<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/Local-SEO-Alligator.png" class="attachment-large size-large wp-post-image" alt="Local SEO Alligator" style="margin-bottom: 15px;" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Local-SEO-Alligator.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/Local-SEO-Alligator-768x432.png 768w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI local packs and ads are shrinking organic space. New data shows why calls drop even when 3-pack positions hold — and what you must change.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/Local-SEO-Alligator.png" class="attachment-large size-large wp-post-image" alt="Local SEO Alligator" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Local-SEO-Alligator.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/Local-SEO-Alligator-768x432.png 768w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>For many local businesses, performance looks healthier than it is.</p>



<p>Rank trackers still show top-three positions. Visibility reports appear steady. Yet calls and website visits from Google Business Profiles are falling — sometimes fast.</p>



<p>This gap is becoming a defining feature of local search today.</p>



<p>Rankings are holding. Visibility and performance aren’t.</p>



<p>The alligator has arrived in local SEO.</p>



<h2 class="wp-block-heading" id="h-the-visibility-crisis-behind-stable-rankings">The visibility crisis behind stable rankings</h2>



<p>Across multiple U.S. industries, traditional local 3-packs are being replaced — or at least supplemented — by AI-powered local packs. These layouts behave differently from the map results we&#8217;ve optimized in the past.</p>



<p>Analysis from Sterling Sky, based on 179 Google Business Profiles, reveals a pattern that’s hard to ignore. Clicks-to-call are dropping sharply for Jepto-managed law firms. </p>



<p>When AI-powered packs replace traditional listings, the landscape shifts in four critical ways:</p>



<ul class="wp-block-list">
<li><strong>Shrinking real estate:</strong> AI packs often surface only two businesses instead of three.</li>



<li><strong>Missing call buttons:</strong> Many AI-generated summaries remove instant click-to-call options, adding friction to the customer journey.</li>



<li><strong>Different businesses appear:</strong> The businesses shown in AI packs often don’t match those in the traditional 3-pack.</li>



<li><strong>Accelerated monetization of local search:</strong> When paid ads are present, traditional 3-packs increasingly lose direct call and website buttons, reducing organic conversion opportunities.</li>
</ul>



<p>A fifth issue compounds the problem:</p>



<ul class="wp-block-list">
<li><strong>Measurement blind spots:</strong> Most rank trackers don’t yet report on AI local packs. A business may rank first in a 3-pack that many users never see.</li>
</ul>



<p>AI local packs surfaced only 32% as many unique businesses as traditional map packs in 2026, according to Sterling Sky. In 88% of the 322 markets analyzed, the total number of visible businesses declined.</p>



<p>At the same time, paid ads continue to take over space once reserved for organic results, signaling a clear shift toward a pay-to-play local landscape.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="797" height="380" src="https://searchengineland.com/wp-content/seloads/2026/02/percent-of-keywords-showing-local-pack-ads.png" alt="" class="wp-image-468324" srcset="https://searchengineland.com/wp-content/seloads/2026/02/percent-of-keywords-showing-local-pack-ads.png 797w, https://searchengineland.com/wp-content/seloads/2026/02/percent-of-keywords-showing-local-pack-ads-768x366.png 768w" sizes="(max-width: 797px) 100vw, 797px" /></figure>



<h2 class="wp-block-heading">What Google Business Profile data shows</h2>



<p>The same pattern appears, especially in the U.S., where Google is aggressively testing new local formats, according to GMBapi.com data. Traditional local 3-pack impressions are increasingly displaced by:</p>



<ul class="wp-block-list">
<li><strong>AI-powered local packs.</strong></li>



<li><strong>Paid placements inside traditional map packs:</strong> Sponsored listings now appear alongside or within the map pack, pushing organic results lower and stripping listings of call and website buttons. This breaks organic customer journeys.</li>



<li><strong>Expanded Google Ads units:</strong> Including Local Services Ads that consume space once reserved for organic visibility.</li>
</ul>



<p>Impression trends still fluctuate due to seasonality, market differences, and occasional API anomalies. But a much clearer signal emerges when you look at GBP actions rather than impressions.</p>



<p>Mentions inside AI-generated results are still counted as impressions — even when they no longer drive calls, clicks, or visits.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="990" height="590" src="https://searchengineland.com/wp-content/seloads/2026/02/gmbapi-us-customer-impressions.png" alt="" class="wp-image-468325" srcset="https://searchengineland.com/wp-content/seloads/2026/02/gmbapi-us-customer-impressions.png 990w, https://searchengineland.com/wp-content/seloads/2026/02/gmbapi-us-customer-impressions-768x458.png 768w" sizes="(max-width: 990px) 100vw, 990px" /></figure>



<p>Some fluctuations are driven by external factors. For example, the June drop ties back to a known Google API issue. Mobile Maps impressions also appear heavily influenced by large advertisers ramping up Google Ads later in the year.</p>



<p>There’s no way to segment these impressions by Google Ads, organic results, or AI Mode.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="990" height="590" src="https://searchengineland.com/wp-content/seloads/2026/02/gmbapi-nl-customers-impressions.png" alt="" class="wp-image-468326" srcset="https://searchengineland.com/wp-content/seloads/2026/02/gmbapi-nl-customers-impressions.png 990w, https://searchengineland.com/wp-content/seloads/2026/02/gmbapi-nl-customers-impressions-768x458.png 768w" sizes="(max-width: 990px) 100vw, 990px" /></figure>



<p>Even there, however, user behaviour is changing. Interaction rates are declining, with fewer direct actions taken from local listings.</p>



<p>Year-on-year comparisons in the US suggest that while impression losses remain moderate and partially seasonal, GBP actions are disproportionately impacted.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="990" height="590" src="https://searchengineland.com/wp-content/seloads/2026/02/gmbapi-us-customers-business-actions.png" alt="" class="wp-image-468327" srcset="https://searchengineland.com/wp-content/seloads/2026/02/gmbapi-us-customers-business-actions.png 990w, https://searchengineland.com/wp-content/seloads/2026/02/gmbapi-us-customers-business-actions-768x458.png 768w" sizes="(max-width: 990px) 100vw, 990px" /></figure>



<p>As a counterfactual, data from the Dutch market — where SERP experimentation remains limited — shows far more stable action trends.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="990" height="590" src="https://searchengineland.com/wp-content/seloads/2026/02/gmbapi-nl-customers-business-actions.png" alt="" class="wp-image-468328" srcset="https://searchengineland.com/wp-content/seloads/2026/02/gmbapi-nl-customers-business-actions.png 990w, https://searchengineland.com/wp-content/seloads/2026/02/gmbapi-nl-customers-business-actions-768x458.png 768w" sizes="(max-width: 990px) 100vw, 990px" /></figure>



<p>The pattern is clear. AI-driven SERP changes, expanding Google Ads, and the removal of call and website buttons from the Map Pack are shrinking organic real estate. Even when visibility looks intact, businesses have fewer chances to earn real user actions.</p>



<h2 class="wp-block-heading">Local SEO is becoming an eligibility problem</h2>



<p>Historically, local optimization centered on familiar ranking factors: proximity, relevance, prominence, reviews, citations, and engagement.</p>



<p>Today, another layer sits above all of them: eligibility.</p>



<p>Many businesses fail to appear in AI-powered local results not because they lack authority, but because Google’s systems decide they aren’t an appropriate match for the specific query context. Research from Yext and insights from practitioners like Claudia Tomina highlight the importance of alignment across three core signals:</p>



<ul class="wp-block-list">
<li>Business name</li>



<li>Primary category</li>



<li>Real-world services and positioning</li>
</ul>



<p>When these fundamentals are misaligned, businesses can be excluded from entire result types — no matter how well optimized the Google Business Profile itself may be.</p>



<h2 class="wp-block-heading">How to future-proof local visibility</h2>



<p>Surviving today&#8217;s zero-click reality means moving beyond reliance on a single, perfectly optimized Google Business Profile. Here’s your new local SEO playbook.</p>



<h3 class="wp-block-heading" id="h-the-eligibility-gatekeeper">The eligibility gatekeeper</h3>



<p>Failure to appear in local packs is now driven more by perceived relevance and classification than by links or review volume.</p>



<h3 class="wp-block-heading" id="h-hyper-local-entity-authority">Hyper-local entity authority</h3>



<p>AI systems cross-reference Reddit, social platforms, forums, and local directories to judge whether a business is legitimate and active. Inconsistent signals across these ecosystems quietly erode visibility.</p>



<h3 class="wp-block-heading" id="h-visual-trust-signals">Visual trust signals</h3>



<p>High-quality, frequently updated photos, and increasingly video, are no longer optional. Google’s AI analyzes visual content to infer services, intent, and categorization.</p>



<h3 class="wp-block-heading" id="h-embrace-the-pay-to-play-reality">Embrace the pay-to-play reality</h3>



<p>It’s a hard truth, but Google Ads — especially Local Services Ads — are now critical to retaining prominent call buttons that organic listings are losing. A hybrid strategy that blends local SEO with paid search isn’t optional. It’s the baseline.</p>



<h2 class="wp-block-heading" id="h-what-this-means-for-local-search-now">What this means for local search now</h2>



<p>Local SEO is no longer a static directory exercise. Google Business Profiles still anchor local discoverability, but they now operate inside a much broader ecosystem shaped by AI validation, constant SERP experimentation, and Google’s accelerating push to monetize local search.</p>



<p>Discovery no longer hinges on where your GBP ranks against nearby competitors. Search systems — including Google’s AI-driven SERP features and large language models like ChatGPT and Gemini — are increasingly trying to understand what a business actually does, not just where it’s listed. </p>



<p>Success is no longer about being the most “optimized” profile. It’s about being widely verified, consistently active, and contextually relevant across the AI-visible ecosystem.</p>



<p>Our observations show little correlation between businesses that rank well in the traditional Map Pack and those favored by Google’s AI-generated local answers that are beginning to replace it. That gap creates a real opportunity for businesses willing to adapt.</p>



<p>In practice, this means pairing local input with central oversight. </p>



<p>Authentic engagement across multiple platforms, locally differentiated content, and real community signals must coexist with brand governance, data consistency, and operational scale. For single-location businesses with deep community roots, this is an advantage. Being genuinely discussed, recommended, and referenced in your local area — online and offline — gets you halfway there.</p>



<p>For agencies and multi-location brands, the challenge is to balance control with local nuance and ensure trusted signals extend beyond Google (e.g., Apple Maps, Tripadvisor, Yelp, Reddit, and other relevant review ecosystems). The real test is producing locally relevant content and citations at scale without losing authenticity.</p>



<p>Rankings may look stable. But performance increasingly lives somewhere else.</p>



<p><strong>The full data.</strong> <a href="https://gmbapi.com/news/local-seo-2026-ai-packs-vanishing-calls/" target="_blank" rel="noopener">Local SEO in 2026: Why Your Rankings are Steady but Your Calls are Vanishing</a></p>



<p><a href="https://gmbapi.com/author/michel/"></a></p>


For many local businesses, performance looks healthier than it is.
Rank trackers still show top-three positions. Visibility reports appear steady. Yet calls and website visits from Google Business Profiles are falling — sometimes fast.
This gap is becoming a defining feature of local search today.
Rankings are holding. Visibility and performance aren’t.
The alligator has arrived in local SEO.
The visibility crisis behind stable rankings
Across multiple U.S. industries, traditional local 3-packs are being replaced — or at least supplemented — by AI-powered local packs. These layouts behave differently from the map results we’ve optimized in the past.
Analysis from Sterling Sky, based on 179 Google Business Profiles, reveals a pattern that’s hard to ignore. Clicks-to-call are dropping sharply for Jepto-managed law firms. 
When AI-powered packs replace traditional listings, the landscape shifts in four critical ways:
Shrinking real estate: AI packs often surface only two businesses instead of three.
Missing call buttons: Many AI-generated summaries remove instant click-to-call options, adding friction to the customer journey.
Different businesses appear: The businesses shown in AI packs often don’t match those in the traditional 3-pack.
Accelerated monetization of local search: When paid ads are present, traditional 3-packs increasingly lose direct call and website buttons, reducing organic conversion opportunities.
A fifth issue compounds the problem:
Measurement blind spots: Most rank trackers don’t yet report on AI local packs. A business may rank first in a 3-pack that many users never see.
AI local packs surfaced only 32% as many unique businesses as traditional map packs in 2026, according to Sterling Sky. In 88% of the 322 markets analyzed, the total number of visible businesses declined.
At the same time, paid ads continue to take over space once reserved for organic results, signaling a clear shift toward a pay-to-play local landscape.




What Google Business Profile data shows
The same pattern appears, especially in the U.S., where Google is aggressively testing new local formats, according to GMBapi.com data. Traditional local 3-pack impressions are increasingly displaced by:
AI-powered local packs.
Paid placements inside traditional map packs: Sponsored listings now appear alongside or within the map pack, pushing organic results lower and stripping listings of call and website buttons. This breaks organic customer journeys.
Expanded Google Ads units: Including Local Services Ads that consume space once reserved for organic visibility.
Impression trends still fluctuate due to seasonality, market differences, and occasional API anomalies. But a much clearer signal emerges when you look at GBP actions rather than impressions.
Mentions inside AI-generated results are still counted as impressions — even when they no longer drive calls, clicks, or visits.




Some fluctuations are driven by external factors. For example, the June drop ties back to a known Google API issue. Mobile Maps impressions also appear heavily influenced by large advertisers ramping up Google Ads later in the year.
There’s no way to segment these impressions by Google Ads, organic results, or AI Mode.




Even there, however, user behaviour is changing. Interaction rates are declining, with fewer direct actions taken from local listings.
Year-on-year comparisons in the US suggest that while impression losses remain moderate and partially seasonal, GBP actions are disproportionately impacted.




As a counterfactual, data from the Dutch market — where SERP experimentation remains limited — shows far more stable action trends.




