% of divisions to 42% of classes. 
Those are useful numbers for understanding how rendering behaves, and they aren’t search visibility. For Search, the relevant figure is 2%.
One caveat on that gap. GoogleOther out-crawled Googlebot roughly 14:1 on HTML pages here. I read that as a crawl-budget artifact of a brand-new domain with no external links, not as evidence that GoogleOther is the more thorough crawler. On an established site, I would expect Googlebot’s share to be much higher.
What the split does show is that a site owner watching raw crawl logs can badly overestimate their search coverage if they count every Google user agent as one crawler.
JavaScript was discovered later than HTML 



GoogleOther accounts for nearly all of Google’s crawling here, so its timings are the only ones with enough volume to measure. Its JS-linked pages were discovered 18% to 27% later than its HTML-linked pages at every depth. 
The lag compounds: Google must fetch the parent, queue it for rendering, execute the script, extract the URLs, and then schedule the children. HTML links skip the middle steps.
Distribution was uneven



GoogleOther’s JS coverage wasn’t spread thin evenly. One JS section got 52 pages crawled while two others sat at one page each for weeks. Rendering appears to be a queue with its own budget, and some branches never came up.
Coverage of each link group over time for both Google crawlers, with GoogleOther flattening at 66% for HTML-linked pages and 48% for JavaScript-linked pages while Googlebot stays near the floor at 5% and 2%.
Share of each group’s pages discovered, by day, split by crawler. The two GoogleOther lines locked in during the initial burst and stayed flat. Googlebot, the indexing crawler, never left the bottom of the chart.



One result ran against my expectations: Once GoogleOther discovered a JS-linked page, it recrawled it as fast as comparable HTML pages, and at two of the three depth levels 24% to 27% faster. The JavaScript delay is entirely front-loaded in discovery. 
Once a page gets past that stage, it’s treated normally, sometimes even better than normal. The pages that never get past it are the ones that hurt. They aren’t ranking slowly. They just aren’t there at all.
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All other crawlers ignored the JavaScript group
My middleware initially lumped Meta-ExternalAgent and Amazonbot into a generic “unknown bot” bucket, but splitting the logs by user agent revealed them as two of the heaviest crawlers on the site, with roughly 2,000 hits each. 



Meta-ExternalAgent is the crawler Meta uses to gather training data for its AI models. Amazonbot feeds Amazon’s search and AI products. Both crawled the HTML group deeply, hit the 10 JS-group section pages, and never went one level further.
HTML versus JavaScript page discovery per crawler, with only Googlebot and GoogleOther showing any JavaScript-linked pages.
Unique hierarchy pages discovered per crawler during Phase 1, with Googlebot and GoogleOther counted separately. Only Google’s two crawlers registered any JavaScript-linked coverage, and almost all of it belongs to GoogleOther, which doesn’t build the search index.



