<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/Are-you-building-software-instead-of-doing-marketing.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Are-you-building-software-instead-of-doing-marketing.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/Are-you-building-software-instead-of-doing-marketing-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Are-you-building-software-instead-of-doing-marketing-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Are-you-building-software-instead-of-doing-marketing-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Your team doesn't need to build every AI workflow. Decide what to automate in-house, buy from a vendor, or outsource.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/Are-you-building-software-instead-of-doing-marketing.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Are-you-building-software-instead-of-doing-marketing.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/Are-you-building-software-instead-of-doing-marketing-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Are-you-building-software-instead-of-doing-marketing-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Are-you-building-software-instead-of-doing-marketing-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">The most valuable 30 minutes in marketing right now are the 30 minutes you spend thinking <em>before</em> you start building any AI automation.</p>



<p class="wp-block-paragraph">Last week, I made the case that <a href="https://searchengineland.com/the-ai-hours-nobody-on-your-marketing-team-is-counting-487349">AI does not delete marketing work, it moves it</a>, and that the bill often gets paid out of the least attributable work your team does. Prompting, checking, editing, fixing, and maintaining are all real work hours, and they come from somewhere.</p>



<p class="wp-block-paragraph">The question now is what to do about it.</p>



<p class="wp-block-paragraph">All of the work your marketing team does with AI comes from three places:</p>



<ul class="wp-block-list">
<li>AI-powered software/tooling from a vendor.</li>



<li>Expert customization and implementation from a consultant who has already built it.</li>



<li>Time from your team to build it in-house.</li>
</ul>



<p class="wp-block-paragraph">Amanda and I work separate client rosters, but the same pattern is showing up in both: Teams aren’t sorting their AI work hours in a targeted manner. Instead, they’re going straight to automating as much as they can in-house.</p>



<p><a href="https://www.growth-memo.com/embed?transparent=1" rel="noopener noreferrer">View embedded content</a>



<p class="wp-block-paragraph">Automating in-house is smart. But skipping the sorting step (which takes 30 minutes or less), is an expensive mistake. Thankfully, it’s an easy fix.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1638" height="2048" src="https://searchengineland.com/wp-content/seloads/2026/09/ai-work-sourcing-matrix-scaled.png" alt="" class="wp-image-488694" srcset="https://searchengineland.com/wp-content/seloads/2026/09/ai-work-sourcing-matrix-scaled.png 1638w, https://searchengineland.com/wp-content/seloads/2026/09/ai-work-sourcing-matrix-768x960.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/ai-work-sourcing-matrix-1229x1536.png 1229w" sizes="(max-width: 1638px) 100vw, 1638px" /></figure>
</div>


<h2 id="h-1-buy-the-tool-when-the-problem-isn-t-just-yours" class="wp-block-heading">1. Buy the tool when the problem isn’t just yours</h2>



<p class="wp-block-paragraph">Rank tracking, citation monitoring, brand mention tracking, crawl diagnostics, and content scoring are not unique to you.</p>



<p class="wp-block-paragraph">Despite $30-40 billion in enterprise investment into genAI, <a href="https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf" target="_blank" rel="noopener">MIT&#8217;s review of enterprise AI projects</a> reveals that 95% of organizations are getting zero return. From the report:</p>



<ul class="wp-block-list">
<li>&#8220;Strategic partnerships achieved a significantly higher share of successful deployments than internal development efforts. While we observed far more BUILD initiatives than BUY initiatives in our sample, with many more organizations exploring internal development… success rates favored external partnerships. Though we lack precise data on total initiative volumes, the pattern suggests that internal development efforts have substantially lower success rates despite being more commonly attempted.&#8221;</li>
</ul>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1790" src="https://searchengineland.com/wp-content/seloads/2026/09/Bought-AI-tools-reached-deployment-twice-as-often-as-in-house-builds.png" alt="Bought AI tools reached deployment twice as often as in-house builds" class="wp-image-488695" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Bought-AI-tools-reached-deployment-twice-as-often-as-in-house-builds.png 2048w, https://searchengineland.com/wp-content/seloads/2026/09/Bought-AI-tools-reached-deployment-twice-as-often-as-in-house-builds-768x671.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Bought-AI-tools-reached-deployment-twice-as-often-as-in-house-builds-1536x1343.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p class="wp-block-paragraph">The missing 1% from the chart above is MIT&#8217;s third structure, hybrid build-buy, where an internal team co-develops with a vendor; the report had too little data to quantify it.</p>



<p class="wp-block-paragraph">The report found that the main reason the projects fail is that the tools do not learn or integrate with how people already work. Making a tool fit is one of the most expensive parts, and a software vendor who’s already done that for 4,000 customers has done work you really don’t need to repeat.</p>



<p class="wp-block-paragraph">When you’re deciding whether to build versus buy, you’re evaluating the software platform fee versus what an hour of your team&#8217;s time really costs, multiplied by the hours it takes to build and maintain the internal version. (Plus the hidden hours nobody is counting: brainstorming, meeting, testing, reiterating, retesting.)</p>



<p class="wp-block-paragraph">Buy the tool when the problem is common. When you do, your team also gets two things you can&#8217;t build for yourself: the vendor maintains the software, and somebody on the other end fixes it when it breaks.</p>



<h2 id="h-2-buy-the-know-how-when-the-workflow-is-yours-alone" class="wp-block-heading">2. Buy the know-how when the workflow is yours alone</h2>



<p class="wp-block-paragraph">Some workflows really are yours, like who signs off on the work before it&#8217;s shipped, how your data is structured, when reporting goes out, how you gather SME input for content, or adding branded CTAs from a pre-approved copy bank.</p>



<p class="wp-block-paragraph">This is where your team can accidentally burn the most hours, because &#8220;nobody else can build this&#8221; gets heard as &#8220;we have to build this ourselves.&#8221;</p>



<p class="wp-block-paragraph">The workflow is yours, yes, but someone who has built 65 of these for 20 different teams already knows which steps usually break, which ones need input from your individual contributors, which ones are worth automating, and which ones look automatable but aren’t.&nbsp;</p>



<p class="wp-block-paragraph">Your team is finding all of that out for the first time, on the clock.</p>



<p class="wp-block-paragraph">Here’s the test: <strong>If the hours you are about to spend produce knowledge you will not need weekly, contract out the knowledge instead.</strong>



<p class="wp-block-paragraph">We both sell consulting individually, so you can read that with the appropriate squint. The point stands true <strong>without</strong> hiring either of us: Buying the know-how looks like a template someone already validated, a contractor for 4 weeks, a consulting hour with a peer who has shipped the same thing, or a resource library built by people who made the mistakes first. </p>



<p class="wp-block-paragraph">What it avoids: Your team discovering the failure modes one at a time.</p>



<h2 id="h-an-ai-tool-you-don-t-have-the-experience-to-check-will-hand-you-wrong-answers-confidently" class="wp-block-heading">An AI tool you don’t have the experience to check will hand you wrong answers confidently</h2>



<p class="wp-block-paragraph">Only <a href="https://technologychecker.io/blog/ai-in-marketing-statistics-use-cases" target="_blank" rel="noopener">13% of marketers</a> fully trust AI output without a human reading it. While that often gets treated as a model maturity problem or AI adoption hurdle, it’s actually a staffing requirement.&nbsp;</p>



<p class="wp-block-paragraph">I (Kevin) ran into a situation where I was trusting my own AI workflow output slightly too much. When I paused and looked at it through a refreshed critical lens, I found a pile of things to correct and reoriented the workflow accordingly.  </p>



<p class="wp-block-paragraph">And I (Amanda) had a case where my client generated a report from GSC data and sent it to me for feedback before bringing it into their weekly meeting. The model read a spike in a specific set of queries as proof of rising AI visibility and was glaringly wrong; the report was nearly on its way to the head of marketing to inform crucial decisions. We caught it. Now every report gets a human read before it leaves.&nbsp;</p>



<p class="wp-block-paragraph">The <a href="https://www.validity.com/resource-center/state-of-crm-data-report-2026/">State of CRM Data Report 2026</a> found the following:</p>



<ul class="wp-block-list">
<li>“Nearly 78% of C-suite and 92% of SVP/VPs respondents say they have acted on an AI recommendation they later suspected was wrong because of bad underlying data, compared with 41% of individual contributors.”</li>
</ul>



<p class="wp-block-paragraph">So if the majority of inputs and outputs need review (and perhaps, even an intervention before an output lands in the CMO’s inbox), someone who knows the subject has to do the reviewing, <strong>which means the tool did not remove the skill required to do the work.</strong> Judging the work is the harder, less-replicable of the tasks.</p>



<p class="wp-block-paragraph">The rule that comes out of both experiences: Never buy or build a tool for a job nobody on your team can verify accuracy for by hand. You won’t be able to tell when it breaks (and it will break confidently).</p>



<h2 id="h-swap-out-one-step-not-one-job" class="wp-block-heading">Swap out one step, not one job</h2>



<p class="wp-block-paragraph">With that in mind, this section is the actual method to use when making the build vs. buy decision, and it’s the part teams can get wrong even if they sort the work correctly.</p>



<p class="wp-block-paragraph">Where failure can creep in at the start: A team names a slow process, decides AI should run the whole thing, and starts building something that does 15 steps. Six weeks later they have in-house software with no real tests, no documentation, and one person who understands how to maintain it, with maybe 2-3 people who know how to run it.</p>



<p class="wp-block-paragraph">A <strong><em>job</em></strong> is a bundle of steps with judgment distributed across all of them.&nbsp;</p>



<p class="wp-block-paragraph">A <strong><em>step</em></strong> has one input, one output, and a check that takes seconds.</p>



<p class="wp-block-paragraph"><a href="https://www.growth-memo.com/p/the-ai-hours-nobody-on-your-marketing">Part 1</a> made the point that every workflow you build becomes a small permanent job to maintain. That’s true. And it&#8217;s an argument for making the job small, not for skipping it.</p>



<p class="wp-block-paragraph">A one-step swap has one input to validate, one output to eyeball, and one thing to fix when the model version changes, while your 15-step workflow has 15 places to break and no fast way to find out which one did.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1620" height="754" src="https://searchengineland.com/wp-content/seloads/2026/09/Productivity-gains-from-automation-are-real.png" alt="Productivity gains from automation are real" class="wp-image-488702" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Productivity-gains-from-automation-are-real.png 1620w, https://searchengineland.com/wp-content/seloads/2026/09/Productivity-gains-from-automation-are-real-768x357.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Productivity-gains-from-automation-are-real-1536x715.png 1536w" sizes="(max-width: 1620px) 100vw, 1620px" /></figure>
</div>


<h3 id="h-how-to-find-the-step-worth-automating" class="wp-block-heading">How to find the step worth automating</h3>



<p class="wp-block-paragraph">Take the process your team wants to automate and write out what actually happens, in order. Then mark each step with three things:</p>



<ul class="wp-block-list">
<li><strong>The input:</strong> What arrives, and where does it come from? A step whose input is &#8220;context from the last meeting&#8221; is not a step yet.</li>



<li><strong>The output:</strong> What leaves, in what format? If the answer is a paragraph of judgment, keep looking.</li>



<li><strong>The check:</strong> How does a person confirm the output is right, and how long does that take? The verification check is real work and takes real time; plan for it.</li>
</ul>



<p class="wp-block-paragraph">The step you want to automate is the one that is slow, repetitive, tightly defined, and checkable at a glance.&nbsp;</p>



<p class="wp-block-paragraph">Here’s an example: I built the new premium members’ area to include any of the <strong>new</strong> tools and skills we’re building, but I automated it to go back into the archives to locate each prior premium asset and build an easy-to-find page for it. </p>



<ul class="wp-block-list">
<li><strong>The input:</strong> Historical Growth Memo posts, specifically gated sections.</li>



<li><strong>The output:</strong> A simple, easily searchable page with a link to the asset and notes on how to use it, along with an area for members to leave feedback/comments for admin.</li>



<li><strong>The check:</strong> A 5-minute review before setting the page live.</li>
</ul>



<p class="wp-block-paragraph">Usually, an automatable step looks like a “small transformation,” raw data into a formatted table, a transcript into tagged quotes or a first draft, internal linking across content clusters, a spreadsheet into a brief skeleton, a GSC export into a list of pages to refresh.</p>



<h3 id="h-what-disqualifies-a-step-from-being-automated" class="wp-block-heading">What disqualifies a step from being automated</h3>



<p class="wp-block-paragraph">I (Kevin) would say there are three types of jobs that people do that people try to automate:</p>



<ul class="wp-block-list">
<li>Reporting (easy to automate).</li>



<li>Synthesizing (hard to automate).</li>



<li>Deciding (semi-hard to automate).</li>
</ul>



<p class="wp-block-paragraph">Anything requiring experienced taste, or any automated task where being wrong could go unnoticed for three months (like automating regular updates to meta titles and descriptions, checked quarterly), is usually an indicator that careful consideration is needed.&nbsp;</p>



<p class="wp-block-paragraph">Use these guidelines to disqualify an in-house build:</p>



<ul class="wp-block-list">
<li>Nobody on the team can do it “by hand,” so nobody can grade or verify the output.</li>



<li>The check takes longer than doing the work (unless you’re producing long-form drafts, which naturally take time for review).</li>



<li>The input changes often, which means you can’t easily maintain the workflow.</li>



<li>It touches a system you do not control, where an update on a Tuesday could break the workflow on a Wednesday.</li>
</ul>



<p class="wp-block-paragraph">Experimenting earns its keep. But your common problems should go to a vendor; there’s a reason why they exist. Problems that are yours and that your team deeply understands can go to someone who has built something similar before.&nbsp;</p>



<p class="wp-block-paragraph">And the stuff that’s left… that’s the actual in-house AI workflow experimentation. Give it an owner, a kill date, and a sentence describing what winning looks like.</p>



<p class="wp-block-paragraph">Everything else is software development, and, well, you didn’t hire your marketing team to write software.</p>



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


The most valuable 30 minutes in marketing right now are the 30 minutes you spend thinking before you start building any AI automation.
Last week, I made the case that AI does not delete marketing work, it moves it, and that the bill often gets paid out of the least attributable work your team does. Prompting, checking, editing, fixing, and maintaining are all real work hours, and they come from somewhere.
The question now is what to do about it.
All of the work your marketing team does with AI comes from three places:
AI-powered software/tooling from a vendor.
Expert customization and implementation from a consultant who has already built it.
Time from your team to build it in-house.
Amanda and I work separate client rosters, but the same pattern is showing up in both: Teams aren’t sorting their AI work hours in a targeted manner. Instead, they’re going straight to automating as much as they can in-house.
View embedded content



Automating in-house is smart. But skipping the sorting step (which takes 30 minutes or less), is an expensive mistake. Thankfully, it’s an easy fix.