The pattern is clear. AI-driven SERP changes, expanding Google Ads, and the removal of call and website buttons from the Map Pack are shrinking organic real estate. Even when visibility looks intact, businesses have fewer chances to earn real user actions.
Local SEO is becoming an eligibility problem
Historically, local optimization centered on familiar ranking factors: proximity, relevance, prominence, reviews, citations, and engagement.
Today, another layer sits above all of them: eligibility.
Many businesses fail to appear in AI-powered local results not because they lack authority, but because Google’s systems decide they aren’t an appropriate match for the specific query context. Research from Yext and insights from practitioners like Claudia Tomina highlight the importance of alignment across three core signals:
Business name
Primary category
Real-world services and positioning
When these fundamentals are misaligned, businesses can be excluded from entire result types — no matter how well optimized the Google Business Profile itself may be.
How to future-proof local visibility
Surviving today’s zero-click reality means moving beyond reliance on a single, perfectly optimized Google Business Profile. Here’s your new local SEO playbook.
The eligibility gatekeeper
Failure to appear in local packs is now driven more by perceived relevance and classification than by links or review volume.
Hyper-local entity authority
AI systems cross-reference Reddit, social platforms, forums, and local directories to judge whether a business is legitimate and active. Inconsistent signals across these ecosystems quietly erode visibility.
Visual trust signals
High-quality, frequently updated photos, and increasingly video, are no longer optional. Google’s AI analyzes visual content to infer services, intent, and categorization.
Embrace the pay-to-play reality
It’s a hard truth, but Google Ads — especially Local Services Ads — are now critical to retaining prominent call buttons that organic listings are losing. A hybrid strategy that blends local SEO with paid search isn’t optional. It’s the baseline.
What this means for local search now
Local SEO is no longer a static directory exercise. Google Business Profiles still anchor local discoverability, but they now operate inside a much broader ecosystem shaped by AI validation, constant SERP experimentation, and Google’s accelerating push to monetize local search.
Discovery no longer hinges on where your GBP ranks against nearby competitors. Search systems — including Google’s AI-driven SERP features and large language models like ChatGPT and Gemini — are increasingly trying to understand what a business actually does, not just where it’s listed. 
Success is no longer about being the most “optimized” profile. It’s about being widely verified, consistently active, and contextually relevant across the AI-visible ecosystem.
Our observations show little correlation between businesses that rank well in the traditional Map Pack and those favored by Google’s AI-generated local answers that are beginning to replace it. That gap creates a real opportunity for businesses willing to adapt.
In practice, this means pairing local input with central oversight. 
Authentic engagement across multiple platforms, locally differentiated content, and real community signals must coexist with brand governance, data consistency, and operational scale. For single-location businesses with deep community roots, this is an advantage. Being genuinely discussed, recommended, and referenced in your local area — online and offline — gets you halfway there.
For agencies and multi-location brands, the challenge is to balance control with local nuance and ensure trusted signals extend beyond Google (e.g., Apple Maps, Tripadvisor, Yelp, Reddit, and other relevant review ecosystems). The real test is producing locally relevant content and citations at scale without losing authenticity.
Rankings may look stable. But performance increasingly lives somewhere else.
The full data. Local SEO in 2026: Why Your Rankings are Steady but Your Calls are Vanishing

AI local packs and ads are shrinking organic space. New data shows why calls drop even when 3-pack positions hold — and what you must change.

Your local rankings look fine. So why are calls disappearing?

<div><img width="800" height="462" src="https://searchengineland.com/wp-content/seloads/2023/03/google-core-update-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/2023/03/google-core-update-1920-800x462.jpg 800w, https://searchengineland.com/wp-content/seloads/2023/03/google-core-update-1920-586x338.jpg 586w, https://searchengineland.com/wp-content/seloads/2023/03/google-core-update-1920-196x113.jpg 196w, https://searchengineland.com/wp-content/seloads/2023/03/google-core-update-1920-768x443.jpg 768w, https://searchengineland.com/wp-content/seloads/2023/03/google-core-update-1920-1536x886.jpg 1536w, https://searchengineland.com/wp-content/seloads/2023/03/google-core-update-1920-150x87.jpg 150w, https://searchengineland.com/wp-content/seloads/2023/03/google-core-update-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Google’s Discover core update prioritizes locally relevant content based on a user’s country while reducing sensational and clickbait content.

<div><img width="800" height="462" src="https://searchengineland.com/wp-content/seloads/2023/03/google-core-update-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/2023/03/google-core-update-1920-800x462.jpg 800w, https://searchengineland.com/wp-content/seloads/2023/03/google-core-update-1920-586x338.jpg 586w, https://searchengineland.com/wp-content/seloads/2023/03/google-core-update-1920-196x113.jpg 196w, https://searchengineland.com/wp-content/seloads/2023/03/google-core-update-1920-768x443.jpg 768w, https://searchengineland.com/wp-content/seloads/2023/03/google-core-update-1920-1536x886.jpg 1536w, https://searchengineland.com/wp-content/seloads/2023/03/google-core-update-1920-150x87.jpg 150w, https://searchengineland.com/wp-content/seloads/2023/03/google-core-update-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Google has released the February 2026 Discover core update, which focuses specifically on how content is surfaced in Google Discover.</p>



<ul class="wp-block-list">
<li>&#8220;This is a broad update to our systems that surface articles in Discover,&#8221; Google <a href="https://developers.google.com/search/blog/2026/02/discover-core-update">wrote</a>.</li>
</ul>



<p>Google said the update is rolling out first to English-language users in the U.S. and will expand to all countries and languages in the coming months. The rollout may take up to two weeks to complete, Google <a href="https://status.search.google.com/incidents/mYbNTqV1ytDc2fA8hUz4">added</a>.</p>



<p><strong>What is expected. </strong>Google said the Discover core update will improve the &#8220;experience in a few key ways,&#8221; including:</p>



<ul class="wp-block-list">
<li>Showing users more locally relevant content from websites based in their country.</li>



<li>Reducing sensational content and clickbait.</li>



<li>Highlighting more in-depth, original, and timely content from sites with demonstrated expertise in a given area, based on Google’s understanding of a site’s content.</li>
</ul>



<p>Because the update prioritizes locally relevant content, it may reduce traffic for non-U.S. websites that publish news for a U.S. audience. That impact may lessen or disappear as the update expands globally.</p>



<p><strong>More details. </strong>Google added that many sites demonstrate deep knowledge across a wide range of subjects, and its systems are built to identify expertise on a topic-by-topic basis. As a result, any site can appear in Discover, whether it covers multiple areas or focuses deeply on a single topic. Google shared an example: </p>



<ul class="wp-block-list">
<li>&#8220;A local news site with a dedicated gardening section could have established expertise in gardening, even though it covers other topics. In contrast, a movie review site that wrote a single article about gardening would likely not.&#8221;</li>
</ul>



<p>Google said it will continue to &#8220;show content that’s personalized based on people’s creator and source preferences.&#8221;</p>



<p>During testing, Google found that &#8220;people find the Discover experience more useful and worthwhile with this update.&#8221;</p>



<p><strong>Expect fluctuations. </strong>With this Discover core update, expect fluctuations in traffic from Google Discover.</p>



<ul class="wp-block-list">
<li>&#8220;Some sites might see increases or decreases; many sites may see no change at all,&#8221; Google said.</li>
</ul>



<p><strong>Rollout. </strong>Google said it is &#8220;releasing this update to English language users in the US, and will expand it to all countries and languages in the months ahead.&nbsp;&#8220;</p>



<p><strong>Why we care. </strong>If you get traffic from Google Discover, you may notice changes in that traffic in the coming days.  Google recommends that if you need guidance, Google has &#8220;general guidance about <a href="https://developers.google.com/search/docs/appearance/core-updates">core updates</a> applies, as does our <a href="https://developers.google.com/search/docs/appearance/google-discover#how-content-appears-in-discover">Get on Discover help page</a>&#8221; in those help documents. </p>


Google has released the February 2026 Discover core update, which focuses specifically on how content is surfaced in Google Discover.
“This is a broad update to our systems that surface articles in Discover,” Google wrote.
Google said the update is rolling out first to English-language users in the U.S. and will expand to all countries and languages in the coming months. The rollout may take up to two weeks to complete, Google added.
What is expected. Google said the Discover core update will improve the “experience in a few key ways,” including:
Showing users more locally relevant content from websites based in their country.
Reducing sensational content and clickbait.
Highlighting more in-depth, original, and timely content from sites with demonstrated expertise in a given area, based on Google’s understanding of a site’s content.
Because the update prioritizes locally relevant content, it may reduce traffic for non-U.S. websites that publish news for a U.S. audience. That impact may lessen or disappear as the update expands globally.
More details. Google added that many sites demonstrate deep knowledge across a wide range of subjects, and its systems are built to identify expertise on a topic-by-topic basis. As a result, any site can appear in Discover, whether it covers multiple areas or focuses deeply on a single topic. Google shared an example: 
“A local news site with a dedicated gardening section could have established expertise in gardening, even though it covers other topics. In contrast, a movie review site that wrote a single article about gardening would likely not.”
Google said it will continue to “show content that’s personalized based on people’s creator and source preferences.”
During testing, Google found that “people find the Discover experience more useful and worthwhile with this update.”
Expect fluctuations. With this Discover core update, expect fluctuations in traffic from Google Discover.
“Some sites might see increases or decreases; many sites may see no change at all,” Google said.
Rollout. Google said it is “releasing this update to English language users in the US, and will expand it to all countries and languages in the months ahead. “
Why we care. If you get traffic from Google Discover, you may notice changes in that traffic in the coming days.  Google recommends that if you need guidance, Google has “general guidance about core updates applies, as does our Get on Discover help page” in those help documents.

Google’s Discover core update prioritizes locally relevant content based on a user’s country while reducing sensational and clickbait content.

Google releases February 2026 Discover core update

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords.jpg" class="attachment-large size-large wp-post-image" alt="Why Google Ads auctions now run on intent, not keywords" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>If you’re still building Google Ads around keywords, you’re behind. Here's what that means for eligibility, structure, and PPC strategy.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords.jpg" class="attachment-large size-large wp-post-image" alt="Why Google Ads auctions now run on intent, not keywords" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Most <a href="https://searchengineland.com/guide/what-is-paid-search">PPC</a> teams still build campaigns the same way: pull a keyword list, set match types, and organize ad groups around search terms. It’s muscle memory.</p>



<p>But Google’s <a href="https://searchengineland.com/guide/ppc/how-the-ppc-ad-auction-works">auction</a> no longer works that way.</p>



<p>Search now behaves more like a conversation than a lookup. In AI Mode, users ask follow-up questions and refine what they’re trying to solve. AI Overviews reason through an answer first, then determine which ads support that answer.</p>



<p>In Google Ads, the auction isn’t triggered by a keyword anymore – it’s triggered by inferred intent.</p>



<p>If you’re still structuring campaigns around exact and phrase match, you’re planning for a system that no longer exists. The new foundation is intent: not the words people type, but the goals behind them.</p>



<p>An intent-first approach gives you a more durable way to design campaigns, creative, and measurement as Google introduces new AI-driven formats.</p>



<p>Keywords aren’t dead, but they’re no longer the blueprint.</p>



<h2 class="wp-block-heading" id="h-the-mechanics-under-the-hood-have-changed">The mechanics under the hood have changed</h2>



<p>Here&#8217;s what&#8217;s actually happening when someone searches now.</p>



<p>Google&#8217;s AI uses a technique called &#8220;query fan out,&#8221; splitting a complex question into subtopics and running multiple concurrent searches to build a comprehensive response.</p>



<p>The auction happens <strong>before</strong> the user even finishes typing.</p>



<p>And crucially, the AI infers commercial intent from purely informational queries.</p>



<p><span style="box-sizing: border-box; margin: 0px; padding: 0px;">For instance, someone asks, &#8220;Why is my pool green?&#8221;</span> They&#8217;re not shopping. They&#8217;re troubleshooting.</p>



<p>But Google&#8217;s reasoning layer detects a problem that products can solve and serves ads for pool-cleaning supplies alongside the explanation. While the user didn&#8217;t search for a product, the AI knew they would need one.</p>



<p>This auction logic is fundamentally different from what we’re accustomed to. It&#8217;s not matching your keyword to the query. It&#8217;s matching your offering to the user’s inferred need state, based on conversational context. </p>



<p>If your campaign structure still assumes people search in isolated, transactional moments, you&#8217;re missing the journey entirely.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1600" height="893" src="https://searchengineland.com/wp-content/seloads/2026/02/Anatomy-of-a-Google-AI-search-query.png" alt="Anatomy of a Google AI search query" class="wp-image-468275" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Anatomy-of-a-Google-AI-search-query.png 1600w, https://searchengineland.com/wp-content/seloads/2026/02/Anatomy-of-a-Google-AI-search-query-768x429.png 768w" sizes="(max-width: 1600px) 100vw, 1600px" /></figure></div>


<p><strong><em>Dig deeper: <a href="https://searchengineland.com/modern-targeting-strategy-google-ads-smx-next-465927">How to build a modern Google Ads targeting strategy like a pro</a></em></strong></p>



<h2 class="wp-block-heading" id="h-what-intent-first-actually-means">What &#8216;intent-first&#8217; actually means</h2>



<p>An intent-first strategy doesn&#8217;t mean you stop doing keyword research. It means you stop treating keywords as the organizing principle.</p>



<p>Instead, you map campaigns to the <strong>why</strong> behind the search. </p>



<ul class="wp-block-list">
<li>What problem is the user trying to solve? </li>



<li>What stage of decision-making are they in? </li>



<li>What job are they hiring your product to do?</li>
</ul>



<p>The same intent can surface through dozens of different queries, and the same query can reflect multiple intents depending on context. </p>



<p>&#8220;Best CRM&#8221; could mean either &#8220;I need feature comparisons&#8221; or &#8220;I&#8217;m ready to buy and want validation.&#8221; Google&#8217;s AI now reads that difference, and your campaign structure should, too.</p>



<p>This is more of a mental model shift than a tactical one. </p>



<p>You&#8217;re still building keyword lists, but you&#8217;re grouping them by intent state rather than match type. </p>



<p>You&#8217;re still writing ad copy, but you&#8217;re speaking to user goals instead of echoing search terms back at them.</p>



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<h2 class="wp-block-heading" id="h-what-changes-in-practice">What changes in practice</h2>



<p>Once campaigns are organized around intent instead of keywords, the downstream implications show up quickly – in eligibility, landing pages, and how the system learns.</p>



<h3 class="wp-block-heading" id="h-campaign-eligibility">Campaign eligibility </h3>



<p>If you want to show up inside AI Overviews or AI Mode, you need broad match keywords, Performance Max, or the newer AI Max for Search campaigns. </p>



<p>Exact and phrase match still work for brand defense and high-visibility placements above the AI summaries, but they won&#8217;t get you into the conversational layer where exploration happens.</p>



<h3 class="wp-block-heading" id="h-landing-page-evolution">Landing page evolution</h3>



<p>It&#8217;s not enough to list product features anymore. If your page explains <strong>why</strong> and <strong>how</strong> someone should use your product (not just <strong>what</strong> it is), you&#8217;re more likely to win the auction. </p>



<p>Google&#8217;s reasoning layer rewards contextual alignment. If the AI built an answer about solving a problem, and your page directly addresses that problem, you&#8217;re in.</p>



<h3 class="wp-block-heading" id="h-asset-volume-and-training-data">Asset volume and training data</h3>



<p>The algorithm prioritizes rich metadata, multiple high-quality images, and optimized shopping feeds with every relevant attribute filled in. </p>



<p>Using Customer Match lists to feed the system first-party data teaches the AI which user segments represent the highest value.</p>



<p>That training affects how aggressively it bids for similar users.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/in-google-ads-automation-everything-is-a-signal-in-2026-468218">In Google Ads automation, everything is a signal in 2026</a></em></strong></p>



