<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/AI-search-is-growing-but-SEO-fundamentals-still-drive-most-traffic.jpg" class="attachment-large size-large wp-post-image" alt="AI search is growing, but SEO fundamentals still drive most traffic" style="margin-bottom: 15px;" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2025/12/AI-search-is-growing-but-SEO-fundamentals-still-drive-most-traffic.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/AI-search-is-growing-but-SEO-fundamentals-still-drive-most-traffic-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/AI-search-is-growing-but-SEO-fundamentals-still-drive-most-traffic-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI is changing search, but traditional SEO still drives most traffic. Real-world data shows which tactics continue to perform.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/AI-search-is-growing-but-SEO-fundamentals-still-drive-most-traffic.jpg" class="attachment-large size-large wp-post-image" alt="AI search is growing, but SEO fundamentals still drive most traffic" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/AI-search-is-growing-but-SEO-fundamentals-still-drive-most-traffic.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/AI-search-is-growing-but-SEO-fundamentals-still-drive-most-traffic-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/AI-search-is-growing-but-SEO-fundamentals-still-drive-most-traffic-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p><a href="https://searchengineland.com/ai-hasnt-killed-search-study-460931">Generative AI</a> is everywhere right now. It dominates conference agendas, fills LinkedIn feeds, and is reshaping how many businesses think about organic search.&nbsp;</p>



<p>Brands are racing to optimize for <a href="https://searchengineland.com/google-ai-overviews-everything-you-need-to-know-449399">AI Overviews</a>, build vector embeddings, map semantic clusters, and rework content models around LLMs.</p>



<p>What gets far less attention is a basic reality: for most websites, AI platforms still drive a small share of overall traffic. </p>



<p>AI search is growing, no question.&nbsp;</p>



<p>But in most cases, total referral sessions from all LLM platforms combined amount to only about 2% to 3% of the organic traffic Google alone delivers.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1128" height="438" src="https://searchengineland.com/wp-content/seloads/2025/12/AI-referral-sessions-vs-Google-organic-clicks.png" alt="AI referral sessions vs Google organic clicks" class="wp-image-466626" srcset="https://searchengineland.com/wp-content/seloads/2025/12/AI-referral-sessions-vs-Google-organic-clicks.png 1128w, https://searchengineland.com/wp-content/seloads/2025/12/AI-referral-sessions-vs-Google-organic-clicks-768x298.png 768w" sizes="(max-width: 1128px) 100vw, 1128px" /></figure></div>


<p>Despite that gap, many teams are spending more time chasing AI strategies than fixing simple, high-impact <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> fundamentals that continue to drive measurable results.&nbsp;</p>



<p>Instead of improving what matters most today, they are overinvesting in the future while underperforming in the present.</p>



<p>This article examines how a narrow focus on AI can obscure proven SEO tactics and highlights practical examples and real-world data showing how those fundamentals still move the needle today.</p>



<h2 class="wp-block-heading" id="h-1-quick-seo-wins-are-still-delivering-outsized-gains">1. Quick SEO wins are still delivering outsized gains</h2>



<p>In an era where everyone is obsessed with things like <a href="https://searchengineland.com/the-shift-to-semantic-seo-what-vectors-mean-for-your-strategy-452766">vector embeddings</a> and semantic relationships, it’s easy to forget that small updates can have a big impact.&nbsp;</p>



<p>For example, <a href="https://searchengineland.com/title-tags-seo-everything-you-need-to-know-2025-451233">title tags</a> are still one of the simplest and most effective <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> levers to pull.&nbsp;</p>



<p>And they are often one of the on-page elements that most websites get wrong, either by targeting the wrong keywords, not including variations, or targeting nothing at all.</p>



<p>Just a few weeks ago, a client saw a win by simply adding “&amp; [keyword]” to the existing title tag on their homepage. Nothing else was changed.</p>



<p>Keyword rankings shot up, as did clicks and impressions for queries containing that keyword.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="518" height="480" src="https://searchengineland.com/wp-content/seloads/2025/12/Results-Updating-existing-title-tags.png" alt="Results - Updating existing title tags" class="wp-image-466627"/></figure></div>

<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1167" height="335" src="https://searchengineland.com/wp-content/seloads/2025/12/Results-Updating-existing-title-tags-Oct-Nov-2025.png" alt="Results - Updating existing title tags Oct-Nov 2025" class="wp-image-466628" srcset="https://searchengineland.com/wp-content/seloads/2025/12/Results-Updating-existing-title-tags-Oct-Nov-2025.png 1167w, https://searchengineland.com/wp-content/seloads/2025/12/Results-Updating-existing-title-tags-Oct-Nov-2025-768x220.png 768w" sizes="(max-width: 1167px) 100vw, 1167px" /></figure></div>


<p>This was all achieved simply by changing the title tag on one page.&nbsp;</p>



<p>Couple that with other tactics, such as on-page copy edits, internal linking, and backlinking across multiple pages, and growth will continue.&nbsp;</p>



<p>It may seem basic, but it still works. </p>



<p>And if you only focus on advanced GEO strategies, you may overlook simple tactics that provide immediate, observable impact.&nbsp;</p>



<h2 class="wp-block-heading" id="h-2-content-freshness-and-authority-still-matter-for-competitive-keywords">2. Content freshness and authority still matter for competitive keywords</h2>



<p>Another tactic that has faded from view with the rise of AI is what’s often called the skyscraper technique.&nbsp;</p>



<p>It involves identifying a set of keywords and the pages that already rank for them, then publishing a materially stronger version designed to outperform the existing results.</p>



<p>It’s true that the web is saturated with content on similar topics, especially for keywords visible in most research tools. </p>



<p>But when a site has sufficient authority, a clear right to win, and content freshness, this approach can still be highly effective.</p>



<p>I’ve seen this work repeatedly.&nbsp;</p>



<p>Here’s Google Search Console data from a recent article we published for a client on a popular, long-standing topic with many competing pages already ranking.&nbsp;</p>



<p>The post climbed to No. 2 almost immediately and began generating net-new clicks and impressions.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="867" height="361" src="https://searchengineland.com/wp-content/seloads/2025/12/Results-Skyscraper-content.png" alt="Results - Skyscraper content" class="wp-image-466629" srcset="https://searchengineland.com/wp-content/seloads/2025/12/Results-Skyscraper-content.png 867w, https://searchengineland.com/wp-content/seloads/2025/12/Results-Skyscraper-content-768x320.png 768w" sizes="(max-width: 867px) 100vw, 867px" /></figure></div>


<p>Why did it work?&nbsp;</p>



<p>The site has strong authority, and much of the content ranking ahead of it was outdated and stale.</p>



<p>If you’re hesitant to publish the thousandth article on an established topic, that hesitation is understandable.&nbsp;</p>



<p>This approach won’t work for every site. But ignoring it entirely can mean passing up clear, high-confidence wins like these.</p>



<div class="nl-inline-form border py-2 px-1 my-2">
	
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<hr class="wp-block-separator has-text-color has-cyan-bluish-gray-color has-css-opacity has-cyan-bluish-gray-background-color has-background"/>



<h2 class="wp-block-heading" id="h-3-user-experience-remains-a-critical-conversion-lever">3. User experience remains a critical conversion lever</h2>



<p>Hype around AI-driven shopping experiences has led some teams to believe traditional website optimization is becoming obsolete.&nbsp;</p>



<p>There is a growing assumption that AI assistants will soon handle most interactions or that users will convert directly within AI platforms without ever reaching a website.</p>



<p>Some of that future is beginning to take shape, particularly for ecommerce brands experimenting with features like <a href="https://searchengineland.com/openai-chatgpt-instant-checkout-462727">Instant Checkout in ChatGPT</a>.&nbsp;</p>



<p>But many websites are not selling products.&nbsp;</p>



<p>And even for those that are, most brands still receive a significant volume of traffic from traditional search and continue to rely on calls to action and on-page signals to drive conversions.</p>



<p>It also makes little difference how a user arrives – via organic search, paid search, AI referrals, or direct visits.&nbsp;</p>



<p>A fast site, a strong user experience, and a clear conversion funnel remain essential.</p>



<p>There are also clear performance gains tied to optimizing these elements.&nbsp;</p>



<p>Here are the results we recently achieved for a client following a simple CTR test:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="522" height="402" src="https://searchengineland.com/wp-content/seloads/2025/12/Results-CTR-test.png" alt="Results - CTR test" class="wp-image-466630"/></figure></div>


<p>Brands that continue to invest in user experience and <a href="https://searchengineland.com/seo-tests-improve-traffic-engagement-conversions-454934">conversion rate optimization</a> will outperform those that do not.&nbsp;</p>



<p>That gap is likely to widen the longer teams wait for AI to fully replace the conversion funnel.</p>



<h2 class="wp-block-heading" id="h-ai-is-reshaping-search-but-what-works-still-matters">AI is reshaping search, but what works still matters</h2>



<p>There is no dispute that AI is reshaping the search landscape.&nbsp;</p>



<p>It’s changing user behavior, influencing SERPs, and complicating attribution models.&nbsp;</p>



<p>The bigger risk for many businesses, however, is not underestimating AI but overcorrecting for it.</p>



<p>Traditional organic search remains the primary traffic source for most websites, and SEO fundamentals still deliver when executed well.&nbsp;</p>



<ul class="wp-block-list">
<li>Quick wins are real.&nbsp;</li>



<li>Higher-quality content continues to be rewarded.&nbsp;</li>



<li>User experience optimization shows no signs of becoming irrelevant.&nbsp;</li>
</ul>



<p>These are just a few examples of tactics that remain effective today.</p>



<p>Importantly, these efforts do not operate in isolation.&nbsp;</p>



<p>Improving a website’s fundamentals can strengthen organic visibility while also supporting <a href="https://searchengineland.com/seo-paid-media-need-each-other-ai-search-461485">paid search performance</a> and <a href="https://searchengineland.com/guide/good-geo-is-good-seo">LLM visibility</a>.</p>



<p>Staying informed about AI developments and planning for what’s ahead is essential.&nbsp;</p>



<p>It should not come at the expense of the strategies that are currently driving measurable growth.</p>

Generative AI is everywhere right now. It dominates conference agendas, fills LinkedIn feeds, and is reshaping how many businesses think about organic search. 
Brands are racing to optimize for AI Overviews, build vector embeddings, map semantic clusters, and rework content models around LLMs.
What gets far less attention is a basic reality: for most websites, AI platforms still drive a small share of overall traffic. 
AI search is growing, no question. 
But in most cases, total referral sessions from all LLM platforms combined amount to only about 2% to 3% of the organic traffic Google alone delivers.

Despite that gap, many teams are spending more time chasing AI strategies than fixing simple, high-impact SEO fundamentals that continue to drive measurable results. 
Instead of improving what matters most today, they are overinvesting in the future while underperforming in the present.
This article examines how a narrow focus on AI can obscure proven SEO tactics and highlights practical examples and real-world data showing how those fundamentals still move the needle today.
1. Quick SEO wins are still delivering outsized gains
In an era where everyone is obsessed with things like vector embeddings and semantic relationships, it’s easy to forget that small updates can have a big impact. 
For example, title tags are still one of the simplest and most effective SEO levers to pull. 
And they are often one of the on-page elements that most websites get wrong, either by targeting the wrong keywords, not including variations, or targeting nothing at all.
Just a few weeks ago, a client saw a win by simply adding “& [keyword]” to the existing title tag on their homepage. Nothing else was changed.
Keyword rankings shot up, as did clicks and impressions for queries containing that keyword.


This was all achieved simply by changing the title tag on one page. 
Couple that with other tactics, such as on-page copy edits, internal linking, and backlinking across multiple pages, and growth will continue. 
It may seem basic, but it still works. 
And if you only focus on advanced GEO strategies, you may overlook simple tactics that provide immediate, observable impact. 
2. Content freshness and authority still matter for competitive keywords
Another tactic that has faded from view with the rise of AI is what’s often called the skyscraper technique. 
It involves identifying a set of keywords and the pages that already rank for them, then publishing a materially stronger version designed to outperform the existing results.
It’s true that the web is saturated with content on similar topics, especially for keywords visible in most research tools. 
But when a site has sufficient authority, a clear right to win, and content freshness, this approach can still be highly effective.
I’ve seen this work repeatedly. 
Here’s Google Search Console data from a recent article we published for a client on a popular, long-standing topic with many competing pages already ranking. 
The post climbed to No. 2 almost immediately and began generating net-new clicks and impressions.

Why did it work? 
The site has strong authority, and much of the content ranking ahead of it was outdated and stale.
If you’re hesitant to publish the thousandth article on an established topic, that hesitation is understandable. 
This approach won’t work for every site. But ignoring it entirely can mean passing up clear, high-confidence wins like these.
Get the newsletter search marketers rely on.
  
