<div><img width="800" height="457" src="https://searchengineland.com/wp-content/seloads/2025/02/google-lens-robot-ai-1920-800x457.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2025/02/google-lens-robot-ai-1920-800x457.jpg 800w, https://searchengineland.com/wp-content/seloads/2025/02/google-lens-robot-ai-1920-592x338.jpg 592w, https://searchengineland.com/wp-content/seloads/2025/02/google-lens-robot-ai-1920-198x113.jpg 198w, https://searchengineland.com/wp-content/seloads/2025/02/google-lens-robot-ai-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/02/google-lens-robot-ai-1920-1536x878.jpg 1536w, https://searchengineland.com/wp-content/seloads/2025/02/google-lens-robot-ai-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>These links appear as pop ups as you hover your mouse over them.

<div><img width="800" height="457" src="https://searchengineland.com/wp-content/seloads/2025/02/google-lens-robot-ai-1920-800x457.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/02/google-lens-robot-ai-1920-800x457.jpg 800w, https://searchengineland.com/wp-content/seloads/2025/02/google-lens-robot-ai-1920-592x338.jpg 592w, https://searchengineland.com/wp-content/seloads/2025/02/google-lens-robot-ai-1920-198x113.jpg 198w, https://searchengineland.com/wp-content/seloads/2025/02/google-lens-robot-ai-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/02/google-lens-robot-ai-1920-1536x878.jpg 1536w, https://searchengineland.com/wp-content/seloads/2025/02/google-lens-robot-ai-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
Google is rolling out new, more visible links within AI Overviews and AI Mode. These new link cards appear in a pop-up window when you hover over them on desktop. They also show more prominent details about the website. 



Google <a href="https://www.seroundtable.com/google-ai-overviews-contextual-overlay-link-cards-40892.html">was testing</a> these earlier and now this new style is live.



What it looks like. Here is a screenshot of these new link pop up menus on hover:



<figure class="wp-block-image size-full"><img decoding="async" width="2048" height="1536" src="https://searchengineland.com/wp-content/seloads/2026/02/AIM-AIO-links-update-scaled.png" alt="" class="wp-image-469442" srcset="https://searchengineland.com/wp-content/seloads/2026/02/AIM-AIO-links-update-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/02/AIM-AIO-links-update-768x576.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/AIM-AIO-links-update-1536x1152.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>



What Google said. Google&#8217;s Robby Stein <a href="https://x.com/rmstein/status/2023865995908313531?s=20">posted</a> on X saying:



<ul class="wp-block-list">
<li>&#8220;New on Search: In AI Overviews and AI Mode, groups of links will automatically appear in a pop-up as you hover over them on desktop, so you can jump right into a website to learn more. And we’ll show more descriptive and prominent link icons within the response across both desktop and mobile.&#8221;</li>



<li>&#8220;Our testing shows this new UI is more engaging, making it easier to get to great content across the web.&#8221;</li>
</ul>



Why we care. This new style does appear to encourage more clicks to websites and I do hope that we will see more traffic from Google&#8217;s AI experiences from these changes.



Of course, we still have no way to measure this in Search Console.

Google is rolling out new, more visible links within AI Overviews and AI Mode. These new link cards appear in a pop-up window when you hover over them on desktop.  They also show more prominent details about the website. 
Google was testing these earlier and now this new style is live.
What it looks like. Here is a screenshot of these new link pop up menus on hover:




What Google said. Google’s Robby Stein posted on X saying:
“New on Search: In AI Overviews and AI Mode, groups of links will automatically appear in a pop-up as you hover over them on desktop, so you can jump right into a website to learn more. And we’ll show more descriptive and prominent link icons within the response across both desktop and mobile.”
“Our testing shows this new UI is more engaging, making it easier to get to great content across the web.”
Why we care. This new style does appear to encourage more clicks to websites and I do hope that we will see more traffic from Google’s AI experiences from these changes.
Of course, we still have no way to measure this in Search Console.

These links appear as pop ups as you hover your mouse over them.

Google launches more visible links in AI Overviews and AI Mode

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/conversational-discovery-engine.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/conversational-discovery-engine.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/conversational-discovery-engine-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/conversational-discovery-engine-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Early data suggests chatbot referrals bring higher-intent guests as Airbnb bets on conversational search and deeper AI integration.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/conversational-discovery-engine.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/conversational-discovery-engine.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/conversational-discovery-engine-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/conversational-discovery-engine-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
Traffic from AI chatbots converts at a higher rate than traffic from Google, according to Airbnb CEO Brian Chesky. He shared this tidbit on the company’s Q4 2025 earnings call:



<ul class="wp-block-list">
<li>“And what we see is that traffic that comes from chatbots convert at a higher rate than traffic that comes from Google,” Chesky said on Feb. 12.</li>
</ul>



Yes, but. He didn’t share specific conversion rates, and the company didn’t quantify chatbot traffic volume. But for Airbnb, early data suggests visitors arriving via AI chatbots may be further along in the booking process than those coming from traditional Google searches.



<ul class="wp-block-list">
<li>Airbnb also didn’t specify which chatbots are driving traffic. Chesky referenced OpenAI’s ChatGPT, Google’s Gemini, Anthropic’s Claude, and others in broader remarks about model availability.</li>
</ul>



Why we care. AI assistants are emerging as a top-of-funnel discovery layer. The quality of that traffic may outperform clicks from traditional search and align with past claims by Google and Microsoft that AI will drive more qualified traffic at lower volume.



AI search ambitions. Chesky described chatbots as “very similar to search” and positioned them as top-of-funnel discovery engines.



<ul class="wp-block-list">
<li>“I think these chatbot platforms are gonna be very similar to search. Gonna be really good top-of-funnel discoveries,” he said.</li>
</ul>



Rather than viewing them as disintermediators, Airbnb sees them as acquisition partners.