1. Buy the tool when the problem isn’t just yours
Rank tracking, citation monitoring, brand mention tracking, crawl diagnostics, and content scoring are not unique to you.
Despite $30-40 billion in enterprise investment into genAI, MIT’s review of enterprise AI projects reveals that 95% of organizations are getting zero return. From the report:
“Strategic partnerships achieved a significantly higher share of successful deployments than internal development efforts. While we observed far more BUILD initiatives than BUY initiatives in our sample, with many more organizations exploring internal development… success rates favored external partnerships. Though we lack precise data on total initiative volumes, the pattern suggests that internal development efforts have substantially lower success rates despite being more commonly attempted.”




The missing 1% from the chart above is MIT’s third structure, hybrid build-buy, where an internal team co-develops with a vendor; the report had too little data to quantify it.
The report found that the main reason the projects fail is that the tools do not learn or integrate with how people already work. Making a tool fit is one of the most expensive parts, and a software vendor who’s already done that for 4,000 customers has done work you really don’t need to repeat.
When you’re deciding whether to build versus buy, you’re evaluating the software platform fee versus what an hour of your team’s time really costs, multiplied by the hours it takes to build and maintain the internal version. (Plus the hidden hours nobody is counting: brainstorming, meeting, testing, reiterating, retesting.)
Buy the tool when the problem is common. When you do, your team also gets two things you can’t build for yourself: the vendor maintains the software, and somebody on the other end fixes it when it breaks.
2. Buy the know-how when the workflow is yours alone
Some workflows really are yours, like who signs off on the work before it’s shipped, how your data is structured, when reporting goes out, how you gather SME input for content, or adding branded CTAs from a pre-approved copy bank.
This is where your team can accidentally burn the most hours, because “nobody else can build this” gets heard as “we have to build this ourselves.”
The workflow is yours, yes, but someone who has built 65 of these for 20 different teams already knows which steps usually break, which ones need input from your individual contributors, which ones are worth automating, and which ones look automatable but aren’t. 
Your team is finding all of that out for the first time, on the clock.
Here’s the test: If the hours you are about to spend produce knowledge you will not need weekly, contract out the knowledge instead.



We both sell consulting individually, so you can read that with the appropriate squint. The point stands true without hiring either of us: Buying the know-how looks like a template someone already validated, a contractor for 4 weeks, a consulting hour with a peer who has shipped the same thing, or a resource library built by people who made the mistakes first. 
What it avoids: Your team discovering the failure modes one at a time.
An AI tool you don’t have the experience to check will hand you wrong answers confidently
Only 13% of marketers fully trust AI output without a human reading it. While that often gets treated as a model maturity problem or AI adoption hurdle, it’s actually a staffing requirement. 
I (Kevin) ran into a situation where I was trusting my own AI workflow output slightly too much. When I paused and looked at it through a refreshed critical lens, I found a pile of things to correct and reoriented the workflow accordingly.  
And I (Amanda) had a case where my client generated a report from GSC data and sent it to me for feedback before bringing it into their weekly meeting. The model read a spike in a specific set of queries as proof of rising AI visibility and was glaringly wrong; the report was nearly on its way to the head of marketing to inform crucial decisions. We caught it. Now every report gets a human read before it leaves. 
The State of CRM Data Report 2026 found the following:
“Nearly 78% of C-suite and 92% of SVP/VPs respondents say they have acted on an AI recommendation they later suspected was wrong because of bad underlying data, compared with 41% of individual contributors.”
So if the majority of inputs and outputs need review (and perhaps, even an intervention before an output lands in the CMO’s inbox), someone who knows the subject has to do the reviewing, which means the tool did not remove the skill required to do the work. Judging the work is the harder, less-replicable of the tasks.
The rule that comes out of both experiences: Never buy or build a tool for a job nobody on your team can verify accuracy for by hand. You won’t be able to tell when it breaks (and it will break confidently).
Swap out one step, not one job
With that in mind, this section is the actual method to use when making the build vs. buy decision, and it’s the part teams can get wrong even if they sort the work correctly.
Where failure can creep in at the start: A team names a slow process, decides AI should run the whole thing, and starts building something that does 15 steps. Six weeks later they have in-house software with no real tests, no documentation, and one person who understands how to maintain it, with maybe 2-3 people who know how to run it.
A job is a bundle of steps with judgment distributed across all of them. 
A step has one input, one output, and a check that takes seconds.
Part 1 made the point that every workflow you build becomes a small permanent job to maintain. That’s true. And it’s an argument for making the job small, not for skipping it.
A one-step swap has one input to validate, one output to eyeball, and one thing to fix when the model version changes, while your 15-step workflow has 15 places to break and no fast way to find out which one did.




How to find the step worth automating
Take the process your team wants to automate and write out what actually happens, in order. Then mark each step with three things:
The input: What arrives, and where does it come from? A step whose input is “context from the last meeting” is not a step yet.
The output: What leaves, in what format? If the answer is a paragraph of judgment, keep looking.
The check: How does a person confirm the output is right, and how long does that take? The verification check is real work and takes real time; plan for it.
The step you want to automate is the one that is slow, repetitive, tightly defined, and checkable at a glance. 
Here’s an example: I built the new premium members’ area to include any of the new tools and skills we’re building, but I automated it to go back into the archives to locate each prior premium asset and build an easy-to-find page for it. 
The input: Historical Growth Memo posts, specifically gated sections.
The output: A simple, easily searchable page with a link to the asset and notes on how to use it, along with an area for members to leave feedback/comments for admin.
The check: A 5-minute review before setting the page live.
Usually, an automatable step looks like a “small transformation,” raw data into a formatted table, a transcript into tagged quotes or a first draft, internal linking across content clusters, a spreadsheet into a brief skeleton, a GSC export into a list of pages to refresh.
What disqualifies a step from being automated
I (Kevin) would say there are three types of jobs that people do that people try to automate:
Reporting (easy to automate).
Synthesizing (hard to automate).
Deciding (semi-hard to automate).
Anything requiring experienced taste, or any automated task where being wrong could go unnoticed for three months (like automating regular updates to meta titles and descriptions, checked quarterly), is usually an indicator that careful consideration is needed. 
Use these guidelines to disqualify an in-house build:
Nobody on the team can do it “by hand,” so nobody can grade or verify the output.
The check takes longer than doing the work (unless you’re producing long-form drafts, which naturally take time for review).
The input changes often, which means you can’t easily maintain the workflow.
It touches a system you do not control, where an update on a Tuesday could break the workflow on a Wednesday.
Experimenting earns its keep. But your common problems should go to a vendor; there’s a reason why they exist. Problems that are yours and that your team deeply understands can go to someone who has built something similar before. 
And the stuff that’s left… that’s the actual in-house AI workflow experimentation. Give it an owner, a kill date, and a sentence describing what winning looks like.
Everything else is software development, and, well, you didn’t hire your marketing team to write software.
This post first appeared on the author’s website and is republished here with permission.

Your team doesn't need to build every AI workflow. Decide what to automate in-house, buy from a vendor, or outsource.

Are you building software instead of doing marketing?

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/20-for-20-matt-van-wagner-cover-.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/20-for-20-matt-van-wagner-cover-.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/20-for-20-matt-van-wagner-cover--768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/20-for-20-matt-van-wagner-cover--1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/20-for-20-matt-van-wagner-cover--1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Paid search veteran Matt Van Wagner reflects on 20 years of Google Ads, lost PPC tactics, rising automation and more

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/20-for-20-matt-van-wagner-cover-.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/20-for-20-matt-van-wagner-cover-.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/20-for-20-matt-van-wagner-cover--768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/20-for-20-matt-van-wagner-cover--1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/20-for-20-matt-van-wagner-cover--1200x675.png 1200w" 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">
<p><a href="https://www.youtube.com/embed/Wmf-IWtGpmo?list=PL-_fgRYQppxMPEDLO0ZLb7_M34_mnNZ2w" rel="noopener noreferrer">Watch this video on YouTube</a>
</div></figure>



<p class="wp-block-paragraph">The search marketing industry has gone from an entrepreneurial “gold rush” to an era dominated by automation, AI and increasingly consolidated platforms.</p>



<p class="wp-block-paragraph">Looking back over that transformation, paid search veteran Matt Van Wagner reflected on the early days of Google Ads, the rise of Search Engine Land, tactics that thankfully disappeared and why advertisers still need to question what Google tells them.</p>



<h2 id="h-search-marketing-started-with-a-gold-rush-mentality" class="wp-block-heading">Search marketing started with a gold-rush mentality</h2>



<p class="wp-block-paragraph">Van Wagner entered search in the early 2000s after working in technology sales and marketing, where traditional channels such as trade publications, conferences and direct mail were becoming increasingly ineffective.</p>



<p class="wp-block-paragraph">His first search conference offered a stark contrast: an industry filled with entrepreneurial energy and people exploring an entirely new way for businesses to reach customers.</p>



<p class="wp-block-paragraph">That experience ultimately convinced him to leave his existing career and start a search advertising agency — at a time when few businesses even knew what a search advertising agency was.</p>



<h2 id="h-selling-google-ads-was-the-easy-part" class="wp-block-heading">Selling Google Ads was the easy part</h2>



<p class="wp-block-paragraph">Once advertisers understood what paid search could do, Van Wagner said convincing companies to try Google advertising required little selling.</p>



<p class="wp-block-paragraph">Demand was plentiful and the emerging agencies were generally collaborative because the market was expanding quickly enough for everyone.</p>



<p class="wp-block-paragraph">The bigger mistake, he said, was that many early agencies simply didn’t charge enough for the value they were providing.</p>



<h2 id="h-search-engine-land-helped-the-industry-grow-up" class="wp-block-heading">Search Engine Land helped the industry grow up</h2>



<p class="wp-block-paragraph">By the time Search Engine Land and SMX arrived, search marketing conferences were beginning to suffer from repetitive speakers and presentations.</p>



<p class="wp-block-paragraph">SMX Advanced took a different approach: rather than repeatedly teaching the fundamentals, it started at an intermediate level and went deeper.</p>



<p class="wp-block-paragraph">That attracted more sophisticated speakers and discussions, while conversations in bars, over coffee and at dinner became almost as valuable as the formal sessions themselves.</p>



<h2 id="h-ppc-tactics-became-far-less-granular" class="wp-block-heading">PPC tactics became far less granular</h2>



<p class="wp-block-paragraph">Some of the practices considered sophisticated in early paid search now look unnecessarily complicated.</p>



<p class="wp-block-paragraph">Single keyword ad groups, duplicated match types and elaborate negative-keyword structures were routinely used to tightly sculpt traffic between campaigns.</p>



<p class="wp-block-paragraph">Automation has fundamentally changed that approach, making many of those old structures largely obsolete.</p>



<h2 id="h-early-google-felt-more-open" class="wp-block-heading">Early Google felt more open</h2>



<p class="wp-block-paragraph">One thing Van Wagner misses about the early search industry is the accessibility of Google itself.</p>



<p class="wp-block-paragraph">Events such as the Google Dance gave marketers opportunities to interact directly with people working on search, while figures such as Matt Cutts became a bridge between Google and the SEO community.</p>



<p class="wp-block-paragraph">Part of that openness came from the fact that Google itself was still figuring search out. Advertisers, SEOs and search engines were effectively exploring the new ecosystem together.</p>



<h2 id="h-google-s-definition-of-success-isn-t-necessarily-yours" class="wp-block-heading">Google’s definition of success isn’t necessarily yours</h2>



<p class="wp-block-paragraph">Advertisers want profitable sales and measurable business outcomes. Google also wants advertisers to spend money.</p>



<p class="wp-block-paragraph">Van Wagner argued that marketers need to remember that distinction when evaluating recommendations, default settings and increasingly automated campaign types.</p>



<p class="wp-block-paragraph">Google may genuinely want its technology to improve advertiser performance, but its commercial incentives and an advertiser’s objectives aren’t automatically identical.</p>



<h2 id="h-broad-match-defaults-remain-a-major-frustration" class="wp-block-heading">Broad match defaults remain a major frustration</h2>



<p class="wp-block-paragraph">One longstanding example is Google’s use of broad match as a default.</p>



<p class="wp-block-paragraph">Van Wagner argued that inexperienced advertisers can easily spend money learning lessons Google’s systems already have enough information to help them avoid.</p>



<p class="wp-block-paragraph">His broader concern is that advertisers increasingly have to spend their own money teaching automated systems things they already know about their businesses.</p>



<h2 id="h-automation-still-needs-advertiser-knowledge" class="wp-block-heading">Automation still needs advertiser knowledge</h2>



<p class="wp-block-paragraph">Van Wagner accepts that automation is moving in one direction and advertisers won’t regain every control they once had.</p>



<p class="wp-block-paragraph">But that doesn’t mean Google should ignore knowledge advertisers already possess.</p>



<p class="wp-block-paragraph">If an advertiser already knows particular queries are irrelevant, for example, forcing an AI system to rediscover that through wasted spend makes little sense. Automation should be able to learn while still incorporating the advertiser’s existing expertise.</p>



<h2 id="h-ginny-marvin-helped-bridge-google-and-advertisers" class="wp-block-heading">Ginny Marvin helped bridge Google and advertisers</h2>



<p class="wp-block-paragraph">Van Wagner highlighted Google Ads Liaison Ginny Marvin as a positive influence on the relationship between Google and the advertising community.</p>



<p class="wp-block-paragraph">Having worked on the advertiser side before joining Google, Marvin understands many of the frustrations marketers experience and, he argued, has been able to advocate for advertisers while still representing Google.</p>



<h2 id="h-bidding-your-way-to-no-1-was-an-expensive-mistake" class="wp-block-heading">Bidding your way to No. 1 was an expensive mistake</h2>