<h2 class="wp-block-heading" id="h-the-gaps-worth-knowing-about">The gaps worth knowing about</h2>



<p>Even as intent-first campaigns unlock new reach, there are still blind spots in reporting, budget constraints, and performance expectations you need to plan around.</p>



<h3 class="wp-block-heading" id="h-no-reporting-segmentation">No reporting segmentation</h3>



<p>Google doesn&#8217;t provide visibility into how ads perform specifically in AI Mode versus traditional search. </p>



<p>You&#8217;re monitoring overall cost-per-conversion and hoping high-funnel clicks convert downstream, but you can&#8217;t isolate which placements are actually driving results.</p>



<h3 class="wp-block-heading" id="h-the-budget-barrier">The budget barrier</h3>



<p>AI-powered campaigns like Performance Max and AI Max need meaningful conversion volume to scale effectively, often 30 conversions in 30 days at a minimum. </p>



<p>Smaller advertisers with limited budgets or longer sales cycles face what some call a &#8220;scissors gap,&#8221; in which they lack the data needed to train algorithms and compete in automated auctions.</p>



<h3 class="wp-block-heading" id="h-funnel-position-matters">Funnel position matters</h3>



<p>AI Mode attracts exploratory, high-funnel behavior. Conversion rates won&#8217;t match bottom-of-the-funnel branded searches. That&#8217;s expected if you&#8217;re planning for it. </p>



<p>It becomes a problem when you&#8217;re chasing immediate ROAS without adjusting how you define success for these placements.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/outsmarting-google-ads-insider-strategies-453124">Outsmarting Google Ads: Insider strategies to navigate changes like a pro</a></em></strong></p>



<h2 class="wp-block-heading" id="h-where-to-start">Where to start</h2>



<p>You don&#8217;t need to rebuild everything overnight.</p>



<p>Pick one campaign where you suspect intent is more complex than the keywords suggest. Map it to user goal states instead of search term buckets. </p>



<p>Test broad match in a limited way. Rewrite one landing page to answer the &#8220;why&#8221; instead of just listing specs.</p>



<p>The shift to intent-first is not a tactic – it&#8217;s a lens. And it&#8217;s the most durable way to plan as Google keeps introducing new AI-driven formats.</p>

Most PPC teams still build campaigns the same way: pull a keyword list, set match types, and organize ad groups around search terms. It’s muscle memory.
But Google’s auction no longer works that way.
Search now behaves more like a conversation than a lookup. In AI Mode, users ask follow-up questions and refine what they’re trying to solve. AI Overviews reason through an answer first, then determine which ads support that answer.
In Google Ads, the auction isn’t triggered by a keyword anymore – it’s triggered by inferred intent.
If you’re still structuring campaigns around exact and phrase match, you’re planning for a system that no longer exists. The new foundation is intent: not the words people type, but the goals behind them.
An intent-first approach gives you a more durable way to design campaigns, creative, and measurement as Google introduces new AI-driven formats.
Keywords aren’t dead, but they’re no longer the blueprint.
The mechanics under the hood have changed
Here’s what’s actually happening when someone searches now.
Google’s AI uses a technique called “query fan out,” splitting a complex question into subtopics and running multiple concurrent searches to build a comprehensive response.
The auction happens before the user even finishes typing.
And crucially, the AI infers commercial intent from purely informational queries.
For instance, someone asks, “Why is my pool green?” They’re not shopping. They’re troubleshooting.
But Google’s reasoning layer detects a problem that products can solve and serves ads for pool-cleaning supplies alongside the explanation. While the user didn’t search for a product, the AI knew they would need one.
This auction logic is fundamentally different from what we’re accustomed to. It’s not matching your keyword to the query. It’s matching your offering to the user’s inferred need state, based on conversational context. 
If your campaign structure still assumes people search in isolated, transactional moments, you’re missing the journey entirely.

Dig deeper: How to build a modern Google Ads targeting strategy like a pro
What ‘intent-first’ actually means
An intent-first strategy doesn’t mean you stop doing keyword research. It means you stop treating keywords as the organizing principle.
Instead, you map campaigns to the why behind the search. 
What problem is the user trying to solve? 
What stage of decision-making are they in? 
What job are they hiring your product to do?
The same intent can surface through dozens of different queries, and the same query can reflect multiple intents depending on context. 
“Best CRM” could mean either “I need feature comparisons” or “I’m ready to buy and want validation.” Google’s AI now reads that difference, and your campaign structure should, too.
This is more of a mental model shift than a tactical one. 
You’re still building keyword lists, but you’re grouping them by intent state rather than match type. 
You’re still writing ad copy, but you’re speaking to user goals instead of echoing search terms back at them.
Get the newsletter search marketers rely on.
  
				
								
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See terms.
What changes in practice
Once campaigns are organized around intent instead of keywords, the downstream implications show up quickly – in eligibility, landing pages, and how the system learns.
Campaign eligibility 
If you want to show up inside AI Overviews or AI Mode, you need broad match keywords, Performance Max, or the newer AI Max for Search campaigns. 
Exact and phrase match still work for brand defense and high-visibility placements above the AI summaries, but they won’t get you into the conversational layer where exploration happens.
Landing page evolution
It’s not enough to list product features anymore. If your page explains why and how someone should use your product (not just what it is), you’re more likely to win the auction. 
Google’s reasoning layer rewards contextual alignment. If the AI built an answer about solving a problem, and your page directly addresses that problem, you’re in.
Asset volume and training data
The algorithm prioritizes rich metadata, multiple high-quality images, and optimized shopping feeds with every relevant attribute filled in. 
Using Customer Match lists to feed the system first-party data teaches the AI which user segments represent the highest value.
That training affects how aggressively it bids for similar users.
Dig deeper: In Google Ads automation, everything is a signal in 2026
The gaps worth knowing about
Even as intent-first campaigns unlock new reach, there are still blind spots in reporting, budget constraints, and performance expectations you need to plan around.
No reporting segmentation
Google doesn’t provide visibility into how ads perform specifically in AI Mode versus traditional search. 
You’re monitoring overall cost-per-conversion and hoping high-funnel clicks convert downstream, but you can’t isolate which placements are actually driving results.
The budget barrier
AI-powered campaigns like Performance Max and AI Max need meaningful conversion volume to scale effectively, often 30 conversions in 30 days at a minimum. 
Smaller advertisers with limited budgets or longer sales cycles face what some call a “scissors gap,” in which they lack the data needed to train algorithms and compete in automated auctions.
Funnel position matters
AI Mode attracts exploratory, high-funnel behavior. Conversion rates won’t match bottom-of-the-funnel branded searches. That’s expected if you’re planning for it. 
It becomes a problem when you’re chasing immediate ROAS without adjusting how you define success for these placements.
Dig deeper: Outsmarting Google Ads: Insider strategies to navigate changes like a pro
Where to start
You don’t need to rebuild everything overnight.
Pick one campaign where you suspect intent is more complex than the keywords suggest. Map it to user goal states instead of search term buckets. 
Test broad match in a limited way. Rewrite one landing page to answer the “why” instead of just listing specs.
The shift to intent-first is not a tactic – it’s a lens. And it’s the most durable way to plan as Google keeps introducing new AI-driven formats.

If you’re still building Google Ads around keywords, you’re behind. Here's what that means for eligibility, structure, and PPC strategy.

Google Ads no longer runs on keywords. It runs on intent.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/google-money-printing-machine.png" class="attachment-large size-large wp-post-image" alt="Google money printing machine" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/google-money-printing-machine.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/google-money-printing-machine-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/google-money-printing-machine-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI-powered search is driving record engagement and ad growth, as executives say new experiences are adding usage, not replacing queries.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/google-money-printing-machine.png" class="attachment-large size-large wp-post-image" alt="Google money printing machine" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/google-money-printing-machine.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/google-money-printing-machine-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/google-money-printing-machine-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google Search is entering an “expansionary moment,” fueled by longer queries, more follow-up questions, and rising use of voice and images. That&#8217;s according to Alphabet&#8217;s executives who spoke on last night&#8217;s Q4 earnings call.</p>



<ul class="wp-block-list">
<li><strong>In other words: </strong>Google Search is shifting toward AI-driven experiences, with more conversations happening inside Google’s own interfaces.</li>
</ul>



<p><strong>Why we care.</strong> AI in Google Search is no longer an experiment. It’s a structural shift that’s changing how people search and reshaping discovery, visibility, and traffic across the web.</p>



<p><strong>By the numbers.</strong> Alphabet&#8217;s Q4 advertising revenue totaled $82.284 billion, up 13.5% from $72.461 billion 2024: </p>



<ul class="wp-block-list">
<li><strong>Google Search &amp; other:</strong> $63.073 billion (up 16.7%)</li>



<li><strong>YouTube: </strong>$11.383 billion (up 8.7%)</li>



<li><strong>Google Network:</strong> $7.828 billion ( down 1.5%)</li>
</ul>



<p>Alphabet&#8217;s 2025 fiscal year advertising revenue totaled $294.691 billion, up 11.4% from $264.590 billion in 2024:</p>



<ul class="wp-block-list">
<li><strong>Google Search &amp; other:</strong> $224,532 billion (up 13.4%)</li>



<li><strong>YouTube: </strong>$40.367 billion (up 11.7%)</li>



<li><strong>Google Network:</strong> $29.792 billion ( down 1.9%)</li>
</ul>



<p><strong>AI Overviews and AI Mode are now core to Search.</strong> Alphabet CEO Sundar Pichai said Google pushed aggressively on AI-powered search features in Q4, highlighting how central they’ve become to the product.</p>



<ul class="wp-block-list">
<li>“We shipped over 250 product launches, within AI mode and AI overviews just last quarter,” Pichai said.</li>
</ul>



<p>This includes Google upgrading AI Overviews to its Gemini 3 model. He said the company has tightly linked AI Overviews with conversational search.</p>



<ul class="wp-block-list">
<li>“We have also made the search experience more cohesive, ensuring the transition from an AI Overview to a conversation in AI Mode is completely seamless,” Pichai said.</li>
</ul>



<p><strong>AI is driving more Google Search usage.</strong> Executives repeatedly described AI-driven search as additive, saying it boosts overall usage rather than replacing traditional queries.</p>



<ul class="wp-block-list">
<li>“Search saw more usage in Q4 than ever before, as AI continues to drive an expansionary moment,” Pichai said.</li>
</ul>



<p>Engagement rises once users interact with AI-powered features, Google said.</p>



<ul class="wp-block-list">
<li>“Once people start using these new experiences, they use them more,” Pichai said.</li>
</ul>



<p><strong>Changing search behavior. </strong>Google shared new data points showing how AI Mode is changing search behavior — making queries longer, more conversational, and increasingly multimodal.</p>



<ul class="wp-block-list">
<li>“Queries in AI Mode are three times longer than traditional searches,” Pichai said.</li>
</ul>



<p>Sessions are also becoming more conversational.</p>



<ul class="wp-block-list">
<li>“We are also seeing sessions become more conversational, with a significant portion of queries in AI Mode, now leading to a follow-up question,” he said.</li>
</ul>



<p>AI Mode is also expanding beyond text.</p>



<ul class="wp-block-list">
<li>“Nearly one in six AI mode queries are now non-text using voice or images,” Pichai said.</li>
</ul>



<p>Google highlighted continued distribution of visual search capabilities, noting that:</p>



<ul class="wp-block-list">
<li>“Circle to Search is now available on over 580 million Android devices,” Pichai said.</li>
</ul>



<p><strong>Gemini isn&#8217;t cannibalizing Search.</strong> As the Gemini app continues to grow, Google says it hasn’t seen signs that users are abandoning Search.</p>



<ul class="wp-block-list">
<li>“We haven’t seen any evidence of cannibalization,” Pichai said.</li>
</ul>



<p>Instead, Google said users move fluidly between Search, AI Overviews, AI Mode, and the Gemini app.</p>



<ul class="wp-block-list">
<li>“The combination of all of that, I think, creates an expansionary moment,” Pichai said.</li>
</ul>


Google Search is entering an “expansionary moment,” fueled by longer queries, more follow-up questions, and rising use of voice and images. That’s according to Alphabet’s executives who spoke on last night’s Q4 earnings call.
In other words: Google Search is shifting toward AI-driven experiences, with more conversations happening inside Google’s own interfaces.
Why we care. AI in Google Search is no longer an experiment. It’s a structural shift that’s changing how people search and reshaping discovery, visibility, and traffic across the web.
By the numbers. Alphabet’s Q4 advertising revenue totaled $82.284 billion, up 13.5% from $72.461 billion 2024: 
Google Search & other: $63.073 billion (up 16.7%)
YouTube: $11.383 billion (up 8.7%)
Google Network: $7.828 billion ( down 1.5%)
Alphabet’s 2025 fiscal year advertising revenue totaled $294.691 billion, up 11.4% from $264.590 billion in 2024:
Google Search & other: $224,532 billion (up 13.4%)
YouTube: $40.367 billion (up 11.7%)
Google Network: $29.792 billion ( down 1.9%)
AI Overviews and AI Mode are now core to Search. Alphabet CEO Sundar Pichai said Google pushed aggressively on AI-powered search features in Q4, highlighting how central they’ve become to the product.
“We shipped over 250 product launches, within AI mode and AI overviews just last quarter,” Pichai said.
This includes Google upgrading AI Overviews to its Gemini 3 model. He said the company has tightly linked AI Overviews with conversational search.
“We have also made the search experience more cohesive, ensuring the transition from an AI Overview to a conversation in AI Mode is completely seamless,” Pichai said.
AI is driving more Google Search usage. Executives repeatedly described AI-driven search as additive, saying it boosts overall usage rather than replacing traditional queries.
“Search saw more usage in Q4 than ever before, as AI continues to drive an expansionary moment,” Pichai said.
Engagement rises once users interact with AI-powered features, Google said.
“Once people start using these new experiences, they use them more,” Pichai said.
Changing search behavior. Google shared new data points showing how AI Mode is changing search behavior — making queries longer, more conversational, and increasingly multimodal.
“Queries in AI Mode are three times longer than traditional searches,” Pichai said.
Sessions are also becoming more conversational.
“We are also seeing sessions become more conversational, with a significant portion of queries in AI Mode, now leading to a follow-up question,” he said.
AI Mode is also expanding beyond text.
“Nearly one in six AI mode queries are now non-text using voice or images,” Pichai said.
Google highlighted continued distribution of visual search capabilities, noting that:
“Circle to Search is now available on over 580 million Android devices,” Pichai said.
Gemini isn’t cannibalizing Search. As the Gemini app continues to grow, Google says it hasn’t seen signs that users are abandoning Search.
“We haven’t seen any evidence of cannibalization,” Pichai said.
Instead, Google said users move fluidly between Search, AI Overviews, AI Mode, and the Gemini app.
“The combination of all of that, I think, creates an expansionary moment,” Pichai said.