<ul class="wp-block-list">
<li>“We think they are gonna be positive for Airbnb,” Chesky added.</li>
</ul>



Chesky described the long-term goal as building an “AI-native experience” where the app “does not just search for you. It knows you.”:



<ul class="wp-block-list">
<li>&#8220;So AI search is live to a very small percent of traffic right now. We are doing a lot of experimentation. The way we do things with AI is much more rapid iteration, not big launches. And over time, we are gonna be experimenting with making AI search more conversational, integrating it into more than trip, and, eventually, we will be looking at sponsor listings as result of that. But we want to first nail AI search.&#8221;</li>
</ul>



AI inside Airbnb. Airbnb isn’t just benefiting from external AI platforms. It’s embedding AI into its operations.



<ul class="wp-block-list">
<li>Its in-house AI customer service agent now resolves nearly one-third of North American support tickets without a human, according to Chesky. The tool is English-only for now but is slated for global, multilingual rollout, including voice support.</li>



<li>Chesky said the goal is for AI to handle “significantly more than 30%” of tickets within a year.</li>



<li>Airbnb is also testing AI-powered conversational search in its app. The feature is live for a small percentage of users and is being iterated quickly rather than launched as a major product release.</li>



<li></li>
</ul>



Sponsored listings on hold for now. Airbnb has long faced questions about launching sponsored listings. On the call, Chesky said traditional ad units may not translate directly into conversational AI environments. The company is prioritizing AI search before designing sponsored placements in that format.



Airbnb&#8217;s search shift. <a href="https://searchengineland.com/airbnb-search-marketing-shift-390069">Airbnb began moving its budget to brand marketing</a> just before the rise of generative AI and AI-powered search. Airbnb bet on broader marketing initiatives, slashing its search marketing spending.

Traffic from AI chatbots converts at a higher rate than traffic from Google, according to Airbnb CEO Brian Chesky. He shared this tidbit on the company’s Q4 2025 earnings call:
“And what we see is that traffic that comes from chatbots convert at a higher rate than traffic that comes from Google,” Chesky said on Feb. 12.
Yes, but. He didn’t share specific conversion rates, and the company didn’t quantify chatbot traffic volume. But for Airbnb, early data suggests visitors arriving via AI chatbots may be further along in the booking process than those coming from traditional Google searches.
Airbnb also didn’t specify which chatbots are driving traffic. Chesky referenced OpenAI’s ChatGPT, Google’s Gemini, Anthropic’s Claude, and others in broader remarks about model availability.
Why we care. AI assistants are emerging as a top-of-funnel discovery layer. The quality of that traffic may outperform clicks from traditional search and align with past claims by Google and Microsoft that AI will drive more qualified traffic at lower volume.
AI search ambitions. Chesky described chatbots as “very similar to search” and positioned them as top-of-funnel discovery engines.
“I think these chatbot platforms are gonna be very similar to search. Gonna be really good top-of-funnel discoveries,” he said.
Rather than viewing them as disintermediators, Airbnb sees them as acquisition partners.
“We think they are gonna be positive for Airbnb,” Chesky added.
Chesky described the long-term goal as building an “AI-native experience” where the app “does not just search for you. It knows you.”:
“So AI search is live to a very small percent of traffic right now. We are doing a lot of experimentation. The way we do things with AI is much more rapid iteration, not big launches. And over time, we are gonna be experimenting with making AI search more conversational, integrating it into more than trip, and, eventually, we will be looking at sponsor listings as result of that. But we want to first nail AI search.”
AI inside Airbnb. Airbnb isn’t just benefiting from external AI platforms. It’s embedding AI into its operations.
Its in-house AI customer service agent now resolves nearly one-third of North American support tickets without a human, according to Chesky. The tool is English-only for now but is slated for global, multilingual rollout, including voice support.
Chesky said the goal is for AI to handle “significantly more than 30%” of tickets within a year.
Airbnb is also testing AI-powered conversational search in its app. The feature is live for a small percentage of users and is being iterated quickly rather than launched as a major product release.





Sponsored listings on hold for now. Airbnb has long faced questions about launching sponsored listings. On the call, Chesky said traditional ad units may not translate directly into conversational AI environments. The company is prioritizing AI search before designing sponsored placements in that format.
Airbnb’s search shift. Airbnb began moving its budget to brand marketing just before the rise of generative AI and AI-powered search. Airbnb bet on broader marketing initiatives, slashing its search marketing spending.

Early data suggests chatbot referrals bring higher-intent guests as Airbnb bets on conversational search and deeper AI integration.

Airbnb says traffic from AI chatbots converts better than Google

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2024/04/TikTok-SEO-The-ultimate-guide-800x450.png" class="attachment-large size-large wp-post-image" alt="TikTok SEO: The ultimate guide" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/04/TikTok-SEO-The-ultimate-guide-800x450.png 800w, https://searchengineland.com/wp-content/seloads/2024/04/TikTok-SEO-The-ultimate-guide-600x338.png 600w, https://searchengineland.com/wp-content/seloads/2024/04/TikTok-SEO-The-ultimate-guide-200x113.png 200w, https://searchengineland.com/wp-content/seloads/2024/04/TikTok-SEO-The-ultimate-guide-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2024/04/TikTok-SEO-The-ultimate-guide-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2024/04/TikTok-SEO-The-ultimate-guide.png 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>TikTok’s new AI-powered ads let entertainment marketers deliver personalized content that boosts engagement and conversions.

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2024/04/TikTok-SEO-The-ultimate-guide-800x450.png" class="attachment-large size-large wp-post-image" alt="TikTok SEO: The ultimate guide" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/04/TikTok-SEO-The-ultimate-guide-800x450.png 800w, https://searchengineland.com/wp-content/seloads/2024/04/TikTok-SEO-The-ultimate-guide-600x338.png 600w, https://searchengineland.com/wp-content/seloads/2024/04/TikTok-SEO-The-ultimate-guide-200x113.png 200w, https://searchengineland.com/wp-content/seloads/2024/04/TikTok-SEO-The-ultimate-guide-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2024/04/TikTok-SEO-The-ultimate-guide-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2024/04/TikTok-SEO-The-ultimate-guide.png 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
TikTok is giving entertainment marketers in Europe new tools to reach audiences with precision, leveraging AI to drive engagement and conversions for streaming and ticketed content.