<p class="wp-block-paragraph">One misconception from early PPC was that advertisers simply needed to outbid everyone to secure the top position.</p>



<p class="wp-block-paragraph">Van Wagner recalled campaigns where advertisers were willing to pay enormous CPCs purely because they wanted to own the No. 1 spot.</p>



<p class="wp-block-paragraph">The better philosophy, he argued, was often to compete for second place and focus on economics rather than prestige.</p>



<h2 id="h-google-instant-showed-the-power-of-product-publicity" class="wp-block-heading">Google Instant showed the power of product publicity</h2>



<p class="wp-block-paragraph">One Search Engine Land article Van Wagner particularly remembers writing examined Google Instant, which began predicting and displaying search results as users typed.</p>



<p class="wp-block-paragraph">His argument was that Google generated enormous media attention from the product change while Microsoft was spending heavily on conventional advertising to promote Bing.</p>



<p class="wp-block-paragraph">The episode demonstrated just how powerful Google&#8217;s ability to turn product changes into mainstream news had become.</p>



<h2 id="h-microsoft-realized-it-couldn-t-reinvent-search-behavior" class="wp-block-heading">Microsoft realized it couldn’t reinvent search behavior</h2>



<p class="wp-block-paragraph">Microsoft faced a different problem: users weren&#8217;t actively deciding which search engine to use every morning.</p>



<p class="wp-block-paragraph">Van Wagner believes Microsoft therefore made a sensible decision to make its advertising platform familiar to Google advertisers rather than forcing them to learn an entirely different system.</p>



<p class="wp-block-paragraph">That reduced friction for marketers running campaigns across both platforms, even if it made differentiation more difficult.</p>



<h2 id="h-google-s-florida-update-changed-seo-overnight" class="wp-block-heading">Google’s Florida update changed SEO overnight</h2>



<p class="wp-block-paragraph">For Van Wagner, Google&#8217;s Florida update was the moment that exposed the risks of relying too heavily on organic search.</p>



<p class="wp-block-paragraph">The update hit shortly before the holiday season and wiped out rankings for businesses and agencies that had built their strategies around SEO techniques that previously worked.</p>



<p class="wp-block-paragraph">Some of those techniques were aggressive, but the episode demonstrated how quickly Google could change the rules — and how much revenue could disappear with them.</p>



<h2 id="h-paid-search-became-an-seo-insurance-policy" class="wp-block-heading">Paid search became an SEO insurance policy</h2>



<p class="wp-block-paragraph">Florida also helped reinforce the value of paid search.</p>



<p class="wp-block-paragraph">Organic visibility could disappear following an algorithm update, while PPC gave advertisers another way to maintain traffic and revenue.</p>



<p class="wp-block-paragraph">For Van Wagner, that uncertainty was one reason he ultimately specialized in paid search rather than continuing to build an SEO business.</p>



<h2 id="h-search-grew-through-collaboration-not-secrecy" class="wp-block-heading">Search grew through collaboration, not secrecy</h2>



<p class="wp-block-paragraph">The early search community was unusually willing to share information.</p>



<p class="wp-block-paragraph">Conference speakers made themselves available after sessions, practitioners exchanged observations when platforms behaved unexpectedly, and competitors frequently helped one another.</p>



<p class="wp-block-paragraph">Van Wagner attributes much of that generosity to the rapidly expanding market: there was enough opportunity that practitioners didn’t feel they needed to guard every insight from competitors.</p>



<h2 id="h-conferences-needed-to-make-room-for-new-voices" class="wp-block-heading">Conferences needed to make room for new voices</h2>



<p class="wp-block-paragraph">As search matured, its conference circuit risked becoming dominated by the same established names.</p>



<p class="wp-block-paragraph">When Van Wagner became involved with speaker selection for SMX Advanced, he tried to make the process more focused on ideas by removing identifying details such as names, genders and companies from submissions during the initial evaluation.</p>



<p class="wp-block-paragraph">The goal was to judge the strength of an idea before the reputation of the person presenting it — helping new speakers break into an industry that had become increasingly difficult to enter.</p>



<h2 id="h-independent-search-journalism-still-matters" class="wp-block-heading">Independent search journalism still matters</h2>



<p class="wp-block-paragraph">Asked what would disappear without Search Engine Land, Van Wagner pointed to its independent editorial voice.</p>



<p class="wp-block-paragraph">For practitioners trying to understand constant platform changes, there remains a difference between advertiser-driven messaging and reporting that can independently question what Google, Microsoft and other platforms are doing.</p>



<p class="wp-block-paragraph">That editorial independence, he argued, is what has made industry publications valuable over the long term.</p>



<h2 id="h-ppc-professionals-need-to-admit-mistakes-faster" class="wp-block-heading">PPC professionals need to admit mistakes faster</h2>



<p class="wp-block-paragraph">Van Wagner also acknowledged one uncomfortable PPC habit: finding creative explanations when something goes wrong.</p>



<p class="wp-block-paragraph">He recalled accidentally applying “loan” as a broad-match negative in an expensive financial campaign and burning through roughly $7,000 in around four hours.</p>



<p class="wp-block-paragraph">His lesson was that practitioners are often better served by simply acknowledging a mistake, explaining what happened and learning from it rather than looking for something — or someone — else to blame.</p>



<h2 id="h-never-assume-google-is-acting-in-your-best-interests" class="wp-block-heading">Never assume Google is acting in your best interests</h2>



<p class="wp-block-paragraph">One myth Van Wagner wants advertisers to abandon is the assumption that Google is automatically acting in their best interests.</p>



<p class="wp-block-paragraph">That doesn’t mean Google representatives are dishonest. It means Google is a business with its own incentives, and advertisers need to independently evaluate recommendations rather than treating them as objective instructions.</p>



<p class="wp-block-paragraph">The industry may have changed dramatically over two decades, but that principle has become more important as advertisers surrender more campaign decisions to algorithms.</p>


Watch this video on YouTube




The search marketing industry has gone from an entrepreneurial “gold rush” to an era dominated by automation, AI and increasingly consolidated platforms.
Looking back over that transformation, paid search veteran Matt Van Wagner reflected on the early days of Google Ads, the rise of Search Engine Land, tactics that thankfully disappeared and why advertisers still need to question what Google tells them.
Search marketing started with a gold-rush mentality
Van Wagner entered search in the early 2000s after working in technology sales and marketing, where traditional channels such as trade publications, conferences and direct mail were becoming increasingly ineffective.
His first search conference offered a stark contrast: an industry filled with entrepreneurial energy and people exploring an entirely new way for businesses to reach customers.
That experience ultimately convinced him to leave his existing career and start a search advertising agency — at a time when few businesses even knew what a search advertising agency was.
Selling Google Ads was the easy part
Once advertisers understood what paid search could do, Van Wagner said convincing companies to try Google advertising required little selling.
Demand was plentiful and the emerging agencies were generally collaborative because the market was expanding quickly enough for everyone.
The bigger mistake, he said, was that many early agencies simply didn’t charge enough for the value they were providing.
Search Engine Land helped the industry grow up
By the time Search Engine Land and SMX arrived, search marketing conferences were beginning to suffer from repetitive speakers and presentations.
SMX Advanced took a different approach: rather than repeatedly teaching the fundamentals, it started at an intermediate level and went deeper.
That attracted more sophisticated speakers and discussions, while conversations in bars, over coffee and at dinner became almost as valuable as the formal sessions themselves.
PPC tactics became far less granular
Some of the practices considered sophisticated in early paid search now look unnecessarily complicated.
Single keyword ad groups, duplicated match types and elaborate negative-keyword structures were routinely used to tightly sculpt traffic between campaigns.
Automation has fundamentally changed that approach, making many of those old structures largely obsolete.
Early Google felt more open
One thing Van Wagner misses about the early search industry is the accessibility of Google itself.
Events such as the Google Dance gave marketers opportunities to interact directly with people working on search, while figures such as Matt Cutts became a bridge between Google and the SEO community.
Part of that openness came from the fact that Google itself was still figuring search out. Advertisers, SEOs and search engines were effectively exploring the new ecosystem together.
Google’s definition of success isn’t necessarily yours
Advertisers want profitable sales and measurable business outcomes. Google also wants advertisers to spend money.
Van Wagner argued that marketers need to remember that distinction when evaluating recommendations, default settings and increasingly automated campaign types.
Google may genuinely want its technology to improve advertiser performance, but its commercial incentives and an advertiser’s objectives aren’t automatically identical.
Broad match defaults remain a major frustration
One longstanding example is Google’s use of broad match as a default.
Van Wagner argued that inexperienced advertisers can easily spend money learning lessons Google’s systems already have enough information to help them avoid.
His broader concern is that advertisers increasingly have to spend their own money teaching automated systems things they already know about their businesses.
Automation still needs advertiser knowledge
Van Wagner accepts that automation is moving in one direction and advertisers won’t regain every control they once had.
But that doesn’t mean Google should ignore knowledge advertisers already possess.
If an advertiser already knows particular queries are irrelevant, for example, forcing an AI system to rediscover that through wasted spend makes little sense. Automation should be able to learn while still incorporating the advertiser’s existing expertise.
Ginny Marvin helped bridge Google and advertisers
Van Wagner highlighted Google Ads Liaison Ginny Marvin as a positive influence on the relationship between Google and the advertising community.
Having worked on the advertiser side before joining Google, Marvin understands many of the frustrations marketers experience and, he argued, has been able to advocate for advertisers while still representing Google.
Bidding your way to No. 1 was an expensive mistake
One misconception from early PPC was that advertisers simply needed to outbid everyone to secure the top position.
Van Wagner recalled campaigns where advertisers were willing to pay enormous CPCs purely because they wanted to own the No. 1 spot.
The better philosophy, he argued, was often to compete for second place and focus on economics rather than prestige.
Google Instant showed the power of product publicity
One Search Engine Land article Van Wagner particularly remembers writing examined Google Instant, which began predicting and displaying search results as users typed.
His argument was that Google generated enormous media attention from the product change while Microsoft was spending heavily on conventional advertising to promote Bing.
The episode demonstrated just how powerful Google’s ability to turn product changes into mainstream news had become.
Microsoft realized it couldn’t reinvent search behavior
Microsoft faced a different problem: users weren’t actively deciding which search engine to use every morning.
Van Wagner believes Microsoft therefore made a sensible decision to make its advertising platform familiar to Google advertisers rather than forcing them to learn an entirely different system.
That reduced friction for marketers running campaigns across both platforms, even if it made differentiation more difficult.
Google’s Florida update changed SEO overnight
For Van Wagner, Google’s Florida update was the moment that exposed the risks of relying too heavily on organic search.
The update hit shortly before the holiday season and wiped out rankings for businesses and agencies that had built their strategies around SEO techniques that previously worked.
Some of those techniques were aggressive, but the episode demonstrated how quickly Google could change the rules — and how much revenue could disappear with them.
Paid search became an SEO insurance policy
Florida also helped reinforce the value of paid search.
Organic visibility could disappear following an algorithm update, while PPC gave advertisers another way to maintain traffic and revenue.
For Van Wagner, that uncertainty was one reason he ultimately specialized in paid search rather than continuing to build an SEO business.
Search grew through collaboration, not secrecy
The early search community was unusually willing to share information.
Conference speakers made themselves available after sessions, practitioners exchanged observations when platforms behaved unexpectedly, and competitors frequently helped one another.
Van Wagner attributes much of that generosity to the rapidly expanding market: there was enough opportunity that practitioners didn’t feel they needed to guard every insight from competitors.
Conferences needed to make room for new voices
As search matured, its conference circuit risked becoming dominated by the same established names.
When Van Wagner became involved with speaker selection for SMX Advanced, he tried to make the process more focused on ideas by removing identifying details such as names, genders and companies from submissions during the initial evaluation.
The goal was to judge the strength of an idea before the reputation of the person presenting it — helping new speakers break into an industry that had become increasingly difficult to enter.
Independent search journalism still matters
Asked what would disappear without Search Engine Land, Van Wagner pointed to its independent editorial voice.
For practitioners trying to understand constant platform changes, there remains a difference between advertiser-driven messaging and reporting that can independently question what Google, Microsoft and other platforms are doing.
That editorial independence, he argued, is what has made industry publications valuable over the long term.
PPC professionals need to admit mistakes faster
Van Wagner also acknowledged one uncomfortable PPC habit: finding creative explanations when something goes wrong.
He recalled accidentally applying “loan” as a broad-match negative in an expensive financial campaign and burning through roughly $7,000 in around four hours.
His lesson was that practitioners are often better served by simply acknowledging a mistake, explaining what happened and learning from it rather than looking for something — or someone — else to blame.
Never assume Google is acting in your best interests
One myth Van Wagner wants advertisers to abandon is the assumption that Google is automatically acting in their best interests.
That doesn’t mean Google representatives are dishonest. It means Google is a business with its own incentives, and advertisers need to independently evaluate recommendations rather than treating them as objective instructions.
The industry may have changed dramatically over two decades, but that principle has become more important as advertisers surrender more campaign decisions to algorithms.