AI-powered search is driving record engagement and ad growth, as executives say new experiences are adding usage, not replacing queries.

Google says AI search is driving an ‘expansionary moment’

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/Local-search-in-the-AI-first-era-From-rankings-to-recommendations-in-2026.jpg" class="attachment-large size-large wp-post-image" alt="Local search in the AI-first era: From rankings to recommendations in 2026" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Local-search-in-the-AI-first-era-From-rankings-to-recommendations-in-2026.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/02/Local-search-in-the-AI-first-era-From-rankings-to-recommendations-in-2026-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/02/Local-search-in-the-AI-first-era-From-rankings-to-recommendations-in-2026-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI now mediates how customers discover and choose local services. Learn how AI systems evaluate trust, data quality, and relevance.

AI is no longer an experimental layer in search. It’s actively mediating how customers discover, evaluate, and choose local businesses, increasingly without a traditional search interaction. 
The real risk is data stagnation. As AI systems act on local data for users, brands that fail to adapt risk declining visibility, data inconsistencies, and loss of control over how locations are represented across AI surfaces.
Learn how AI is changing local search and what you can do to stay visible in this new landscape. 
How AI search is different from traditional search

We are experiencing a platform shift where machine inference, not database retrieval, drives decisions. At the same time, AI is moving beyond screens into real-world execution. 
AI now powers navigation systems, in-car assistants, logistics platforms, and autonomous decision-making.
In this environment, incorrect or fragmented location data does not just degrade search. 
It leads to missed turns, failed deliveries, inaccurate recommendations, and lost revenue. Brands don’t simply lose visibility. They get bypassed.
Business implications in an AI-first, zero-click decision layer 
Local search has become an AI-first, zero-click decision layer. 
Multi-location brands now win or lose based on whether AI systems can confidently recommend a location as the safest, most relevant answer. 
That confidence is driven by structured data quality, Google Business Profile excellence, reviews, engagement, and real-world signals such as availability and proximity.
For 2026, the enterprise risk is not experimentation. It’s inertia. 
Brands that fail to industrialize and centralize local data, content, and reputation operations will see declining AI visibility, fragmented brand representation, and lost conversion opportunities without knowing why.
Paradigm shifts to understand 
Here are four key ways the growth in AI search is changing the local journey:
AI answers are the new front door: Local discovery increasingly starts and ends inside AI answers and Google surfaces, where users select a business directly.
Context beats rankings: AI weighs conversation history, user intent, location context, citations, and engagement signals, not just position.
Zero-click journeys dominate: Most local actions now happen on-SERP (GBP, AI Overviews, service features), making on-platform optimization mission-critical.
Local search in 2026 is about being chosen, not clicked: Enterprises that combine entity intelligence, operational rigor by centralizing data and creating consistency, and on-SERP conversion discipline will remain visible and preferred as AI becomes the primary decision-maker.
Businesses that don’t grasp these changes quickly won’t fall behind quietly. They’ll be algorithmically bypassed.
Dig deeper: The enterprise blueprint for winning visibility in AI search
How AI composes local results (and why it matters)
AI systems build memory through entity and context graphs. Brands with clean, connected location, service, and review data become default answers.
Local queries increasingly fall into two intent categories: objective and subjective. 
Objective queries focus on verifiable facts:

“Is the downtown branch open right now?”
“Do you offer same-day service?”
“Is this product in stock nearby?”
Subjective queries rely on interpretation and sentiment:

“Best Italian restaurant near me”
“Top-rated bank in Denver”
“Most family-friendly hotel”
This distinction matters because AI systems treat risk differently depending on intent.
For objective queries, AI models prioritize first-party sources and structured data to reduce hallucination risk. These answers often drive direct actions like calls, visits, and bookings without a traditional website visit ever occurring.
For subjective queries, AI relies more heavily on reviews, third-party commentary, and editorial consensus. This data normally comes from various other channels, such as UGC sites.  
Dig deeper: How to deploy advanced schema at scale
Source authority matters
Industry research has shown that for objective local queries, brand websites and location-level pages act as primary “truth anchors.”
 When an AI system needs to confirm hours, services, amenities, or availability, it prioritizes explicit, structured core data over inferred mentions.
Consider a simple example. If a user asks, “Find a coffee shop near me that serves oat milk and is open until 9,” the AI must reason across location, inventory, and hours simultaneously. 
If those facts are not clearly linked and machine-readable, the brand cannot be confidently recommended.
This is why freshness, relevance, and machine clarity, powered by entity-rich structured data, help AI systems interpret the right response. 
Set yourself up for success
Ensure your data is fresh, relevant, and clear with these tips:
Build a centralized entity and context graph and syndicate it consistently across GBP, listings, schema, and content.
Industrialize local data and entities by developing one source of truth for locations, services, attributes, inventory – continuously audited and AI-normalized.
Make content AI-readable and hyper-local with structured FAQs, services, and how-to content by location, optimized for conversational and multimodal queries.
Treat GBP as a product surface with standardized photos, services, offers, and attributes — localized and continuously optimized.
Operationalize reviews and reputation by implementing always-on review generation, AI-assisted responses, and sentiment intelligence feeding CX and operations.
Adopt AI-first measurement and governance to track AI visibility, local answer share, and on-SERP conversions — not just rankings and traffic.
Dig deeper: From search to answer engines: How to optimize for the next era of discovery
The evolution of local search from listings management to an enterprise local journey
Historically, local search was managed as a collection of disconnected tactics: listings accuracy, review monitoring, and periodic updates to location pages. 
That operating model is increasingly misaligned with how local discovery now works.
Local discovery has evolved into an end-to-end enterprise journey – one that spans data integrity, experience delivery, governance, and measurement across AI-driven surfaces. 
Listings, location pages, structured data, reviews, and operational workflows now work together to determine whether a brand is trusted, cited, and repeatedly surfaced by AI systems.
Introducing local 4.0
Local 4.0 is a practical operating model for AI-first local discovery at an enterprise scale. The focus of this framework is to ensure your brand is callable, verifiable, and safe for AI systems to recommend. 
To understand why this matters, it helps to look at how local has evolved:

Local 1.0 – Listings and basic NAP consistency: The goal was presence – being indexed and included.
Local 2.0 – Map pack optimization and reviews: Visibility was driven by proximity, profile completeness, and reputation.
Local 3.0 – Location pages, content, and ROI: Local became a traffic and conversion driver tied to websites.
Local 4.0 – AI-mediated discovery and recommendation: Local becomes decision infrastructure, not a channel.
Local 4.0 is a new operating model for AI-first local discovery at enterprise scale. The focus is on understanding, verifying, and recommending based on consumer intent.  
Understandable by AI systems (clean, structured, connected data).
Verifiable across platforms (consistent facts, citations, reviews).
Safe to recommend in real-world decision contexts.
In an AI-mediated environment, brands are no longer merely present. They are selected, reused, or ignored – often without a click. This is the core transformation enterprise leaders must internalize as they plan for 2026.
Dig deeper: AI and local search: The new rules of visibility and ROI
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See terms.
The local 4.0 journey for enterprise brands

Step 1: Discovery, consistency, and control
Discovery in an AI-driven environment is fundamentally about trust. When data is inconsistent or noisy, AI systems treat it as a risk signal and deprioritize it.
Core elements include:
Consistency across websites, profiles, directories, and attributes.
Listings as verification infrastructure.
Location pages as primary AI data sources.
Structured data and indexing as the machine clarity layer.

Why ‘legacy’ sources still matter
Listings act as verification infrastructure. Interestingly, research suggests that LLMs often cross-reference data against highly structured legacy directories (such as MapQuest or the Yellow Pages). 
While human traffic to these sites has waned, AI systems utilize them as “truth anchors” because their data is rigidly structured and verified. 
If your hours are wrong on MapQuest, an AI agent may downgrade its confidence in your Google Business Profile, viewing the discrepancy as a risk.
Discovery is no longer about being crawled. It’s about being trusted and reused. Governance matters because ownership, workflows, and data quality now directly affect brand risk.
Dig deeper: 4 pillars of an effective enterprise AI strategy 
Step 2: Engagement and freshness 
AI systems increasingly reward data that is current, efficiently crawled, and easy to validate.
Stale content is no longer neutral. When an AI system encounters outdated information – such as incorrect hours, closed locations, or unavailable services – it may deprioritize or avoid that entity in future recommendations.
For enterprises, freshness must be operationalized, not managed manually. This requires tightly connecting the CMS with protocols like IndexNow, so updates are discovered and reflected by AI systems in near real time.
Beyond updates, enterprises must deliberately design for local-level engagement and signal velocity. Fresh, locally relevant content – such as events, offers, service updates, and community activity – should be surfaced on location pages, structured with schema, and distributed across platforms.
In an AI-first environment, freshness is trust, and trust determines whether a location is surfaced, reused, or skipped entirely.
Unlocking ‘trapped’ data
A major challenge for enterprise brands is “trapped” data, which is vital information, often locked behind PDFs, menu images, or static event calendars.
For example, a restaurant group may upload a PDF of their monthly live music schedule. To a human, this is visible. To a search crawler, it’s often opaque. In an AI-first era, this data must be extracted and structured. 
If an agent cannot read the text inside the PDF, it cannot answer the query: “Find a bar with live jazz tonight.”
Key focus areas include:
Continuous content freshness.
Efficient indexing and crawl pathways.
Dynamic local updates such as events, availability, and offerings.
At enterprise scale, manual workflows break. Freshness is no longer tactical. It’s a competitive requirement.
Dig deeper: Chunk, cite, clarify, build: A content framework for AI search
Step 3: Experience and local relevance
AI does not select the best brand. It selects the location that best resolves intent.
Generic brand messaging consistently loses out to locally curated content. AI retrieval is context-driven and prioritizes specific attributes such as parking availability, accessibility, accepted insurance, or local services.
This exposes a structural problem for many enterprises: information is fragmented across systems and teams.
Solving AI-driven relevance requires organizing data as a context graph. This means connecting services, attributes, FAQs, policies, and location details into a coherent, machine-readable system that maps to customer intent rather than departmental ownership. 
Enterprises should also consider omnichannel marketing approaches to achieve consistency.   
Dig deeper: Integrating SEO into omnichannel marketing for seamless engagement
Step 4: Measurement that executives can trust
As AI-driven and zero-click journeys increase, traditional SEO metrics lose relevance. Attribution becomes fragmented across search, maps, AI interfaces, and third-party platforms.
Precision tracking gives way to directional confidence.
Executive-level KPIs should focus on:
AI visibility and recommendation presence.
Citation accuracy and consistency.
Location-level actions (calls, directions, bookings).
Incremental revenue or lead quality lift.
The goal is not perfect attribution. It’s confidence that local discovery is working and revenue risk is being mitigated.
Dig deeper: 7 focus areas as AI transforms search and the customer journey in 2026
Why local 4.0 needs to be the enterprise response
Fragmentation is a material revenue risk. When local data is inconsistent or disconnected, AI systems have lower confidence in it and are less likely to reuse or recommend those locations. 
Treating local data as a living, governed asset and establishing a single, authoritative source of truth early prevents incorrect information from propagating across AI-driven ecosystems and avoids the costly remediation required to fix issues after they scale.
AI-mediated discovery is now the default – and local 4.0 gives enterprises control, confidence, and competitiveness by aligning data, experience, and governance into the AI discovery flywheel.
This isn’t about chasing trends; it’s about ensuring your brand is accurately represented and confidently chosen wherever customers discover you next.
Dig deeper: How to select a CMS that powers SEO, personalization and growth
Local 4.0 is integral to the localized AI discovery flywheel

AI-mediated discovery is becoming the default interface between customers and local brands.
Local 4.0 provides a framework for control, confidence, and competitiveness in that environment. It aligns data, experience, and governance around how AI systems actually operate through reasoning, verification, and reuse.
This is not about chasing AI trends. It’s about ensuring your brand is correctly represented and confidently recommended wherever customers discover you next.

AI now mediates how customers discover and choose local services. Learn how AI systems evaluate trust, data quality, and relevance.

How AI is reshaping local search and what enterprises must do now

<div><img width="1536" height="1024" src="https://searchengineland.com/wp-content/seloads/2026/02/Human-Judgment-vs-Machine-Output.png" class="attachment-large size-large wp-post-image" alt="Human Judgment vs Machine Output" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Human-Judgment-vs-Machine-Output.png 1536w, https://searchengineland.com/wp-content/seloads/2026/02/Human-Judgment-vs-Machine-Output-768x512.png 768w" sizes="(max-width: 1536px) 100vw, 1536px" /></div>Automation can save SEO teams time—but at what cost? Explore where AI boosts speed, where it erodes trust, and how to protect real authority.