				
								
					MktoForms2.loadForm("https://app-sj02.marketo.com", "727-ZQE-044", 16298, function(form) {
						// form.onSubmit(function(){
						// });
						
						// form.onSuccess(function (values, followUpUrl) {
						// });
					});
				
			
See terms.
3. User experience remains a critical conversion lever
Hype around AI-driven shopping experiences has led some teams to believe traditional website optimization is becoming obsolete. 
There is a growing assumption that AI assistants will soon handle most interactions or that users will convert directly within AI platforms without ever reaching a website.
Some of that future is beginning to take shape, particularly for ecommerce brands experimenting with features like Instant Checkout in ChatGPT. 
But many websites are not selling products. 
And even for those that are, most brands still receive a significant volume of traffic from traditional search and continue to rely on calls to action and on-page signals to drive conversions.
It also makes little difference how a user arrives – via organic search, paid search, AI referrals, or direct visits. 
A fast site, a strong user experience, and a clear conversion funnel remain essential.
There are also clear performance gains tied to optimizing these elements. 
Here are the results we recently achieved for a client following a simple CTR test:

Brands that continue to invest in user experience and conversion rate optimization will outperform those that do not. 
That gap is likely to widen the longer teams wait for AI to fully replace the conversion funnel.
AI is reshaping search, but what works still matters
There is no dispute that AI is reshaping the search landscape. 
It’s changing user behavior, influencing SERPs, and complicating attribution models. 
The bigger risk for many businesses, however, is not underestimating AI but overcorrecting for it.
Traditional organic search remains the primary traffic source for most websites, and SEO fundamentals still deliver when executed well. 
Quick wins are real. 
Higher-quality content continues to be rewarded. 
User experience optimization shows no signs of becoming irrelevant. 
These are just a few examples of tactics that remain effective today.
Importantly, these efforts do not operate in isolation. 
Improving a website’s fundamentals can strengthen organic visibility while also supporting paid search performance and LLM visibility.
Staying informed about AI developments and planning for what’s ahead is essential. 
It should not come at the expense of the strategies that are currently driving measurable growth.

AI is changing search, but traditional SEO still drives most traffic. Real-world data shows which tactics continue to perform.

AI search is growing, but SEO fundamentals still drive most traffic

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/06/Googles-token-auction-When-LLMs-write-the-ads-in-real-time.png" class="attachment-large size-large wp-post-image" alt="Google’s token auction: When LLMs write the ads in real time" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/06/Googles-token-auction-When-LLMs-write-the-ads-in-real-time.png 1920w, https://searchengineland.com/wp-content/seloads/2025/06/Googles-token-auction-When-LLMs-write-the-ads-in-real-time-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/06/Googles-token-auction-When-LLMs-write-the-ads-in-real-time-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google's latest update gives large advertisers clearer, cross-account insight into how PMax allocates spend and performs across channels.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/06/Googles-token-auction-When-LLMs-write-the-ads-in-real-time.png" class="attachment-large size-large wp-post-image" alt="Google’s token auction: When LLMs write the ads in real time" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/06/Googles-token-auction-When-LLMs-write-the-ads-in-real-time.png 1920w, https://searchengineland.com/wp-content/seloads/2025/06/Googles-token-auction-When-LLMs-write-the-ads-in-real-time-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/06/Googles-token-auction-When-LLMs-write-the-ads-in-real-time-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google appears to be rolling out the Performance Max Channel Performance report at the MCC level, giving agencies and large advertisers a long-awaited view of channel-level performance across multiple accounts.</p>



<p><strong>What’s new:</strong> The <a href="https://searchengineland.com/google-begins-rolling-out-performance-max-channel-performance-reporting-456251">Channel Performance report</a>, previously limited to individual accounts, is now surfacing in some manager (MCC) accounts. Google had previously confirmed the feature was coming, but this marks one of the first confirmed sightings in live environments.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1280" height="1101" src="https://searchengineland.com/wp-content/seloads/2025/12/1766405667353.png" alt="" class="wp-image-466635" srcset="https://searchengineland.com/wp-content/seloads/2025/12/1766405667353.png 1280w, https://searchengineland.com/wp-content/seloads/2025/12/1766405667353-768x661.png 768w" sizes="(max-width: 1280px) 100vw, 1280px" /></figure>



<p><strong>Why we care.</strong> MCC-level visibility allows agencies to analyze how Performance Max allocates spend and drives results across channels—Search, Display, YouTube, Discover, Gmail, and Shopping—without logging into each account individually. That’s a major efficiency gain for teams managing large portfolios.</p>



<p><strong>What to watch</strong>. When and how quickly the feature becomes available across all MCCs, and whether Google expands the report with deeper metrics or export options.</p>



<p><strong>First seen.</strong> This update was first picked up by head of Ecommerce Insights at Smarter Ecommerce, Mike Ryan, who very recently published a guide on <a href="https://searchengineland.com/google-channel-performance-report-pmax-campaigns-466298">How to use Google&#8217;s Channel Performance reports</a>.</p>



<p><strong>Bottom line</strong>. MCC-level Channel Performance reporting signals another step toward making Performance Max less of a black box—especially for agencies that need cross-account insight at scale.</p>


Google appears to be rolling out the Performance Max Channel Performance report at the MCC level, giving agencies and large advertisers a long-awaited view of channel-level performance across multiple accounts.
What’s new: The Channel Performance report, previously limited to individual accounts, is now surfacing in some manager (MCC) accounts. Google had previously confirmed the feature was coming, but this marks one of the first confirmed sightings in live environments.




Why we care. MCC-level visibility allows agencies to analyze how Performance Max allocates spend and drives results across channels—Search, Display, YouTube, Discover, Gmail, and Shopping—without logging into each account individually. That’s a major efficiency gain for teams managing large portfolios.
What to watch. When and how quickly the feature becomes available across all MCCs, and whether Google expands the report with deeper metrics or export options.
First seen. This update was first picked up by head of Ecommerce Insights at Smarter Ecommerce, Mike Ryan, who very recently published a guide on How to use Google’s Channel Performance reports.
Bottom line. MCC-level Channel Performance reporting signals another step toward making Performance Max less of a black box—especially for agencies that need cross-account insight at scale.

Google's latest update gives large advertisers clearer, cross-account insight into how PMax allocates spend and performs across channels.

Google expands Performance Max channel reporting to MCCs

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/Why-Google-is-deleting-reviews-at-record-levels.png" class="attachment-large size-large wp-post-image" alt="Why Google is deleting reviews at record levels" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/Why-Google-is-deleting-reviews-at-record-levels.png 1920w, https://searchengineland.com/wp-content/seloads/2025/12/Why-Google-is-deleting-reviews-at-record-levels-768x432.png 768w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Reviews are disappearing faster as moderation systems, industry risk, and geography shape which reviews stay visible.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/Why-Google-is-deleting-reviews-at-record-levels.png" class="attachment-large size-large wp-post-image" alt="Why Google is deleting reviews at record levels" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/Why-Google-is-deleting-reviews-at-record-levels.png 1920w, https://searchengineland.com/wp-content/seloads/2025/12/Why-Google-is-deleting-reviews-at-record-levels-768x432.png 768w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>In 2025, Google is removing reviews at unprecedented rates – and it is not accidental.</p>



<p>Our <a href="https://gmbapi.com/news/google-reviews-deleted-ai-legal-takedowns/" target="_blank" rel="noopener">industry analysis of 60,000 Google Business Profiles</a> shows that deletions are being driven by a mix of:</p>



<ul class="wp-block-list">
<li>Automated moderation.</li>



<li>Industry-wide risk factors.</li>



<li>Increased enforcement against incentivized reviews.</li>



<li>Local regulatory pressure.</li>
</ul>



<p>Together, these forces have significant implications for businesses and local search visibility.</p>



<h2 class="wp-block-heading" id="h-review-deletions-are-on-the-up-globally">Review deletions are on the up globally</h2>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="512" height="256" src="https://searchengineland.com/wp-content/seloads/2025/12/Weekly-deleted-reviews-Jan-to-Jul-2025.png" alt="Weekly deleted reviews - Jan to Jul 2025" class="wp-image-466560"/></figure></div>


<p>Data collected from tens of thousands of Google Business Profile listings across multiple countries by GMBapi.com show a sharp increase in deleted reviews between January and July 2025.&nbsp;</p>



<p>The surge began accelerating toward the end of Q1 and gained momentum mid-year, with a growing share of monitored locations experiencing at least one review removal in a given week.</p>



<p>This is not limited to negative feedback.&nbsp;</p>



<p>While one-star reviews continue to be taken down, five-star reviews now account for a sizable share of deletions.&nbsp;</p>



<p>That pattern suggests Google is applying stricter enforcement, including on positive reviews, as it works to maintain authenticity and trust.&nbsp;</p>



<p>More recently, Google has begun asking members of its Local Guide community whether businesses are incentivizing reviews, likely in response to AI-driven flags for suspicious activity.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/google-deleting-five-star-reviews-449665">Google’s review deletions: Why 5-star reviews are disappearing</a></em></strong></p>



<h2 class="wp-block-heading" id="h-not-all-industries-are-treated-the-same">Not all industries are treated the same</h2>



<p>Review deletion patterns vary significantly by business category.</p>



<p>Restaurants account for the highest volume of deleted reviews, followed by home services, brick-and-mortar retail, and construction.&nbsp;</p>



<p>These categories generate large volumes of reviews, and removals occur across both recent and older submissions.&nbsp;</p>



<p>That distribution points to ongoing enforcement, not isolated cleanup efforts.</p>



<p>By contrast, medical services, beauty, and professional services see fewer deletions overall.&nbsp;</p>



<p>However, closer analysis reveals distinct and consistent patterns within those categories.</p>



<h2 class="wp-block-heading" id="h-what-review-ratings-reveal-about-industry-bias">What review ratings reveal about industry bias<br></h2>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1390" height="690" src="https://searchengineland.com/wp-content/seloads/2025/12/Top-10-meta-categories-Deleted-review-rating-mix.png" alt="Top 10 meta categories- Deleted review rating mix" class="wp-image-466555" srcset="https://searchengineland.com/wp-content/seloads/2025/12/Top-10-meta-categories-Deleted-review-rating-mix.png 1390w, https://searchengineland.com/wp-content/seloads/2025/12/Top-10-meta-categories-Deleted-review-rating-mix-768x381.png 768w" sizes="(max-width: 1390px) 100vw, 1390px" /></figure></div>


<p>Looking at deleted reviews as a share of total removals within each category reveals distinct moderation patterns.</p>



<p>In restaurants and general retail, deleted reviews are relatively evenly distributed across one- to five-star ratings.&nbsp;</p>



<p>By contrast, medical services and home services show a strong skew toward five-star review deletions, with far fewer removals in the middle of the rating spectrum.&nbsp;</p>



<p>That imbalance suggests positive reviews in higher-risk or regulated categories face closer scrutiny, likely tied to concerns around trust, safety, and compliance.</p>



<p>These differences do not appear to stem from manual, category-specific policy decisions.&nbsp;</p>



<p>Instead, they reflect how Google’s automated systems adjust enforcement based on perceived industry risk.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/google-reviews-keywords-rich-local-seo-wins-464418">7 local SEO wins you get from keyword-rich Google reviews</a></em></strong></p>



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<h2 class="wp-block-heading" id="h-timing-matters-early-vs-retroactive-deletions">Timing matters: Early vs. retroactive deletions</h2>



<p>The age of a review plays a significant role in when it is removed.</p>



<p>In medical and home services, a large share of deleted reviews disappear within the first six months after posting.&nbsp;</p>



<p>That timing points to early intervention by automated systems evaluating language, reviewer behavior, and other risk signals.</p>



<p>Restaurants and brick-and-mortar retail show a different pattern.&nbsp;</p>



<p>Many deleted reviews in these categories are more than two years old, suggesting retroactive enforcement as detection systems improve or new suspicious patterns emerge.&nbsp;</p>



<p>It may also reflect efforts to refresh older review profiles.</p>



<p>For businesses, this means reviews can disappear long after they are posted, often without warning.</p>



<h2 class="wp-block-heading" id="h-geography-adds-further-complexity">Geography adds further complexity</h2>



<p>Industry alone does not tell the full story. Location matters.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1390" height="690" src="https://searchengineland.com/wp-content/seloads/2025/12/Top-10-meta-categories-by-deleted-reviews-stacked-by-rating.png" alt="Top 10 meta categories by deleted reviews (stacked by rating)" class="wp-image-466561" srcset="https://searchengineland.com/wp-content/seloads/2025/12/Top-10-meta-categories-by-deleted-reviews-stacked-by-rating.png 1390w, https://searchengineland.com/wp-content/seloads/2025/12/Top-10-meta-categories-by-deleted-reviews-stacked-by-rating-768x381.png 768w" sizes="(max-width: 1390px) 100vw, 1390px" /></figure></div>


<p>In English-speaking markets such as the U.S., UK, Canada, and Australia, deleted reviews skew heavily toward five-star ratings.&nbsp;</p>



<p>That trend aligns with increased AI-driven moderation aimed at reducing review spam and incentivized positive feedback.</p>



<p>Germany stands apart.&nbsp;</p>



<p>Analysis of thousands of German business listings shows a higher share of deleted reviews are low-rated, and most are removed within weeks of posting.&nbsp;</p>



<p>This pattern aligns with Germany’s strict defamation laws, which permit businesses to legally challenge negative reviews and require platforms to take prompt action upon notification.</p>



<p>In short:</p>



<ul class="wp-block-list">
<li>AI-driven enforcement dominates in many English-speaking markets.</li>



<li>Legal takedowns play a much larger role in Germany.</li>
</ul>



<h2 class="wp-block-heading" id="h-what-this-means-for-local-seo-and-small-business-owners">What this means for local SEO and small business owners</h2>



<p>The rise in review deletions creates two primary challenges.</p>



<ul class="wp-block-list">
<li><strong>Trust erosion:</strong> When legitimate reviews, whether positive or negative, disappear without explanation, confidence in review platforms begins to weaken.</li>



<li><strong>Data distortion:</strong> Deleted reviews affect star ratings, performance benchmarks, and conversion signals that businesses rely on for local SEO and reputation management.</li>
</ul>



<p>For SEO practitioners, small businesses, and multi-location brands, review monitoring is no longer optional.&nbsp;</p>



<p>Understanding when, where, and which reviews are removed is now as important as generating them.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/google-reviews-local-seo-unleash-potential-429883">Why Google reviews will power up your local SEO</a></em></strong></p>