Paid search veteran Matt Van Wagner reflects on 20 years of Google Ads, lost PPC tactics, rising automation and more

Matt Van Wagner shares what’s changed, what disappeared and what PPC still gets wrong

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/09/google-phone-live.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/google-phone-live.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/09/google-phone-live-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/09/google-phone-live-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google just released its Gemini 3.8 Live and 3.8 Live Extended Thinking models and 3.8 Live is now powering Search Live on the Google app.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/09/google-phone-live.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/google-phone-live.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/09/google-phone-live-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/09/google-phone-live-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Google <a href="https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-8-live-gemini-3-8-live-extended-thinking/">announced</a> the release of Gemini 3.8 Live and 3.8 Live Extended Thinking models and 3.8 Live is now powering Search Live on the Google app.  Google said3.8 Live is rolling out starting today on <a href="https://searchengineland.com/google-launches-search-live-with-talk-and-listen-within-google-app-457224">Search Live</a>.</p>



<p class="wp-block-paragraph">Rajan Patel, VP, Engineering for Search at Google, <a href="https://x.com/rajanpatel/status/2099914244221489368">wrote</a>, &#8220;New Gemini audio models just dropped – 3.8 Live is now powering real-time conversations in Search Live.&#8221; </p>



<p class="wp-block-paragraph">He added that this gives you:</p>



<ul class="wp-block-list">
<li>More helpful responses, complete with web links to dive deeper</li>



<li>Fluid multilingual support (you can switch languages mid-conversation)</li>



<li>More natural, free-flowing interactions</li>
</ul>



<p class="wp-block-paragraph"><strong>How it works. </strong>Here is a video of it in action:</p>



<figure class="wp-block-video"><video height="720" width="1280" controls src="https://searchengineland.com/wp-content/seloads/2026/09/gemini-38-search-live.mp4"></video></figure>



<p class="wp-block-paragraph">Then open the Google app, tap the new “Live” icon and verbally ask your question. You’ll hear an AI-generated audio response and you can follow up with another question. Google will also show links right on your screen so you can dig deeper to those links, if you are watching the screen.</p>



<p class="wp-block-paragraph">Plus, if you want to look at the text version, there is a “transcript” link to see the conversation later. Tap the “transcript” button to view the text response and continue to ask questions by typing. If you want to revisit a Search Live response, pick up where you left off by heading to your AI Mode history.&nbsp;</p>



<p class="wp-block-paragraph"><strong>Why we care. </strong>Google continues to add Gemini to power parts of Google Search; from many search features, including AI Overviews, AI Mode, and many other components within Google Search, now also Search Live.</p>



<p class="wp-block-paragraph">These models continue to get better and the results will change as these models change.</p>


Google announced the release of Gemini 3.8 Live and 3.8 Live Extended Thinking models and 3.8 Live is now powering Search Live on the Google app.  Google said3.8 Live is rolling out starting today on Search Live.
Rajan Patel, VP, Engineering for Search at Google, wrote, “New Gemini audio models just dropped – 3.8 Live is now powering real-time conversations in Search Live.” 
He added that this gives you:
More helpful responses, complete with web links to dive deeper
Fluid multilingual support (you can switch languages mid-conversation)
More natural, free-flowing interactions
How it works. Here is a video of it in action:




Then open the Google app, tap the new “Live” icon and verbally ask your question. You’ll hear an AI-generated audio response and you can follow up with another question. Google will also show links right on your screen so you can dig deeper to those links, if you are watching the screen.
Plus, if you want to look at the text version, there is a “transcript” link to see the conversation later. Tap the “transcript” button to view the text response and continue to ask questions by typing. If you want to revisit a Search Live response, pick up where you left off by heading to your AI Mode history. 
Why we care. Google continues to add Gemini to power parts of Google Search; from many search features, including AI Overviews, AI Mode, and many other components within Google Search, now also Search Live.
These models continue to get better and the results will change as these models change.

Google just released its Gemini 3.8 Live and 3.8 Live Extended Thinking models and 3.8 Live is now powering Search Live on the Google app.

Gemini 3.8 Live powers Google Search Live

<div><img width="1600" height="900" src="https://searchengineland.com/wp-content/seloads/2026/09/PPC-Live-The-Podcast-ft-Mike-Ryan.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/PPC-Live-The-Podcast-ft-Mike-Ryan.png 1600w, https://searchengineland.com/wp-content/seloads/2026/09/PPC-Live-The-Podcast-ft-Mike-Ryan-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/PPC-Live-The-Podcast-ft-Mike-Ryan-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/PPC-Live-The-Podcast-ft-Mike-Ryan-1200x675.png 1200w" sizes="(max-width: 1600px) 100vw, 1600px" /></div>Mike Ryan shares lessons from AI Max mistakes, campaign over-segmentation and advertiser overreactions to Google Ads changes.

<div><img width="1600" height="900" src="https://searchengineland.com/wp-content/seloads/2026/09/PPC-Live-The-Podcast-ft-Mike-Ryan.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/PPC-Live-The-Podcast-ft-Mike-Ryan.png 1600w, https://searchengineland.com/wp-content/seloads/2026/09/PPC-Live-The-Podcast-ft-Mike-Ryan-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/PPC-Live-The-Podcast-ft-Mike-Ryan-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/PPC-Live-The-Podcast-ft-Mike-Ryan-1200x675.png 1200w" sizes="(max-width: 1600px) 100vw, 1600px" /></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">
<p><a href="https://www.youtube.com/embed/mRMqzdo_ZZc?feature=oembed" rel="noopener noreferrer">Watch this video on YouTube</a>
</div></figure>



<p class="wp-block-paragraph">Google Ads’ increasingly automated campaign tools can uncover new opportunities, but advertisers shouldn’t mistake optimization for strategy.</p>



<p class="wp-block-paragraph" id="h-">That was one of the central lessons shared by Mike Ryan, head of e-commerce insights at Smarter Ecommerce (SMEC), during a conversation on the PPC Live podcast about mistakes, AI Max and how advertisers should approach Google’s rapidly evolving automation.</p>



<p class="wp-block-paragraph">Ryan shared how AI Max dramatically expanded a client’s traffic by targeting a much larger competitor. Performance looked good, but the advertiser had intentionally avoided those searches to prevent a bidding war — business context AI Max couldn’t understand.</p>



<h2 id="h-trust-ai-but-verify-what-it-s-doing" class="wp-block-heading">Trust AI, but verify what it’s doing</h2>



<p class="wp-block-paragraph">The experience reinforced Ryan’s “trust but verify” approach. New Google Ads technology should be monitored closely from day one, including checking search terms rather than assuming strong headline performance means everything is working as intended.</p>



<h2 id="h-ai-max-and-search-partners-sparked-a-false-alarm" class="wp-block-heading">AI Max and Search Partners sparked a false alarm</h2>



<p class="wp-block-paragraph">Ryan initially concluded from several campaigns that AI Max favored Search Partner Network and shared the finding on LinkedIn. More data later showed that wasn’t generally true, illustrating the danger of turning early observations into broad conclusions.</p>



<h2 id="h-viral-ppc-claims-are-difficult-to-take-back" class="wp-block-heading">Viral PPC claims are difficult to take back</h2>



<p class="wp-block-paragraph">A correction rarely gets the reach of the original controversial claim. Ryan discussed the responsibility that comes with having an industry audience, particularly when advertisers may use his observations to make real campaign decisions.</p>



<h2 id="h-ai-max-has-guardrails-advertisers-need-to-use-them" class="wp-block-heading">AI Max has guardrails — advertisers need to use them</h2>



<p class="wp-block-paragraph">Advertisers can control unwanted AI Max behavior through negative keywords, brand inclusions and exclusions, and Search Partner Network settings. Reporting such as AI Max match type and match source can also reveal exactly where expansion is coming from.</p>



<h2 id="h-over-segmenting-campaigns-can-starve-google-s-ai" class="wp-block-heading">Over-segmenting campaigns can starve Google’s AI</h2>



<p class="wp-block-paragraph">Highly granular account structures can leave individual campaigns without enough conversion data for automated bidding. Ryan said his research points to at least 30 monthly conversions — ideally 60 or more — for Smart Bidding to work consistently.</p>



<h2 id="h-campaign-segmentation-should-be-strategic" class="wp-block-heading">Campaign segmentation should be strategic</h2>



<p class="wp-block-paragraph">Ryan argued that advertisers have moved from tactical segmentation toward strategic segmentation. Every additional campaign or split should have a reason to exist rather than simply recreating the highly granular structures common in older Google Ads strategies.</p>



<h2 id="h-cfo-driven-structures-can-clash-with-google-ads" class="wp-block-heading">CFO-driven structures can clash with Google Ads</h2>



<p class="wp-block-paragraph">One account Ryan encountered had been divided into extremely granular margin buckets dictated by its CFO, each with its own ROAS target. The structure sounded sophisticated but fragmented the data so heavily that the algorithm struggled to achieve those targets.</p>



<h2 id="h-custom-labels-are-an-overlooked-source-of-business-context" class="wp-block-heading">Custom labels are an overlooked source of business context</h2>



<p class="wp-block-paragraph">Custom labels can feed information Google doesn’t inherently know — including margins, sell-through rates and return rates — into campaign strategy. That can help advertisers optimize around actual business profitability rather than simply recorded conversions.</p>



<h2 id="h-performance-max-channel-controls-could-cause-confusion" class="wp-block-heading">Performance Max channel controls could cause confusion</h2>



<p class="wp-block-paragraph">Ryan discussed an emerging Performance Max feature allowing advertisers to adjust the importance of individual channels. He warned that increasing a channel’s “importance” may actually give Google greater tolerance around a CPA or ROAS target — potentially producing a different outcome than advertisers expect.</p>



<h2 id="h-advertisers-panicked-over-google-s-august-17-change" class="wp-block-heading">Advertisers panicked over Google’s August 17 change</h2>



<p class="wp-block-paragraph">Ryan’s early research found advertisers increased target ROAS around <a href="https://searchengineland.com/google-expands-smart-bidding-exploration-adds-promotion-mode-480165">Google’s August 17 update</a> — including campaigns that weren’t limited by budget and therefore didn’t necessarily need changes. He argued uncertainty around the update caused advertisers to become unnecessarily conservative.</p>



<h2 id="h-that-panic-may-have-created-an-opportunity" class="wp-block-heading">That panic may have created an opportunity</h2>



<p class="wp-block-paragraph">If advertisers collectively raise ROAS targets and reduce spend, competitors could potentially move in the opposite direction. Ryan suggested some advertisers might have benefited from lowering their ROAS targets and capturing market share while others pulled back.</p>



<h2 id="h-the-august-17-impact-needed-more-time" class="wp-block-heading">The August 17 impact needed more time</h2>



<p class="wp-block-paragraph">Ryan pushed back against immediate claims about the impact of the update. Google advised advertisers to wait one to two conversion cycles, meaning many conclusions circulating shortly after the change were being drawn before sufficient data existed.</p>



<h2 id="h-curiosity-is-becoming-a-core-ppc-skill" class="wp-block-heading">Curiosity is becoming a core PPC skill</h2>



<p class="wp-block-paragraph">Ryan’s overarching lesson was to remain skeptical and curious as advertising becomes more automated. Strong performance, new controls and early industry claims all deserve investigation rather than being accepted at face value.</p>


Watch this video on YouTube




Google Ads’ increasingly automated campaign tools can uncover new opportunities, but advertisers shouldn’t mistake optimization for strategy.
That was one of the central lessons shared by Mike Ryan, head of e-commerce insights at Smarter Ecommerce (SMEC), during a conversation on the PPC Live podcast about mistakes, AI Max and how advertisers should approach Google’s rapidly evolving automation.
Ryan shared how AI Max dramatically expanded a client’s traffic by targeting a much larger competitor. Performance looked good, but the advertiser had intentionally avoided those searches to prevent a bidding war — business context AI Max couldn’t understand.
Trust AI, but verify what it’s doing
The experience reinforced Ryan’s “trust but verify” approach. New Google Ads technology should be monitored closely from day one, including checking search terms rather than assuming strong headline performance means everything is working as intended.
AI Max and Search Partners sparked a false alarm
Ryan initially concluded from several campaigns that AI Max favored Search Partner Network and shared the finding on LinkedIn. More data later showed that wasn’t generally true, illustrating the danger of turning early observations into broad conclusions.
Viral PPC claims are difficult to take back
A correction rarely gets the reach of the original controversial claim. Ryan discussed the responsibility that comes with having an industry audience, particularly when advertisers may use his observations to make real campaign decisions.
AI Max has guardrails — advertisers need to use them
Advertisers can control unwanted AI Max behavior through negative keywords, brand inclusions and exclusions, and Search Partner Network settings. Reporting such as AI Max match type and match source can also reveal exactly where expansion is coming from.
Over-segmenting campaigns can starve Google’s AI
Highly granular account structures can leave individual campaigns without enough conversion data for automated bidding. Ryan said his research points to at least 30 monthly conversions — ideally 60 or more — for Smart Bidding to work consistently.
Campaign segmentation should be strategic
Ryan argued that advertisers have moved from tactical segmentation toward strategic segmentation. Every additional campaign or split should have a reason to exist rather than simply recreating the highly granular structures common in older Google Ads strategies.
CFO-driven structures can clash with Google Ads
One account Ryan encountered had been divided into extremely granular margin buckets dictated by its CFO, each with its own ROAS target. The structure sounded sophisticated but fragmented the data so heavily that the algorithm struggled to achieve those targets.
Custom labels are an overlooked source of business context
Custom labels can feed information Google doesn’t inherently know — including margins, sell-through rates and return rates — into campaign strategy. That can help advertisers optimize around actual business profitability rather than simply recorded conversions.
Performance Max channel controls could cause confusion
Ryan discussed an emerging Performance Max feature allowing advertisers to adjust the importance of individual channels. He warned that increasing a channel’s “importance” may actually give Google greater tolerance around a CPA or ROAS target — potentially producing a different outcome than advertisers expect.
Advertisers panicked over Google’s August 17 change
Ryan’s early research found advertisers increased target ROAS around Google’s August 17 update — including campaigns that weren’t limited by budget and therefore didn’t necessarily need changes. He argued uncertainty around the update caused advertisers to become unnecessarily conservative.
That panic may have created an opportunity
If advertisers collectively raise ROAS targets and reduce spend, competitors could potentially move in the opposite direction. Ryan suggested some advertisers might have benefited from lowering their ROAS targets and capturing market share while others pulled back.
The August 17 impact needed more time
Ryan pushed back against immediate claims about the impact of the update. Google advised advertisers to wait one to two conversion cycles, meaning many conclusions circulating shortly after the change were being drawn before sufficient data existed.
Curiosity is becoming a core PPC skill
Ryan’s overarching lesson was to remain skeptical and curious as advertising becomes more automated. Strong performance, new controls and early industry claims all deserve investigation rather than being accepted at face value.

Mike Ryan shares lessons from AI Max mistakes, campaign over-segmentation and advertiser overreactions to Google Ads changes.