<div><img width="1536" height="1024" src="https://searchengineland.com/wp-content/seloads/2026/02/Human-Judgment-vs-Machine-Output.png" class="attachment-large size-large wp-post-image" alt="Human Judgment vs Machine Output" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Human-Judgment-vs-Machine-Output.png 1536w, https://searchengineland.com/wp-content/seloads/2026/02/Human-Judgment-vs-Machine-Output-768x512.png 768w" sizes="(max-width: 1536px) 100vw, 1536px" /></div>
<p>Right now, it’s hard to find a marketing conversation that doesn’t include two letters: AI. </p>



<p>SEOs, strategists, and marketing leaders everywhere are asking the same question in different ways:</p>



<ul class="wp-block-list">
<li>How do we use AI to cut manpower, streamline work, move faster, and boost efficiency?</li>
</ul>



<p>Much of that thinking makes sense. If you run a business, you can’t ignore a tool that turns hours of grunt work into minutes. You’d be foolish to try.</p>



<p>But we’re spending too much time asking, &#8220;Can AI do this?&#8221; and not enough time asking, &#8220;Should AI do this?&#8221;</p>



<p>Once the initial excitement fades, some uncomfortable questions show up.</p>



<ul class="wp-block-list">
<li>If every title tag, meta description, landing page, and blog post comes from AI, where does differentiation come from?</li>



<li>If every outreach email, proposal, and report is machine-generated, what happens to trust? </li>



<li>If AI agents start talking to other AI agents on our behalf, what happens to judgment, creativity, and the human side of business?</li>
</ul>



<p>This isn’t anti-AI. I use AI. My team uses AI. You probably do, too.</p>



<p>This is about using AI well, using it intentionally, and not automating so much that you accidentally automate away the things that make you valuable.</p>



<h2 class="wp-block-heading" id="h-what-automating-too-much-looks-like-in-seo">What &#8216;automating too much&#8217; looks like in SEO</h2>



<p>The slippery part of automation? It rarely starts with big decisions. It starts with small ones that feel harmless.</p>



<p>First, you automate the boring admin. Then the repetitive writing. Then the analysis. Then client communication. Then, quietly, decision-making.</p>



<p>In SEO, “too much” often looks like this:</p>



<ul class="wp-block-list">
<li>Meta titles and descriptions generated at scale, with barely any review.</li>



<li>Content briefs created by AI from SERP summaries, then passed straight to an AI writer for drafting.</li>



<li>On-page changes rolled out across templates because “the model recommended it.”</li>



<li>Link building outreach written by AI, sent at volume, and ignored at volume.</li>



<li>Reporting that is technically accurate but disconnected from what the business actually cares about.</li>
</ul>



<p>If this sounds harsh, that’s because it happens fast. </p>



<p>The promise is always “we’ll save time.” What usually happens is you save time and lose something else. Most often, you lose the sense that your marketing has a brain behind it.</p>



<h2 class="wp-block-heading" id="h-the-sameness-problem-if-everyone-uses-the-same-tools-who-wins">The sameness problem: if everyone uses the same tools, who wins?</h2>



<p>This is the question I keep coming back to.</p>



<p>If everyone uses AI to create everything, the web fills up with content that looks and sounds the same. It might be polished. It might even be technically “good.” But it becomes interchangeable.</p>



<p>That creates two problems: </p>



<ul class="wp-block-list">
<li><strong>Users get bored.</strong> They read one page, then another, and it’s the same advice dressed up with slightly different words. You might win a click. You’ll struggle to build a relationship.</li>



<li><strong>Search engines and language models still need ways to tell you apart.</strong> When content converges, the real differentiators become things like:
<ul class="wp-block-list">
<li>Brand recognition.</li>



<li>Original data or firsthand experience.</li>



<li>Clear expertise and accountability.</li>



<li>Signals that other people trust you.</li>



<li>Distinct angles and opinions.</li>
</ul>
</li>
</ul>



<p>The irony? </p>



<p>Heavy automation often strips those things out. It produces “fine” content quickly, but it also produces content that could have come from anyone.</p>



<p>If your goal is authority, being indistinguishable isn’t neutral. It’s a liability.</p>



<h2 class="wp-block-heading" id="h-when-ai-starts-quoting-ai-reality-gets-blurry">When AI starts quoting AI, reality gets blurry</h2>



<p>This is where things start to get strange.</p>



<p>We’re already heading into a world where AI tools summarize content, other tools re-summarize those summaries, and someone publishes the result as if it’s new insight. It becomes a loop.</p>



<p>If you’ve ever asked a tool to write a blog post and it felt familiar but hard to place, that’s usually why. It isn’t creating knowledge from scratch. It’s remixing patterns.</p>



<p>Now imagine that happening at scale. Search engines crawl pages. Models summarize them. Businesses publish new pages based on those summaries. Agents use those pages to answer questions. Repeat.</p>



<p>Remove humans from the loop for too long, and you risk an internet that feels like it’s talking to itself. Plenty of words. Very little substance.</p>



<p>From an SEO perspective, that’s a serious problem. When the web floods with similar information, value shifts away from &#8220;who wrote the neatest explanation&#8221; and toward &#8220;who has something real to add.&#8221;</p>



<p>That’s why I keep coming back to the same point. The question isn’t “can AI do this?” It’s “should we use AI here, or should a human own this?”</p>



<h2 class="wp-block-heading" id="h-the-creativity-and-judgment-problem">The creativity and judgment problem</h2>



<p>There&#8217;s a quieter risk we don&#8217;t talk about enough.</p>



<p>If you let AI write every proposal, every contract, every strategy deck, and every content plan, you start outsourcing judgment.</p>



<p>You may still be the one who clicks “generate” and “send,” but the thinking has moved somewhere else.</p>



<p>Over time, you lose the habit of critical thinking. Not because you can&#8217;t think, but because you stop practicing. It&#8217;s the same way GPS makes you worse at directions. You can still drive, but you stop building the skill.</p>



<p>In SEO, judgment is one of our most valuable assets. Knowing:</p>



<ul class="wp-block-list">
<li>What to prioritize. </li>



<li>What to ignore. </li>



<li>When a dip is normal and when it is a warning sign. </li>



<li>When the data is lying because the tracking is broken.</li>
</ul>



<p>AI can support decisions, but it can&#8217;t own them. If you automate that away, you risk becoming a delivery machine instead of a strategist. And authority doesn&#8217;t come from delivery.</p>



<h2 class="wp-block-heading" id="h-the-trust-problem-clients-do-not-just-buy-outputs">The trust problem: clients do not just buy outputs</h2>



<p>Here&#8217;s a reality check agency owners feel in their bones.</p>



<p>Clients don&#8217;t stay because you can do the work. They stay because they:</p>



<ul class="wp-block-list">
<li>Trust you. </li>



<li>Feel looked after. </li>



<li>Believe you have their best interests at heart. </li>



<li>Like working with you.</li>
</ul>



<p>It&#8217;s business, but it&#8217;s still human.</p>



<p>When you automate too much of the client experience, your service can start to feel cheap. Not in price, but in care.</p>



<ul class="wp-block-list">
<li>If every email sounds generated, clients notice. </li>



<li>If every report is a generic summary with no opinion, clients notice. </li>



<li>If every deliverable looks like it came straight from a tool, clients start asking why they are paying you instead of the tool.</li>
</ul>



<p>The same thing happens in-house. Stakeholders want confidence. They want interpretation. They want someone to say, “This is what matters, and this is what we should do next.”</p>



<p>AI is excellent at producing outputs. It isn&#8217;t good at reassurance, context, or accountability. Those are human services, even when the work is digital.</p>



<h2 class="wp-block-heading" id="h-the-accuracy-and-responsibility-problem">The accuracy and responsibility problem</h2>



<p>If you automate content production without proper oversight, eventually you&#8217;ll publish something wrong.</p>



<p>Sometimes it&#8217;s small. A definition that is slightly off. A stat that is outdated. A recommendation that doesn&#8217;t fit the situation.</p>



<p>Sometimes it&#8217;s serious. Incorrect medical advice. Legal misinformation. Financial guidance that should never have gone live.</p>



<p>Even in low-risk niches, accuracy matters. When your content is wrong, trust erodes. When it&#8217;s wrong with confidence, trust disappears faster.</p>



<p>The more you scale AI output, the harder quality control becomes. That is where automation turns dangerous. You can produce content at speed, but you may not spot the decay until performance drops or, worse, a customer calls it out publicly.</p>



<p>Authority is fragile. It takes time to build and seconds to lose. Automation increases that risk because mistakes don&#8217;t stay small. They scale.</p>



<h2 class="wp-block-heading" id="h-the-confidentiality-problem-that-nobody-wants-to-admit">The confidentiality problem that nobody wants to admit</h2>



<p>This is the part that often gets brushed aside in the rush to “implement AI.”</p>



<p>SEO and marketing work regularly involves sensitive information—sales data, customer feedback, conversion rates, pricing strategies, internal documents, and product roadmaps. Paste that into an AI tool without thinking, and you create risk.</p>



<p>Sometimes that risk is contractual. Sometimes it’s regulatory. Sometimes it’s reputational.</p>



<p>Even if your AI tools are configured securely, you still need an internal policy. Nothing fancy. Just clear rules on what can and can’t be shared, who can approve it, and how outputs are reviewed.</p>



<p>If you’re building authority as a brand, the last thing you want is to lose trust because you treated sensitive information casually in the name of efficiency.</p>



<h2 class="wp-block-heading" id="h-the-window-of-opportunity-and-why-it-will-not-last-forever">The window of opportunity, and why it will not last forever</h2>



<p>Right now, there’s a window. Most businesses are still learning how to use AI well. That gives brands that move carefully a real edge.</p>



<p>That window won’t stay open.</p>



<p>In a few years, the market will be flooded with AI-generated content and AI-assisted services. The tools will be cheaper and more accessible. The baseline will rise.</p>



<p>When that happens, “we use AI” won’t be a differentiator anymore. It’ll sound like saying, “we use email.”</p>



<p>The real differentiator will be how you use it.</p>



<p>Do you use AI to churn out more of the same?</p>



<p>Or do you use it to buy back time so you can create things others can’t?</p>



<p>That’s the opportunity. AI can strip out the grunt work and give you time back. What you do with that time is where authority is built.</p>



<h2 class="wp-block-heading" id="h-where-seo-fits-in-less-doing-more-directing">Where SEO fits in: less doing, more directing</h2>



<p>I suspect the SEO role is shifting.</p>



<p>Not away from execution entirely, but away from being valued purely for output. When a tool can generate a content draft, the value shifts to the person who can judge whether it’s the right draft — for the right audience, with the right angle, on the right page, at the right time.</p>



<p>In other words, the SEO becomes a director, not just a doer.</p>



<p>That looks like this:</p>



<ul class="wp-block-list">
<li>Knowing which content is worth creating—and which isn’t.</li>



<li>Understanding the user journey and where search fits into it.</li>



<li>Building content strategies anchored in real business value.</li>



<li>Designing workflows that protect quality while increasing speed.</li>



<li>Helping teams use AI responsibly without removing human judgment.</li>
</ul>



<p>If you’re trying to build authority, this shift is good news. It rewards expertise and judgment. It rewards people who can see the bigger picture and make decisions that go beyond “more content.”</p>



<h2 class="wp-block-heading" id="h-the-upside-take-away-the-grunt-work-keep-the-thinking">The upside: take away the grunt work, keep the thinking</h2>



<p>AI is excellent at certain jobs. And if we’re honest, a lot of SEO work is repetitive and draining. That’s where AI shines.</p>



<p>AI can help you:</p>



<ul class="wp-block-list">
<li>Summarize and cluster keyword research faster.</li>



<li>Create first drafts of meta descriptions that a human then edits properly.</li>



<li>Turn messy notes into a structure you can actually work with.</li>



<li>Generate alternative title options quickly so you can choose the strongest one.</li>



<li>Create scripts for short videos or webinars from existing material.</li>



<li>Analyze patterns in performance data and flag areas worth investigating.</li>



<li>Speed up technical tasks like regex, formulas, documentation, and QA checklists.</li>
</ul>



<p>This is the sweet spot. Use AI to reduce friction and strip out the boring work. Then spend your time on the things that actually create differentiation.</p>



<p>In my experience, the best use of AI in SEO isn’t replacing humans. It’s giving humans more time to do the human parts properly.</p>



<h2 class="wp-block-heading" id="h-personalization-the-dream-and-the-risk">Personalization: The dream and the risk</h2>



<p>There’s a lot of talk about personalized results. A future where each person gets answers tailored to their preferences, context, history, and intent.</p>



<p>That future may arrive. In some ways, it’s already here. Search results and recommendations aren’t neutral. They’re shaped by behavior and patterns.</p>



<p>Personalization could be great for users. It also raises the bar for brands.</p>



<p>If every user sees a slightly different answer, it gets harder to compete with generic content. Generic content fades into the background because it isn’t specific enough to be chosen.</p>



<p>That brings us back to the same truth: unique value wins. Real expertise wins. Original experience wins. Trust wins.</p>



<p>Automation can help you scale personalization — but only if the thinking behind it is solid. Automate personalization badly, and all you get is faster irrelevance.</p>



<h2 class="wp-block-heading" id="h-a-practical-way-to-decide-what-should-be-automated">A practical way to decide what should be automated</h2>



<p>So how do we move from “can AI do this?” to “should AI do this?”</p>



<p>The better approach is to decide what must stay human, what can be assisted, and what can be automated safely.</p>



<p>These are the questions I use when making that call:</p>



<ul class="wp-block-list">
<li>What happens if this is wrong? If the cost of being wrong is high, a human needs to own it.</li>



<li>Is this customer-facing? The more visible it is, the more it should sound like you and reflect your judgment.</li>



<li>Does this require empathy or nuance? If yes, automate less.</li>



<li>Does this require your unique perspective? If yes, automate less.</li>



<li>Is this reversible? If it’s easy to undo, you can afford to experiment.</li>



<li>Does it involve sensitive information? If yes, tighten control.</li>



<li>Will automation make us look like everyone else? If yes, be cautious. You may be trading speed for differentiation.</li>
</ul>



<p>These questions are simple, but they lead to far better decisions than, “the tool can do it, so let’s do it.”</p>



<h2 class="wp-block-heading" id="h-what-i-would-and-would-not-automate-in-seo">What I would and would not automate in SEO</h2>



<p>To make this practical, here’s where I’d draw the line for most teams.</p>



<p>I’d happily automate or heavily assist:</p>



<ul class="wp-block-list">
<li><strong>Early-stage research</strong>, like summarizing competitors, clustering topics, and extracting themes from customer feedback.</li>



<li><strong>Drafting tasks that a human will edit</strong>, such as meta descriptions, outlines, and first drafts of support content.</li>



<li><strong>Repetitive admin work</strong>, including documentation, tagging, and reporting templates.</li>



<li><strong>Technical helper tasks</strong>, like formulas, regex, and scripts—as long as a human reviews the output.</li>
</ul>



<p>I would not fully automate:</p>



<ul class="wp-block-list">
<li><strong>Strategy:</strong> Deciding what matters and why.</li>



<li><strong>Positioning:</strong> The angle that gives your brand a clear point of view.</li>



<li><strong>Final customer-facing messaging</strong>: Especially anything that represents your voice and level of care.</li>



<li><strong>Claims that require evidence:</strong> If you can’t prove it, don’t publish it.</li>



<li><strong>Client relationships:</strong> The conversations, reassurance, and trust-building that keep people with you.</li>
</ul>



<p>If you automate those, you may increase output, but you’ll often decrease loyalty. And loyalty is a form of authority.</p>



<h2 class="wp-block-heading" id="h-the-real-risk-is-not-ai-it-is-thoughtlessness">The real risk is not AI. It is thoughtlessness.</h2>



<p>The biggest risk isn’t that AI will take your job. It’s that you use it in a way that makes you replaceable.</p>



<p>If your brand turns into a machine that churns out generic output, it becomes hard to care. </p>



<ul class="wp-block-list">
<li>Hard for search engines to prioritize. </li>



<li>Hard for language models to cite. </li>



<li>Hard for clients to justify paying for.</li>
</ul>



<p>If you want to build authority, you have to protect what makes you different. Your judgment. Your experience. Your voice. Your evidence. Your relationships.</p>



<p>AI can help if you use it to create space for better thinking. It can hurt if you use it to avoid thinking altogether.</p>



<h2 class="wp-block-heading" id="h-human-involvement">Human involvement</h2>



<p>It’s easy to get excited about AI doing everything. Saving on headcount. Producing output 24/7. Removing bottlenecks.</p>



<p>But the more important question is what you lose when you remove too much human involvement. Do you lose:</p>



<ul class="wp-block-list">
<li>Differentiation?</li>



<li>Trust?</li>



<li>The ability to think critically?</li>



<li>The relationships that keep clients loyal?</li>
</ul>



<p>For most of us, the goal isn’t more marketing. The goal is marketing that works — for people we actually want to work with — in a way we can be proud of.</p>