What’s happening. TikTok is introducing two new ad types for European campaigns:



<ul class="wp-block-list">
<li>Streaming Ads: AI-driven ads for streaming platforms that show personalized content based on user engagement. Formats include a four-title video carousel or a multi-title media card. With 80% of TikTok users saying the app influences their streaming choices, these ads can directly shape viewing decisions.</li>



<li>New Title Launch: Targets high-intent users using signals like genre preference and price sensitivity, helping marketers convert cultural moments into ticket sales, subscriptions, or event attendance.</li>
</ul>



Context. The rollout coincides with the 76th Berlinale International Film Festival, underscoring TikTok’s growing role in entertainment marketing. In 2025, an average of 6.5 million daily posts were shared about film and TV on TikTok, with 15 of the top 20 European box office films last year being viral hits on the platform.



Why we care. TikTok’s new AI-powered ad formats let streaming platforms and entertainment brands target users with highly personalized content, increasing the likelihood of engagement and conversions.



With 80% of users saying TikTok influences their viewing choices (according to TikTok data), these tools can directly shape audience behavior, helping marketers turn cultural moments into subscriptions, ticket sales, or higher viewership. It’s a chance to leverage TikTok’s viral influence for measurable campaign impact.



The bottom line. For entertainment marketers, TikTok’s AI-driven ad formats provide new ways to engage audiences, boost viewership, and turn trending content into measurable results.



Dig deeper. <a href="https://www.socialmediatoday.com/news/tiktok-adds-new-ad-types-for-entertainment-marketers/812135/">TikTok Adds New Ad Types for Entertainment Marketers</a>

TikTok is giving entertainment marketers in Europe new tools to reach audiences with precision, leveraging AI to drive engagement and conversions for streaming and ticketed content.
What’s happening. TikTok is introducing two new ad types for European campaigns:
Streaming Ads: AI-driven ads for streaming platforms that show personalized content based on user engagement. Formats include a four-title video carousel or a multi-title media card. With 80% of TikTok users saying the app influences their streaming choices, these ads can directly shape viewing decisions.
New Title Launch: Targets high-intent users using signals like genre preference and price sensitivity, helping marketers convert cultural moments into ticket sales, subscriptions, or event attendance.
Context. The rollout coincides with the 76th Berlinale International Film Festival, underscoring TikTok’s growing role in entertainment marketing. In 2025, an average of 6.5 million daily posts were shared about film and TV on TikTok, with 15 of the top 20 European box office films last year being viral hits on the platform.
Why we care. TikTok’s new AI-powered ad formats let streaming platforms and entertainment brands target users with highly personalized content, increasing the likelihood of engagement and conversions.
With 80% of users saying TikTok influences their viewing choices (according to TikTok data), these tools can directly shape audience behavior, helping marketers turn cultural moments into subscriptions, ticket sales, or higher viewership. It’s a chance to leverage TikTok’s viral influence for measurable campaign impact.
The bottom line. For entertainment marketers, TikTok’s AI-driven ad formats provide new ways to engage audiences, boost viewership, and turn trending content into measurable results.
Dig deeper. TikTok Adds New Ad Types for Entertainment Marketers

TikTok’s new AI-powered ads let entertainment marketers deliver personalized content that boosts engagement and conversions.

TikTok launches AI-powered ad options for entertainment marketers

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/01/Inside-Metas-AI-driven-advertising-system-How-Andromeda-and-GEM-work-together.jpg" class="attachment-large size-large wp-post-image" alt="Inside Meta’s AI-driven advertising system: How Andromeda and GEM work together" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/01/Inside-Metas-AI-driven-advertising-system-How-Andromeda-and-GEM-work-together.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/01/Inside-Metas-AI-driven-advertising-system-How-Andromeda-and-GEM-work-together-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/01/Inside-Metas-AI-driven-advertising-system-How-Andromeda-and-GEM-work-together-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Meta is embedding Manus AI into Ads Manager, giving advertisers built-in AI tools to streamline reporting, research, and campaign optimization.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/01/Inside-Metas-AI-driven-advertising-system-How-Andromeda-and-GEM-work-together.jpg" class="attachment-large size-large wp-post-image" alt="Inside Meta’s AI-driven advertising system: How Andromeda and GEM work together" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/01/Inside-Metas-AI-driven-advertising-system-How-Andromeda-and-GEM-work-together.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/01/Inside-Metas-AI-driven-advertising-system-How-Andromeda-and-GEM-work-together-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/01/Inside-Metas-AI-driven-advertising-system-How-Andromeda-and-GEM-work-together-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
Meta Platforms is embedding newly acquired AI agent tech directly into Ads Manager, giving advertisers built-in automation tools for research and reporting as the company looks to show faster returns on its AI investments.



What’s happening. Some advertisers are seeing in-stream prompts to activate Manus AI inside Ads Manager.



<ul class="wp-block-list">
<li>Manus is now available to all advertisers via the Tools menu.</li>



<li>Select users are also getting pop-up alerts encouraging in-workflow adoption.</li>



<li>The feature rollout signals deeper integration ahead.</li>
</ul>



What is Manus. Manus AI is designed to power AI agents that can perform tasks like report building and audience research, effectively acting as an assistant within the ad workflow.



Why we care. Manus AI brings AI-powered automation directly into Meta Platforms Ads Manager, making tasks like report-building, audience research, and campaign analysis faster and more efficient.



Meta is currently prioritizing tying AI investment to measurable ad performance, giving advertisers new ways to optimize campaigns and potentially gain a competitive edge by testing workflow efficiencies early.



Between the lines. Meta is under pressure to demonstrate practical value from its aggressive AI spending. Advertising remains its clearest path to monetization, and embedding Manus into everyday ad tools offers a direct way to tie AI investment to performance gains.



Zoom out. The move aligns with CEO Mark Zuckerberg’s push to weave AI across Meta’s product stack. By positioning Manus as a performance tool for advertisers, Meta is betting that workflow efficiencies will translate into stronger ad results — and a clearer AI revenue story.