Google Ads AI needs guardrails, not blind trust ft Mike Ryan

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/09/Google-Chomre-Analytics.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Google-Chomre-Analytics.png 1672w, https://searchengineland.com/wp-content/seloads/2026/09/Google-Chomre-Analytics-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Google-Chomre-Analytics-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Google-Chomre-Analytics-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Chrome’s new ad metrics give advertisers more visibility into ad clutter, page quality and inventory value.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/09/Google-Chomre-Analytics.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Google-Chomre-Analytics.png 1672w, https://searchengineland.com/wp-content/seloads/2026/09/Google-Chomre-Analytics-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Google-Chomre-Analytics-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Google-Chomre-Analytics-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google Chrome is giving advertisers and publishers a new way to quantify just how ad-heavy a website feels to real users.</p>



<p class="wp-block-paragraph">The new data could eventually help advertisers distinguish between quality inventory and pages overloaded with ads, potentially adding another signal for deciding where their media dollars should go.</p>



<p class="wp-block-paragraph"><strong>Driving the news</strong>. Google added four experimental advertising metrics to its Chrome User Experience Report (CrUX), its public dataset based on real-world Chrome user experiences.</p>



<p class="wp-block-paragraph">The new measurements are:</p>



<ul class="wp-block-list">
<li><strong>Ad Count:</strong> Average number of ads visible in the viewport.</li>



<li><strong>Ad Density:</strong> Average percentage of the viewport occupied by ads.</li>



<li><strong>Ad Weight – Network:</strong> Data consumed by advertising, measured in bytes.</li>



<li><strong>Ad Weight – CPU:</strong> Processing resources consumed by ads, measured in milliseconds.</li>
</ul>



<p class="wp-block-paragraph"><strong>Why we care.</strong> Advertisers could use these metrics to spot ad-heavy pages where clutter and poor performance may hurt attention and campaign results. More transparency into ad density and resource usage could help buyers assess inventory quality, avoid waste and direct spend toward better ad experiences.</p>



<p class="wp-block-paragraph"><strong>How it works</strong>. Chrome identifies ads using a combination of network-level filtering and analysis of script execution.</p>



<p class="wp-block-paragraph">Chrome can classify URLs matching its ad filter list as advertising and identify resources or frames created by scripts already determined to be ad-related.</p>



<p class="wp-block-paragraph">For ad count and density, Chrome samples the visible viewport once per second. Network and CPU usage accumulate over the user&#8217;s session, with CrUX ultimately reporting results at the 75th percentile.</p>



<p class="wp-block-paragraph"><strong>The advertiser angle</strong>. Google says feedback from the advertising industry suggests the metrics could help marketers evaluate the value of ad opportunities and make better ad product decisions.</p>



<p class="wp-block-paragraph">Google Ads&#8217; Display &amp; Video 360 is supporting the initiative, saying the additional transparency around page-layout quality could help improve quality and advertiser outcomes.</p>



<p class="wp-block-paragraph"><strong>The publisher angle</strong>. The measurements could also give publishers with cleaner ad experiences more evidence to differentiate their inventory.</p>



<p class="wp-block-paragraph">Industry supporters including WPP, The Guardian, Mediavine, Index Exchange and Adelaide said the data could help connect better user experiences with media quality, performance and value.</p>



<p class="wp-block-paragraph"><strong>Yes, but</strong>. Don&#8217;t expect a new &#8220;good&#8221; or &#8220;bad&#8221; ad-experience score just yet.</p>



<p class="wp-block-paragraph">All four metrics are currently labeled <strong>experimental</strong>. They&#8217;re not part of Core Web Vitals, and Google hasn&#8217;t established recommended thresholds for any of them.</p>



<p class="wp-block-paragraph">Google also hasn&#8217;t said whether DV360 is testing the signals for bidding, inventory valuation or reporting.</p>



<p class="wp-block-paragraph"><strong>Worth watching</strong>. The obvious question is whether these measurements eventually move beyond transparency and start influencing how advertisers value, and potentially bid on inventory.</p>



<p class="wp-block-paragraph">Google told Digiday that independent ad platforms will receive access to the CrUX data at the same time as Google&#8217;s own advertising businesses.</p>



<p class="wp-block-paragraph"><strong>The bottom line</strong>. Chrome is turning the subjective experience of &#8220;this page has way too many ads&#8221; into measurable data. If advertisers begin using those signals to value inventory, publishers may have a stronger financial incentive to keep ad loads under control.</p>



<p class="wp-block-paragraph"><strong>Dig deeper. </strong>



<ul class="wp-block-list">
<li><a href="https://digiday.com/media-buying/google-debuts-ad-detection-tool-letting-marketers-better-assess-ad-experiences-in-chrome/">Google debuts ‘ad detection tool,’ letting marketers better assess ad experiences in Chrome</a></li>



<li><a href="https://developer.chrome.com/blog/crux-ad-metrics?hl=en">New ad metrics in Chrome User Experience Report<br></a><a href="https://digiday.com/media-buying/google-debuts-ad-detection-tool-letting-marketers-better-assess-ad-experiences-in-chrome/"><br></a></li>
</ul>


Google Chrome is giving advertisers and publishers a new way to quantify just how ad-heavy a website feels to real users.
The new data could eventually help advertisers distinguish between quality inventory and pages overloaded with ads, potentially adding another signal for deciding where their media dollars should go.
Driving the news. Google added four experimental advertising metrics to its Chrome User Experience Report (CrUX), its public dataset based on real-world Chrome user experiences.
The new measurements are:
Ad Count: Average number of ads visible in the viewport.
Ad Density: Average percentage of the viewport occupied by ads.
Ad Weight – Network: Data consumed by advertising, measured in bytes.
Ad Weight – CPU: Processing resources consumed by ads, measured in milliseconds.
Why we care. Advertisers could use these metrics to spot ad-heavy pages where clutter and poor performance may hurt attention and campaign results. More transparency into ad density and resource usage could help buyers assess inventory quality, avoid waste and direct spend toward better ad experiences.
How it works. Chrome identifies ads using a combination of network-level filtering and analysis of script execution.
Chrome can classify URLs matching its ad filter list as advertising and identify resources or frames created by scripts already determined to be ad-related.
For ad count and density, Chrome samples the visible viewport once per second. Network and CPU usage accumulate over the user’s session, with CrUX ultimately reporting results at the 75th percentile.
The advertiser angle. Google says feedback from the advertising industry suggests the metrics could help marketers evaluate the value of ad opportunities and make better ad product decisions.
Google Ads’ Display & Video 360 is supporting the initiative, saying the additional transparency around page-layout quality could help improve quality and advertiser outcomes.
The publisher angle. The measurements could also give publishers with cleaner ad experiences more evidence to differentiate their inventory.
Industry supporters including WPP, The Guardian, Mediavine, Index Exchange and Adelaide said the data could help connect better user experiences with media quality, performance and value.
Yes, but. Don’t expect a new “good” or “bad” ad-experience score just yet.
All four metrics are currently labeled experimental. They’re not part of Core Web Vitals, and Google hasn’t established recommended thresholds for any of them.
Google also hasn’t said whether DV360 is testing the signals for bidding, inventory valuation or reporting.
Worth watching. The obvious question is whether these measurements eventually move beyond transparency and start influencing how advertisers value, and potentially bid on inventory.
Google told Digiday that independent ad platforms will receive access to the CrUX data at the same time as Google’s own advertising businesses.
The bottom line. Chrome is turning the subjective experience of “this page has way too many ads” into measurable data. If advertisers begin using those signals to value inventory, publishers may have a stronger financial incentive to keep ad loads under control.
Dig deeper. 




Google debuts ‘ad detection tool,’ letting marketers better assess ad experiences in Chrome
New ad metrics in Chrome User Experience Report

Chrome’s new ad metrics give advertisers more visibility into ad clutter, page quality and inventory value.

Google Chrome launches new metrics to measure ad-heavy websites

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/The-follow-up-query-Rethinking-SEO-for-conversational-search.png" class="attachment-large size-large wp-post-image" alt="The follow-up query- Rethinking SEO for conversational search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/The-follow-up-query-Rethinking-SEO-for-conversational-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/The-follow-up-query-Rethinking-SEO-for-conversational-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/The-follow-up-query-Rethinking-SEO-for-conversational-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/The-follow-up-query-Rethinking-SEO-for-conversational-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>A search can now become a chain of questions, comparisons, and decisions. Build content that supports what users ask next.


A search can now become a chain of questions, comparisons, and decisions. Build content that supports what users ask next.

The follow-up query: Rethinking SEO for conversational search

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/What-happens-before-search-strongly-shapes-the-decision.png" class="attachment-large size-large wp-post-image" alt="What happens before search strongly shapes the decision" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/What-happens-before-search-strongly-shapes-the-decision.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/What-happens-before-search-strongly-shapes-the-decision-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/What-happens-before-search-strongly-shapes-the-decision-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/What-happens-before-search-strongly-shapes-the-decision-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Search can confirm a decision that was shaped before the query. Here’s where that influence comes from and what you can do.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/What-happens-before-search-strongly-shapes-the-decision.png" class="attachment-large size-large wp-post-image" alt="What happens before search strongly shapes the decision" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/What-happens-before-search-strongly-shapes-the-decision.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/What-happens-before-search-strongly-shapes-the-decision-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/What-happens-before-search-strongly-shapes-the-decision-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/What-happens-before-search-strongly-shapes-the-decision-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Search isn’t shrinking, but its role in the purchase funnel is changing. </p>



<p class="wp-block-paragraph">People are searching in fundamentally different ways, using cameras, conversations, and keywords to find what they want. At the same time, discovery is happening before many searches, as recommendation engines curate content and help people develop preferences before they ever type a query.</p>



<p class="wp-block-paragraph">The searches marketers see may be less about discovering what to buy and more about confirming a decision that’s already taking shape. Those earlier stages are difficult to observe, so the activity that helped someone develop a preference can disappear from the purchase path.</p>



<p class="wp-block-paragraph">A few weeks ago, I was on a walk in my neighborhood, enjoying a Pacific Northwest summer day. Along my usual route, I noticed a beautiful plant that I loved but didn’t recognize. Just a few years ago, I would have had to return home, type a clumsy description like “purple flower Northwest beautiful” into a search engine, and then scroll through dozens of pictures before finding the plant’s name.</p>



<p class="wp-block-paragraph">This time, I simply pointed my phone camera at the plant, had Google Lens identify it for me, and then bought one at my local plant store the next day. No ad. No search bar. Just a camera and an answer.</p>



<p class="wp-block-paragraph">Nothing about my experience is unusual anymore, and it should change how every digital marketer views search’s role in the purchase funnel.</p>



<h2 id="h-search-is-changing-alongside-the-funnel" class="wp-block-heading">Search is changing alongside the funnel</h2>



<p class="wp-block-paragraph">The demand generation funnel has become <a href="https://searchengineland.com/google-ads-demand-gen-playbook-464902">flatter, noisier, and more complicated</a>. The touchpoints have sorted themselves into a consistent sequence.</p>



<p class="wp-block-paragraph">The traditional funnel was often described as a straight line from awareness → interest → desire → action, with search activity doing the most work in the interest stage, when people were still figuring out what they wanted.</p>



<p class="wp-block-paragraph">Now the funnel looks something like <em>passive exposure → preference development → confirmation searches → purchase</em>. Search is still a critical component of a buying decision, but it plays a smaller role in discovery.&nbsp;</p>



<p class="wp-block-paragraph">That job has shifted to recommendation engines that can predict user intent and curate personalized content before the first query. By the time searches happen, they’re confirming a decision that’s already been made.</p>



<p class="wp-block-paragraph">I see this play out in client accounts. The queries that are closest to purchase look more like caveman language, i.e., “brand name product model,” indicating that consumers know exactly what they’re looking for when it’s finally time to buy.</p>



<p class="wp-block-paragraph">The catch here is that the first two stages of the new funnel are ones we can’t really observe. If someone watches a series of videos on social media, develops an interest in a product, and then conducts a Google search before purchase, that activity will appear to be a branded search conversion. The content that helped them form their decision never appears in the purchase path.</p>



<p class="wp-block-paragraph">Consequently, most marketers still budget as if search does the discovery work because that’s what their data tells them.</p>


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<h2 id="h-examining-youtube-s-unique-position" class="wp-block-heading">Examining YouTube’s unique position</h2>



<p class="wp-block-paragraph">As search plays a different role in the funnel, <a href="https://searchengineland.com/youtube-seo-what-you-need-to-know-449431">YouTube</a> is taking on a larger role in discovery. Two things make it different: machine trust and human trust.&nbsp;</p>



<ul class="wp-block-list">
<li><strong>Machine trust:</strong> A study of over 100 million AI citations found that YouTube was the<a href="https://otterly.ai/blog/youtube-ai-citation-study-2026/"> second most-cited social media platform</a>, behind only Reddit. AI answer engines trust YouTube so much because of its massive library of content, including elements like video transcripts, which provide AI models with structured text they can display in answers.</li>



<li><strong>Human trust: </strong>Consumers have more confidence in YouTube than other social platforms. Studies show that<a href="https://www.emarketer.com/content/consumers-trust-youtube-most"> more than two-thirds of consumers</a> find YouTube “somewhat or very trustworthy” when it comes to making purchase decisions.</li>
</ul>



<p class="wp-block-paragraph">Creators on YouTube also supply social proof, which now plays a critical role in purchasing decisions, particularly among younger buyers. <a href="https://www.marketingdive.com/news/gen-z-relies-on-influencers-for-purchase-decisions-kantar-says/582890/">Up to 70% of Gen Z</a> follow an influencer, and 44% made a purchase based on an influencer’s recommendation.</p>



<p class="wp-block-paragraph">YouTube enjoys the unique position of being highly trusted by both consumers and the machines making recommendations, and Google has taken steps to ensure the platform gets credit for its role in discovery. In January, it <a href="https://searchengineland.com/google-adds-new-youtube-brand-campaign-measurement-tools-481320">rolled out the Attributed Branded Search metric</a>, which enables marketers to link YouTube ad exposure to a branded search within a specific timeframe.</p>



<p class="wp-block-paragraph">ABS only captures exposure to <a href="https://searchengineland.com/youtube-advertising-the-ultimate-guide-436845">YouTube advertising</a>. The organic layer featuring review videos, product unboxings, and comparison videos remains invisible. If the measurable portion is driving tangible results, imagine what the rest of the channel is doing.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: </em></strong><a href="https://searchengineland.com/youtube-seo-ai-overviews-467253"><strong><em>YouTube is no longer optional for SEO in the age of AI Overviews</em></strong></a>



<h2 id="h-discovery-extends-well-beyond-youtube" class="wp-block-heading">Discovery extends well beyond YouTube</h2>