<h2 class="wp-block-heading" id="h-the-forces-reshaping-review-visibility">The forces reshaping review visibility</h2>



<p>Three developments are shaping review visibility:</p>



<ul class="wp-block-list">
<li>More automated moderation, with AI evaluating reviews in real time and retroactively.</li>



<li>Greater legal influence in regions with strict defamation laws.</li>



<li>Increased reliance on third-party monitoring tools as businesses seek independent records of review deletion activity.</li>
</ul>



<p>As moderation becomes more automated and more influenced by local law, sentiment alone will not guarantee review visibility.&nbsp;</p>



<p>In local SEO, reviews – especially recent ones with detailed context – remain a critical authority signal for both users and search engines.</p>



<p>Staying ahead now means not only collecting new reviews, but also closely tracking and understanding removals.&nbsp;</p>



<p>Reputation management increasingly requires attention on both fronts.</p>

In 2025, Google is removing reviews at unprecedented rates – and it is not accidental.
Our industry analysis of 60,000 Google Business Profiles shows that deletions are being driven by a mix of:
Automated moderation.
Industry-wide risk factors.
Increased enforcement against incentivized reviews.
Local regulatory pressure.
Together, these forces have significant implications for businesses and local search visibility.
Review deletions are on the up globally

Data collected from tens of thousands of Google Business Profile listings across multiple countries by GMBapi.com show a sharp increase in deleted reviews between January and July 2025. 
The surge began accelerating toward the end of Q1 and gained momentum mid-year, with a growing share of monitored locations experiencing at least one review removal in a given week.
This is not limited to negative feedback. 
While one-star reviews continue to be taken down, five-star reviews now account for a sizable share of deletions. 
That pattern suggests Google is applying stricter enforcement, including on positive reviews, as it works to maintain authenticity and trust. 
More recently, Google has begun asking members of its Local Guide community whether businesses are incentivizing reviews, likely in response to AI-driven flags for suspicious activity.
Dig deeper: Google’s review deletions: Why 5-star reviews are disappearing
Not all industries are treated the same
Review deletion patterns vary significantly by business category.
Restaurants account for the highest volume of deleted reviews, followed by home services, brick-and-mortar retail, and construction. 
These categories generate large volumes of reviews, and removals occur across both recent and older submissions. 
That distribution points to ongoing enforcement, not isolated cleanup efforts.
By contrast, medical services, beauty, and professional services see fewer deletions overall. 
However, closer analysis reveals distinct and consistent patterns within those categories.
What review ratings reveal about industry bias





Looking at deleted reviews as a share of total removals within each category reveals distinct moderation patterns.
In restaurants and general retail, deleted reviews are relatively evenly distributed across one- to five-star ratings. 
By contrast, medical services and home services show a strong skew toward five-star review deletions, with far fewer removals in the middle of the rating spectrum. 
That imbalance suggests positive reviews in higher-risk or regulated categories face closer scrutiny, likely tied to concerns around trust, safety, and compliance.
These differences do not appear to stem from manual, category-specific policy decisions. 
Instead, they reflect how Google’s automated systems adjust enforcement based on perceived industry risk.
Dig deeper: 7 local SEO wins you get from keyword-rich Google reviews
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See terms.
Timing matters: Early vs. retroactive deletions
The age of a review plays a significant role in when it is removed.
In medical and home services, a large share of deleted reviews disappear within the first six months after posting. 
That timing points to early intervention by automated systems evaluating language, reviewer behavior, and other risk signals.
Restaurants and brick-and-mortar retail show a different pattern. 
Many deleted reviews in these categories are more than two years old, suggesting retroactive enforcement as detection systems improve or new suspicious patterns emerge. 
It may also reflect efforts to refresh older review profiles.
For businesses, this means reviews can disappear long after they are posted, often without warning.
Geography adds further complexity
Industry alone does not tell the full story. Location matters.

In English-speaking markets such as the U.S., UK, Canada, and Australia, deleted reviews skew heavily toward five-star ratings. 
That trend aligns with increased AI-driven moderation aimed at reducing review spam and incentivized positive feedback.
Germany stands apart. 
Analysis of thousands of German business listings shows a higher share of deleted reviews are low-rated, and most are removed within weeks of posting. 
This pattern aligns with Germany’s strict defamation laws, which permit businesses to legally challenge negative reviews and require platforms to take prompt action upon notification.
In short:
AI-driven enforcement dominates in many English-speaking markets.
Legal takedowns play a much larger role in Germany.
What this means for local SEO and small business owners
The rise in review deletions creates two primary challenges.
Trust erosion: When legitimate reviews, whether positive or negative, disappear without explanation, confidence in review platforms begins to weaken.
Data distortion: Deleted reviews affect star ratings, performance benchmarks, and conversion signals that businesses rely on for local SEO and reputation management.
For SEO practitioners, small businesses, and multi-location brands, review monitoring is no longer optional. 
Understanding when, where, and which reviews are removed is now as important as generating them.
Dig deeper: Why Google reviews will power up your local SEO
The forces reshaping review visibility
Three developments are shaping review visibility:
More automated moderation, with AI evaluating reviews in real time and retroactively.
Greater legal influence in regions with strict defamation laws.
Increased reliance on third-party monitoring tools as businesses seek independent records of review deletion activity.
As moderation becomes more automated and more influenced by local law, sentiment alone will not guarantee review visibility. 
In local SEO, reviews – especially recent ones with detailed context – remain a critical authority signal for both users and search engines.
Staying ahead now means not only collecting new reviews, but also closely tracking and understanding removals. 
Reputation management increasingly requires attention on both fronts.

Reviews are disappearing faster as moderation systems, industry risk, and geography shape which reviews stay visible.

Why Google is deleting reviews at record levels

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/Decoding-the-machine-gaze-Image-SEO-for-multimodal-AI.png" class="attachment-large size-large wp-post-image" alt="Decoding the machine gaze- Image SEO for multimodal AI" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/Decoding-the-machine-gaze-Image-SEO-for-multimodal-AI.png 1920w, https://searchengineland.com/wp-content/seloads/2025/12/Decoding-the-machine-gaze-Image-SEO-for-multimodal-AI-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/12/Decoding-the-machine-gaze-Image-SEO-for-multimodal-AI-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Images are now parsed like language. OCR, visual context and pixel-level quality shape how AI systems interpret and surface content.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/Decoding-the-machine-gaze-Image-SEO-for-multimodal-AI.png" class="attachment-large size-large wp-post-image" alt="Decoding the machine gaze- Image SEO for multimodal AI" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/Decoding-the-machine-gaze-Image-SEO-for-multimodal-AI.png 1920w, https://searchengineland.com/wp-content/seloads/2025/12/Decoding-the-machine-gaze-Image-SEO-for-multimodal-AI-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/12/Decoding-the-machine-gaze-Image-SEO-for-multimodal-AI-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>For the past decade, <a href="https://searchengineland.com/guide/image-optimization-for-seo">image SEO</a> was largely a matter of technical hygiene:</p>



<ul class="wp-block-list">
<li>Compressing JPEGs to appease impatient visitors.</li>



<li>Writing alt text for accessibility.</li>



<li>Implementing lazy loading to keep LCP scores in the green. </li>
</ul>



<p>While these practices remain foundational to a healthy site, the rise of large, multimodal models such as <a href="https://searchengineland.com/what-is-chatgpt-and-why-seos-should-care-392165">ChatGPT</a> and <a href="https://searchengineland.com/use-google-gemini-seo-433357">Gemini</a> has introduced new possibilities and challenges.</p>



<p><a href="https://searchengineland.com/designing-a-multimodal-search-experience-a-cmo-playbook-459009">Multimodal search</a> embeds content types into a shared vector space.&nbsp;</p>



<p>We are now optimizing for the “machine gaze.”&nbsp;</p>



<p>Generative search makes most content machine-readable by segmenting media into chunks and extracting text from visuals through optical character recognition (OCR).&nbsp;</p>



<p>Images must be legible to the machine eye.&nbsp;</p>



<p>If an AI cannot parse the text on product packaging due to low contrast or hallucinates details because of poor resolution, that is a serious problem.</p>



<p>This article deconstructs the machine gaze, shifting the focus from loading speed to machine readability.</p>



<h2 class="wp-block-heading" id="h-technical-hygiene-still-matters">Technical hygiene still matters</h2>



<p>Before optimizing for machine comprehension, we must respect the gatekeeper: performance.&nbsp;</p>



<p>Images are a double-edged sword.&nbsp;</p>



<p>They drive engagement but are often the primary cause of layout instability and slow speeds.&nbsp;</p>



<p>The standard for “good enough” has moved beyond WebP.&nbsp;</p>



<p>Once the asset loads, the real work begins.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/multimodal-discovery-redefining-seo-456816">How multimodal discovery is redefining SEO in the AI era</a></em></strong></p>



<h2 class="wp-block-heading" id="h-designing-for-the-machine-eye-pixel-level-readability">Designing for the machine eye: Pixel-level readability</h2>



<p>To <a href="https://searchengineland.com/seo-guide-large-language-models-413227">large language models</a> (LLMs), images, audio, and video are sources of structured data. </p>



<p>They use a process called visual tokenization to break an image into a grid of patches, or visual tokens, converting raw pixels into a sequence of vectors.</p>



<p>This unified modeling allows AI to process “a picture of a [image token] on a table” as a single coherent sentence.</p>



<p>These systems rely on OCR to extract text directly from visuals.&nbsp;</p>



<p>This is where quality becomes a ranking factor.</p>



<p>If an image is heavily compressed with lossy artifacts, the resulting visual tokens become noisy.</p>



<p>Poor resolution can cause the model to misinterpret those tokens, leading to hallucinations in which the AI confidently describes objects or text that do not actually exist because the “visual words” were unclear.</p>



<h2 class="wp-block-heading" id="h-reframing-alt-text-as-grounding">Reframing alt text as grounding</h2>



<p>For large language models, alt text serves a new function: grounding.&nbsp;</p>



<p>It acts as a semantic signpost that forces the model to resolve ambiguous visual tokens, helping confirm its interpretation of an image.</p>



<p>As Zhang, Zhu, and Tambe <a href="https://arxiv.org/pdf/2509.23109" target="_blank" rel="noopener">noted</a>:</p>



<ul class="wp-block-list">
<li>“By inserting text tokens near relevant visual patches, we create semantic signposts that reveal true content-based cross-modal attention scores, guiding the model.” </li>
</ul>



<p><strong>Tip: </strong>By describing the physical aspects of the image – the lighting, the layout, and the text on the object – you provide the high-quality training data that helps the machine eye correlate visual tokens with text tokens.</p>



<h2 class="wp-block-heading" id="h-the-ocr-failure-points-audit">The OCR failure points audit</h2>



<p>Search agents like Google Lens and Gemini use OCR to read ingredients, instructions, and features directly from images.&nbsp;</p>



<p>They can then answer complex user queries.&nbsp;</p>



<p>As a result, image SEO now extends to physical packaging.</p>



<p>Current labeling regulations – FDA 21 CFR 101.2 and <a href="https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32011R1169" target="_blank" rel="noopener">EU 1169/2011</a> – allow type sizes as small as 4.5 pt to 6 pt, or 0.9 mm, on compact packaging. </p>



<ul class="wp-block-list">
<li>“In case of packaging or containers the largest surface of which has an area of less than 80 cm², the x-height of the font size referred to in paragraph 2 shall be equal to or greater than 0.9 mm.” </li>
</ul>



<p>While this satisfies the human eye, it fails the machine gaze.&nbsp;</p>



<p>The <a href="https://community.cognex.com/s/article/in-sight-what-is-the-minimum-pixel-resolution-recommended-to-ocr-human-readable-text#:~:text=Information,%7C%20Cognex%20Support%20Community" target="_blank" rel="noopener">minimum pixel resolution</a> required for OCR-readable text is far higher. </p>



<p>Character height should be at least 30 pixels.&nbsp;</p>



<p><a href="https://arxiv.org/pdf/2506.20168" target="_blank" rel="noopener">Low contrast</a> is also an issue. Contrast should reach 40 grayscale values. </p>



<p>Be wary of stylized fonts, which can cause OCR systems to mistake a lowercase “l” for a “1” or a “b” for an “8.”</p>



<p>Beyond contrast, <a href="https://www.mdpi.com/1424-8220/25/8/2449" target="_blank" rel="noopener">reflective finishes</a> create additional problems. </p>



<p>Glossy packaging reflects light, producing glare that obscures text.&nbsp;</p>



<p>Packaging should be treated as a machine-readability feature.</p>



<p>If an AI cannot parse a packaging photo because of glare or a script font, it may hallucinate information or, worse, omit the product entirely.</p>



<h2 class="wp-block-heading" id="h-originality-as-a-proxy-for-experience-and-effort">Originality as a proxy for experience and effort</h2>



<p>Originality can feel like a subjective creative trait, but it can be quantified as a measurable data point.</p>



<p>Original images act as a canonical signal.&nbsp;</p>



<p>The Google Cloud Vision API includes a feature called <a href="https://docs.cloud.google.com/vision/docs/detecting-web" target="_blank" rel="noopener">WebDetection</a>, which returns lists of fullMatchingImages – exact duplicates found across the web – and pagesWithMatchingImages. </p>



<p>If your URL has the earliest index date for a unique set of visual tokens (i.e., a specific product angle), Google credits your page as the origin of that visual information, boosting its “experience” score.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/visual-content-seo-images-videos-447306">Visual content and SEO: How to use images and videos</a></em></strong></p>



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<h2 class="wp-block-heading" id="h-the-co-occurrence-audit">The co-occurrence audit</h2>



<p>AI identifies every object in an image and uses their relationships to infer attributes about a brand, price point, and target audience.&nbsp;</p>