The bottom line. For advertisers, <a href="https://www.socialmediatoday.com/news/meta-launches-manus-ai-integration-in-ads-manager/812288/">Manus</a> adds another layer of built-in automation worth testing. Early adopters may uncover time savings and optimization gains as Meta continues expanding AI inside its ad ecosystem.

Meta Platforms is embedding newly acquired AI agent tech directly into Ads Manager, giving advertisers built-in automation tools for research and reporting as the company looks to show faster returns on its AI investments.
What’s happening. Some advertisers are seeing in-stream prompts to activate Manus AI inside Ads Manager.
Manus is now available to all advertisers via the Tools menu.
Select users are also getting pop-up alerts encouraging in-workflow adoption.
The feature rollout signals deeper integration ahead.
What is Manus. Manus AI is designed to power AI agents that can perform tasks like report building and audience research, effectively acting as an assistant within the ad workflow.
Why we care. Manus AI brings AI-powered automation directly into Meta Platforms Ads Manager, making tasks like report-building, audience research, and campaign analysis faster and more efficient.
Meta is currently prioritizing tying AI investment to measurable ad performance, giving advertisers new ways to optimize campaigns and potentially gain a competitive edge by testing workflow efficiencies early.
Between the lines. Meta is under pressure to demonstrate practical value from its aggressive AI spending. Advertising remains its clearest path to monetization, and embedding Manus into everyday ad tools offers a direct way to tie AI investment to performance gains.
Zoom out. The move aligns with CEO Mark Zuckerberg’s push to weave AI across Meta’s product stack. By positioning Manus as a performance tool for advertisers, Meta is betting that workflow efficiencies will translate into stronger ad results — and a clearer AI revenue story.
The bottom line. For advertisers, Manus adds another layer of built-in automation worth testing. Early adopters may uncover time savings and optimization gains as Meta continues expanding AI inside its ad ecosystem.

Meta is embedding Manus AI into Ads Manager, giving advertisers built-in AI tools to streamline reporting, research, and campaign optimization.

Meta adds Manus AI tools into Ads Manager

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/11/The-Google-Ads-Demand-Gen-playbook-for-todays-fractured-consumer-journey.png" class="attachment-large size-large wp-post-image" alt="The Google Ads Demand Gen playbook for today’s fractured consumer journey" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/11/The-Google-Ads-Demand-Gen-playbook-for-todays-fractured-consumer-journey.png 1920w, https://searchengineland.com/wp-content/seloads/2025/11/The-Google-Ads-Demand-Gen-playbook-for-todays-fractured-consumer-journey-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/11/The-Google-Ads-Demand-Gen-playbook-for-todays-fractured-consumer-journey-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google is turning Lookalike audiences into AI signals that expand reach and could significantly change campaign performance.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/11/The-Google-Ads-Demand-Gen-playbook-for-todays-fractured-consumer-journey.png" class="attachment-large size-large wp-post-image" alt="The Google Ads Demand Gen playbook for today’s fractured consumer journey" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/11/The-Google-Ads-Demand-Gen-playbook-for-todays-fractured-consumer-journey.png 1920w, https://searchengineland.com/wp-content/seloads/2025/11/The-Google-Ads-Demand-Gen-playbook-for-todays-fractured-consumer-journey-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/11/The-Google-Ads-Demand-Gen-playbook-for-todays-fractured-consumer-journey-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
A core targeting lever in Google Demand Gen campaigns is changing. Starting March 2026, Lookalike audiences will act as optimization signals — not hard constraints — potentially widening reach and leaning more heavily on automation to drive conversions.



What is happening. Per an update to Google’s Help documentation, Lookalike segments in Demand Gen are moving from strict similarity-based targeting to an AI-driven suggestion model.



<ul class="wp-block-list">
<li>Before: Advertisers selected a similarity tier (narrow, balanced, broad), and campaigns targeted users strictly within that Lookalike pool.</li>



<li>After: The same tiers act as signals. Google’s system can expand beyond the Lookalike list to reach users it predicts are likely to convert.</li>
</ul>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="800" height="307" src="https://searchengineland.com/wp-content/seloads/2026/02/Lookalike-audience.jpeg" alt="" class="wp-image-469407" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Lookalike-audience.jpeg 800w, https://searchengineland.com/wp-content/seloads/2026/02/Lookalike-audience-768x295.jpeg 768w" sizes="(max-width: 800px) 100vw, 800px" /></figure></div>


Between the lines. This effectively reframes Lookalikes from a fence to a compass. Instead of limiting delivery to a defined cohort, advertisers are feeding intent signals into Google’s automation and allowing it to search for performance outside preset boundaries.



How this interacts with Optimized Targeting. The new Lookalike-as-signal approach resembles Optimized Targeting — but it doesn’t replace it.



<ul class="wp-block-list">
<li>When advertisers layer Optimized Targeting on top, Google says the system may expand reach even further.</li>



<li>In practice, this stacks multiple automation signals, increasing the algorithm’s freedom to pursue lower CPA or higher conversion volume.</li>
</ul>



Opt-out option. Advertisers who want to preserve legacy behavior can request continued access to strict Lookalike targeting through a dedicated opt-out form. Without that request, campaigns will default to the new signal-based model.



Why we care. This update changes how much control advertisers will have over who their ads reach in Google Demand Gen campaigns. Lookalike audiences will no longer strictly limit targeting — they’ll guide AI expansion — which can significantly affect scale, CPA, and overall performance.



It also signals a broader shift toward automation, similar to trends driven by Meta Platforms. Advertisers will need to test carefully, rethink audience strategies, and decide whether to embrace the added reach or opt out to preserve tighter targeting.



Zoom out. The shift mirrors a broader industry trend toward AI-first audience expansion, similar to moves by Meta Platforms over the past few years. Platforms are steadily trading granular manual controls for machine-led optimization.



Why Google is doing this. Digital markerter Dario Zannoni, has two reasons as to why Google is doing this:



<ul class="wp-block-list">
<li>Strict Lookalike targeting can cap scale and constrain performance in conversion-focused campaigns.</li>



<li>Maintaining high-quality similarity models is increasingly complex, making broader automation more attractive.</li>
</ul>



The bottom line. For performance marketers, this is another step toward automation-centric buying. While reduced control may be uncomfortable, comparable platform changes have often produced performance gains in mainstream use cases. Expect a new testing cycle as advertisers measure how expanded Lookalike signals affect CPA, reach, and incremental conversions.