<p class="wp-block-paragraph">This huge invisible discovery layer extends well beyond YouTube. Much of that discovery happens outside the channels brands control.</p>



<p class="wp-block-paragraph">AI tools don’t repeat your version of the brand. They repeat what the entire web says about you. Earned media drove <a href="https://muckrack.com/blog/what-is-ai-reading-may-2026">84% of AI citations</a>, according to a Muck Rack review of 25 million ChatGPT, Claude, and Gemini responses across multiple industries.</p>



<p class="wp-block-paragraph">Those third-party mentions, YouTube review videos, and Reddit threads form the raw material for the AI answers and feed recommendations that drive today’s purchasing decisions.</p>



<p class="wp-block-paragraph">The practical outcome of this shift is that brand reputation management and search visibility have merged into a single role.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: </em></strong><a href="https://searchengineland.com/social-search-visibility-evolution-471685"><strong><em>Why social search visibility is the next evolution of discoverability</em></strong></a>



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<h2 id="h-how-do-brands-drive-discovery" class="wp-block-heading">How do brands drive discovery?</h2>



<p class="wp-block-paragraph">Brands still have an enormous role to play in driving discovery. However, those activities must extend beyond paid advertising. Here are three ways you can <a href="https://searchengineland.com/build-brand-worth-finding-discovery-signals-481644">influence discovery</a>:</p>



<ul class="wp-block-list">
<li>Build a video library that answers questions about your product and your brand regardless of whether you’re running YouTube ads. These will become a database AI answers can draw from.</li>



<li>Data health has become a non-negotiable. The algorithm only knows what you tell it. Consequently, your product feeds should provide the algorithms with as much information as possible. It’s also critical to build brand consistency across the channels you control.</li>



<li>Show up in conversations you don’t own. Reddit threads involving your brand are feeding AI Overviews whether you participate or not.</li>
</ul>



<p class="wp-block-paragraph">Providing consistent and complete information across channels will give algorithms and AI tools something reliable to work with.</p>



<h2 id="h-don-t-abandon-search" class="wp-block-heading">Don’t abandon search</h2>



<p class="wp-block-paragraph">A reshaped funnel shouldn’t be the reason you pull back from search. Google query volume reached an all-time high in early 2026 even as search’s position in the funnel changed. </p>



<p class="wp-block-paragraph">The mistake won’t come from overinvesting in search. Instead, the mistake will come from expecting search to accomplish something it&#8217;s no longer suited for.</p>



<p class="wp-block-paragraph">Rather than retreating from search, this is an opportunity to reimagine your marketing mix within a new framework. If search now occupies a different funnel position, how can you allocate your marketing resources to cover the upper-funnel work that still must be done?&nbsp;</p>



<p class="wp-block-paragraph">The answer to this question will be different for every marketing team, but it’s a conversation that everyone should be having right now.</p>



<h2 id="h-search-behavior-won-t-stand-still" class="wp-block-heading">Search behavior won’t stand still</h2>



<p class="wp-block-paragraph">A few years ago, I never would have imagined that I could search for a plant’s name with a camera built into my phone, much less have a back-and-forth conversation with my computer. But here we are.</p>



<p class="wp-block-paragraph">Marketers are working on the cutting edge, watching new technology change user behavior right before our eyes. Everything could shift again by this time next year. Nobody knows for sure. But the larger trend is clear: people are increasingly searching to confirm what they’ve already decided.</p>

Search isn’t shrinking, but its role in the purchase funnel is changing. 
People are searching in fundamentally different ways, using cameras, conversations, and keywords to find what they want. At the same time, discovery is happening before many searches, as recommendation engines curate content and help people develop preferences before they ever type a query.
The searches marketers see may be less about discovering what to buy and more about confirming a decision that’s already taking shape. Those earlier stages are difficult to observe, so the activity that helped someone develop a preference can disappear from the purchase path.
A few weeks ago, I was on a walk in my neighborhood, enjoying a Pacific Northwest summer day. Along my usual route, I noticed a beautiful plant that I loved but didn’t recognize. Just a few years ago, I would have had to return home, type a clumsy description like “purple flower Northwest beautiful” into a search engine, and then scroll through dozens of pictures before finding the plant’s name.
This time, I simply pointed my phone camera at the plant, had Google Lens identify it for me, and then bought one at my local plant store the next day. No ad. No search bar. Just a camera and an answer.
Nothing about my experience is unusual anymore, and it should change how every digital marketer views search’s role in the purchase funnel.
Search is changing alongside the funnel
The demand generation funnel has become flatter, noisier, and more complicated. The touchpoints have sorted themselves into a consistent sequence.
The traditional funnel was often described as a straight line from awareness → interest → desire → action, with search activity doing the most work in the interest stage, when people were still figuring out what they wanted.
Now the funnel looks something like passive exposure → preference development → confirmation searches → purchase. Search is still a critical component of a buying decision, but it plays a smaller role in discovery. 
That job has shifted to recommendation engines that can predict user intent and curate personalized content before the first query. By the time searches happen, they’re confirming a decision that’s already been made.
I see this play out in client accounts. The queries that are closest to purchase look more like caveman language, i.e., “brand name product model,” indicating that consumers know exactly what they’re looking for when it’s finally time to buy.
The catch here is that the first two stages of the new funnel are ones we can’t really observe. If someone watches a series of videos on social media, develops an interest in a product, and then conducts a Google search before purchase, that activity will appear to be a branded search conversion. The content that helped them form their decision never appears in the purchase path.
Consequently, most marketers still budget as if search does the discovery work because that’s what their data tells them.
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Examining YouTube’s unique position
As search plays a different role in the funnel, YouTube is taking on a larger role in discovery. Two things make it different: machine trust and human trust. 
Machine trust: A study of over 100 million AI citations found that YouTube was the second most-cited social media platform, behind only Reddit. AI answer engines trust YouTube so much because of its massive library of content, including elements like video transcripts, which provide AI models with structured text they can display in answers.
Human trust: Consumers have more confidence in YouTube than other social platforms. Studies show that more than two-thirds of consumers find YouTube “somewhat or very trustworthy” when it comes to making purchase decisions.
Creators on YouTube also supply social proof, which now plays a critical role in purchasing decisions, particularly among younger buyers. Up to 70% of Gen Z follow an influencer, and 44% made a purchase based on an influencer’s recommendation.
YouTube enjoys the unique position of being highly trusted by both consumers and the machines making recommendations, and Google has taken steps to ensure the platform gets credit for its role in discovery. In January, it rolled out the Attributed Branded Search metric, which enables marketers to link YouTube ad exposure to a branded search within a specific timeframe.
ABS only captures exposure to YouTube advertising. The organic layer featuring review videos, product unboxings, and comparison videos remains invisible. If the measurable portion is driving tangible results, imagine what the rest of the channel is doing.
Dig deeper: YouTube is no longer optional for SEO in the age of AI Overviews



Discovery extends well beyond YouTube
This huge invisible discovery layer extends well beyond YouTube. Much of that discovery happens outside the channels brands control.
AI tools don’t repeat your version of the brand. They repeat what the entire web says about you. Earned media drove 84% of AI citations, according to a Muck Rack review of 25 million ChatGPT, Claude, and Gemini responses across multiple industries.
Those third-party mentions, YouTube review videos, and Reddit threads form the raw material for the AI answers and feed recommendations that drive today’s purchasing decisions.
The practical outcome of this shift is that brand reputation management and search visibility have merged into a single role.
Dig deeper: Why social search visibility is the next evolution of discoverability




	
	
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How do brands drive discovery?
Brands still have an enormous role to play in driving discovery. However, those activities must extend beyond paid advertising. Here are three ways you can influence discovery:
Build a video library that answers questions about your product and your brand regardless of whether you’re running YouTube ads. These will become a database AI answers can draw from.
Data health has become a non-negotiable. The algorithm only knows what you tell it. Consequently, your product feeds should provide the algorithms with as much information as possible. It’s also critical to build brand consistency across the channels you control.
Show up in conversations you don’t own. Reddit threads involving your brand are feeding AI Overviews whether you participate or not.
Providing consistent and complete information across channels will give algorithms and AI tools something reliable to work with.
Don’t abandon search
A reshaped funnel shouldn’t be the reason you pull back from search. Google query volume reached an all-time high in early 2026 even as search’s position in the funnel changed. 
The mistake won’t come from overinvesting in search. Instead, the mistake will come from expecting search to accomplish something it’s no longer suited for.
Rather than retreating from search, this is an opportunity to reimagine your marketing mix within a new framework. If search now occupies a different funnel position, how can you allocate your marketing resources to cover the upper-funnel work that still must be done? 
The answer to this question will be different for every marketing team, but it’s a conversation that everyone should be having right now.
Search behavior won’t stand still
A few years ago, I never would have imagined that I could search for a plant’s name with a camera built into my phone, much less have a back-and-forth conversation with my computer. But here we are.
Marketers are working on the cutting edge, watching new technology change user behavior right before our eyes. Everything could shift again by this time next year. Nobody knows for sure. But the larger trend is clear: people are increasingly searching to confirm what they’ve already decided.

Search can confirm a decision that was shaped before the query. Here’s where that influence comes from and what you can do.

What happens before search strongly shapes the decision

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/Claude-for-SEO-5-ways-to-automate-repetitive-work.png" class="attachment-large size-large wp-post-image" alt="Claude for SEO- 5 ways to automate repetitive work" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Claude-for-SEO-5-ways-to-automate-repetitive-work.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/Claude-for-SEO-5-ways-to-automate-repetitive-work-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Claude-for-SEO-5-ways-to-automate-repetitive-work-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Claude-for-SEO-5-ways-to-automate-repetitive-work-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Find out where Claude delivers the biggest time savings across SEO, from analyzing data and localizing content to maintaining websites.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/Claude-for-SEO-5-ways-to-automate-repetitive-work.png" class="attachment-large size-large wp-post-image" alt="Claude for SEO- 5 ways to automate repetitive work" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Claude-for-SEO-5-ways-to-automate-repetitive-work.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/Claude-for-SEO-5-ways-to-automate-repetitive-work-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Claude-for-SEO-5-ways-to-automate-repetitive-work-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Claude-for-SEO-5-ways-to-automate-repetitive-work-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">After working on my <a href="https://searchengineland.com/ai-hreflang-xml-sitemaps-480181">hreflang XML sitemap project with Gemini</a>, my company decided to invest in paid <a href="https://searchengineland.com/use-claude-for-seo-dont-let-claude-do-seo-485931">Claude</a> accounts. I took the opportunity to see what else Claude could do through its desktop app and found some great uses for it. </p>



<p class="wp-block-paragraph">Here are five ways I’ve been using it in my everyday SEO work.</p>



<h2 id="h-1-implement-a-daily-briefing" class="wp-block-heading">1. Implement a daily briefing</h2>



<p class="wp-block-paragraph">The first thing I set up was a daily intelligence brief. Using lessons learned from other AI tools, I crafted a detailed outline of the types of news I wanted to see, including industry updates, competitor news, mergers, and acquisitions.</p>



<p class="wp-block-paragraph">The key to getting more useful information and less fluff is to be specific and use the output as a guide for improvement. Don’t accept schlock — you’re paying for the tool, so teach it to do what you need.</p>



<p class="wp-block-paragraph">For instance, I have Gemini checking atmospheric conditions for radio wave propagation in my amateur radio hobby, and it took several adjustments to get useful output.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/claude-code-seo-work-470668">How to turn Claude Code into your SEO command center</a></em></strong>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0101ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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<h2 id="h-2-connect-to-data-sources-for-faster-analysis" class="wp-block-heading">2. Connect to data sources for faster analysis</h2>



<p class="wp-block-paragraph">Yes, I can dig into Google Analytics and analyze the data, but how much faster would it be if I could just ask Claude to look at the data and answer questions for me? It turns out it’s much faster.</p>



<p class="wp-block-paragraph">The folks at Rank Math shared instructions on <a href="https://www.youtube.com/watch?v=rf7zkcQ0ncM" target="_blank" rel="noopener">how to make a direct connection without using third-party middleware</a>. This tip has saved me a lot of time by allowing me to ask for what I need and quickly get an answer.</p>



<h2 id="h-3-create-a-better-and-faster-hreflang-xml-sitemap" class="wp-block-heading">3. Create a better and faster hreflang XML sitemap</h2>



<p class="wp-block-paragraph">I decided to see how Claude would tackle the hreflang XML sitemap project. Like I did in my Gemini example, I told it what I wanted, fed it the latest versions of the sitemaps, and asked how it would approach the problem.</p>



<p class="wp-block-paragraph">Claude surprised me by asking only for links to XML sitemaps as input. Using its own resources, Claude gathered all the pages and built the sitemap for me without any further intervention.</p>



<p class="wp-block-paragraph">I double-checked the output and found it to be excellent on the first go, avoiding the need for the back and forth I experienced with Gemini — a huge time saver.</p>



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<h2 id="h-4-generate-translate-and-localize-content" class="wp-block-heading">4. Generate, translate, and localize content</h2>



<p class="wp-block-paragraph">Our team supports three main businesses across several regions. This requires content updates to be replicated across websites, translated, and localized as appropriate. We need to track this work as it goes through the pipeline so nothing falls through the cracks.</p>



<p class="wp-block-paragraph">This is where Claude has been a tremendous benefit. I took an example of content that had been generated, vetted, and approved by one of our in-house experts and decided to see if Claude could handle automating some of the international SEO process. The results were far beyond my expectations.</p>



<p class="wp-block-paragraph">As usual, beginning with the end in mind, I uploaded examples of previous translation and localization work to show what I needed done. Next, I uploaded a new item and gave it specific instructions:</p>



<ul class="wp-block-list">
<li>Look at the content and identify the page on the main .com website to understand the service and its context.</li>



<li>Check the regional websites and determine if that same service is offered.</li>



<li>If it is, create a translated/regionalized version using our standard template.</li>



<li>Create tasks in our workflow tracker (we use Asana) and space out due dates according to a standard formula.</li>



<li>Upload the content docs to the created task.</li>
</ul>



<p class="wp-block-paragraph">I was pleasantly surprised by how quickly Claude was able to do this. It accomplished everything I needed except uploading the document to the ticket. The translations were on par with what we were getting with Google Translate, as verified by our in-country experts.</p>