<p>So yes, ask, “Can AI do this?” It’s a useful question.</p>



<p>Then ask, “Should AI do this?” That’s the one that protects your authority.</p>



<p>And if you’re unsure, start small. Automate the grunt work. Keep the thinking. Keep the voice. Keep the care.</p>



<p>That’s how you get the best of AI without automating away what makes you valuable.</p>


Right now, it’s hard to find a marketing conversation that doesn’t include two letters: AI. 
SEOs, strategists, and marketing leaders everywhere are asking the same question in different ways:
How do we use AI to cut manpower, streamline work, move faster, and boost efficiency?
Much of that thinking makes sense. If you run a business, you can’t ignore a tool that turns hours of grunt work into minutes. You’d be foolish to try.
But we’re spending too much time asking, “Can AI do this?” and not enough time asking, “Should AI do this?”
Once the initial excitement fades, some uncomfortable questions show up.
If every title tag, meta description, landing page, and blog post comes from AI, where does differentiation come from?
If every outreach email, proposal, and report is machine-generated, what happens to trust? 
If AI agents start talking to other AI agents on our behalf, what happens to judgment, creativity, and the human side of business?
This isn’t anti-AI. I use AI. My team uses AI. You probably do, too.
This is about using AI well, using it intentionally, and not automating so much that you accidentally automate away the things that make you valuable.
What ‘automating too much’ looks like in SEO
The slippery part of automation? It rarely starts with big decisions. It starts with small ones that feel harmless.
First, you automate the boring admin. Then the repetitive writing. Then the analysis. Then client communication. Then, quietly, decision-making.
In SEO, “too much” often looks like this:
Meta titles and descriptions generated at scale, with barely any review.
Content briefs created by AI from SERP summaries, then passed straight to an AI writer for drafting.
On-page changes rolled out across templates because “the model recommended it.”
Link building outreach written by AI, sent at volume, and ignored at volume.
Reporting that is technically accurate but disconnected from what the business actually cares about.
If this sounds harsh, that’s because it happens fast. 
The promise is always “we’ll save time.” What usually happens is you save time and lose something else. Most often, you lose the sense that your marketing has a brain behind it.
The sameness problem: if everyone uses the same tools, who wins?
This is the question I keep coming back to.
If everyone uses AI to create everything, the web fills up with content that looks and sounds the same. It might be polished. It might even be technically “good.” But it becomes interchangeable.
That creates two problems: 
Users get bored. They read one page, then another, and it’s the same advice dressed up with slightly different words. You might win a click. You’ll struggle to build a relationship.
Search engines and language models still need ways to tell you apart. When content converges, the real differentiators become things like:

Brand recognition.
Original data or firsthand experience.
Clear expertise and accountability.
Signals that other people trust you.
Distinct angles and opinions.
The irony? 
Heavy automation often strips those things out. It produces “fine” content quickly, but it also produces content that could have come from anyone.
If your goal is authority, being indistinguishable isn’t neutral. It’s a liability.
When AI starts quoting AI, reality gets blurry
This is where things start to get strange.
We’re already heading into a world where AI tools summarize content, other tools re-summarize those summaries, and someone publishes the result as if it’s new insight. It becomes a loop.
If you’ve ever asked a tool to write a blog post and it felt familiar but hard to place, that’s usually why. It isn’t creating knowledge from scratch. It’s remixing patterns.
Now imagine that happening at scale. Search engines crawl pages. Models summarize them. Businesses publish new pages based on those summaries. Agents use those pages to answer questions. Repeat.
Remove humans from the loop for too long, and you risk an internet that feels like it’s talking to itself. Plenty of words. Very little substance.
From an SEO perspective, that’s a serious problem. When the web floods with similar information, value shifts away from “who wrote the neatest explanation” and toward “who has something real to add.”
That’s why I keep coming back to the same point. The question isn’t “can AI do this?” It’s “should we use AI here, or should a human own this?”
The creativity and judgment problem
There’s a quieter risk we don’t talk about enough.
If you let AI write every proposal, every contract, every strategy deck, and every content plan, you start outsourcing judgment.
You may still be the one who clicks “generate” and “send,” but the thinking has moved somewhere else.
Over time, you lose the habit of critical thinking. Not because you can’t think, but because you stop practicing. It’s the same way GPS makes you worse at directions. You can still drive, but you stop building the skill.
In SEO, judgment is one of our most valuable assets. Knowing:
What to prioritize. 
What to ignore. 
When a dip is normal and when it is a warning sign. 
When the data is lying because the tracking is broken.
AI can support decisions, but it can’t own them. If you automate that away, you risk becoming a delivery machine instead of a strategist. And authority doesn’t come from delivery.
The trust problem: clients do not just buy outputs
Here’s a reality check agency owners feel in their bones.
Clients don’t stay because you can do the work. They stay because they:
Trust you. 
Feel looked after. 
Believe you have their best interests at heart. 
Like working with you.
It’s business, but it’s still human.
When you automate too much of the client experience, your service can start to feel cheap. Not in price, but in care.
If every email sounds generated, clients notice. 
If every report is a generic summary with no opinion, clients notice. 
If every deliverable looks like it came straight from a tool, clients start asking why they are paying you instead of the tool.
The same thing happens in-house. Stakeholders want confidence. They want interpretation. They want someone to say, “This is what matters, and this is what we should do next.”
AI is excellent at producing outputs. It isn’t good at reassurance, context, or accountability. Those are human services, even when the work is digital.
The accuracy and responsibility problem
If you automate content production without proper oversight, eventually you’ll publish something wrong.
Sometimes it’s small. A definition that is slightly off. A stat that is outdated. A recommendation that doesn’t fit the situation.
Sometimes it’s serious. Incorrect medical advice. Legal misinformation. Financial guidance that should never have gone live.
Even in low-risk niches, accuracy matters. When your content is wrong, trust erodes. When it’s wrong with confidence, trust disappears faster.
The more you scale AI output, the harder quality control becomes. That is where automation turns dangerous. You can produce content at speed, but you may not spot the decay until performance drops or, worse, a customer calls it out publicly.
Authority is fragile. It takes time to build and seconds to lose. Automation increases that risk because mistakes don’t stay small. They scale.
The confidentiality problem that nobody wants to admit
This is the part that often gets brushed aside in the rush to “implement AI.”
SEO and marketing work regularly involves sensitive information—sales data, customer feedback, conversion rates, pricing strategies, internal documents, and product roadmaps. Paste that into an AI tool without thinking, and you create risk.
Sometimes that risk is contractual. Sometimes it’s regulatory. Sometimes it’s reputational.
Even if your AI tools are configured securely, you still need an internal policy. Nothing fancy. Just clear rules on what can and can’t be shared, who can approve it, and how outputs are reviewed.
If you’re building authority as a brand, the last thing you want is to lose trust because you treated sensitive information casually in the name of efficiency.
The window of opportunity, and why it will not last forever
Right now, there’s a window. Most businesses are still learning how to use AI well. That gives brands that move carefully a real edge.
That window won’t stay open.
In a few years, the market will be flooded with AI-generated content and AI-assisted services. The tools will be cheaper and more accessible. The baseline will rise.
When that happens, “we use AI” won’t be a differentiator anymore. It’ll sound like saying, “we use email.”
The real differentiator will be how you use it.
Do you use AI to churn out more of the same?
Or do you use it to buy back time so you can create things others can’t?
That’s the opportunity. AI can strip out the grunt work and give you time back. What you do with that time is where authority is built.
Where SEO fits in: less doing, more directing
I suspect the SEO role is shifting.
Not away from execution entirely, but away from being valued purely for output. When a tool can generate a content draft, the value shifts to the person who can judge whether it’s the right draft — for the right audience, with the right angle, on the right page, at the right time.
In other words, the SEO becomes a director, not just a doer.
That looks like this:
Knowing which content is worth creating—and which isn’t.
Understanding the user journey and where search fits into it.
Building content strategies anchored in real business value.
Designing workflows that protect quality while increasing speed.
Helping teams use AI responsibly without removing human judgment.
If you’re trying to build authority, this shift is good news. It rewards expertise and judgment. It rewards people who can see the bigger picture and make decisions that go beyond “more content.”
The upside: take away the grunt work, keep the thinking
AI is excellent at certain jobs. And if we’re honest, a lot of SEO work is repetitive and draining. That’s where AI shines.
AI can help you:
Summarize and cluster keyword research faster.
Create first drafts of meta descriptions that a human then edits properly.
Turn messy notes into a structure you can actually work with.
Generate alternative title options quickly so you can choose the strongest one.
Create scripts for short videos or webinars from existing material.
Analyze patterns in performance data and flag areas worth investigating.
Speed up technical tasks like regex, formulas, documentation, and QA checklists.
This is the sweet spot. Use AI to reduce friction and strip out the boring work. Then spend your time on the things that actually create differentiation.
In my experience, the best use of AI in SEO isn’t replacing humans. It’s giving humans more time to do the human parts properly.
Personalization: The dream and the risk
There’s a lot of talk about personalized results. A future where each person gets answers tailored to their preferences, context, history, and intent.
That future may arrive. In some ways, it’s already here. Search results and recommendations aren’t neutral. They’re shaped by behavior and patterns.
Personalization could be great for users. It also raises the bar for brands.
If every user sees a slightly different answer, it gets harder to compete with generic content. Generic content fades into the background because it isn’t specific enough to be chosen.
That brings us back to the same truth: unique value wins. Real expertise wins. Original experience wins. Trust wins.
Automation can help you scale personalization — but only if the thinking behind it is solid. Automate personalization badly, and all you get is faster irrelevance.
A practical way to decide what should be automated
So how do we move from “can AI do this?” to “should AI do this?”
The better approach is to decide what must stay human, what can be assisted, and what can be automated safely.
These are the questions I use when making that call:
What happens if this is wrong? If the cost of being wrong is high, a human needs to own it.
Is this customer-facing? The more visible it is, the more it should sound like you and reflect your judgment.
Does this require empathy or nuance? If yes, automate less.
Does this require your unique perspective? If yes, automate less.
Is this reversible? If it’s easy to undo, you can afford to experiment.
Does it involve sensitive information? If yes, tighten control.
Will automation make us look like everyone else? If yes, be cautious. You may be trading speed for differentiation.
These questions are simple, but they lead to far better decisions than, “the tool can do it, so let’s do it.”
What I would and would not automate in SEO
To make this practical, here’s where I’d draw the line for most teams.
I’d happily automate or heavily assist:
Early-stage research, like summarizing competitors, clustering topics, and extracting themes from customer feedback.
Drafting tasks that a human will edit, such as meta descriptions, outlines, and first drafts of support content.
Repetitive admin work, including documentation, tagging, and reporting templates.
Technical helper tasks, like formulas, regex, and scripts—as long as a human reviews the output.
I would not fully automate:
Strategy: Deciding what matters and why.
Positioning: The angle that gives your brand a clear point of view.
Final customer-facing messaging: Especially anything that represents your voice and level of care.
Claims that require evidence: If you can’t prove it, don’t publish it.
Client relationships: The conversations, reassurance, and trust-building that keep people with you.
If you automate those, you may increase output, but you’ll often decrease loyalty. And loyalty is a form of authority.
The real risk is not AI. It is thoughtlessness.
The biggest risk isn’t that AI will take your job. It’s that you use it in a way that makes you replaceable.
If your brand turns into a machine that churns out generic output, it becomes hard to care. 
Hard for search engines to prioritize. 
Hard for language models to cite. 
Hard for clients to justify paying for.
If you want to build authority, you have to protect what makes you different. Your judgment. Your experience. Your voice. Your evidence. Your relationships.
AI can help if you use it to create space for better thinking. It can hurt if you use it to avoid thinking altogether.
Human involvement
It’s easy to get excited about AI doing everything. Saving on headcount. Producing output 24/7. Removing bottlenecks.
But the more important question is what you lose when you remove too much human involvement. Do you lose:
Differentiation?
Trust?
The ability to think critically?
The relationships that keep clients loyal?
For most of us, the goal isn’t more marketing. The goal is marketing that works — for people we actually want to work with — in a way we can be proud of.
So yes, ask, “Can AI do this?” It’s a useful question.
Then ask, “Should AI do this?” That’s the one that protects your authority.
And if you’re unsure, start small. Automate the grunt work. Keep the thinking. Keep the voice. Keep the care.
That’s how you get the best of AI without automating away what makes you valuable.

Automation can save SEO teams time—but at what cost? Explore where AI boosts speed, where it erodes trust, and how to protect real authority.

Why SEO teams need to ask ‘should we use AI?’ not just ‘can we?’

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/julie-cover.png" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/julie-cover.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/julie-cover-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/julie-cover-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Learn why first-party data plays an increasingly important role in how automated ad campaigns are optimized and measured.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/julie-cover.png" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/julie-cover.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/julie-cover-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/julie-cover-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe title="Julie Warnecke discusses the importance of first-party data" width="500" height="281" src="https://www.youtube.com/embed/Ai1ANLY-UO0?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div></figure>



<p>As AI-driven bidding and automation transform paid media, first-party data has become the most powerful lever advertisers control. </p>



<p>In this conversation with Search Engine Land, Julie Warneke, founder and CEO of Found Search Marketing, explained why first-party data now underpins profitable advertising — no matter how Google’s position on third-party cookies evolves.</p>



<h2 class="wp-block-heading" id="h-what-first-party-data-really-is-and-isn-t">What first-party data really is — and isn’t</h2>



<p>First-party data is customer information that an advertiser owns directly, usually housed in a CRM. It includes:</p>



<ul class="wp-block-list">
<li>Lead details.</li>



<li>Purchase history.</li>



<li>Revenue.</li>



<li>Customer value collected through websites, forms, or physical locations. </li>
</ul>



<p>It doesn&#8217;t include platform-owned or browser-based data that advertisers can’t fully control.</p>



<h2 class="wp-block-heading" id="h-why-first-party-data-matters-more-than-ever">Why first-party data matters more than ever</h2>



<p>Digital advertising has moved from paying for impressions, to clicks, to actions — and now to outcomes. The real goal is no longer conversions alone, but profitable conversions, according to Warneke. </p>



<p>As AI systems process far more signals than humans can handle, advertisers who supply high-quality customer data gain a clear advantage.</p>



<h2 class="wp-block-heading" id="h-cpcs-may-rise-but-profitability-can-too">CPCs may rise — but profitability can too</h2>



<p>Rising cost-per-clicks are a fact of paid media. First-party data doesn’t always reduce CPCs, but it improves what matters more: conversion quality, revenue, and return on ad spend. </p>



<p>By optimizing for downstream business outcomes instead of surface-level metrics, advertisers can justify higher costs with stronger results.</p>



<h2 class="wp-block-heading" id="h-how-first-party-data-improves-roas">How first-party data improves ROAS</h2>