First seen. This update was spotted by Zannoni who shared his thoughts on <a href="https://www.linkedin.com/posts/dariozannoni_important-changes-coming-in-march-for-lookalike-activity-7428997929341976576-HCg7/?utm_source=share&amp;utm_medium=member_desktop&amp;rcm=ACoAAAAOXwgBGvKJ5PG5kuwaTmFUJWimyBar5pw">LinkedIn</a>. 



Dig deeper. <a href="https://support.google.com/google-ads/answer/13541369?hl=en-GB">Use Lookalike segments to grow your audience</a>

A core targeting lever in Google Demand Gen campaigns is changing. Starting March 2026, Lookalike audiences will act as optimization signals — not hard constraints — potentially widening reach and leaning more heavily on automation to drive conversions.
What is happening. Per an update to Google’s Help documentation, Lookalike segments in Demand Gen are moving from strict similarity-based targeting to an AI-driven suggestion model.
Before: Advertisers selected a similarity tier (narrow, balanced, broad), and campaigns targeted users strictly within that Lookalike pool.
After: The same tiers act as signals. Google’s system can expand beyond the Lookalike list to reach users it predicts are likely to convert.

Between the lines. This effectively reframes Lookalikes from a fence to a compass. Instead of limiting delivery to a defined cohort, advertisers are feeding intent signals into Google’s automation and allowing it to search for performance outside preset boundaries.
How this interacts with Optimized Targeting. The new Lookalike-as-signal approach resembles Optimized Targeting — but it doesn’t replace it.
When advertisers layer Optimized Targeting on top, Google says the system may expand reach even further.
In practice, this stacks multiple automation signals, increasing the algorithm’s freedom to pursue lower CPA or higher conversion volume.
Opt-out option. Advertisers who want to preserve legacy behavior can request continued access to strict Lookalike targeting through a dedicated opt-out form. Without that request, campaigns will default to the new signal-based model.
Why we care. This update changes how much control advertisers will have over who their ads reach in Google Demand Gen campaigns. Lookalike audiences will no longer strictly limit targeting — they’ll guide AI expansion — which can significantly affect scale, CPA, and overall performance.
It also signals a broader shift toward automation, similar to trends driven by Meta Platforms. Advertisers will need to test carefully, rethink audience strategies, and decide whether to embrace the added reach or opt out to preserve tighter targeting.
Zoom out. The shift mirrors a broader industry trend toward AI-first audience expansion, similar to moves by Meta Platforms over the past few years. Platforms are steadily trading granular manual controls for machine-led optimization.
Why Google is doing this. Digital markerter Dario Zannoni, has two reasons as to why Google is doing this:
Strict Lookalike targeting can cap scale and constrain performance in conversion-focused campaigns.
Maintaining high-quality similarity models is increasingly complex, making broader automation more attractive.
The bottom line. For performance marketers, this is another step toward automation-centric buying. While reduced control may be uncomfortable, comparable platform changes have often produced performance gains in mainstream use cases. Expect a new testing cycle as advertisers measure how expanded Lookalike signals affect CPA, reach, and incremental conversions.
First seen. This update was spotted by Zannoni who shared his thoughts on LinkedIn. 
Dig deeper. Use Lookalike segments to grow your audience

Google is turning Lookalike audiences into AI signals that expand reach and could significantly change campaign performance.

Google shifts Lookalike to AI signals in Demand Gen

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/ai-search-stack.png" class="attachment-large size-large wp-post-image" alt="AI search stack" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/ai-search-stack.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/ai-search-stack-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/ai-search-stack-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Behind the AI interface, a staged system narrows tens of thousands of documents to a few, showing that visibility hinges on classic signals.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/ai-search-stack.png" class="attachment-large size-large wp-post-image" alt="AI search stack" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/ai-search-stack.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/ai-search-stack-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/ai-search-stack-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
Jeff Dean says Google’s AI Search still works like classic Search: narrow the web to relevant pages, rank them, then let a model generate the answer.



In an interview on Latent Space: The AI Engineer Podcast, Google’s chief AI scientist explained how Google&#8217;s AI systems work and how much they rely on traditional search infrastructure.



The architecture: filter first, reason last. Visibility still depends on clearing ranking thresholds. Content must enter the broad candidate pool, then survive deeper reranking before it can be used in an AI-generated response. Put simply, AI doesn’t replace ranking. It sits on top of it.



Dean said an LLM-powered system doesn’t read the entire web at once. It starts with Google’s full index, then uses lightweight methods to identify a large candidate pool — tens of thousands of documents. Dean said:



<ul class="wp-block-list">
<li>&#8220;You identify a subset of them that are relevant with very lightweight kinds of methods. You’re down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is the final 10 results or 10 results plus other kinds of information.&#8221;</li>
</ul>



Stronger ranking systems narrow that set further. Only after multiple filtering rounds does the most capable model analyze a much smaller group of documents and generate an answer. Dean said:



<ul class="wp-block-list">
<li>“And I think an LLM-based system is not going to be that dissimilar, right? You’re going to attend to trillions of tokens, but you’re going to want to identify what are the 30,000-ish documents that are with the maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked me to do?&#8221;</li>
</ul>



Dean called this the “illusion” of attending to trillions of tokens. In practice, it’s a staged pipeline: retrieve, rerank, synthesize. Dean said:



<ul class="wp-block-list">
<li>“Google search gives you &#8230; not the illusion, but you are searching the internet, but you’re finding a very small subset of things that are relevant.”</li>
</ul>



Matching: from keywords to meaning. Nothing new here, but we heard another reminder that covering a topic clearly and comprehensively matters more than repeating exact-match phrases.



Dean explained how LLM-based representations changed how Google matches queries to content.