<p>This makes product adjacency a ranking signal. To evaluate it, you need to audit your visual entities.</p>



<p>You can test this using tools such as the Google Vision API.&nbsp;</p>



<p>For a systematic audit of an entire media library, you need to pull the raw JSON using the <a href="https://docs.cloud.google.com/vision/docs/object-localizer" target="_blank" rel="noopener">OBJECT_LOCALIZATION</a> feature. </p>



<p>The API returns object labels such as “watch,” “plastic bag” and “disposable cup.”</p>



<p>Google provides <a href="https://docs.cloud.google.com/vision/docs/object-localizer" target="_blank" rel="noopener">this example</a>, where the API returns the following information for the objects in the image:</p>



<figure class="wp-block-table"><table class="has-fixed-layout"><tbody><tr><td><strong>Name</strong></td><td><strong>mid</strong></td><td><strong>Score</strong></td><td><strong>Bounds</strong></td></tr><tr><td>Bicycle wheel</td><td>/m/01bqk0</td><td>0.89648587</td><td>(0.32076266, 0.78941387), (0.43812272, 0.78941387), (0.43812272, 0.97331065), (0.32076266, 0.97331065)</td></tr><tr><td>Bicycle</td><td>/m/0199g</td><td>0.886761</td><td>(0.312, 0.6616471), (0.638353, 0.6616471), (0.638353, 0.9705882), (0.312, 0.9705882)</td></tr><tr><td>Bicycle wheel</td><td>/m/01bqk0</td><td>0.6345275</td><td>(0.5125398, 0.760708), (0.6256646, 0.760708), (0.6256646, 0.94601655), (0.5125398, 0.94601655)</td></tr></tbody></table></figure>



<p>Good to know: <strong>mid </strong>contains a machine-generated identifier (MID) corresponding to a label&#8217;s <a href="https://googleblog.blogspot.com/2012/05/introducing-knowledge-graph-things-not.html">Google Knowledge Graph</a> entry.&nbsp;</p>



<p>The API does not know whether this context is good or bad.&nbsp;</p>



<p>You do, so check whether the visual neighbors are telling the same story as your price tag.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1152" src="https://searchengineland.com/wp-content/seloads/2025/12/lord-leathercraft-blue-leather-watch-band.webp" alt="Lord Leathercraft blue leather watch band" class="wp-image-466512" srcset="https://searchengineland.com/wp-content/seloads/2025/12/lord-leathercraft-blue-leather-watch-band.webp 2048w, https://searchengineland.com/wp-content/seloads/2025/12/lord-leathercraft-blue-leather-watch-band-768x432.webp 768w, https://searchengineland.com/wp-content/seloads/2025/12/lord-leathercraft-blue-leather-watch-band-1536x864.webp 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure></div>


<p>By photographing a blue leather watch next to a vintage brass compass and a warm wood-grain surface, Lord Leathercraft engineers a specific semantic signal: heritage exploration.&nbsp;</p>



<p>The co-occurrence of analog mechanics, aged metal, and tactile suede infers a persona of timeless adventure and old-world sophistication.</p>



<p>Photograph that same watch next to a neon energy drink and a plastic digital stopwatch, and the narrative shifts through dissonance.&nbsp;</p>



<p>The visual context now signals mass-market utility, diluting the entity’s perceived value.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/products-machine-readable-multimodal-ai-search-465151">How to make products machine-readable for multimodal AI search</a></em></strong></p>



<h2 class="wp-block-heading" id="h-quantifying-emotional-resonance">Quantifying emotional resonance</h2>



<p>Beyond objects, these models are increasingly adept at reading sentiment.&nbsp;</p>



<p>APIs, such as Google Cloud Vision, can quantify emotional attributes by assigning confidence scores to emotions like “joy,” “sorrow,” and “surprise” detected in human faces.&nbsp;</p>



<p>This creates a new optimization vector: emotional alignment.&nbsp;</p>



<p>If you are selling fun summer outfits, but the models appear moody or neutral – a common trope in high-fashion photography – the AI may de-prioritize the image for that query because the visual sentiment conflicts with search intent.</p>



<p>For a quick spot check without writing code, use <a href="https://cloud.google.com/vision" target="_blank" rel="noopener">Google Cloud Vision’s live drag-and-drop demo</a> to review the four primary emotions: joy, sorrow, anger, and surprise. </p>



<p>For positive intents, such as “happy family dinner,” you want the joy attribute to register as <code>VERY_LIKELY</code>. </p>



<p>If it reads <code>POSSIBLE</code> or <code>UNLIKELY</code>, the signal is too weak for the machine to confidently index the image as happy.</p>



<p>For a more rigorous audit:</p>



<ul class="wp-block-list">
<li>Run a batch of images through the API. </li>



<li>Look specifically at the faceAnnotations object in the JSON response by sending a FACE_DETECTION feature request. </li>



<li>Review the likelihood fields. </li>
</ul>



<p>The API returns these values as enums or fixed categories. </p>



<p>This example comes directly from the <a href="https://docs.cloud.google.com/vision/docs/detecting-faces#vision_face_detection-drest" target="_blank" rel="noopener">official documentation</a>:</p>



<pre class="wp-block-code"><code>          "rollAngle": 1.5912293,
          "panAngle": -22.01964,
          "tiltAngle": -1.4997566,
          "detectionConfidence": 0.9310801,
          "landmarkingConfidence": 0.5775582,
          "joyLikelihood": "VERY_LIKELY",
          "sorrowLikelihood": "VERY_UNLIKELY",
          "angerLikelihood": "VERY_UNLIKELY",
          "surpriseLikelihood": "VERY_UNLIKELY",
          "underExposedLikelihood": "VERY_UNLIKELY",
          "blurredLikelihood": "VERY_UNLIKELY",
          "headwearLikelihood": "POSSIBLE"
</code></pre>



<p>The API grades emotion on a fixed scale.&nbsp;</p>



<p>The goal is to move primary images from <code>POSSIBLE</code> to <code>LIKELY</code> or <code>VERY_LIKELY</code> for the target emotion.</p>



<ul class="wp-block-list">
<li><code>UNKNOWN</code> (data gap).</li>



<li><code>VERY_UNLIKELY</code> (strong negative signal).</li>



<li><code>UNLIKELY</code>.</li>



<li><code>POSSIBLE</code> (neutral or ambiguous).</li>



<li><code>LIKELY</code>.</li>



<li><code>VERY_LIKELY</code> (strong positive signal – target this).</li>
</ul>



<h2 class="wp-block-heading" id="h-use-these-benchmarks">Use these benchmarks</h2>



<p>You cannot optimize for emotional resonance if the machine can barely see the human.&nbsp;</p>



<p>If <code>detectionConfidence</code> is below 0.60, the AI is struggling to identify a face. </p>



<p>As a result, any emotion readings tied to that face are statistically unreliable noise.</p>



<ul class="wp-block-list">
<li><strong>0.90+ (Ideal):</strong> High-definition, front-facing, well-lit. The AI is certain. Trust the sentiment score.</li>



<li><strong>0.70-0.89 (Acceptable):</strong> Good enough for background faces or secondary lifestyle shots.</li>



<li><strong>&lt; 0.60 (Failure):</strong> The face is likely too small, blurry, side-profile, or blocked by shadows or sunglasses. </li>
</ul>



<p>While Google documentation does not provide this guidance, and Microsoft offers <a href="https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview-identity" target="_blank" rel="noopener">limited access to its Azure AI Face service</a>, Amazon Rekognition documentation <a href="https://docs.aws.amazon.com/rekognition/latest/dg/face-feature-differences.html" target="_blank" rel="noopener">notes that</a>: </p>



<ul class="wp-block-list">
<li>“[A] lower threshold (e.g., 80%) might suffice for identifying family members in photos.”</li>
</ul>



<h2 class="wp-block-heading" id="h-closing-the-semantic-gap-between-pixels-and-meaning">Closing the semantic gap between pixels and meaning</h2>



<p>Treat visual assets with the same editorial rigor and strategic intent as primary content.&nbsp;</p>



<p>The semantic gap between image and text is disappearing.&nbsp;</p>



<p>Images are processed as part of the language sequence.</p>



<p>The quality, clarity, and semantic accuracy of the pixels themselves now matter as much as the keywords on the page.</p>



<p></p>

For the past decade, image SEO was largely a matter of technical hygiene:
Compressing JPEGs to appease impatient visitors.
Writing alt text for accessibility.
Implementing lazy loading to keep LCP scores in the green. 
While these practices remain foundational to a healthy site, the rise of large, multimodal models such as ChatGPT and Gemini has introduced new possibilities and challenges.
Multimodal search embeds content types into a shared vector space. 
We are now optimizing for the “machine gaze.” 
Generative search makes most content machine-readable by segmenting media into chunks and extracting text from visuals through optical character recognition (OCR). 
Images must be legible to the machine eye. 
If an AI cannot parse the text on product packaging due to low contrast or hallucinates details because of poor resolution, that is a serious problem.
This article deconstructs the machine gaze, shifting the focus from loading speed to machine readability.
Technical hygiene still matters
Before optimizing for machine comprehension, we must respect the gatekeeper: performance. 
Images are a double-edged sword. 
They drive engagement but are often the primary cause of layout instability and slow speeds. 
The standard for “good enough” has moved beyond WebP. 
Once the asset loads, the real work begins.
Dig deeper: How multimodal discovery is redefining SEO in the AI era
Designing for the machine eye: Pixel-level readability
To large language models (LLMs), images, audio, and video are sources of structured data. 
They use a process called visual tokenization to break an image into a grid of patches, or visual tokens, converting raw pixels into a sequence of vectors.
This unified modeling allows AI to process “a picture of a [image token] on a table” as a single coherent sentence.
These systems rely on OCR to extract text directly from visuals. 
This is where quality becomes a ranking factor.
If an image is heavily compressed with lossy artifacts, the resulting visual tokens become noisy.
Poor resolution can cause the model to misinterpret those tokens, leading to hallucinations in which the AI confidently describes objects or text that do not actually exist because the “visual words” were unclear.
Reframing alt text as grounding
For large language models, alt text serves a new function: grounding. 
It acts as a semantic signpost that forces the model to resolve ambiguous visual tokens, helping confirm its interpretation of an image.
As Zhang, Zhu, and Tambe noted:
“By inserting text tokens near relevant visual patches, we create semantic signposts that reveal true content-based cross-modal attention scores, guiding the model.” 
Tip: By describing the physical aspects of the image – the lighting, the layout, and the text on the object – you provide the high-quality training data that helps the machine eye correlate visual tokens with text tokens.
The OCR failure points audit
Search agents like Google Lens and Gemini use OCR to read ingredients, instructions, and features directly from images. 
They can then answer complex user queries. 
As a result, image SEO now extends to physical packaging.
Current labeling regulations – FDA 21 CFR 101.2 and EU 1169/2011 – allow type sizes as small as 4.5 pt to 6 pt, or 0.9 mm, on compact packaging. 
“In case of packaging or containers the largest surface of which has an area of less than 80 cm², the x-height of the font size referred to in paragraph 2 shall be equal to or greater than 0.9 mm.” 
While this satisfies the human eye, it fails the machine gaze. 
The minimum pixel resolution required for OCR-readable text is far higher. 
Character height should be at least 30 pixels. 
Low contrast is also an issue. Contrast should reach 40 grayscale values. 
Be wary of stylized fonts, which can cause OCR systems to mistake a lowercase “l” for a “1” or a “b” for an “8.”
Beyond contrast, reflective finishes create additional problems. 
Glossy packaging reflects light, producing glare that obscures text. 
Packaging should be treated as a machine-readability feature.
If an AI cannot parse a packaging photo because of glare or a script font, it may hallucinate information or, worse, omit the product entirely.
Originality as a proxy for experience and effort
Originality can feel like a subjective creative trait, but it can be quantified as a measurable data point.
Original images act as a canonical signal. 
The Google Cloud Vision API includes a feature called WebDetection, which returns lists of fullMatchingImages – exact duplicates found across the web – and pagesWithMatchingImages. 
If your URL has the earliest index date for a unique set of visual tokens (i.e., a specific product angle), Google credits your page as the origin of that visual information, boosting its “experience” score.
Dig deeper: Visual content and SEO: How to use images and videos
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See terms.
The co-occurrence audit
AI identifies every object in an image and uses their relationships to infer attributes about a brand, price point, and target audience. 
This makes product adjacency a ranking signal. To evaluate it, you need to audit your visual entities.
You can test this using tools such as the Google Vision API. 
For a systematic audit of an entire media library, you need to pull the raw JSON using the OBJECT_LOCALIZATION feature. 
The API returns object labels such as “watch,” “plastic bag” and “disposable cup.”
Google provides this example, where the API returns the following information for the objects in the image:

NamemidScoreBounds
Bicycle wheel/m/01bqk00.89648587(0.32076266, 0.78941387), (0.43812272, 0.78941387), (0.43812272, 0.97331065), (0.32076266, 0.97331065)
Bicycle/m/0199g0.886761(0.312, 0.6616471), (0.638353, 0.6616471), (0.638353, 0.9705882), (0.312, 0.9705882)
Bicycle wheel/m/01bqk00.6345275(0.5125398, 0.760708), (0.6256646, 0.760708), (0.6256646, 0.94601655), (0.5125398, 0.94601655)





Good to know: mid contains a machine-generated identifier (MID) corresponding to a label’s Google Knowledge Graph entry. 
The API does not know whether this context is good or bad. 
You do, so check whether the visual neighbors are telling the same story as your price tag.