The zeros held for the entire phase. Bingbot was active on 20 of the 27 days of Phase 1, recrawled the 10 JS-group section pages on 18 of those days, and never followed one injected link.
GPTBot crawled the entire HTML group and 0% of the JS hierarchy.
PerplexityBot stayed on the homepage and robots.txt for all of Phase 1. It reached its first hierarchy page on day 40, well after the recovery portion of the test began.
Something odd happened with Bingbot
Bingbot did reach three JS-group hierarchy pages on days 26 and 27, after three weeks of daily recrawls that never went past the section level. However, the crawl pattern points to discovery through another path rather than JavaScript execution, since Bingbot’s coverage of those sections never expanded beyond those isolated URLs.
Microsoft has documented an evergreen, Edge-based Bingbot that can render JavaScript since 2019, but across 20 days of active crawling on this site, it never followed a single injected link, so whatever rendering capacity exists wasn’t applied here.
User requests from AI could not reach JavaScript links
ChatGPT-User deserves attention because it represents real user demand. This is the agent that fetches pages when someone asks ChatGPT a question.
During the experiment, real user queries pulled it to dozens of HTML-linked pages. Pages behind JavaScript links were unreachable for those same queries. So the pages a real person was actively trying to reach were exactly the ones it couldn’t get to.
Crawl logs don’t reveal indexing
Everything above is discovery, not indexing. A page can be crawled and still not make it into the index, and indexing determines whether the page shows up in Search at all. 
Server logs are the right tool for proving what a crawler could reach, which is the question this experiment was built to answer, but they’re the wrong tool for proving visibility. Search Console is the arbiter there.
Phase 2: Not all crawlers passed the recovery test
After 27 days, I converted all JavaScript links to hard-coded HTML and watched what happened. Nothing about the pages themselves changed here. The URLs, the content, and the server were identical to Phase 1. The one thing I touched was the links, which now sat in the HTML instead of being injected by JavaScript.
Some crawlers were quick to catch up
GPTBot responded within two days. It swept every formerly blocked section in a single sweep lasting under three hours and picked up 250 pages it had never crawled before, against zero in 27 days of Phase 1.
Bingbot followed with a systematic sweep, adding 212 pages across the same sections it had ignored for weeks, reaching class level in sections where it had never gone past the entrance.
Meta-ExternalAgent joined by day 41 with a sweep of its own.
Others were not
Amazonbot had finished its crawl and left the site on day 21, six days before the conversion, so its JS-group coverage ended at zero.
ClaudeBot stayed at zero in a different way: It kept recrawling the site after the fix, hundreds of hits through day 41, but only on pages it already knew. It never picked up a single newly linked page. Whatever internal map ClaudeBot drew on its first pass, it seemed to keep working from that same map for weeks, even as it carried on hitting the site.
Cumulative page discovery before and after the conversion to HTML links after 27 days, showing immediate recovery by GPTBot and Bingbot, a flat GoogleOther line, and a Googlebot line at the floor.
Cumulative hierarchy pages found in the 10 JS-group sections, with the Google crawlers shown separately. After the HTML conversion, GPTBot swept 250 pages in one pass, and Bingbot and Meta-ExternalAgent climbed steadily, while both Google lines stayed flat. Amazonbot left before the fix. ClaudeBot returned only to recrawl pages it already knew.



The slowest responder, ironically, was Google. Its aggressive first-visit budget was already spent. By day 41, Googlebot had visited exactly one page in the converted sections, and GoogleOther had visited none. 
Fixing link architecture after launch recovered AI crawler coverage quickly, but with Google, that first crawl of a brand-new site looks a lot harder to win back a second time.
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What I’d tell a client
Iceberg-style diagram showing every HTML-linked page found by at least one crawler at every depth, against GoogleOther coverage of JavaScript-linked pages falling from 67% to 42% as depth increases and Googlebot coverage at 5% or below.
The whole experiment in one picture. Above the waterline, every crawler saw everything. Below it, every HTML-linked page was found by at least one crawler, while the JavaScript-linked side was reached only by Google, and mostly by GoogleOther rather than by the crawler that builds the index.



This is one site, one niche, one 41-day window, and a fresh domain with no external links pointing at the buried pages. Established sites with strong signals may see Googlebot invest more in rendering. 
The non-Google results leave less room for interpretation: Eight crawlers failed to execute the JavaScript and follow the injected links across every crawl burst and recrawl cycle.
My takeaways:
Audit what your raw HTML actually contains. Disable JavaScript and click through your critical paths, or compare the raw source against the rendered DOM. If navigation to revenue pages only exists after hydration, every AI crawler is blind to it.
Treat “Google renders JS” as the exception, and a partial one. Rendering happened, but the crawler that feeds Search reached 2% of my JS-linked pages. Check which Google user agent your logs are actually showing you before you count that coverage as visibility.
Prioritize server-side rendering for links above content. A widget that renders in JavaScript just makes a page slightly worse. Navigation that renders in JavaScript is a different problem because it can drop whole sections of your site out of every index that feeds an AI assistant.
Fix it sooner rather than later. GPTBot re-swept within 48 hours of my Phase 2 conversion, and Bingbot within a week. Google took weeks and still hadn’t returned to the converted sections by day 41.
The rendering question used to be about whether Google could see your content. With AI assistants becoming a real referral channel and the web reshaping itself around agents, the question is now whether other crawlers can. If your links live in JavaScript, they can’t.

A 41-day experiment reveals how JavaScript-only navigation limits AI crawler discovery and why fixing it later can be harder.

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