Older systems relied more on exact word overlap. With LLM representations, Google can move beyond the idea that particular words must appear on the page and instead evaluate whether a page — or even a paragraph — is topically relevant to a query. Dean said:



<ul class="wp-block-list">
<li>“Going to an LLM-based representation of text and words and so on enables you to get out of the explicit hard notion of particular words having to be on the page. But really getting at the notion of this topic of this page or this page paragraph is highly relevant to this query.”</li>
</ul>



That shift lets Search connect queries to answers even when wording differs. Relevance increasingly centers on intent and subject matter, not just keyword presence.



Query expansion didn’t start with AI. Dean pointed to 2001, when Google moved its index into memory across enough machines to make query expansion cheap and fast. Dean said:



<ul class="wp-block-list">
<li>“One of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Because if you don’t have the page in your index, you’re going to not do well. </li>



<li>&#8220;And then we also needed to scale our capacity because we were, our traffic was growing quite extensively. So we had a sharded system where you have more and more shards as the index grows, you have like 30 shards. Then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. And then as traffic grows, you add more and more replicas of each of those.</li>



<li>And so we eventually did the math that realized that in a data center where we had say 60 shards and 20 copies of each shard, we now had 1,200 machines with disks. And we did the math and we’re like, Hey, one copy of that index would actually fit in memory across 1,200 machines. So in 2001, we &#8230; put our entire index in memory and what that enabled from a quality perspective was amazing.</li>
</ul>



Before that, adding terms was expensive because it required disk access. Once the index lived in memory, Google could expand a short query into dozens of related terms — adding synonyms and variations to better capture meaning. Dean said:



<ul class="wp-block-list">
<li>“Before, you had to be really careful about how many different terms you looked at for a query, because every one of them would involve a disk seek.</li>



<li>“Once you have the whole index in memory, it’s totally fine to have 50 terms you throw into the query from the user’s original three- or four-word query. Because now you can add synonyms like restaurant and restaurants and cafe and bistro and all these things.</li>



<li>“And you can suddenly start &#8230; getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was &#8230; 2001, very much pre-LLM, but really it was about softening the strict definition of what the user typed in order to get at the meaning.&#8221;</li>
</ul>



That change pushed Search toward intent and semantic matching years before LLMs. AI Mode (and its other AI experiences) continues Google&#8217;s ongoing shift toward meaning-based retrieval, enabled by better systems and more compute.



Freshness as a core advantage. Dean said one of Search’s biggest transformations was update speed. Early systems refreshed pages as rarely as once a month. Over time, Google built infrastructure that can update pages in under a minute. Dean said:



<ul class="wp-block-list">
<li>“In the early days of Google, we were growing the index quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most.&#8221;</li>
</ul>



That improved results for news queries and affected the main search experience. Users expect current information, and the system is designed to deliver it. Dean said:



<ul class="wp-block-list">
<li>“If you’ve got last month’s news index, it’s not actually that useful.”</li>
</ul>



Google uses systems to decide how often to crawl a page, balancing how likely it is to change with how valuable the latest version is. Even pages that change infrequently may be crawled often if they’re important enough. Dean said:



<ul class="wp-block-list">
<li>“There’s a whole &#8230; system behind the scenes that’s trying to decide update rates and importance of the pages. So, even if the update rate seems low, you might still want to recrawl important pages quite often because the likelihood they change might be low, but the value of having updated is high.”</li>
</ul>



Why we care. AI answers don’t bypass ranking, crawl prioritization, or relevance signals. They depend on them. Eligibility, quality, and freshness still determine which pages are retrieved and narrowed. LLMs change how content is synthesized and presented — but the competition to enter the underlying candidate set remains a search problem.



The interview. <a href="https://www.youtube.com/watch?v=F_1oDPWxpFQ">Owning the AI Pareto Frontier — Jeff Dean</a>



<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="The AI Frontier: from Gemini 3 Deep Think distilling to Flash — Jeff Dean" width="500" height="281" src="https://www.youtube.com/embed/F_1oDPWxpFQ?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>