By photographing a blue leather watch next to a vintage brass compass and a warm wood-grain surface, Lord Leathercraft engineers a specific semantic signal: heritage exploration. 
The co-occurrence of analog mechanics, aged metal, and tactile suede infers a persona of timeless adventure and old-world sophistication.
Photograph that same watch next to a neon energy drink and a plastic digital stopwatch, and the narrative shifts through dissonance. 
The visual context now signals mass-market utility, diluting the entity’s perceived value.
Dig deeper: How to make products machine-readable for multimodal AI search
Quantifying emotional resonance
Beyond objects, these models are increasingly adept at reading sentiment. 
APIs, such as Google Cloud Vision, can quantify emotional attributes by assigning confidence scores to emotions like “joy,” “sorrow,” and “surprise” detected in human faces. 
This creates a new optimization vector: emotional alignment. 
If you are selling fun summer outfits, but the models appear moody or neutral – a common trope in high-fashion photography – the AI may de-prioritize the image for that query because the visual sentiment conflicts with search intent.
For a quick spot check without writing code, use Google Cloud Vision’s live drag-and-drop demo to review the four primary emotions: joy, sorrow, anger, and surprise. 
For positive intents, such as “happy family dinner,” you want the joy attribute to register as VERY_LIKELY. 
If it reads POSSIBLE or UNLIKELY, the signal is too weak for the machine to confidently index the image as happy.
For a more rigorous audit:
Run a batch of images through the API. 
Look specifically at the faceAnnotations object in the JSON response by sending a FACE_DETECTION feature request. 
Review the likelihood fields. 
The API returns these values as enums or fixed categories. 
This example comes directly from the official documentation:
          "rollAngle": 1.5912293,
          "panAngle": -22.01964,
          "tiltAngle": -1.4997566,
          "detectionConfidence": 0.9310801,
          "landmarkingConfidence": 0.5775582,
          "joyLikelihood": "VERY_LIKELY",
          "sorrowLikelihood": "VERY_UNLIKELY",
          "angerLikelihood": "VERY_UNLIKELY",
          "surpriseLikelihood": "VERY_UNLIKELY",
          "underExposedLikelihood": "VERY_UNLIKELY",
          "blurredLikelihood": "VERY_UNLIKELY",
          "headwearLikelihood": "POSSIBLE"

The API grades emotion on a fixed scale. 
The goal is to move primary images from POSSIBLE to LIKELY or VERY_LIKELY for the target emotion.
UNKNOWN (data gap).
VERY_UNLIKELY (strong negative signal).
UNLIKELY.
POSSIBLE (neutral or ambiguous).
LIKELY.
VERY_LIKELY (strong positive signal – target this).
Use these benchmarks
You cannot optimize for emotional resonance if the machine can barely see the human. 
If detectionConfidence is below 0.60, the AI is struggling to identify a face. 
As a result, any emotion readings tied to that face are statistically unreliable noise.
0.90+ (Ideal): High-definition, front-facing, well-lit. The AI is certain. Trust the sentiment score.
0.70-0.89 (Acceptable): Good enough for background faces or secondary lifestyle shots.
< 0.60 (Failure): The face is likely too small, blurry, side-profile, or blocked by shadows or sunglasses. 
While Google documentation does not provide this guidance, and Microsoft offers limited access to its Azure AI Face service, Amazon Rekognition documentation notes that: 
“[A] lower threshold (e.g., 80%) might suffice for identifying family members in photos.”
Closing the semantic gap between pixels and meaning
Treat visual assets with the same editorial rigor and strategic intent as primary content. 
The semantic gap between image and text is disappearing. 
Images are processed as part of the language sequence.
The quality, clarity, and semantic accuracy of the pixels themselves now matter as much as the keywords on the page.

Images are now parsed like language. OCR, visual context and pixel-level quality shape how AI systems interpret and surface content.

Image SEO for multimodal AI

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/How-to-build-search-visibility-before-demand-exists.png" class="attachment-large size-large wp-post-image" alt="How to build search visibility before demand exists" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/How-to-build-search-visibility-before-demand-exists.png 1920w, https://searchengineland.com/wp-content/seloads/2025/12/How-to-build-search-visibility-before-demand-exists-768x432.png 768w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Search demand forms before keywords appear. See how Exploding Topics, social search, and PR support early authority building.

Discovery now happens before search demand is visible in Google.
In 2026, interest forms across social feeds, communities, and AI-generated answers – long before it shows up as keyword search volume. 
By the time demand appears in SEO tools, the opportunity to shape how a concept is understood has already passed.
This creates a problem for how search marketing research is typically done. 
Keyword tools, search volume, and Google Trends are lagging indicators. 
They reveal what people cared about yesterday, not what they are starting to explore now. 
In a landscape shaped by AI Overviews, social SERPs, and shrinking organic real estate, arriving late means competing inside narratives already defined by someone else.
Exploding Topics sits upstream of this shift. 
It helps surface emerging themes, behaviors, and conversations while they are still forming – before they harden into keywords, content clusters, and product categories. 
Used properly, it is not just a trend tool. It is a way to plan SEO, content, digital PR, and social-led search proactively.
This article breaks down how to use Exploding Topics to identify future entities, validate them through social search, and build search visibility before demand peaks.
Use Exploding Topics Trend Analytics to identify future entities – not just topics
Most marketers who use Exploding Topics already understand its value for content ideation, and we will cover that. 
But its bigger opportunity is identifying future entities – concepts that search engines and AI systems will soon recognize as distinct “things,” not just keyword variations.
This matters because modern search no longer operates purely on keywords. 
Google’s AI Overviews, ChatGPT, and other LLM-powered systems organize information around entities and relationships. 
Once an entity is established, the narrative around it hardens. 
Arrive late, and you are competing inside a story that has already been defined. 
Exploding Topics gives you visibility early enough to act before that happens.
Example: Weighted sleep masks

In Exploding Topics, you might notice “weighted sleep mask” rising steadily. 
Search volume remains low, and most keyword tools understate its importance. 
At a glance, it looks like a niche product trend that is easy to ignore.
Look closer, and the signals are stronger:
The phrase is consistent and repeatable.
Adjacent topics are rising alongside it, including deep pressure sleep, anxiety sleep tools, and vagus nerve stimulation.
Questions that signal intent are increasing.
Early discussion focuses on understanding the concept, not just buying a product.
This is the point where something shifts from being a product with an adjective to a named solution. In other words, it is becoming an entity.
The traditional play
Most brands wait until:
Search demand becomes obvious, acting in December 2025 rather than July 2025.
Competitors launch dedicated product pages.
Affiliates and publishers surface “best” and “vs.” content.
Only then do they create:
A category page.
A “What is a weighted sleep mask?” article or social-search activation.
SEO content designed to chase presence, such as FAQs, SERP features, and rankings.
By this point, the entity already exists, and the story around it has largely been written by someone else. 
In this case, NodPod is clearly dominating the entity.
Acting earlier, while the entity is forming
Using Exploding Topics well means acting earlier, while the entity is still being defined. Instead of starting with a product page, you:
Publish a clear, authoritative explanation of what a weighted sleep mask is.
Explain why deep pressure can help with sleep and anxiety.
Address who it is for – and who it is not.
Create supporting content that adds context, such as comparisons with weighted blankets or safety considerations.
This work can be done quickly and at scale through reactive PR and social search activations. 
You are not optimizing for keywords yet. 
You are teaching social algorithms, search engines, and AI systems what the concept means and associating your brand with that explanation from the start.
This is how brands can win at search in 2026 and beyond. 
This early, proactive approach:
Helps search systems understand new concepts faster.
Increases the chance your framing is reused in AI-generated answers.
Positions your brand as the authority on the entity – not just a seller within the conversation.
Dig deeper: Beyond Google: How to put a total search strategy together
Validate emerging entities through social search
Identifying an emerging entity is only the first step. 
The real risk is not being early to a conversation. It is being early to something that never takes off.
This is where many SEO teams stall. 
They wait for search volume and arrive too late, publish on instinct and hope demand follows, or freeze under uncertainty and do nothing.
There is a better middle ground: validate emerging entities through social search research and activation tests before scaling them into owned SEO and on-site experiences.
Exploding Topics is straightforward. It shows what might matter. Social platforms tell you whether your audience actually cares.
How social search becomes your validation layer
Once Exploding Topics surfaces a potential emerging entity, the next step is not Keyword Planner. 
It is native search across platforms such as TikTok, Reddit, and YouTube, using either built-in trend tools or basic platform search.
You are looking for signals like:
Multiple creators independently explaining the same concept.
Comment sections filled with questions such as “Does this actually work?” or “Is this safe?”.
Repeated framing, metaphors, or demonstrations.
Early how-to or comparison content, even if production quality is low.
These signals point to intent. 
Curiosity is turning into understanding. 
Historically, this phase has always preceded measurable search demand.
Revisiting the weighted sleep mask example
After spotting “weighted sleep mask” in Exploding Topics, you might search for it on TikTok.
What you want to see is a lack of heavy brand advertising. 
Mature ecommerce pushes or TikTok Shop funnels suggest the market is already established. 
Instead, look for creators – not brand channels – testing products, discussing solutions, and exploring the underlying problem.
Focus on videos that explain pains, needs, and motivations, such as why pressure may help with anxiety. 
Check the comments for comparisons to other solutions. 
Look for questions raised in videos and comment threads.
Tools like Buzzabout.AI can help do this at scale through topic analysis and AI-assisted research.
These signals answer two critical questions:
Are people actively trying to understand this concept?
What language, framing, and objections are forming before SEO data exists?
That is validation.
Rethinking how SEO strategy gets built
This is where search strategy shifts. 
Instead of asking, “Is there enough volume to justify content creation?” the better question is, “Is there enough curiosity to justify building authority early?”
If social signals are weak:
Pause.
De-risk by testing with creators outside your owned channels.
Avoid heavy investment in content that takes months to rank.
If signals are strong:
Scale with confidence.
Work with creators and activate brand channels.
Invest in entity pages, hubs, FAQs, comparisons, and PLP optimization.
In this model, fast-moving social platforms become the testing layer. 
SEO is not the experiment, it’is the compounding layer.
Dig deeper: Social and UGC: The trust engines powering search everywhere
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See terms.
Editorial digital PR that earns links and LLM citations
Most digital PR still works backward.
A trend reaches mainstream awareness.
Journalists write about it.
Brands scramble to comment.
PR teams try to extract links from a story that already exists. 
The result is short-term coverage, diluted impact, and little lasting search advantage.
Exploding Topics makes it possible to reverse that dynamic by surfacing editorial narratives before they are obvious and positioning your brand as one of the sources that helps define them.
In 2026, this matters more than ever. 
Links still matter, but they are no longer the only outcome that counts. 
Brand mentions, explanations, and citations increasingly feed the systems behind AI Overviews, ChatGPT, Perplexity, and other LLM-driven discovery experiences.
Why early narratives outperform reactive PR
When a topic is everywhere, journalists are aggregating. When a topic is emerging, they are still asking questions.
Exploding Topics surfaces concepts at the stage where:
There is no consensus narrative.
Definitions are inconsistent.
Journalists are looking for clarity, not quotes.
“What is this?” stories have not yet been written.
This is the point where brands can move from commenting on a conversation to shaping it.
From trend-jacker to narrative owner
Instead of pitching “our brand’s take on X,” you lead with early signals you are seeing, why a concept is emerging now, and what it suggests about consumer behavior or the market.
The difference is subtle but important.
You are no longer reacting to coverage that already exists. 
You are creating the framing that journalists, publishers, and, eventually, AI systems reuse. 
LLMs do not learn from rankings alone. 
They learn from editorial context, repeated explanations, and how trusted publications describe and define emerging concepts over time.
Done consistently, this approach compounds. 
As your brand becomes associated with spotting and explaining emerging narratives early, you move from reactive commentary to trusted source. 
Journalists begin to recognize where useful insight comes from, and that trust carries into more established coverage later on. You are no longer pitching for inclusion. 
Your perspective is actively sought out.
The result is early narrative ownership and stronger access when mainstream coverage follows.
An editorial window before mainstream coverage
Before “weighted sleep mask” became a crowded ecommerce term in early 2025, there was a clear editorial window.
Journalists had not yet published stories asking:
“What is a weighted sleep mask?”
“Are weighted sleep masks safe?”
“Do they actually work for anxiety?” 
That was the opportunity.
A PR-led approach at this stage includes:
Supplying journalists with expert explanations of deep pressure and sleep.
Sharing early insight into why the product category is emerging.
Contextualizing it alongside weighted blankets and other anxiety tools.
The result is not just coverage. It connects PR to search, curiosity, and discovery by helping define the concept itself. 
That earns links, builds brand mentions, and signals authority around emerging entities that LLMs are more likely to cite and summarize over time.
Dig deeper: Why PR is becoming more essential for AI search visibility
Content roadmaps and briefs that don’t rely on search volume
Search volume is a poor starting point for content briefing.
It reflects interest only after a topic is established, language has stabilized, and the SERP is already crowded. 
Used as a primary input, it pushes teams to chase demand instead of building authority. 
That is why so many brands end up rewriting the same “What is X?” post year after year.
Better briefs start upstream. 
They use Exploding Topics to spot what is forming and social search to understand how people are trying to make sense of it.
Reframing the briefing process
The core shift is moving away from briefs built around keywords and volumes and toward briefs built around audience intent.
That means focusing on three things:
Problems people are beginning to articulate.
Concepts that are not yet clearly defined or are actively debated.
Language that is inconsistent, emotional, or exploratory.
When content is approached this way, the objective changes. 
It is no longer “create X to rank for Y.” 
It becomes “explain X so the audience does not experience Y.” 
That shift matters.
Designing content that compounds instead of expiring
The goal for SEO content teams in 2026 and beyond should be to brief content that defines a concept clearly. That includes:
Connecting it to adjacent ideas.
Comparing it to established solutions.
Answering questions within conversations that are still forming.
This does not always require written content. 
The same work can happen through social search activations or digital PR.
Approached this way, content grows into demand rather than chasing it.
Instead of being rewritten every time search volume changes, it evolves through updates, expansion, and, where possible, stronger internal linking. 
As interest grows, the content does not need replacing. It needs refining. 
This is the type of material AI and LLMs tend to reference – timely, clear, explanatory, and grounded in real questions.
Publication isn’t the end
Publishing and waiting for content to rank is no longer the end of the brief.
Teams need a clear plan for distribution and reuse.
For emerging topics, that means contributing insight in relevant Reddit threads, Discord communities, niche forums, and creator comment sections. 
Not to drop links, but to answer questions, share explanations, and test framing in public. 
Those conversations feed back into the content itself, improving clarity and increasing the likelihood that your explanation is the one others repeat.
With a social search activation approach, brands can scale messaging quickly by working with partners who interpret and distribute the brief in their own voice. 
When this works, SEO content stops being static and starts acting like a living reference point – one that contributes to culture and builds lasting brand recognition.
Dig deeper: Beyond SERP visibility: 7 success criteria for organic search in 2026
Where this leaves SEO in 2026
Search demand does not appear fully formed. 
It develops across social platforms, communities, and AI-driven discovery long before it registers as keyword volume.
Exploding Topics helps surface what is emerging. 
Social search shows whether people are trying to understand it. 
Digital PR shapes how those ideas are defined and cited. 
SEO compounds that work by reinforcing narratives that are already taking shape, rather than trying to test or invent them after the fact.
In this model, SEO is the layer that turns early insight and clear explanation into durable visibility across Google, social platforms, and AI-generated answers.
Search no longer starts on Google. The teams that act on that reality will influence what people search for next.