<p>When advertisers feed Google data tied to revenue and customer value, AI bidding systems can prioritize users who resemble high-value customers — often using signals far beyond demographics or geography. </p>



<p>The result is traffic that converts better, even if advertisers never see or control the underlying signals.</p>



<h2 class="wp-block-heading" id="h-performance-max-leads-the-way">Performance Max leads the way</h2>



<p>Among campaign types, Performance Max (PMax) currently benefits the most from first-party data activation.  </p>



<p>PMax performs best when advertisers move away from manual optimizations and instead focus on supplying accurate, consistent data, then let the system learn, Warneke noted.</p>



<h2 class="wp-block-heading" id="h-smbs-aren-t-locked-out-but-they-need-the-right-setup">SMBs aren’t locked out — but they need the right setup</h2>



<p>Small and mid-sized businesses aren’t disadvantaged by limited first-party data volume. Warneke shared examples of success with customer lists as small as 100 records. </p>



<p>The real hurdle for SMBs is infrastructure — specifically proper tracking, consent management, and reliable data pipelines.</p>



<h2 class="wp-block-heading" id="h-the-biggest-mistakes-advertisers-are-making">The biggest mistakes advertisers are making</h2>



<p>Two issues stand out: </p>



<ul class="wp-block-list">
<li><strong>Weak data capture:</strong> Many brands still depend on browser-side tracking, which increasingly fails — especially on iOS. </li>



<li><strong>Broken feedback loops:</strong> Others upload CRM data sporadically instead of building continuous data flows that let AI systems learn and improve over time.</li>
</ul>



<h2 class="wp-block-heading" id="h-what-marketers-should-do-next">What marketers should do next</h2>



<p>Warneke&#8217;s advice: Step back and audit how data is captured, stored, and sent back to platforms, then improve it incrementally. </p>



<p>There’s no need to overhaul everything at once or risk the entire budget. Even testing with 5–7% of spend can create a learning roadmap that delivers long-term gains.</p>



<h2 class="wp-block-heading" id="h-bottom-line">Bottom line</h2>



<p>AI optimizes toward the signals it receives — good or bad. Advertisers who own and refine their first-party data can shape outcomes in their favor, while those who don’t risk being optimized into inefficiency.</p>


As AI-driven bidding and automation transform paid media, first-party data has become the most powerful lever advertisers control. 
In this conversation with Search Engine Land, Julie Warneke, founder and CEO of Found Search Marketing, explained why first-party data now underpins profitable advertising — no matter how Google’s position on third-party cookies evolves.
What first-party data really is — and isn’t
First-party data is customer information that an advertiser owns directly, usually housed in a CRM. It includes:
Lead details.
Purchase history.
Revenue.
Customer value collected through websites, forms, or physical locations. 
It doesn’t include platform-owned or browser-based data that advertisers can’t fully control.
Why first-party data matters more than ever
Digital advertising has moved from paying for impressions, to clicks, to actions — and now to outcomes. The real goal is no longer conversions alone, but profitable conversions, according to Warneke. 
As AI systems process far more signals than humans can handle, advertisers who supply high-quality customer data gain a clear advantage.
CPCs may rise — but profitability can too
Rising cost-per-clicks are a fact of paid media. First-party data doesn’t always reduce CPCs, but it improves what matters more: conversion quality, revenue, and return on ad spend. 
By optimizing for downstream business outcomes instead of surface-level metrics, advertisers can justify higher costs with stronger results.
How first-party data improves ROAS
When advertisers feed Google data tied to revenue and customer value, AI bidding systems can prioritize users who resemble high-value customers — often using signals far beyond demographics or geography. 
The result is traffic that converts better, even if advertisers never see or control the underlying signals.
Performance Max leads the way
Among campaign types, Performance Max (PMax) currently benefits the most from first-party data activation.  
PMax performs best when advertisers move away from manual optimizations and instead focus on supplying accurate, consistent data, then let the system learn, Warneke noted.
SMBs aren’t locked out — but they need the right setup
Small and mid-sized businesses aren’t disadvantaged by limited first-party data volume. Warneke shared examples of success with customer lists as small as 100 records. 
The real hurdle for SMBs is infrastructure — specifically proper tracking, consent management, and reliable data pipelines.
The biggest mistakes advertisers are making
Two issues stand out: 
Weak data capture: Many brands still depend on browser-side tracking, which increasingly fails — especially on iOS. 
Broken feedback loops: Others upload CRM data sporadically instead of building continuous data flows that let AI systems learn and improve over time.
What marketers should do next
Warneke’s advice: Step back and audit how data is captured, stored, and sent back to platforms, then improve it incrementally. 
There’s no need to overhaul everything at once or risk the entire budget. Even testing with 5–7% of spend can create a learning roadmap that delivers long-term gains.
Bottom line
AI optimizes toward the signals it receives — good or bad. Advertisers who own and refine their first-party data can shape outcomes in their favor, while those who don’t risk being optimized into inefficiency.

Learn why first-party data plays an increasingly important role in how automated ad campaigns are optimized and measured.

How first-party data drives better outcomes in AI-powered advertising

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/08/How-to-tell-if-Google-Ads-automation-helps-or-hurts-your-campaigns.jpg" class="attachment-large size-large wp-post-image" alt="How to tell if Google Ads automation helps or hurts your campaigns" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/08/How-to-tell-if-Google-Ads-automation-helps-or-hurts-your-campaigns.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/08/How-to-tell-if-Google-Ads-automation-helps-or-hurts-your-campaigns-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/08/How-to-tell-if-Google-Ads-automation-helps-or-hurts-your-campaigns-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google Ads’ new multi-party approval feature adds an extra safeguard against unauthorized or malicious account changes.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/08/How-to-tell-if-Google-Ads-automation-helps-or-hurts-your-campaigns.jpg" class="attachment-large size-large wp-post-image" alt="How to tell if Google Ads automation helps or hurts your campaigns" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/08/How-to-tell-if-Google-Ads-automation-helps-or-hurts-your-campaigns.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/08/How-to-tell-if-Google-Ads-automation-helps-or-hurts-your-campaigns-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/08/How-to-tell-if-Google-Ads-automation-helps-or-hurts-your-campaigns-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google Ads introduced multi-party approval, a security feature that requires a second administrator to approve high-risk account actions. These actions include adding or removing users and changing user roles.</p>



<p><strong>Why we care. </strong>As ad accounts grow in size and value, access control becomes a serious risk. One unauthorized, malicious, or accidental change can disrupt campaigns, permissions, or billing in minutes. Multi-party approval reduces that risk by requiring a second admin to approve high-impact actions. It adds strong protection without slowing daily work. For agencies and large teams, it prevents costly mistakes and significantly improves account security.</p>



<p><strong>How it works</strong>. When an admin initiates a sensitive change, Google Ads automatically creates an approval request. Other eligible admins receive an in-product notification. One of them must approve or deny the request within 20 days. If no one responds, the request expires, and the change is blocked.</p>



<p><strong>Status tracking</strong>. Each request is clearly labeled as Complete, Denied, or Expired. This makes it easy to see what was approved and what didn’t go through.</p>



<p><strong>Where to find it</strong>. You can view and manage approval requests from <em>Access and security</em> within the Admin menu.</p>



<p><strong>The bigger picture</strong>. The update reflects growing concern around account security, especially for agencies and large advertisers managing multiple users, partners, and permissions. With <a href="https://searchengineland.com/google-ads-mcc-hijacks-are-surging-465186">advertisers recently reporting costly hacks</a>, this is a welcome update.</p>



<p><strong>The Google Ads help doc</strong>. <a href="https://support.google.com/google-ads/answer/16891189" target="_blank" rel="noopener">About Multi-party approval for Google Ads</a></p>


Google Ads introduced multi-party approval, a security feature that requires a second administrator to approve high-risk account actions. These actions include adding or removing users and changing user roles.
Why we care. As ad accounts grow in size and value, access control becomes a serious risk. One unauthorized, malicious, or accidental change can disrupt campaigns, permissions, or billing in minutes. Multi-party approval reduces that risk by requiring a second admin to approve high-impact actions. It adds strong protection without slowing daily work. For agencies and large teams, it prevents costly mistakes and significantly improves account security.
How it works. When an admin initiates a sensitive change, Google Ads automatically creates an approval request. Other eligible admins receive an in-product notification. One of them must approve or deny the request within 20 days. If no one responds, the request expires, and the change is blocked.
Status tracking. Each request is clearly labeled as Complete, Denied, or Expired. This makes it easy to see what was approved and what didn’t go through.
Where to find it. You can view and manage approval requests from Access and security within the Admin menu.
The bigger picture. The update reflects growing concern around account security, especially for agencies and large advertisers managing multiple users, partners, and permissions. With advertisers recently reporting costly hacks, this is a welcome update.
The Google Ads help doc. About Multi-party approval for Google Ads

Google Ads’ new multi-party approval feature adds an extra safeguard against unauthorized or malicious account changes.

Google Ads tightens access control with multi-party approval

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026.jpg" class="attachment-large size-large wp-post-image" alt="In Google Ads automation, everything is a signal in 2026" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Automation performance depends on signal quality. Learn how Google Ads prioritizes signals, how pollution starts, and how to correct drift.

In 2015, PPC was a game of direct control. You told Google exactly which keywords to target, set manual bids at the keyword level, and capped spend with a daily budget. If you were good with spreadsheets and understood match types, you could build and manage 30,000-keyword accounts all day long. 
Those days are gone.
In 2026, platform automation is no longer a helpful assistant. It’s the primary driver of performance. Fighting that reality is a losing battle. 
Automation has leveled the playing field and, in many cases, given PPC marketers back their time. But staying effective now requires a different skill set: understanding how automated systems learn and how your data shapes their decisions.
This article breaks down how signals actually work inside Google Ads, how to identify and protect high-quality signals, and how to prevent automation from drifting into the wrong pockets of performance.
Automation runs on signals, not settings
Google’s automation isn’t a black box where you drop in a budget and hope for the best. It’s a learning system that gets smarter based on the signals you provide. 
Feed it strong, accurate signals, and it will outperform any manual approach.
Feed it poor or misleading data, and it will efficiently automate failure.
That’s the real dividing line in modern PPC. AI and automation run on signals. If a system can observe, measure, or infer something, it can use it to guide bidding and targeting.
Google’s official documentation still frames “audience signals” primarily as the segments advertisers manually add to products like Performance Max or Demand Gen. 
That definition isn’t wrong, but it’s incomplete. It reflects a legacy, surface-level view of inputs and not how automation actually learns at scale.
Dig deeper: Google Ads PMax: The truth about audience signals and search themes
What actually qualifies as a signal?
In practice, every element inside a Google Ads account functions as a signal. 
Structure, assets, budgets, pacing, conversion quality, landing page behavior, feed health, and real-time query patterns all shape how the AI interprets intent and decides where your money goes. 
Nothing is neutral. Everything contributes to the model’s understanding of who you want, who you don’t, and what outcomes you value.
So when we talk about “signals,” we’re not just talking about first-party data or demographic targeting. 
We’re talking about the full ecosystem of behavioral, structural, and quality indicators that guide the algorithm’s decision-making.
Here’s what actually matters:
Conversion actions and values: These are 100% necessary. They tell Google Ads what defines success for your specific business and which outcomes carry the most weight for your bottom line.
Keyword signals: These indicate search intent. Based on research shared by Brad Geddes at a recent Paid Search Association webinar, even “low-volume” keywords serve as vital signals. They help the system understand the semantic neighborhood of your target audience.
Ad creative signals: This goes beyond RSA word choice. I believe the platform now analyzes the environment within your images. If you show a luxury kitchen, the algorithm identifies those visual cues to find high-end customers. I base this hypothesis on my experience running a YouTube channel. I’ve watched how the algorithm serves content based on visual environments, not just metadata.
Landing page signals: Beyond copy, elements like color palettes, imagery, and engagement metrics signal how well your destination aligns with the user’s initial intent. This creates a feedback loop that tells Google whether the promise of the ad was kept.
Bid strategies and budgets: Your bidding strategy is another core signal for the AI. It tells the system whether you’re prioritizing efficiency, volume, or raw profit. Your budget signals your level of market commitment. It tells the system how much permission it has to explore and test.
In 2026, we’ve moved beyond the daily cap mindset. With the expansion of campaign total budgets to Search and Shopping, we are now signaling a total commitment window to Google.
In the announcement, UK retailer Escentual.com used this approach to signal a fixed promotional budget, resulting in a 16% traffic lift because the AI was given permission to pace spend based on real-time demand rather than arbitrary 24-hour cycles.
All of these elements function as signals because they actively shape the ad account’s learning environment.
Anything the ad platform can observe, measure, or infer becomes part of how it predicts intent, evaluates quality, and allocates budget. 
If a component influences who sees your ads, how they behave, or what outcomes the algorithm optimizes toward, it functions as a signal.
The auction-time reality: Finding the pockets
To understand why signal quality has become critical, you need to understand what’s actually happening every time someone searches.
Google’s auction-time bidding doesn’t set one bid for “mobile users in New York.” 
It calculates a unique bid for every single auction based on billions of signal combinations at that precise millisecond. This considers the user, not simply the keyword.
We are no longer looking for “black-and-white” performance.
We are finding pockets of performance and users who are predicted to take the outcomes we define as our goals in the platform.
The AI evaluates the specific intersection of a user on iOS 17, using Chrome, in London, at 8 p.m., who previously visited your pricing page. 
Because the bidding algorithm cross-references these attributes, it generates a precise bid. This level of granularity is impossible for humans to replicate. 
But this is also the “garbage in, garbage out” reality. Without quality signals, the system is forced to guess.
Dig deeper: How to build a modern Google Ads targeting strategy like a pro
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See terms.
The signal hierarchy: What Google actually listens to
If every element in a Google Ads account functions as a signal, we also have to acknowledge that not all signals carry equal weight.
Some signals shape the core of the model’s learning. Others simply refine it.
Based on my experience managing accounts spending six and seven figures monthly, this is the hierarchy that actually matters.
Conversion signals reign supreme
Your tracking is the most important data point. The algorithm needs a baseline of 30 to 50 conversions per month to recognize patterns. For B2B advertisers, this often requires shifting from high-funnel form fills to down-funnel CRM data.
As Andrea Cruz noted in her deep dive on Performance Max for B2B, optimizing for a “qualified lead” or “appointment booked” is the only way to ensure the AI doesn’t just chase cheap, irrelevant clicks.
Enhanced conversions and first-party data
We are witnessing a “death by a thousand cuts,” where browser restrictions from Safari and Firefox, coupled with aggressive global regulations, have dismantled the third-party cookie. 
Without enhanced conversions or server-side tracking, you are essentially flying blind, because the invisible trackers of the past are being replaced by a model where data must be earned through transparent value exchanges.
First-party audience signals
Your customer lists tell Google, “Here is who converted. Now go find more people like this.” 
Quality trumps quantity here. A stale or tiny list won’t be as effective as a list that is updated in real time.
Custom segments provide context
Using keywords and URLs to build segments creates a digital footprint of your ideal customer. 
This is especially critical in niche industries where Google’s prebuilt audiences are too broad or too generic.
These segments help the system understand the neighborhood your best prospects live in online.
To simplify this hierarchy, I’ve mapped out the most common signals used in 2026 by their actual weight in the bidding engine:

Signal categorySpecific input 
(The “what”)Weight/impactWhy it matters in 2026
Primary (Truth)Offline conversion imports (CRM)CriticalTrains the AI on profit, not just “leads.”
Primary (Truth)Value-based bidding (tROAS)CriticalSignals which products actually drive margin.
Secondary (Context)First-party customer match listsHighProvides a “Seed Audience” for the AI to model.
Secondary (Context)Visual environment (images/video)HighAI scans images to infer user “lifestyle” and price tier.
Tertiary (Intent)Low-volume/long-tail keywordsMediumDefines the “semantic neighborhood” of the search.
Tertiary (Intent)Landing page color and speedMediumSignals trust and relevance feedback loops.
Pollutant (Noise)“Soft” conversions (scrolls/clicks)NegativeDilutes intent. Trains AI to find “cheap clickers.”