Jeff Dean says Google’s AI Search still works like classic Search: narrow the web to relevant pages, rank them, then let a model generate the answer.
In an interview on Latent Space: The AI Engineer Podcast, Google’s chief AI scientist explained how Google’s AI systems work and how much they rely on traditional search infrastructure.
The architecture: filter first, reason last. Visibility still depends on clearing ranking thresholds. Content must enter the broad candidate pool, then survive deeper reranking before it can be used in an AI-generated response. Put simply, AI doesn’t replace ranking. It sits on top of it.
Dean said an LLM-powered system doesn’t read the entire web at once. It starts with Google’s full index, then uses lightweight methods to identify a large candidate pool — tens of thousands of documents. Dean said:
“You identify a subset of them that are relevant with very lightweight kinds of methods. You’re down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is the final 10 results or 10 results plus other kinds of information.”
Stronger ranking systems narrow that set further. Only after multiple filtering rounds does the most capable model analyze a much smaller group of documents and generate an answer. Dean said:
“And I think an LLM-based system is not going to be that dissimilar, right? You’re going to attend to trillions of tokens, but you’re going to want to identify what are the 30,000-ish documents that are with the maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked me to do?”
Dean called this the “illusion” of attending to trillions of tokens. In practice, it’s a staged pipeline: retrieve, rerank, synthesize. Dean said:
“Google search gives you … not the illusion, but you are searching the internet, but you’re finding a very small subset of things that are relevant.”
Matching: from keywords to meaning. Nothing new here, but we heard another reminder that covering a topic clearly and comprehensively matters more than repeating exact-match phrases.
Dean explained how LLM-based representations changed how Google matches queries to content.
Older systems relied more on exact word overlap. With LLM representations, Google can move beyond the idea that particular words must appear on the page and instead evaluate whether a page — or even a paragraph — is topically relevant to a query. Dean said:
“Going to an LLM-based representation of text and words and so on enables you to get out of the explicit hard notion of particular words having to be on the page. But really getting at the notion of this topic of this page or this page paragraph is highly relevant to this query.”
That shift lets Search connect queries to answers even when wording differs. Relevance increasingly centers on intent and subject matter, not just keyword presence.
Query expansion didn’t start with AI. Dean pointed to 2001, when Google moved its index into memory across enough machines to make query expansion cheap and fast. Dean said:
“One of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Because if you don’t have the page in your index, you’re going to not do well. 
“And then we also needed to scale our capacity because we were, our traffic was growing quite extensively. So we had a sharded system where you have more and more shards as the index grows, you have like 30 shards. Then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. And then as traffic grows, you add more and more replicas of each of those.
And so we eventually did the math that realized that in a data center where we had say 60 shards and 20 copies of each shard, we now had 1,200 machines with disks. And we did the math and we’re like, Hey, one copy of that index would actually fit in memory across 1,200 machines. So in 2001, we … put our entire index in memory and what that enabled from a quality perspective was amazing.
Before that, adding terms was expensive because it required disk access. Once the index lived in memory, Google could expand a short query into dozens of related terms — adding synonyms and variations to better capture meaning. Dean said:
“Before, you had to be really careful about how many different terms you looked at for a query, because every one of them would involve a disk seek.
“Once you have the whole index in memory, it’s totally fine to have 50 terms you throw into the query from the user’s original three- or four-word query. Because now you can add synonyms like restaurant and restaurants and cafe and bistro and all these things.
“And you can suddenly start … getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was … 2001, very much pre-LLM, but really it was about softening the strict definition of what the user typed in order to get at the meaning.”
That change pushed Search toward intent and semantic matching years before LLMs. AI Mode (and its other AI experiences) continues Google’s ongoing shift toward meaning-based retrieval, enabled by better systems and more compute.
Freshness as a core advantage. Dean said one of Search’s biggest transformations was update speed. Early systems refreshed pages as rarely as once a month. Over time, Google built infrastructure that can update pages in under a minute. Dean said:
“In the early days of Google, we were growing the index quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most.”
That improved results for news queries and affected the main search experience. Users expect current information, and the system is designed to deliver it. Dean said:
“If you’ve got last month’s news index, it’s not actually that useful.”
Google uses systems to decide how often to crawl a page, balancing how likely it is to change with how valuable the latest version is. Even pages that change infrequently may be crawled often if they’re important enough. Dean said:
“There’s a whole … system behind the scenes that’s trying to decide update rates and importance of the pages. So, even if the update rate seems low, you might still want to recrawl important pages quite often because the likelihood they change might be low, but the value of having updated is high.”
Why we care. AI answers don’t bypass ranking, crawl prioritization, or relevance signals. They depend on them. Eligibility, quality, and freshness still determine which pages are retrieved and narrowed. LLMs change how content is synthesized and presented — but the competition to enter the underlying candidate set remains a search problem.
The interview. Owning the AI Pareto Frontier — Jeff Dean

Behind the AI interface, a staged system narrows tens of thousands of documents to a few, showing that visibility hinges on classic signals.

Google’s Jeff Dean: AI Search relies on classic ranking and retrieval

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/The-return-of-long-tail-SEO-in-the-AI-era.png" class="attachment-large size-large wp-post-image" alt="The return of long-tail SEO in the AI era" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/The-return-of-long-tail-SEO-in-the-AI-era.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/The-return-of-long-tail-SEO-in-the-AI-era-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/The-return-of-long-tail-SEO-in-the-AI-era-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>LLMs still rely on search, shifting SEO from head terms to the long tail. Here’s how to use AI to uncover real customer questions and win.

LLMs still rely on search, shifting SEO from head terms to the long tail. Here’s how to use AI to uncover real customer questions and win.

Why AI optimization is just long-tail SEO done right

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920-800x450.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>This is now live for all users, so check it out.

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920-800x450.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2016/02/google-tools1-ss-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
Over two months ago, Google began testing its <a href="https://searchengineland.com/google-search-console-tests-ai-powered-configuration-to-create-dynamic-reports-465637">AI-powered configuration tool</a>. It allows you to ask AI questions about the Google Search Console performance reports and it would bring back answers for you. Well, Google is now rolling out this tool for all.



Google <a href="https://www.linkedin.com/posts/the-search-consoles-new-ai-powered-configuration-share-7429518168726974464-2nTQ/">said</a> on LinkedIn, &#8220;The Search Console&#8217;s new AI-powered configuration is now available to everyone!&#8221;



AI-powered configuration.&nbsp;AI-powered configuration “lets you describe the analysis you want to see in natural language. Your inputs are then transformed into the appropriate filters and settings, instantly configuring the report for you,” Google&nbsp;<a href="https://developers.google.com/search/blog/2025/12/ai-powered-configuration">said</a>.



Rolling out now. If you login to your Search Console account and click on the performance report, you may see a note at the top that says &#8220;New! Customize your Performance report using Al.&#8221;



<figure class="wp-block-image size-full"><img decoding="async" width="2048" height="928" src="https://searchengineland.com/wp-content/seloads/2026/02/google-search-console-ai-powered-configuration1-scaled.png" alt="" class="wp-image-469352" srcset="https://searchengineland.com/wp-content/seloads/2026/02/google-search-console-ai-powered-configuration1-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/02/google-search-console-ai-powered-configuration1-768x348.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/google-search-console-ai-powered-configuration1-1536x696.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>



When you click on it, you get into the AI tool:



<figure class="wp-block-image size-full"><img decoding="async" width="2048" height="1066" src="https://searchengineland.com/wp-content/seloads/2026/02/google-search-console-ai-powered-configuration-scaled.png" alt="" class="wp-image-469354" srcset="https://searchengineland.com/wp-content/seloads/2026/02/google-search-console-ai-powered-configuration-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/02/google-search-console-ai-powered-configuration-768x400.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/google-search-console-ai-powered-configuration-1536x800.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>



More details.&nbsp;As we reported earlier, Google said “The AI-powered configuration feature is designed to streamline your analysis by handling three key elements for you.”