<p class="wp-block-paragraph">Strike another blow against inefficiency.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/seo-process-ai-powered-tools-480473">Turn your SEO process into AI-powered tools</a></em></strong>



<h2 id="h-5-replace-an-outdated-wordpress-plugin" class="wp-block-heading">5. Replace an outdated WordPress plugin</h2>



<p class="wp-block-paragraph">While setting up a new WordPress website for one of our businesses, I found that one of the plugins we were using hadn’t been updated in several years and had known security issues. Thankfully, other security measures in place significantly mitigated the risk, but it’s always better to fix the problem than rely on other means.</p>



<p class="wp-block-paragraph">I found a suitable substitute and thought about how long it would take my team and me to go through the 20+ websites we support and update the plugins. Even if it only took an hour per website, that’s more than 20 hours of dedicated, hands-on, fingers-to-keys work.</p>



<p class="wp-block-paragraph">Before starting the process, I asked Claude if it could do it for me. Again, I was pleasantly surprised.</p>



<p class="wp-block-paragraph">Using Claude’s Chrome extension, I logged into a staging environment for one of our smaller websites and told Claude what to do. It found all the places the plugin was being used, swapped it out with the new plugin, and did a quick visual check. There was one small discrepancy with the first attempt, which was quickly fixed when I pointed out the problem to Claude.</p>



<p class="wp-block-paragraph">“Would you like me to check the other plugins for any security issues?” it asked when completed. Of course I would. It found an additional plugin that was unused and no longer supported. That was quickly deactivated and deleted.</p>



<p class="wp-block-paragraph">My only time requirement was about five minutes per site of hands-on work as I:</p>



<ul class="wp-block-list">
<li>Pulled a copy of each production website from staging.</li>



<li>Turned Claude loose.</li>



<li>Visually checked URLs that were updated.</li>



<li>Pushed the newly updated staging version to production.</li>
</ul>



<p class="wp-block-paragraph">Everything went on in the background as I worked on other tasks, and I didn’t have to get anyone else from my team involved.</p>



<p class="wp-block-paragraph">I bet you’d be willing to trade getting the same result from five minutes of work vs. an hour any day of the week.</p>


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<h2 id="h-how-to-work-effectively-with-ai" class="wp-block-heading">How to work effectively with AI</h2>



<p class="wp-block-paragraph">AI tools can feel overwhelming, but they’re an incredibly useful asset when used properly. Follow these practical guidelines when implementing AI, and you’ll be blazing through those tedious SEO tasks in no time.</p>



<h3 id="h-be-the-strategist-and-let-ai-be-the-doer" class="wp-block-heading">Be the strategist and let AI be the doer</h3>



<p class="wp-block-paragraph">Don’t just demand a final product. Discuss the architecture, the edge cases, and the logic first, and be specific. Treat the AI like an intern by showing examples of final products when possible.</p>



<h3 id="h-use-the-proper-tool-for-the-proper-job" class="wp-block-heading">Use the proper tool for the proper job</h3>



<p class="wp-block-paragraph">Not all AIs are alike, and some are better at some tasks than others. Read, learn, and experiment. There are a lot of us out there using AI tools, and people have found some amazing results using one platform that completely failed on another.</p>



<h3 id="h-check-double-check-and-correct-when-needed" class="wp-block-heading">Check, double-check, and correct when needed</h3>



<p class="wp-block-paragraph">Much like embracing the iterative process I referred to in my last article about using AI tools, don’t be afraid to check thoroughly and correct when needed.</p>



<p class="wp-block-paragraph">The reality that AI is really still in its infancy becomes readily apparent when it fails. You may have to spoon-feed information at first to get the result you want. Remember, you don’t have to be polite — be direct and clear for faster results.</p>

After working on my hreflang XML sitemap project with Gemini, my company decided to invest in paid Claude accounts. I took the opportunity to see what else Claude could do through its desktop app and found some great uses for it. 
Here are five ways I’ve been using it in my everyday SEO work.
1. Implement a daily briefing
The first thing I set up was a daily intelligence brief. Using lessons learned from other AI tools, I crafted a detailed outline of the types of news I wanted to see, including industry updates, competitor news, mergers, and acquisitions.
The key to getting more useful information and less fluff is to be specific and use the output as a guide for improvement. Don’t accept schlock — you’re paying for the tool, so teach it to do what you need.
For instance, I have Gemini checking atmospheric conditions for radio wave propagation in my amateur radio hobby, and it took several adjustments to get useful output.
Dig deeper: How to turn Claude Code into your SEO command center



  
AI recommends.
      
See your AI visibility
    
2. Connect to data sources for faster analysis
Yes, I can dig into Google Analytics and analyze the data, but how much faster would it be if I could just ask Claude to look at the data and answer questions for me? It turns out it’s much faster.
The folks at Rank Math shared instructions on how to make a direct connection without using third-party middleware. This tip has saved me a lot of time by allowing me to ask for what I need and quickly get an answer.
3. Create a better and faster hreflang XML sitemap
I decided to see how Claude would tackle the hreflang XML sitemap project. Like I did in my Gemini example, I told it what I wanted, fed it the latest versions of the sitemaps, and asked how it would approach the problem.
Claude surprised me by asking only for links to XML sitemaps as input. Using its own resources, Claude gathered all the pages and built the sitemap for me without any further intervention.
I double-checked the output and found it to be excellent on the first go, avoiding the need for the back and forth I experienced with Gemini — a huge time saver.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
4. Generate, translate, and localize content
Our team supports three main businesses across several regions. This requires content updates to be replicated across websites, translated, and localized as appropriate. We need to track this work as it goes through the pipeline so nothing falls through the cracks.
This is where Claude has been a tremendous benefit. I took an example of content that had been generated, vetted, and approved by one of our in-house experts and decided to see if Claude could handle automating some of the international SEO process. The results were far beyond my expectations.
As usual, beginning with the end in mind, I uploaded examples of previous translation and localization work to show what I needed done. Next, I uploaded a new item and gave it specific instructions:
Look at the content and identify the page on the main .com website to understand the service and its context.
Check the regional websites and determine if that same service is offered.
If it is, create a translated/regionalized version using our standard template.
Create tasks in our workflow tracker (we use Asana) and space out due dates according to a standard formula.
Upload the content docs to the created task.
I was pleasantly surprised by how quickly Claude was able to do this. It accomplished everything I needed except uploading the document to the ticket. The translations were on par with what we were getting with Google Translate, as verified by our in-country experts.
Strike another blow against inefficiency.
Dig deeper: Turn your SEO process into AI-powered tools



5. Replace an outdated WordPress plugin
While setting up a new WordPress website for one of our businesses, I found that one of the plugins we were using hadn’t been updated in several years and had known security issues. Thankfully, other security measures in place significantly mitigated the risk, but it’s always better to fix the problem than rely on other means.
I found a suitable substitute and thought about how long it would take my team and me to go through the 20+ websites we support and update the plugins. Even if it only took an hour per website, that’s more than 20 hours of dedicated, hands-on, fingers-to-keys work.
Before starting the process, I asked Claude if it could do it for me. Again, I was pleasantly surprised.
Using Claude’s Chrome extension, I logged into a staging environment for one of our smaller websites and told Claude what to do. It found all the places the plugin was being used, swapped it out with the new plugin, and did a quick visual check. There was one small discrepancy with the first attempt, which was quickly fixed when I pointed out the problem to Claude.
“Would you like me to check the other plugins for any security issues?” it asked when completed. Of course I would. It found an additional plugin that was unused and no longer supported. That was quickly deactivated and deleted.
My only time requirement was about five minutes per site of hands-on work as I:
Pulled a copy of each production website from staging.
Turned Claude loose.
Visually checked URLs that were updated.
Pushed the newly updated staging version to production.
Everything went on in the background as I worked on other tasks, and I didn’t have to get anyone else from my team involved.
I bet you’d be willing to trade getting the same result from five minutes of work vs. an hour any day of the week.
customers won’t either.
      
See your AI visibility
    
How to work effectively with AI
AI tools can feel overwhelming, but they’re an incredibly useful asset when used properly. Follow these practical guidelines when implementing AI, and you’ll be blazing through those tedious SEO tasks in no time.
Be the strategist and let AI be the doer
Don’t just demand a final product. Discuss the architecture, the edge cases, and the logic first, and be specific. Treat the AI like an intern by showing examples of final products when possible.
Use the proper tool for the proper job
Not all AIs are alike, and some are better at some tasks than others. Read, learn, and experiment. There are a lot of us out there using AI tools, and people have found some amazing results using one platform that completely failed on another.
Check, double-check, and correct when needed
Much like embracing the iterative process I referred to in my last article about using AI tools, don’t be afraid to check thoroughly and correct when needed.
The reality that AI is really still in its infancy becomes readily apparent when it fails. You may have to spoon-feed information at first to get the result you want. Remember, you don’t have to be polite — be direct and clear for faster results.

Find out where Claude delivers the biggest time savings across SEO, from analyzing data and localizing content to maintaining websites.

Claude for SEO: 5 ways to automate repetitive work

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/AI-Overviews-and-paid-search-A-three-bucket-framework.png" class="attachment-large size-large wp-post-image" alt="AI Overviews and paid search- A three-bucket framework" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/AI-Overviews-and-paid-search-A-three-bucket-framework.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/AI-Overviews-and-paid-search-A-three-bucket-framework-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/AI-Overviews-and-paid-search-A-three-bucket-framework-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/AI-Overviews-and-paid-search-A-three-bucket-framework-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Classify queries by intent and AI visibility to decide where to bid, influence the answer, or shift budget to demand generation.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/AI-Overviews-and-paid-search-A-three-bucket-framework.png" class="attachment-large size-large wp-post-image" alt="AI Overviews and paid search- A three-bucket framework" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/AI-Overviews-and-paid-search-A-three-bucket-framework.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/AI-Overviews-and-paid-search-A-three-bucket-framework-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/AI-Overviews-and-paid-search-A-three-bucket-framework-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/AI-Overviews-and-paid-search-A-three-bucket-framework-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph"><a href="https://searchengineland.com/guide/google-ai-overviews">AI Overviews</a> reshaped the search results page, yet most paid search playbooks still run as if nothing happened.</p>



<p class="wp-block-paragraph">You still hear the same default rule: bid up where performance looks strong, bid down where it looks weak. That assumes every query is the same kind of opportunity, a small auction for the same kind of click. In B2B and industrial accounts, where sales cycles run for months, conversion data is thin, and much of the demand is informational, that assumption falls apart.</p>



<p class="wp-block-paragraph">When Google answers a question directly on the results page, the value of a paid click varies across your keywords. </p>



<ul class="wp-block-list">
<li>Some queries still lead to a real commercial decision, and winning the top spot above the answer is worth paying for. </li>



<li>Others get resolved inside the AI answer itself, so the goal shifts from buying a click to influencing the answer and building a brand strong enough to be included. </li>



<li>A third group has moved so far toward zero-click that paid search on its own can no longer rebuild the journey.</li>
</ul>



<p class="wp-block-paragraph">Those are three different problems, and a single bid lever can&#8217;t solve all three. Trying to is how accounts spend budget on the wrong surface. I separate them into three buckets: fight, influence, and generate demand.</p>



<h2 id="h-the-framework-fight-influence-and-generate-demand" class="wp-block-heading">The framework: Fight, influence, and generate demand</h2>



<p class="wp-block-paragraph">Each bucket describes a different mechanism on the results page, so each one calls for a different approach.</p>



<h3 id="h-bucket-1-fight" class="wp-block-heading">Bucket 1: Fight</h3>



<p class="wp-block-paragraph">Fight is for bottom-of-funnel queries where intent is clearly commercial, and a click still turns into direct leads and sales: product + modifier, supplier shortlists, &#8220;buy,&#8221; &#8220;quote,&#8221; &#8220;distributor,&#8221; and brand terms combined with buying intent. Here, the job hasn&#8217;t changed much. You want to win the auction that sits above the answer, and you want the ad itself to answer the buying question.</p>



<p class="wp-block-paragraph">This is where paid competition concentrates, and the data show it. The likelihood of an ad appearing <a href="https://seranking.com/blog/google-ai-mode-ads/" target="_blank" rel="noopener">rose steadily with cost per click</a>, according to a July SE Ranking study of commercial queries in Google&#8217;s U.S. AI Mode:</p>



<ul class="wp-block-list">
<li>About 54% at $10 or more.</li>



<li>Around 24% for keywords under $2.</li>



<li>32% between $2 and $10.</li>
</ul>



<p class="wp-block-paragraph"><a href="https://searchengineland.com/ai-mode-google-next-ads-engine-471967">AI Mode</a> isn&#8217;t the same surface as AI Overviews, but what they share is the pattern: paid inventory follows commercial weight. Where the market already pays more for a click, AI-era ad surfaces appear more often. You rarely compete alone, since about seven in 10 answers with an ad in that study showed two advertisers in the block.</p>



<p class="wp-block-paragraph">Fight is expensive for a reason, so treat it that way. </p>



<ul class="wp-block-list">
<li>Reserve the premium for terms where winning absolute top still beats your incremental cost per acquisition. Use bid simulators to estimate the cost of that jump in position first. </li>



<li>Watch Absolute Top impression share and impression share lost to rank, because that&#8217;s how you see the AI block pushing you down the page. </li>
</ul>



<p class="wp-block-paragraph">What you shouldn&#8217;t do is pour Fight budget into informational queries the Overview has already answered.</p>


<a href="https://www.semrush.com/analytics/traffic/paid-search?utm_campaign=ic_sel_0101ppc&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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        See exactly how <span>your competitors win</span>.
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        Uncover the keywords, ads, landing pages, and strategies driving your competitors&#8217; paid search success—and find your next opportunity to outperform them.
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      <span id="semrush-one-cta">Analyze your competitors</span>
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<h3 id="h-bucket-2-influence" class="wp-block-heading">Bucket 2: Influence</h3>



<p class="wp-block-paragraph">Influence is for queries where the Overview, or a longer conversational answer, does the educating. Very often, the user won&#8217;t click. </p>



<p class="wp-block-paragraph">The goal here isn&#8217;t to gain a cheap visit anymore. It&#8217;s to be part of how the answer gets built: cited as a source or presented as one of the brands named inside the answer.</p>