Search demand forms before keywords appear. See how Exploding Topics, social search, and PR support early authority building.

How to build search visibility before demand exists

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-800x450.jpg" class="attachment-large size-large wp-post-image" alt="Microsoft Ads" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Microsoft’s new asset-level editorial review lets compliant ad components run even when others are disapproved reducing campaign disruption.

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-800x450.jpg" class="attachment-large size-large wp-post-image" alt="Microsoft Ads" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2024/01/Microsoft-Ads.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Microsoft Advertising is now rolling out asset-level editorial review. This will allow you to view policy approvals for each ad component, rather than the entire ad, reducing delays caused by a single non-compliant element.</p>



<ul class="wp-block-list">
<li>Asset-level ad reviews were <a href="https://searchengineland.com/microsoft-ads-gets-granular-with-new-asset-level-reviews-457690">first spotted in June</a> and <a href="https://about.ads.microsoft.com/en/blog/post/november-2025/asset-level-editorial-review-and-other-updates-for-november">officially introduced</a> last month.</li>
</ul>



<p><strong>What’s new</strong>. Advertisers can now see headlines, descriptions, and images reviewed separately inside the Microsoft Advertising interface. If one asset violates policy, only that component is blocked, while compliant assets continue to serve.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2025/12/asset-level-review.jpg" alt="" class="wp-image-466583" srcset="https://searchengineland.com/wp-content/seloads/2025/12/asset-level-review.jpg 1280w, https://searchengineland.com/wp-content/seloads/2025/12/asset-level-review-768x432.jpg 768w" sizes="(max-width: 1280px) 100vw, 1280px" /></figure>



<p><strong>How it works</strong>. Microsoft Advertising:</p>



<ul class="wp-block-list">
<li>Flags disapproved assets directly in the dashboard.</li>



<li>Displays warning banners when components are blocked.</li>



<li>Shows clear asset-level status alongside campaign details.</li>
</ul>



<p><strong>Why we care.</strong> This change reduces campaign disruption and speeds up approvals. You can pinpoint and fix the exact asset causing an issue instead of rebuilding or resubmitting entire ads, helping campaigns go live faster.</p>


Microsoft Advertising is now rolling out asset-level editorial review. This will allow you to view policy approvals for each ad component, rather than the entire ad, reducing delays caused by a single non-compliant element.
Asset-level ad reviews were first spotted in June and officially introduced last month.
What’s new. Advertisers can now see headlines, descriptions, and images reviewed separately inside the Microsoft Advertising interface. If one asset violates policy, only that component is blocked, while compliant assets continue to serve.




How it works. Microsoft Advertising:
Flags disapproved assets directly in the dashboard.
Displays warning banners when components are blocked.
Shows clear asset-level status alongside campaign details.
Why we care. This change reduces campaign disruption and speeds up approvals. You can pinpoint and fix the exact asset causing an issue instead of rebuilding or resubmitting entire ads, helping campaigns go live faster.

Microsoft’s new asset-level editorial review lets compliant ad components run even when others are disapproved reducing campaign disruption.

Microsoft launches asset-level ad reviews

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920-800x450.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Google’s policy update expands pharmaceutical advertising access in programmatic channels and shifts greater compliance risk to advertisers.

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920-800x450.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2015/09/google-mobile3-ss-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Google will update its Pharmaceutical policy for AdMob Authorized Buyers in January, allowing prescription drug and prescription drug service ads in select markets without Google certification while clarifying what remains strictly prohibited.</p>



<p><strong>What’s changing</strong>. The policy will be renamed “Pharmaceutical products and services” and updated to let Authorized Buyers promote prescription drugs and prescription drug services in select countries where local law allows, without the Google certification normally required in Google Ads.</p>



<ul class="wp-block-list">
<li>While access is expanding, the rules are not loosening. The policy language is being reorganized to improve clarity and readability, not to relax enforcement.</li>
</ul>



<p><strong>Why we care.</strong> This update expands access to pharmaceutical inventory without Google certification, opening new reach and adding competitive pressure in programmatic auctions. It also shifts more compliance responsibility to advertisers, raising the risk of violations if geo-targeting and creative controls aren’t tightly managed. Even non-pharma advertisers may feel the impact as increased pharma demand can affect pricing, brand safety, and placement strategies.</p>



<p><strong>What’s still banned</strong>. These types of ads remain prohibited across Google Partner Inventory:</p>



<ul class="wp-block-list">
<li>Clinical trials.</li>



<li>Miracle cures.</li>



<li>Unapproved supplements.</li>



<li>Illegal drugs.</li>



<li>Drug paraphernalia.</li>



<li>Addiction treatment and recovery services.</li>



<li>Crisis hotlines.</li>



<li>Speculative or experimental medical treatments (including stem cell and gene therapies) </li>
</ul>



<p><strong>Between the lines</strong>. Google is widening access while pushing responsibility downstream. By removing certification requirements for Authorized Buyers but keeping strict geographic and content controls, Google places compliance risk squarely on buyers and publishers.</p>



<p><strong>What to do now.</strong> App publishers using AdMob should review category blocking and ad controls to prevent unwanted pharma ads, especially as more inventory becomes eligible. Buyers should prepare for country-by-country enforcement and carefully audit creatives.</p>



<p><strong>Bottom line</strong>. <a href="https://support.google.com/authorizedbuyers/answer/6100448" target="_blank" rel="noopener">Google is opening the door wider for pharmaceutical advertising</a> in programmatic environments — but the rules are still complex, localized, and unforgiving for those who get them wrong.</p>


Google will update its Pharmaceutical policy for AdMob Authorized Buyers in January, allowing prescription drug and prescription drug service ads in select markets without Google certification while clarifying what remains strictly prohibited.
What’s changing. The policy will be renamed “Pharmaceutical products and services” and updated to let Authorized Buyers promote prescription drugs and prescription drug services in select countries where local law allows, without the Google certification normally required in Google Ads.
While access is expanding, the rules are not loosening. The policy language is being reorganized to improve clarity and readability, not to relax enforcement.
Why we care. This update expands access to pharmaceutical inventory without Google certification, opening new reach and adding competitive pressure in programmatic auctions. It also shifts more compliance responsibility to advertisers, raising the risk of violations if geo-targeting and creative controls aren’t tightly managed. Even non-pharma advertisers may feel the impact as increased pharma demand can affect pricing, brand safety, and placement strategies.
What’s still banned. These types of ads remain prohibited across Google Partner Inventory:
Clinical trials.
Miracle cures.
Unapproved supplements.
Illegal drugs.
Drug paraphernalia.
Addiction treatment and recovery services.
Crisis hotlines.
Speculative or experimental medical treatments (including stem cell and gene therapies) 
Between the lines. Google is widening access while pushing responsibility downstream. By removing certification requirements for Authorized Buyers but keeping strict geographic and content controls, Google places compliance risk squarely on buyers and publishers.
What to do now. App publishers using AdMob should review category blocking and ad controls to prevent unwanted pharma ads, especially as more inventory becomes eligible. Buyers should prepare for country-by-country enforcement and carefully audit creatives.
Bottom line. Google is opening the door wider for pharmaceutical advertising in programmatic environments — but the rules are still complex, localized, and unforgiving for those who get them wrong.

Google’s policy update expands pharmaceutical advertising access in programmatic channels and shifts greater compliance risk to advertisers.

Google to loosen pharma ad rules for AdMob authorized buyers

<div><img width="800" height="462" src="https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920-800x462.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920-800x462.jpg 800w, https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920-586x338.jpg 586w, https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920-196x113.jpg 196w, https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920-768x443.jpg 768w, https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920-1536x886.jpg 1536w, https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920-150x87.jpg 150w, https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Google accused SerpApi of 'circumventing security measures protecting others’ copyrighted content that appears in Google search results.'

<div><img width="800" height="462" src="https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920-800x462.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920-800x462.jpg 800w, https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920-586x338.jpg 586w, https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920-196x113.jpg 196w, https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920-768x443.jpg 768w, https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920-1536x886.jpg 1536w, https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920-150x87.jpg 150w, https://searchengineland.com/wp-content/seloads/2023/04/google-ninja-robot-crawler-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Google said today that it is suing SerpApi, accusing the company of bypassing security protections to scrape, harvest, and resell copyrighted content from Google Search results.</p>



<p><strong>The allegations:</strong> Google said SerpApi:</p>



<ul class="wp-block-list">
<li>Circumvented Google’s security measures and industry-standard crawling controls.</li>



<li>Ignored website directives that specify whether content can be accessed.</li>



<li>Used cloaking, rotating bot identities, and large bot networks to scrape content at scale.</li>



<li>Took licensed content from Search features, including images and real-time data, and resold it for profit.</li>
</ul>



<p><strong>What Google is saying. </strong>“Stealthy scrapers like SerpApi override [crawling] directives and give sites no choice at all,” Google wrote, calling the alleged scraping “brazen” and “unlawful.” Google said SerpApi&#8217;s activity “increased dramatically over the past year.”</p>



<p><strong>Catch up quick.</strong> Google&#8217;s lawsuit <a href="https://searchengineland.com/reddit-sues-perplexity-serpapi-scraping-google-463681">similar legal action taken by Reddit</a>, which named SerpApi, Perplexity, Oxylabs, and AWMProxy and accused them of scraping Reddit content through Google Search results while hiding their identities to bypass restrictions.</p>



<ul class="wp-block-list">
<li>Reddit licenses its data to Google and OpenAI, but alleges others tried to sidestep those deals.</li>



<li>Reddit said it set a “trap” post visible only to Google’s crawler that later appeared in Perplexity results — evidence of scraping, according to the complaint.</li>



<li><a href="https://searchengineland.com/serpapi-reddit-lawsuit-response-463962">SerpApi denied wrongdoing</a> and said it operates lawfully.</li>
</ul>



<p><strong>What SerpApi has said previously</strong>. SerpApi argued that “public search data should be accessible,” framing its work as protected by the First Amendment and warning that lawsuits like Reddit’s threaten the “free and open web.”</p>



<p><strong>Why we care.</strong> If Google wins, reliable SERP data could become harder to get, more expensive, or both — especially for teams that rely on tools powered by services like SerpApi. As AI already reduces clicks and transparency, Google now appears intent on making it even harder for brands to understand how Search works, how they appear in results, and how to measure success.</p>



<p><strong>Update: SerpApi responds.</strong> SerpApi founder and CEO Julien Khaleghy shared this statement with Search Engine Land:</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p>&#8220;SerpApi has not been served with Google&#8217;s complaint, and prior to filing, Google did not contact us to raise any concerns or explore a constructive resolution.</p>



<p>For more than eight years, SerpApi has provided developers, researchers, and businesses with access to public search data. The information we provide is the same information any person can see in their browser without signing in. We believe this lawsuit is an effort to stifle competition from the innovators who rely on our services to build next-generation AI, security, browsers, productivity, and many other applications.</p>



<p>As we state on our website: &#8216;The crawling and parsing of public data is protected by the First Amendment of the United States Constitution.&#8217; We work closely with our attorneys to ensure our services comply with all applicable laws, including fair use principles.</p>



<p>SerpApi stands firmly behind its business model and will vigorously defend itself in court.&#8221;</p>
</blockquote>
</blockquote>


Google said today that it is suing SerpApi, accusing the company of bypassing security protections to scrape, harvest, and resell copyrighted content from Google Search results.
The allegations: Google said SerpApi:
Circumvented Google’s security measures and industry-standard crawling controls.
Ignored website directives that specify whether content can be accessed.
Used cloaking, rotating bot identities, and large bot networks to scrape content at scale.
Took licensed content from Search features, including images and real-time data, and resold it for profit.
What Google is saying. “Stealthy scrapers like SerpApi override [crawling] directives and give sites no choice at all,” Google wrote, calling the alleged scraping “brazen” and “unlawful.” Google said SerpApi’s activity “increased dramatically over the past year.”
Catch up quick. Google’s lawsuit similar legal action taken by Reddit, which named SerpApi, Perplexity, Oxylabs, and AWMProxy and accused them of scraping Reddit content through Google Search results while hiding their identities to bypass restrictions.
Reddit licenses its data to Google and OpenAI, but alleges others tried to sidestep those deals.
Reddit said it set a “trap” post visible only to Google’s crawler that later appeared in Perplexity results — evidence of scraping, according to the complaint.
SerpApi denied wrongdoing and said it operates lawfully.
What SerpApi has said previously. SerpApi argued that “public search data should be accessible,” framing its work as protected by the First Amendment and warning that lawsuits like Reddit’s threaten the “free and open web.”
Why we care. If Google wins, reliable SERP data could become harder to get, more expensive, or both — especially for teams that rely on tools powered by services like SerpApi. As AI already reduces clicks and transparency, Google now appears intent on making it even harder for brands to understand how Search works, how they appear in results, and how to measure success.
Update: SerpApi responds. SerpApi founder and CEO Julien Khaleghy shared this statement with Search Engine Land:
“SerpApi has not been served with Google’s complaint, and prior to filing, Google did not contact us to raise any concerns or explore a constructive resolution.
For more than eight years, SerpApi has provided developers, researchers, and businesses with access to public search data. The information we provide is the same information any person can see in their browser without signing in. We believe this lawsuit is an effort to stifle competition from the innovators who rely on our services to build next-generation AI, security, browsers, productivity, and many other applications.
As we state on our website: ‘The crawling and parsing of public data is protected by the First Amendment of the United States Constitution.’ We work closely with our attorneys to ensure our services comply with all applicable laws, including fair use principles.
SerpApi stands firmly behind its business model and will vigorously defend itself in court.”