<ul class="wp-block-list">
<li>Selecting metrics:&nbsp;Choose which of the four available metrics – Clicks, Impressions, Average CTR, and Average Position –&nbsp;to display based on your question.</li>



<li>Applying filters:&nbsp;Narrow down data by query, page, country, device, search appearance, or date range.</li>



<li>Configuring comparisons:&nbsp;Set up complex comparisons (like custom date ranges) without manual setup.</li>
</ul>



Why we care. This is only supported in the Performance report for Search results. It isn’t available for Discover or News reports, yet. Plus, it is AI, so the answers may not be perfect. But it can be fun to play with and get you thinking about things you may not have thought about yet.



So give it a try.

Over two months ago, Google began testing its AI-powered configuration tool. It allows you to ask AI questions about the Google Search Console performance reports and it would bring back answers for you. Well, Google is now rolling out this tool for all.
Google said on LinkedIn, “The Search Console’s new AI-powered configuration is now available to everyone!”
AI-powered configuration. AI-powered configuration “lets you describe the analysis you want to see in natural language. Your inputs are then transformed into the appropriate filters and settings, instantly configuring the report for you,” Google said.
Rolling out now. If you login to your Search Console account and click on the performance report, you may see a note at the top that says “New! Customize your Performance report using Al.”




When you click on it, you get into the AI tool:




More details. As we reported earlier, Google said “The AI-powered configuration feature is designed to streamline your analysis by handling three key elements for you.”
Selecting metrics: Choose which of the four available metrics – Clicks, Impressions, Average CTR, and Average Position – to display based on your question.
Applying filters: Narrow down data by query, page, country, device, search appearance, or date range.
Configuring comparisons: Set up complex comparisons (like custom date ranges) without manual setup.
Why we care. This is only supported in the Performance report for Search results. It isn’t available for Discover or News reports, yet. Plus, it is AI, so the answers may not be perfect. But it can be fun to play with and get you thinking about things you may not have thought about yet.
So give it a try.

This is now live for all users, so check it out.

Google Search Console AI-powered configuration rolling out

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/Rand-Fishkin-proved-AI-recommendations-are-inconsistent-–-heres-why-and-how-to-fix-it.png" class="attachment-large size-large wp-post-image" alt="Rand Fishkin proved AI recommendations are inconsistent – here’s why and how to fix it" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Rand-Fishkin-proved-AI-recommendations-are-inconsistent-–-heres-why-and-how-to-fix-it.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/Rand-Fishkin-proved-AI-recommendations-are-inconsistent-–-heres-why-and-how-to-fix-it-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/Rand-Fishkin-proved-AI-recommendations-are-inconsistent-–-heres-why-and-how-to-fix-it-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI outputs vary because confidence varies. Corroboration and entity optimization turn inconsistent AI visibility into consistent presence.

AI outputs vary because confidence varies. Corroboration and entity optimization turn inconsistent AI visibility into consistent presence.

Rand Fishkin proved AI recommendations are inconsistent – here’s why and how to fix it

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google Ads now lets advertisers connect external data to conversions, improving measurement and performance.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/google-data-center-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
Google Ads is rolling out a beta feature that lets advertisers connect external data sources directly inside conversion action settings, tightening the link between first-party data and campaign measurement.



How it works. A new section in conversion action details — labeled “Get deeper insights about your customers’ behavior to improve measurement” — prompts advertisers to connect external databases to their Google tag.



<figure class="wp-block-image size-full"><img decoding="async" width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2026/02/data-source-integration.jpg" alt="" class="wp-image-469329" srcset="https://searchengineland.com/wp-content/seloads/2026/02/data-source-integration.jpg 1280w, https://searchengineland.com/wp-content/seloads/2026/02/data-source-integration-768x432.jpg 768w" sizes="(max-width: 1280px) 100vw, 1280px" /></figure>



<ul class="wp-block-list">
<li>Supported integrations include platforms like BigQuery and MySQL</li>



<li>The goal is to enrich conversion metrics and improve performance signals</li>



<li>The feature appears in a highlighted prompt within data attribution settings</li>



<li>Rollout is gradual and currently marked as Beta</li>
</ul>



Why we care. Direct integrations could reduce friction in syncing offline or backend data with ad measurement. This beta from Google Ads makes it easier to connect first-party data directly to conversion tracking, which can improve measurement accuracy and campaign optimization. 



By integrating sources like BigQuery or MySQL, brands can feed richer customer data into their signals, helping offset data loss from privacy changes. In practical terms, better data in means smarter bidding, clearer attribution, and potentially stronger ROI.



Between the lines. Embedding data connections inside conversion settings — rather than requiring separate pipelines — makes advanced measurement more accessible to everyday advertisers, not just enterprise teams.



Zoom out. As ad platforms compete on measurement accuracy, native data integrations are becoming a key differentiator, especially for brands investing heavily in proprietary customer data.

Google Ads is rolling out a beta feature that lets advertisers connect external data sources directly inside conversion action settings, tightening the link between first-party data and campaign measurement.
How it works. A new section in conversion action details — labeled “Get deeper insights about your customers’ behavior to improve measurement” — prompts advertisers to connect external databases to their Google tag.





Supported integrations include platforms like BigQuery and MySQL
The goal is to enrich conversion metrics and improve performance signals
The feature appears in a highlighted prompt within data attribution settings
Rollout is gradual and currently marked as Beta
Why we care. Direct integrations could reduce friction in syncing offline or backend data with ad measurement. This beta from Google Ads makes it easier to connect first-party data directly to conversion tracking, which can improve measurement accuracy and campaign optimization. 
By integrating sources like BigQuery or MySQL, brands can feed richer customer data into their signals, helping offset data loss from privacy changes. In practical terms, better data in means smarter bidding, clearer attribution, and potentially stronger ROI.
Between the lines. Embedding data connections inside conversion settings — rather than requiring separate pipelines — makes advanced measurement more accessible to everyday advertisers, not just enterprise teams.
Zoom out. As ad platforms compete on measurement accuracy, native data integrations are becoming a key differentiator, especially for brands investing heavily in proprietary customer data.

Google Ads now lets advertisers connect external data to conversions, improving measurement and performance.

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Read the Latest Trends and Headlines in Digital Marketing.

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

Free Consultation

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

All About
Digital Marketing