Dig deeper: Auditing and optimizing Google Ads in an age of limited data
Beware of signal pollution
Signal pollution occurs when low-quality, conflicting, or misleading signals contaminate the data Google’s AI uses to learn. 
It’s what happens when the system receives signals that don’t accurately represent your ideal client, your real conversion quality, or the true intent you want to attract in your ad campaigns.
Signal pollution doesn’t just “confuse” the bidding algorithm. It actively trains it in the wrong direction. 
It dilutes your high-value signals, expands your reach into low-intent audiences, and forces the model to optimize toward outcomes you don’t actually want.
Common sources include:
Bad conversion data, including junk leads, unqualified form fills, and misfires.
Overly broad structures that blend high- and low-intent traffic.
Creative that attracts the wrong people.
Landing page behavior that signals low relevance or low trust.
Budget or pacing patterns that imply you’re willing to pay for volume over quality.
Feed issues that distort product relevance.
Audience segments that don’t match your real buyer.
These sources create the initial pollution. But when marketers try to compensate for underperformance by feeding the machine more data, the root cause never gets addressed. 
That’s when soft conversions like scrolls or downloads get added as primary signals, and none of them correlate to revenue.
Like humans, algorithms focus on the metrics they are fed.
If you mix soft signals with high-intent revenue data, you dilute the profile of your ideal customer. 
You end up winning thousands of cheap, low-value auctions that look great in a report but fail to move the needle on the P&L. 
Your job is to be the gatekeeper, ensuring only the most profitable signals reach the bidding engine.
When signal pollution takes hold, the algorithm doesn’t just underperform. The ads start drifting toward the wrong users, and performance begins to decline. 
Before you can build a strong signal strategy, you have to understand how to spot that drift early and correct it before it compounds.
How to detect and correct algorithm drift
Algorithm drift happens when Google’s automation starts optimizing toward the wrong outcomes because the signals it’s receiving no longer match your real advertising goals. 
Drift doesn’t show up as a dramatic crash. It shows up as a slow shift in who you reach, what queries you win, and which conversions the system prioritizes. It looks like a gradual deterioration of lead quality.
To stay in control, you need a simple way to spot drift early and correct it before the machine locks in the wrong pattern.
Early warning signs of drift include:
A sudden rise in cheap conversions that don’t correlate with revenue.
A shift in search terms toward lower-intent or irrelevant queries.
A drop in average order value or lead quality.
A spike in new-user volume with no matching lift in sales.
A campaign that looks healthy in-platform but feels wrong in the CRM or P&L.
These are all indicators that the system is optimizing toward the wrong signals.
To correct drift without resetting learning:
Tighten your conversion signals: Remove soft conversions, misfires, or anything that doesn’t map to revenue. The machine can’t unlearn bad data, but you can stop feeding it.
Reinforce the right audience patterns:  Upload fresh customer lists, refresh custom segments, and remove stale data. Drift often comes from outdated or diluted audience signals.
Adjust structure to isolate intent:  If a campaign blends high- and low-intent traffic, split it. Give the ad platform a cleaner environment to relearn the right patterns.
Refresh creative to repel the wrong users: Creative is a signal. If the wrong people are clicking, your ads are attracting them. Update imagery, language, and value props to realign intent.
Let the system stabilize before making another change: After a correction, give the campaign 5-10 days to settle. Overcorrecting creates more drift.
Your job isn’t to fight automation in Google Ads, it’s to guide it. 
Drift happens when the machine is left unsupervised with weak or conflicting signals. Strong signal hygiene keeps the system aligned with your real business outcomes.
Once you can detect drift and correct it quickly, you’re finally in a position to build a signal strategy that compounds over time instead of constantly resetting.
The next step is structuring your ad account so every signal reinforces the outcomes you actually want.
Dig deeper: How to tell if Google Ads automation helps or hurts your campaigns
Building a strategy that actually works in 2026 with signals
If you want to build a signal strategy that becomes a competitive advantage, you have to start with the foundations.
For lead gen
Implement offline conversion imports. The difference between optimizing for a “form fill” and a “$50K closed deal” is the difference between wasting budget and growing a business. 
When “journey-aware bidding” eventually rolls out, it will be a game-changer because we can feed more data about the individual steps of a sale.
For ecommerce
Use value-based bidding. Don’t just count conversions. Differentiate between a customer buying a $20 accessory and one buying a $500 hero product.
Segment your data
Don’t just dump everyone into one list. A list of 5,000 recent purchasers is worth far more than 50,000 people who visited your homepage two years ago. 
Stale data hurts performance by teaching the algorithm to find people who matched your business 18 months ago, not today.
Separate brand and nonbrand campaigns
Brand traffic carries radically different intent and conversion rates than nonbrand. 
Mixing these campaigns forces the algorithm to average two incompatible behaviors, which muddies your signals and inflates your ROAS expectations. 
Brand should be isolated so it doesn’t subsidize poor nonbrand performance or distort bidding decisions in the ad platform.
Don’t mix high-ticket and low-ticket products under one ROAS target
A $600 product and a $20 product do not behave the same in auction-time bidding. 
When you put them in the same campaign with a single 4x ROAS target, the algorithm will get confused. 
This trains the system away from your hero products and toward low-value volume.
Centralize campaigns for data density, but only when the data belongs together
Google’s automation performs best when it has enough data to be consistent and high-quality data to recognize patterns. That means fewer, stronger campaigns are better as long as the signals inside them are aligned. 
Centralize campaigns when products share similar price points, margins, audiences, and intent. Decentralize campaigns when mixing them would pollute the signal pool.
The competitive advantage of 2026
When everyone has access to the same automation, the only real advantage left is the quality of the signals you feed it. 
Your job is to protect those signals, diagnose pollution early, and correct drift before the system locks onto the wrong patterns.
Once you build a deliberate signal strategy, Google’s automation stops being a constraint and becomes leverage. You stay in the loop, and the machine does the heavy lifting.

Automation performance depends on signal quality. Learn how Google Ads prioritizes signals, how pollution starts, and how to correct drift.

In Google Ads automation, everything is a signal in 2026

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/ai-future-ad-free-vs-supported.png" class="attachment-large size-large wp-post-image" alt="AI ad free vs. ad supported" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/ai-future-ad-free-vs-supported.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/ai-future-ad-free-vs-supported-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/ai-future-ad-free-vs-supported-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Anthropic argues ads inside AI chats would erode trust, warp incentives, and clash with how people actually use assistants like Claude.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/ai-future-ad-free-vs-supported.png" class="attachment-large size-large wp-post-image" alt="AI ad free vs. ad supported" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/ai-future-ad-free-vs-supported.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/ai-future-ad-free-vs-supported-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/ai-future-ad-free-vs-supported-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Anthropic is drawing the line against advertising in AI chatbots. Claude will remain ad-free, the company said, even as rival AI platforms experiment with sponsored messages and branded placements inside conversations.</p>



<ul class="wp-block-list">
<li>Ads inside AI chats would erode trust, warp incentives, and clash with how people actually use assistants like Claude (for work, problem-solving, and sensitive topics), Anthropic said in a new blog post.</li>
</ul>



<p><strong>Why we care.</strong> Anthropic’s position removes Claude, and its user base of 30 million, from the AI advertising equation. Brands shouldn’t expect sponsored links, conversations, or responses inside Claude. Meanwhile, ChatGPT is about to give brands the opportunity to reach an estimated 800 million weekly users. </p>



<p><strong>What’s happening.</strong>  AI conversations are fundamentally different from search results or social feeds, where users expect a mix of organic and paid content, Anthropic said:</p>



<ul class="wp-block-list">
<li>Many Claude interactions involve personal issues, complex technical work, or high-stakes thinking. Dropping ads into those moments would feel intrusive and could quietly influence responses in ways users can’t easily detect.</li>



<li>Ad incentives tend to expand over time, gradually optimizing for engagement rather than genuine usefulness.</li>
</ul>



<p><strong>Incentives matter.</strong> This is a business-model decision, not just a product preference, Anthropic said:</p>



<ul class="wp-block-list">
<li>An ad-free assistant can focus entirely on what helps the user — even if that means a short exchange or no follow-up at all. </li>



<li>An ad-supported model, by contrast, creates pressure to surface monetizable moments or keep users engaged longer than necessary.</li>



<li>Once ads enter the system, users may start questioning whether recommendations are driven by help or by commerce.</li>
</ul>



<p><strong>Anthropic isn’t rejecting commerce.</strong> Claude will still help users research, compare, and buy products when they ask. The company is also exploring “agentic commerce,” where the AI completes tasks like bookings or purchases on a user’s behalf.</p>



<ul class="wp-block-list">
<li>Commerce should be triggered by the user, not by advertisers, Anthropic said.</li>



<li>The same rule applies to third-party integrations like Figma or Asana. These tools will remain user-directed, not sponsored.</li>
</ul>



<p><strong>Super Bowl ad. </strong>Anthropic is making the argument publicly and aggressively. In a Super Bowl debut, the company mocked intrusive AI advertising by inserting fake product pitches into personal conversations. The ad closed with a clear message: “Ads are coming to AI. But not to Claude.”</p>



<ul class="wp-block-list">
<li>The campaign appears to be a direct shot at OpenAI, which has announced plans to introduce ads into ChatGPT. </li>



<li>Here&#8217;s the ad:</li>
</ul>



<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe title="Can I get a six pack quickly?" width="500" height="281" src="https://www.youtube.com/embed/kQRu7DdTTVA?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div></figure>



<p><strong>Claude&#8217;s blog post.</strong> <a href="https://www.anthropic.com/news/claude-is-a-space-to-think">Claude is a space to think</a></p>



<p><strong>OpenAI responds.</strong> OpenAI CEO <a href="https://x.com/sama/status/2019139174339928189" target="_blank" rel="noopener">Sam Altman posted some thoughts on X</a>. Some of the highlights:</p>



<ul class="wp-block-list">
<li>&#8220;&#8230;I wonder why Anthropic would go for something so clearly dishonest. Our most important principle for ads says that we won’t do exactly this; we would obviously never run ads in the way Anthropic depicts them. We are not stupid and we know our users would reject that.</li>



<li>&#8220;I guess it’s on brand for Anthropic doublespeak to use a deceptive ad to critique theoretical deceptive ads that aren’t real, but a Super Bowl ad is not where I would expect it.</li>



<li>&#8220;Anthropic serves an expensive product to rich people. We are glad they do that and we are doing that too, but we also feel strongly that we need to bring AI to billions of people who can’t pay for subscriptions.</li>



<li>&#8220;We will continue to work hard to make even more intelligence available for lower and lower prices to our users.&#8221;</li>
</ul>


Anthropic is drawing the line against advertising in AI chatbots. Claude will remain ad-free, the company said, even as rival AI platforms experiment with sponsored messages and branded placements inside conversations.
Ads inside AI chats would erode trust, warp incentives, and clash with how people actually use assistants like Claude (for work, problem-solving, and sensitive topics), Anthropic said in a new blog post.
Why we care. Anthropic’s position removes Claude, and its user base of 30 million, from the AI advertising equation. Brands shouldn’t expect sponsored links, conversations, or responses inside Claude. Meanwhile, ChatGPT is about to give brands the opportunity to reach an estimated 800 million weekly users. 
What’s happening.  AI conversations are fundamentally different from search results or social feeds, where users expect a mix of organic and paid content, Anthropic said:
Many Claude interactions involve personal issues, complex technical work, or high-stakes thinking. Dropping ads into those moments would feel intrusive and could quietly influence responses in ways users can’t easily detect.
Ad incentives tend to expand over time, gradually optimizing for engagement rather than genuine usefulness.
Incentives matter. This is a business-model decision, not just a product preference, Anthropic said:
An ad-free assistant can focus entirely on what helps the user — even if that means a short exchange or no follow-up at all. 
An ad-supported model, by contrast, creates pressure to surface monetizable moments or keep users engaged longer than necessary.
Once ads enter the system, users may start questioning whether recommendations are driven by help or by commerce.
Anthropic isn’t rejecting commerce. Claude will still help users research, compare, and buy products when they ask. The company is also exploring “agentic commerce,” where the AI completes tasks like bookings or purchases on a user’s behalf.
Commerce should be triggered by the user, not by advertisers, Anthropic said.
The same rule applies to third-party integrations like Figma or Asana. These tools will remain user-directed, not sponsored.
Super Bowl ad. Anthropic is making the argument publicly and aggressively. In a Super Bowl debut, the company mocked intrusive AI advertising by inserting fake product pitches into personal conversations. The ad closed with a clear message: “Ads are coming to AI. But not to Claude.”
The campaign appears to be a direct shot at OpenAI, which has announced plans to introduce ads into ChatGPT. 
Here’s the ad:






Claude’s blog post. Claude is a space to think
OpenAI responds. OpenAI CEO Sam Altman posted some thoughts on X. Some of the highlights:
“…I wonder why Anthropic would go for something so clearly dishonest. Our most important principle for ads says that we won’t do exactly this; we would obviously never run ads in the way Anthropic depicts them. We are not stupid and we know our users would reject that.
“I guess it’s on brand for Anthropic doublespeak to use a deceptive ad to critique theoretical deceptive ads that aren’t real, but a Super Bowl ad is not where I would expect it.
“Anthropic serves an expensive product to rich people. We are glad they do that and we are doing that too, but we also feel strongly that we need to bring AI to billions of people who can’t pay for subscriptions.
“We will continue to work hard to make even more intelligence available for lower and lower prices to our users.”

Anthropic argues ads inside AI chats would erode trust, warp incentives, and clash with how people actually use assistants like Claude.

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