Google accused SerpApi of 'circumventing security measures protecting others’ copyrighted content that appears in Google search results.'

Google sues SerpApi over scraping and reselling Search data

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2025/12/blue-bing-writing-1920.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/blue-bing-writing-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/blue-bing-writing-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/blue-bing-writing-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Duplicate or similar content on your site makes it hard for AI search engines to interpret your intent signals. 

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2025/12/blue-bing-writing-1920.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/blue-bing-writing-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/blue-bing-writing-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/blue-bing-writing-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Duplicate content can hurt your visibility in AI Search. Fabrice Canel and Krishna Madhavan from Microsoft <a href="https://blogs.bing.com/webmaster/December-2025/Does-Duplicate-Content-Hurt-SEO-and-AI-Search-Visibility" target="_blank" rel="noopener">explained</a> that when content is duplicated, AI systems struggle to interpret signals, which reduces &#8220;the likelihood that the correct version will be selected or summarized.&#8221;.</p>



<p>This mirrors how duplicate or near-duplicate content affects traditional search rankings. AI Search on Bing and Google relies on the same underlying signals, so duplication can blur intent and create confusion about which version matters most.</p>



<p><strong>The issue with duplicate content and AI Search. </strong>Here are some bullet points from the Bing blog post on why duplicate or very similar content can cause issues with that content showing in AI Search:</p>



<ul class="wp-block-list">
<li>AI search builds on the same signals that support traditional SEO,&nbsp;but adds additional layers,&nbsp;especially&nbsp;in&nbsp;satisfying&nbsp;intent.</li>



<li>When several pages repeat the same information, those intent signals become harder for AI systems to interpret, reducing the likelihood that the correct version will be selected or summarized.</li>



<li>When multiple pages cover the same topic with similar wording, structure, and metadata, AI systems cannot easily&nbsp;determine&nbsp;which version&nbsp;aligns best with the user’s intent. This reduces the chances that your preferred page will be chosen as a grounding source.</li>



<li>LLMs group near-duplicate URLs into a single cluster and then choose one page to&nbsp;represent&nbsp;the set. If the differences between pages are minimal, the model may select a version that is outdated or not the one you intended to highlight.&nbsp;</li>



<li>Campaign pages, audience segments, and localized versions can satisfy different intents, but only if those differences are meaningful. When variations reuse the same content, models have fewer signals to match each page with a unique&nbsp;user&nbsp;need.&nbsp;</li>



<li>AI systems favor fresh, up-to-date content, but duplicates can slow how quickly changes are reflected. When crawlers revisit duplicate or low-value URLs instead of&nbsp;updated&nbsp;pages,&nbsp;new information&nbsp;may take longer to reach the systems that support AI summaries and comparisons. Clearer intent strengthens AI visibility by helping models understand which version to trust and surface.&nbsp;</li>
</ul>



<p><strong>Syndicated content. </strong>Many people don’t realize that syndicated content — content you publish on your site and allow others to republish — can also create problems. Syndicated content counts as duplicate content, at least by Microsoft’s definition.</p>



<ul class="wp-block-list">
<li>&#8220;When your articles are republished on other sites, identical copies can exist across domains, making it harder for search engines and AI systems to identify the original source,&#8221; Microsoft wrote.</li>
</ul>



<p><strong>How do you reduce duplicate content? </strong>When it comes to syndicated content, you can try to ask the syndication partner to:</p>



<ul class="wp-block-list">
<li>Add a canonical tag from what they published on their site, to the original version on your site</li>



<li>You can ask them to rework the content, so it is not too similar </li>



<li>You can ask them to noindex the content, so search engines don&#8217;t see it</li>
</ul>



<p><strong>Campaign pages. </strong>Microsoft also said that &#8220;Campaign pages can become duplicate content when multiple versions target the same intent and differ only by minor changes, such as headlines, imagery, or audience messaging.&#8221;  So you want to make sure to be very careful about your internal site&#8217;s page organization and URL structure.</p>



<ul class="wp-block-list">
<li>Select one primary campaign page to collect links and engagement.&nbsp;</li>



<li>Use canonical&nbsp;tags on variations that&nbsp;do not&nbsp;represent&nbsp;a distinct search intent</li>



<li>Only keep separate pages when intent clearly changes, such as seasonal offers, localized pricing, or comparison-focused content.&nbsp;</li>



<li>Consolidate&nbsp;or&nbsp;301&nbsp;redirect older or redundant campaign pages&nbsp;that no longer serve a unique purpose.&nbsp;</li>
</ul>



<p><strong>Localization pages. </strong>Yes, localization can also create duplicate content. If many pages say the same thing and only swap the city or location, they’re too similar and can cause issues.</p>



<ul class="wp-block-list">
<li>&#8220;Localization creates duplicate content when regional or language pages are nearly identical and do not provide meaningful differences for users in each market,&#8221; Microsoft wrote.</li>
</ul>



<p>To fix it, Microsoft suggests:</p>



<ul class="wp-block-list">
<li>Localize with meaningful changes such as terminology, examples, regulations, or product details.&nbsp;</li>



<li>Avoid creating multiple pages in the same language that serve the same purpose.&nbsp;</li>



<li>Use&nbsp;hreflang&nbsp;to define&nbsp;language and regional&nbsp;targeting</li>
</ul>



<p><strong>Other technical SEO issues. </strong>Technical issues can also create duplicate content. A single page can end up with multiple URLs, which splits signals. Search engines can often sort this out, but you shouldn’t rely on them.</p>



<p>Take control by making sure each piece of content has a clear URL. Common causes include URL parameters, HTTP vs. HTTPS versions, uppercase and lowercase URLs, trailing slashes, printer-friendly pages, staging or test sites, and similar variations.</p>



<p>To fix this, Microsoft suggests:</p>



<ul class="wp-block-list">
<li>Use 301 redirects to&nbsp;consolidate&nbsp;variants into a single preferred URL.&nbsp;</li>



<li>Apply&nbsp;canonical&nbsp;tags when multiple versions must remain accessible.&nbsp;</li>



<li>Enforce consistent URL structures across the site.&nbsp;</li>



<li>Prevent staging or archive URLs from being crawled or indexed.&nbsp;</li>
</ul>



<p><strong>Why we care. </strong>Duplicate content in SEO is not a new topic and it will carry over from traditional search to AI search.  Many of you have a lot of experience dealing with duplicate or nearly identical content and how that negatively impacts indexing and ranking.</p>



<p></p>


Duplicate content can hurt your visibility in AI Search. Fabrice Canel and Krishna Madhavan from Microsoft explained that when content is duplicated, AI systems struggle to interpret signals, which reduces “the likelihood that the correct version will be selected or summarized.”.
This mirrors how duplicate or near-duplicate content affects traditional search rankings. AI Search on Bing and Google relies on the same underlying signals, so duplication can blur intent and create confusion about which version matters most.
The issue with duplicate content and AI Search. Here are some bullet points from the Bing blog post on why duplicate or very similar content can cause issues with that content showing in AI Search:
AI search builds on the same signals that support traditional SEO, but adds additional layers, especially in satisfying intent.
When several pages repeat the same information, those intent signals become harder for AI systems to interpret, reducing the likelihood that the correct version will be selected or summarized.
When multiple pages cover the same topic with similar wording, structure, and metadata, AI systems cannot easily determine which version aligns best with the user’s intent. This reduces the chances that your preferred page will be chosen as a grounding source.
LLMs group near-duplicate URLs into a single cluster and then choose one page to represent the set. If the differences between pages are minimal, the model may select a version that is outdated or not the one you intended to highlight. 
Campaign pages, audience segments, and localized versions can satisfy different intents, but only if those differences are meaningful. When variations reuse the same content, models have fewer signals to match each page with a unique user need. 
AI systems favor fresh, up-to-date content, but duplicates can slow how quickly changes are reflected. When crawlers revisit duplicate or low-value URLs instead of updated pages, new information may take longer to reach the systems that support AI summaries and comparisons. Clearer intent strengthens AI visibility by helping models understand which version to trust and surface. 
Syndicated content. Many people don’t realize that syndicated content — content you publish on your site and allow others to republish — can also create problems. Syndicated content counts as duplicate content, at least by Microsoft’s definition.
“When your articles are republished on other sites, identical copies can exist across domains, making it harder for search engines and AI systems to identify the original source,” Microsoft wrote.
How do you reduce duplicate content? When it comes to syndicated content, you can try to ask the syndication partner to:
Add a canonical tag from what they published on their site, to the original version on your site
You can ask them to rework the content, so it is not too similar 
You can ask them to noindex the content, so search engines don’t see it
Campaign pages. Microsoft also said that “Campaign pages can become duplicate content when multiple versions target the same intent and differ only by minor changes, such as headlines, imagery, or audience messaging.”  So you want to make sure to be very careful about your internal site’s page organization and URL structure.
Select one primary campaign page to collect links and engagement. 
Use canonical tags on variations that do not represent a distinct search intent
Only keep separate pages when intent clearly changes, such as seasonal offers, localized pricing, or comparison-focused content. 
Consolidate or 301 redirect older or redundant campaign pages that no longer serve a unique purpose. 
Localization pages. Yes, localization can also create duplicate content. If many pages say the same thing and only swap the city or location, they’re too similar and can cause issues.
“Localization creates duplicate content when regional or language pages are nearly identical and do not provide meaningful differences for users in each market,” Microsoft wrote.
To fix it, Microsoft suggests:
Localize with meaningful changes such as terminology, examples, regulations, or product details. 
Avoid creating multiple pages in the same language that serve the same purpose. 
Use hreflang to define language and regional targeting
Other technical SEO issues. Technical issues can also create duplicate content. A single page can end up with multiple URLs, which splits signals. Search engines can often sort this out, but you shouldn’t rely on them.
Take control by making sure each piece of content has a clear URL. Common causes include URL parameters, HTTP vs. HTTPS versions, uppercase and lowercase URLs, trailing slashes, printer-friendly pages, staging or test sites, and similar variations.
To fix this, Microsoft suggests:
Use 301 redirects to consolidate variants into a single preferred URL. 
Apply canonical tags when multiple versions must remain accessible. 
Enforce consistent URL structures across the site. 
Prevent staging or archive URLs from being crawled or indexed. 
Why we care. Duplicate content in SEO is not a new topic and it will carry over from traditional search to AI search.  Many of you have a lot of experience dealing with duplicate or nearly identical content and how that negatively impacts indexing and ranking.

Duplicate or similar content on your site makes it hard for AI search engines to interpret your intent signals.

Microsoft Bing explains how duplicate content hurts AI search visibility

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/Your-next-customer-might-not-be-human-Designing-journeys-for-people-and-AI-agents.jpg" class="attachment-large size-large wp-post-image" alt="Your next customer might not be human: Designing journeys for people and AI agents" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/Your-next-customer-might-not-be-human-Designing-journeys-for-people-and-AI-agents.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/Your-next-customer-might-not-be-human-Designing-journeys-for-people-and-AI-agents-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/Your-next-customer-might-not-be-human-Designing-journeys-for-people-and-AI-agents-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI agents increasingly filter options and shape shortlists before people ever engage, shifting where visibility and influence are earned.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/Your-next-customer-might-not-be-human-Designing-journeys-for-people-and-AI-agents.jpg" class="attachment-large size-large wp-post-image" alt="Your next customer might not be human: Designing journeys for people and AI agents" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/Your-next-customer-might-not-be-human-Designing-journeys-for-people-and-AI-agents.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/Your-next-customer-might-not-be-human-Designing-journeys-for-people-and-AI-agents-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/Your-next-customer-might-not-be-human-Designing-journeys-for-people-and-AI-agents-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>For the past decade, customer journey design has assumed one thing: the customer is human.&nbsp;</p>



<p>A real person. Messy. Emotional. Overloaded.&nbsp;</p>



<p>Someone who needs clarity, reassurance, and a sense of progress to keep moving forward.</p>



<p>But in 2026, that assumption no longer holds.</p>



<p>AI agents are starting to influence how people search, compare, choose, and buy.&nbsp;</p>



<p>They filter results, generate shortlists, book services, and soon may even negotiate on our behalf.</p>



<p>While much of AI usage is still task-based, it marks a critical shift.&nbsp;</p>



<p>Once AI becomes part of how information is gathered, filtered, and prioritized, it begins to shape the decisions that follow.</p>



<p>That means the customer journey is no longer a single track. It is now a dual journey, one for the person and one for the agent.</p>



<p>This article does not promise all the answers. The landscape is changing fast.&nbsp;</p>



<p>What it can do is explore why this shift matters, what dual journeys look like, and how marketers can start designing for both.</p>



<h2 class="wp-block-heading" id="h-the-customer-is-no-longer-the-only-decision-maker">The customer is no longer the only decision-maker</h2>



<p>When an AI agent filters 200 options down to just three, your customer is only seeing the surface of that decision.&nbsp;</p>



<p>Visibility is being won or lost long before the person is even aware of it.</p>



<p>Agents are not just another channel. They are becoming gatekeepers in the decision-making process.&nbsp;</p>



<p>That means your journey now has two layers:</p>



<ul class="wp-block-list">
<li>The human journey.</li>



<li>The agent journey.</li>
</ul>



<p>They overlap, but they work in very different ways.</p>



<h2 class="wp-block-heading" id="h-humans-and-agents-decide-in-fundamentally-different-ways">Humans and agents decide in fundamentally different ways</h2>



<p>The human journey is emotional and unpredictable.&nbsp;</p>



<p>We get distracted. We procrastinate.&nbsp;</p>



<p>We need reassurance. We are influenced by how something feels, not just what it says.</p>



<p>Agents are different. They don&#8217;t:</p>



<ul class="wp-block-list">
<li>Get distracted.</li>



<li>Need clarity or persuasion.</li>



<li>Feel risk. They evaluate it.</li>



<li>Get overwhelmed. They operate on logic, rules, and data.</li>
</ul>



<p>This means the same piece of content could succeed with one and fail with the other.&nbsp;</p>



<p>A person might skim your copy and feel like it “just gets them,” while an agent scans your entire site, finds a key attribute missing, and removes you from the shortlist.</p>



<p>When you treat these two journeys as one, you risk missing both.</p>



<h2 class="wp-block-heading" id="h-it-s-time-to-map-two-paths">It’s time to map two paths</h2>



<p><strong>The human journey:</strong> You know this one. It includes:</p>



<ul class="wp-block-list">
<li>Emotional drivers.</li>



<li>Trust signals.</li>



<li>Clarity and reassurance.</li>



<li>Cognitive load management.</li>



<li>Social proof.</li>



<li>Personal preferences and brand affinity.</li>
</ul>



<p>This is the part of the journey that reflects how humans think, feel, and decide.</p>



<p><strong>The agent journey: </strong>This is newer territory, and it requires a different playbook:</p>



<ul class="wp-block-list">
<li>Structured, machine-readable data.</li>



<li>Consistent schema and clean attributes.</li>



<li>Up-to-date information and availability.</li>



<li>Clear logic and authority signals.</li>
</ul>



<p>Agents are not reading your copy. They are parsing your data.&nbsp;</p>



<p>If that data is not structured well, you may be invisible, no matter how compelling your brand story is.</p>



<h2 class="wp-block-heading" id="h-agents-are-not-necessarily-doing-the-whole-journey-yet">Agents are not necessarily doing the whole journey yet</h2>



<p>One of the biggest unknowns right now is how much of the journey an agent will actually take on. That will depend heavily on human trust.</p>



<p>In the short term, agents are more likely to be used for filtering, refining, and shortlisting, not making the final call.&nbsp;</p>



<p>A person might say, “Find me the best two options with free delivery,” or, “I need something that can arrive tomorrow without paying extra.”&nbsp;</p>



<p>The agent’s role is to surface the right recommendations, not make the decision.</p>



<p>That means the data the agent sees and passes back is what the human will be judging.&nbsp;</p>



<div class="nl-inline-form border py-2 px-1 my-2">
	
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<hr class="wp-block-separator has-text-color has-cyan-bluish-gray-color has-css-opacity has-cyan-bluish-gray-background-color has-background"/>



<p>If your content does not clearly show that free delivery is available, or that next-day shipping applies above a certain spend, you may miss out.&nbsp;</p>



<p>Even if the human were happy with your offer, they would never hear about it if the agent could not find or interpret that information.</p>



<p>This is why marketers need to design for different lengths of agent journeys.&nbsp;</p>



<p>In some cases, agents act as helpers that scan, filter, and report back.&nbsp;</p>



<p>In others, they may go much further.&nbsp;</p>



<p>In both scenarios, agents still need access to the most critical marketing messages.&nbsp;</p>



<p>Even on the cleanest, most structured path, those value points need to be front and center.</p>



<h2 class="wp-block-heading" id="h-why-this-matters-now">Why this matters now</h2>



<p>Most brands are still designing only for people, which creates gaps for agents.</p>



<p>Here are a few scenarios already emerging:</p>



<ul class="wp-block-list">
<li>Agents bypass brands because key data fields are missing.</li>



<li>Pages do not match the structures agents are trained to look for.</li>



<li>Competitors win simply because their data is cleaner and easier to parse.</li>
</ul>



<p>Designing only for agents is also a mistake. People still make the final call in many decisions, and emotion continues to play a major role in building trust.</p>



<p>This is not a trade-off. It is not either-or. It is both-and.</p>



<p>You now have two audiences: the human and the agent acting on their behalf. You need to show up for both.</p>



<h2 class="wp-block-heading" id="h-how-to-start-designing-dual-journeys">How to start designing dual journeys</h2>



<p>There is no perfect playbook yet. But here are six early principles to start experimenting with.</p>



<ul class="wp-block-list">
<li><strong>Make your information work at two levels: </strong>Copy should connect with people. Structure should support agents. One does not replace the other. They work in tandem.</li>



<li><strong>Reduce ambiguity:</strong> People can cope with some gray area. Agents cannot. Be as clear and unambiguous as possible, especially around key product and service data.</li>



<li><strong>Create agent-friendly versions of your propositions:</strong> You do not need separate pages, just separate representations. Think emotion and empathy for humans, logic and validation for agents.</li>



<li><strong>Identify where agents already shape your journey: </strong>From filtering products to estimating costs, agents are already stepping in. Start designing with those moments in mind.</li>



<li><strong>Redefine trust:</strong> People trust stories, tone, and design. Agents trust structured data, freshness, and citation strength. Treat both as equally important.</li>



<li><strong>Test and learn:</strong> No one is getting this exactly right yet. The brands that start testing now will have a meaningful head start when it matters most.</li>
</ul>



<h2 class="wp-block-heading" id="h-what-you-can-practically-do-in-2026">What you can practically do in 2026</h2>



<p>Here is a short list to get moving:</p>



<ul class="wp-block-list">
<li>Audit your site for both readability for people and interpretability for agents.</li>



<li>Map your current journeys and overlay the agent version.</li>



<li>Close data gaps that would prevent an agent from recommending you.</li>



<li>Make sure key attributes are structured and complete.</li>



<li>Include critical value propositions in structured formats, such as delivery rules, price thresholds, availability, and authority signals.</li>



<li>Help teams see agents as an audience, not just a technical layer.</li>



<li>Experiment with conversational search to better understand how agents interpret information.</li>
</ul>



<p>This shift may be as significant as mobile, just less visible. Many decisions are now being made before the customer ever reaches you.</p>



<h2 class="wp-block-heading" id="h-the-point-of-convergence-between-people-and-agents">The point of convergence between people and agents</h2>



<p>If you design only for humans, you will lose the agent.</p>



<p>If you design only for agents, you will miss the human.</p>



<p>The real opportunity sits in the middle, designing for both.</p>



<p>This will become a core skill for marketers over the next 12 to 18 months.&nbsp;</p>



<p>No one has all the answers yet, but the direction is clear. Agents are becoming part of the customer journey.</p>



<p>The brands that start designing for that reality now will be the ones that remain visible, chosen, and relevant.</p>

For the past decade, customer journey design has assumed one thing: the customer is human. 
A real person. Messy. Emotional. Overloaded. 
Someone who needs clarity, reassurance, and a sense of progress to keep moving forward.
But in 2026, that assumption no longer holds.
AI agents are starting to influence how people search, compare, choose, and buy. 
They filter results, generate shortlists, book services, and soon may even negotiate on our behalf.
While much of AI usage is still task-based, it marks a critical shift. 
Once AI becomes part of how information is gathered, filtered, and prioritized, it begins to shape the decisions that follow.
That means the customer journey is no longer a single track. It is now a dual journey, one for the person and one for the agent.
This article does not promise all the answers. The landscape is changing fast. 
What it can do is explore why this shift matters, what dual journeys look like, and how marketers can start designing for both.
The customer is no longer the only decision-maker
When an AI agent filters 200 options down to just three, your customer is only seeing the surface of that decision. 
Visibility is being won or lost long before the person is even aware of it.
Agents are not just another channel. They are becoming gatekeepers in the decision-making process. 
That means your journey now has two layers:
The human journey.
The agent journey.
They overlap, but they work in very different ways.
Humans and agents decide in fundamentally different ways
The human journey is emotional and unpredictable. 
We get distracted. We procrastinate. 
We need reassurance. We are influenced by how something feels, not just what it says.
Agents are different. They don’t:
Get distracted.
Need clarity or persuasion.
Feel risk. They evaluate it.
Get overwhelmed. They operate on logic, rules, and data.
This means the same piece of content could succeed with one and fail with the other. 
A person might skim your copy and feel like it “just gets them,” while an agent scans your entire site, finds a key attribute missing, and removes you from the shortlist.
When you treat these two journeys as one, you risk missing both.
It’s time to map two paths
The human journey: You know this one. It includes:
Emotional drivers.
Trust signals.
Clarity and reassurance.
Cognitive load management.
Social proof.
Personal preferences and brand affinity.
This is the part of the journey that reflects how humans think, feel, and decide.
The agent journey: This is newer territory, and it requires a different playbook:
Structured, machine-readable data.
Consistent schema and clean attributes.
Up-to-date information and availability.
Clear logic and authority signals.
Agents are not reading your copy. They are parsing your data. 
If that data is not structured well, you may be invisible, no matter how compelling your brand story is.
Agents are not necessarily doing the whole journey yet
One of the biggest unknowns right now is how much of the journey an agent will actually take on. That will depend heavily on human trust.
In the short term, agents are more likely to be used for filtering, refining, and shortlisting, not making the final call. 
A person might say, “Find me the best two options with free delivery,” or, “I need something that can arrive tomorrow without paying extra.” 
The agent’s role is to surface the right recommendations, not make the decision.
That means the data the agent sees and passes back is what the human will be judging. 
Get the newsletter search marketers rely on.
  
				
								
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See terms.
If your content does not clearly show that free delivery is available, or that next-day shipping applies above a certain spend, you may miss out. 
Even if the human were happy with your offer, they would never hear about it if the agent could not find or interpret that information.
This is why marketers need to design for different lengths of agent journeys. 
In some cases, agents act as helpers that scan, filter, and report back. 
In others, they may go much further. 
In both scenarios, agents still need access to the most critical marketing messages. 
Even on the cleanest, most structured path, those value points need to be front and center.
Why this matters now
Most brands are still designing only for people, which creates gaps for agents.
Here are a few scenarios already emerging:
Agents bypass brands because key data fields are missing.
Pages do not match the structures agents are trained to look for.
Competitors win simply because their data is cleaner and easier to parse.
Designing only for agents is also a mistake. People still make the final call in many decisions, and emotion continues to play a major role in building trust.
This is not a trade-off. It is not either-or. It is both-and.
You now have two audiences: the human and the agent acting on their behalf. You need to show up for both.
How to start designing dual journeys
There is no perfect playbook yet. But here are six early principles to start experimenting with.
Make your information work at two levels: Copy should connect with people. Structure should support agents. One does not replace the other. They work in tandem.
Reduce ambiguity: People can cope with some gray area. Agents cannot. Be as clear and unambiguous as possible, especially around key product and service data.
Create agent-friendly versions of your propositions: You do not need separate pages, just separate representations. Think emotion and empathy for humans, logic and validation for agents.
Identify where agents already shape your journey: From filtering products to estimating costs, agents are already stepping in. Start designing with those moments in mind.
Redefine trust: People trust stories, tone, and design. Agents trust structured data, freshness, and citation strength. Treat both as equally important.
Test and learn: No one is getting this exactly right yet. The brands that start testing now will have a meaningful head start when it matters most.
What you can practically do in 2026
Here is a short list to get moving:
Audit your site for both readability for people and interpretability for agents.
Map your current journeys and overlay the agent version.
Close data gaps that would prevent an agent from recommending you.
Make sure key attributes are structured and complete.
Include critical value propositions in structured formats, such as delivery rules, price thresholds, availability, and authority signals.
Help teams see agents as an audience, not just a technical layer.
Experiment with conversational search to better understand how agents interpret information.
This shift may be as significant as mobile, just less visible. Many decisions are now being made before the customer ever reaches you.
The point of convergence between people and agents
If you design only for humans, you will lose the agent.
If you design only for agents, you will miss the human.
The real opportunity sits in the middle, designing for both.
This will become a core skill for marketers over the next 12 to 18 months. 
No one has all the answers yet, but the direction is clear. Agents are becoming part of the customer journey.
The brands that start designing for that reality now will be the ones that remain visible, chosen, and relevant.

AI agents increasingly filter options and shape shortlists before people ever engage, shifting where visibility and influence are earned.

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Read the Latest Trends and Headlines in Digital Marketing.

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

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