<p class="wp-block-paragraph">As Shashi Thakur put it at Google Marketing Live EMEA 2026, the best ads must be answers. In an Influence query, your ad brief starts from the question the user is really asking, rather than from a keyword list, and it means accepting that SEO and PPC address the same challenge, not competing against each other.</p>



<p class="wp-block-paragraph">In B2B and industrial accounts, the queries that trigger AI answer blocks are still mostly informational. In one of our accounts, the intent split of the terms surfacing these blocks looked like this:</p>



<figure class="wp-block-table"><table><tbody><tr><td><strong>Intent</strong></td><td><strong>Share</strong></td></tr><tr><td>Informational</td><td>83.3%</td></tr><tr><td>Learn and solve</td><td>10.0%</td></tr><tr><td>Commercial</td><td>6.7%</td></tr></tbody></table></figure>



<div aria-hidden="true" class="wp-block-spacer"></div>



<p class="wp-block-paragraph">For &#8220;learn and solve&#8221; and some &#8220;informational&#8221; terms, plan for share of the answer instead of maximum CPC.</p>



<p class="wp-block-paragraph">There is a budget consequence worth stating plainly. On influence queries, we work very closely with SEO, and we&#8217;re usually not interested in paying to appear where organic is already winning. Being visible twice, in paid and in the answer, barely happens anyway.</p>



<p class="wp-block-paragraph">When the data pushes us to move money into the Fight and Generate demand buckets, we deliberately avoid being aggressive on terms where SEO already earns the visibility. That keeps paid spend pointed at the gaps we&#8217;re interested in covering.</p>



<h3 id="h-bucket-3-generate-demand" class="wp-block-heading">Bucket 3: Generate demand</h3>



<p class="wp-block-paragraph">Generate demand is for queries where zero-click has already won and paid search alone won&#8217;t rebuild the top of the funnel. Informational demand that used to create assisted journeys now often ends on Google, with no visit to anyone. </p>



<p class="wp-block-paragraph">You have two options. You can:</p>



<ul class="wp-block-list">
<li>Keep bidding while your CPCs climb and volume falls.</li>



<li>Fund the work that happens before the search: Demand Gen, YouTube, first-party content, and the review and community presence that shows up later when the same person searches with commercial intent.</li>
</ul>



<p class="wp-block-paragraph">This work is about recognizing that some keywords have stopped being capture channels and have become a signal of a demand gap. The capture still happens later, in Fight, once the person is ready to act. </p>



<p class="wp-block-paragraph">Generate demand fills the pool that Fight then fishes from. Measured only on last-click Search ROAS, this work always looks weak because its payoff appears as brand searches and better-qualified pipeline weeks later.</p>



<figure class="wp-block-table"><table><tbody><tr><td><strong>Bucket</strong></td><td><strong>The job</strong></td><td><strong>The paid response</strong></td></tr><tr><td><strong>Fight</strong></td><td>Clear purchase intent, where the click still feeds the sales pipeline directly</td><td>Pay for absolute top when your brand isn&#8217;t already winning the answer box</td></tr><tr><td><strong>Influence</strong></td><td>Top and mid-funnel, where the answer shapes consideration</td><td>Win share of the answer through citation and SEO coordination, not raw CPC</td></tr><tr><td><strong>Generate demand</strong></td><td>Zero-click is winning and Search alone can&#8217;t rebuild the funnel</td><td>Build demand before the search, through Demand Gen, YouTube, and awareness</td></tr></tbody></table></figure>



<div aria-hidden="true" class="wp-block-spacer"></div>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/when-ai-overviews-contradict-paid-search-ads-484323">What happens when AI Overviews contradict paid search ads?</a></em></strong>



<h2 id="h-why-one-bid-rule-fails-after-ai-overviews" class="wp-block-heading">Why one bid rule fails after AI Overviews</h2>



<p class="wp-block-paragraph">AI Overviews sit at the top of the page, sharing space that used to belong to ads and organic results. The answer often satisfies the user before they scroll, changing the economics of paid search in three ways.</p>



<h3 id="h-1-click-through-falls-on-informational-and-mid-funnel-queries" class="wp-block-heading">1. Click-through falls on informational and mid-funnel queries</h3>



<p class="wp-block-paragraph">When users get a usable answer without leaving Google, both organic and paid lose clicks. The effect is stronger when your brand isn&#8217;t mentioned in the AI Overview. </p>



<p class="wp-block-paragraph">The clicks that remain tend to be better qualified, but the drop in volume usually exceeds the lift in quality, so total conversions decline.</p>



<h3 id="h-2-cost-per-click-goes-up" class="wp-block-heading">2. Cost per click goes up</h3>



<p class="wp-block-paragraph">There are fewer premium positions at the top. More advertisers are shifting their budgets to paid search to offset lost visibility. </p>



<p class="wp-block-paragraph">The pressure is strongest in B2B and services, and on comparative queries such as &#8220;best X&#8221; or &#8220;X reviews.&#8221; Pure transactional queries hold up better because users still need to click through to buy or request a quote.</p>



<h3 id="h-3-position-gets-harder-to-read" class="wp-block-heading">3. Position gets harder to read</h3>



<p class="wp-block-paragraph">There are often only a few useful slots above the AI Overview or AI Mode answer, and falling below the Overview can sharply reduce click-through, especially on mobile. Absolute Top impression share and impression share lost to rank become especially useful for understanding that pressure.</p>



<p class="wp-block-paragraph">Underneath these changes is a shift in behavior. People can research and decide within a single answer experience, shortening the window to capture and persuade them. Intent still lives in your account, but the economics attached to each type of intent have changed. That&#8217;s why you should classify a query before you touch the bid.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/paid-search-pivots-google-ai-overviews-470193">4 strategic paid search pivots to survive Google’s AI Overviews</a></em></strong>



<div class="nl-inline-form border py-2 px-1 my-2">
	
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			<p class="inline-form-text text-center mb-0">Get the newsletter search marketers rely on.</p>
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			<p class="text-center mb-0"><a class="nl-terms" href="https://searchengineland.com/termsofuse" target="_blank" aria-label="opens in a new tab">See terms.</a>
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<hr class="wp-block-separator has-text-color has-cyan-bluish-gray-color has-css-opacity has-cyan-bluish-gray-background-color has-background"/>



<h2 id="h-how-to-classify-your-terms-in-practice" class="wp-block-heading">How to classify your terms in practice</h2>



<p class="wp-block-paragraph">The framework only pays off if your account can distinguish between valuable and low-value traffic, so two foundations come first.</p>



<p class="wp-block-paragraph">One is semantic coherence: keyword, ad, and landing page as a single unit of meaning, with each ad group built around one clear concept. Mixing concepts teaches the system the wrong matches, and that gets worse the moment you hand control to broad match, Performance Max, or AI Max.</p>



<p class="wp-block-paragraph">The other is conversion quality: primary conversions that reflect real business value, lead scoring, and offline or CRM signals wherever you can pass them back. Smart Bidding and the AI surfaces amplify whatever you give them, so thin data makes every bucket noisier.</p>



<p class="wp-block-paragraph">With those in place, classification becomes a routine you can repeat:</p>



<ul class="wp-block-list">
<li>Pull your search terms and map intent, separating clear commercial intent from research and from purely informational queries.</li>



<li>Use SEO data, Search Console, AI Overview Reports, plus manual SERP checks as a proxy for where Overviews appear and whether your brand is cited.</li>



<li>Sort each term into Fight, Influence, or Generate demand, and revisit the sorting regularly because the SERP keeps changing.</li>
</ul>



<figure class="wp-block-table"><table><tbody><tr><td><strong>Step</strong></td><td><strong>Action</strong></td><td><strong>Data Sources</strong></td><td><strong>Output</strong></td></tr><tr><td><strong>1. Map intent</strong></td><td>Pull search terms and classify by intent type.</td><td>Google Ads keywords and search terms (+ Search Console).</td><td>Terms grouped by: commercial, research, informational.</td></tr><tr><td><strong>2. Identify AI presence</strong></td><td>Check where AI Overviews appear and whether your brand is cited.</td><td>AI Overview reports, manual SERP checks.</td><td>Map of AI Overview presence by term.</td></tr><tr><td><strong>3. Sort into buckets</strong></td><td>Assign each term to a strategic bucket.</td><td>Intent mapping + AI presence data.</td><td>Fight, Influence, or Generate demand list.</td></tr><tr><td><strong>4. Revisit Regularly</strong></td><td>Re-sort terms as SERP landscape changes.</td><td>Ongoing SERP monitoring, AI Overview reports.</td><td>Update bucket assignments and review budget assigned to each.</td></tr></tbody></table></figure>



<div aria-hidden="true" class="wp-block-spacer"></div>



<p class="wp-block-paragraph">On Fight terms, accept a position premium only where absolute top still wins your incremental cost per acquisition.</p>



<p class="wp-block-paragraph">On Influence terms, align ad copy with the question the Overview answers, coordinate with SEO on content and citation, and pull back paid pressure where organic already wins.</p>



<p class="wp-block-paragraph">On Generate demand terms, move budget into YouTube, Demand Gen or content when the Search clicks are gone but the query still matters and measure through brand search and assisted pipeline.</p>



<p class="wp-block-paragraph">AI Max and broad match sit on top of this classification. They don’t replace it. Google needs broad match or keywordless campaigns for your ads to appear around AI answer surfaces, which expands reach while reducing control over what you show. </p>



<p class="wp-block-paragraph">Use them where you already know the bucket and your tracking is clean, and introduce them through small, gradual tests.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/get-google-ads-ai-overviews-478737">How to get your Google Ads seen in AI Overviews</a></em></strong>


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        Every click they win is <span>a customer you lose</span>.
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        See where competitors are investing, which keywords drive their results, and how to capture more of the market.
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<h2 id="h-put-the-budget-where-the-opportunity-is" class="wp-block-heading">Put the budget where the opportunity is</h2>



<p class="wp-block-paragraph">Accounts that apply one rule to every query will keep funding the wrong mechanism, seeing it as rising costs they can&#8217;t explain. Accounts that re-segment and move money between buckets and into channels outside the auction spend less time arguing with a results page that has already answered the user. </p>



<p class="wp-block-paragraph">In B2B and industrial search especially, shifting from optimizing bids to reallocating budget across the three buckets is the difference between defending last year&#8217;s setup and building for how people search now.</p>

AI Overviews reshaped the search results page, yet most paid search playbooks still run as if nothing happened.
You still hear the same default rule: bid up where performance looks strong, bid down where it looks weak. That assumes every query is the same kind of opportunity, a small auction for the same kind of click. In B2B and industrial accounts, where sales cycles run for months, conversion data is thin, and much of the demand is informational, that assumption falls apart.
When Google answers a question directly on the results page, the value of a paid click varies across your keywords. 
Some queries still lead to a real commercial decision, and winning the top spot above the answer is worth paying for. 
Others get resolved inside the AI answer itself, so the goal shifts from buying a click to influencing the answer and building a brand strong enough to be included. 
A third group has moved so far toward zero-click that paid search on its own can no longer rebuild the journey.
Those are three different problems, and a single bid lever can’t solve all three. Trying to is how accounts spend budget on the wrong surface. I separate them into three buckets: fight, influence, and generate demand.
The framework: Fight, influence, and generate demand
Each bucket describes a different mechanism on the results page, so each one calls for a different approach.
Bucket 1: Fight
Fight is for bottom-of-funnel queries where intent is clearly commercial, and a click still turns into direct leads and sales: product + modifier, supplier shortlists, “buy,” “quote,” “distributor,” and brand terms combined with buying intent. Here, the job hasn’t changed much. You want to win the auction that sits above the answer, and you want the ad itself to answer the buying question.
This is where paid competition concentrates, and the data show it. The likelihood of an ad appearing rose steadily with cost per click, according to a July SE Ranking study of commercial queries in Google’s U.S. AI Mode:
About 54% at $10 or more.
Around 24% for keywords under $2.
32% between $2 and $10.
AI Mode isn’t the same surface as AI Overviews, but what they share is the pattern: paid inventory follows commercial weight. Where the market already pays more for a click, AI-era ad surfaces appear more often. You rarely compete alone, since about seven in 10 answers with an ad in that study showed two advertisers in the block.
Fight is expensive for a reason, so treat it that way. 
Reserve the premium for terms where winning absolute top still beats your incremental cost per acquisition. Use bid simulators to estimate the cost of that jump in position first. 
Watch Absolute Top impression share and impression share lost to rank, because that’s how you see the AI block pushing you down the page. 
What you shouldn’t do is pour Fight budget into informational queries the Overview has already answered.
your competitors win.
      
Analyze your competitors
    
Bucket 2: Influence
Influence is for queries where the Overview, or a longer conversational answer, does the educating. Very often, the user won’t click. 
The goal here isn’t to gain a cheap visit anymore. It’s to be part of how the answer gets built: cited as a source or presented as one of the brands named inside the answer.
As Shashi Thakur put it at Google Marketing Live EMEA 2026, the best ads must be answers. In an Influence query, your ad brief starts from the question the user is really asking, rather than from a keyword list, and it means accepting that SEO and PPC address the same challenge, not competing against each other.
In B2B and industrial accounts, the queries that trigger AI answer blocks are still mostly informational. In one of our accounts, the intent split of the terms surfacing these blocks looked like this:

IntentShare
Informational83.3%
Learn and solve10.0%
Commercial6.7%






For “learn and solve” and some “informational” terms, plan for share of the answer instead of maximum CPC.
There is a budget consequence worth stating plainly. On influence queries, we work very closely with SEO, and we’re usually not interested in paying to appear where organic is already winning. Being visible twice, in paid and in the answer, barely happens anyway.
When the data pushes us to move money into the Fight and Generate demand buckets, we deliberately avoid being aggressive on terms where SEO already earns the visibility. That keeps paid spend pointed at the gaps we’re interested in covering.
Bucket 3: Generate demand
Generate demand is for queries where zero-click has already won and paid search alone won’t rebuild the top of the funnel. Informational demand that used to create assisted journeys now often ends on Google, with no visit to anyone. 
You have two options. You can:
Keep bidding while your CPCs climb and volume falls.
Fund the work that happens before the search: Demand Gen, YouTube, first-party content, and the review and community presence that shows up later when the same person searches with commercial intent.
This work is about recognizing that some keywords have stopped being capture channels and have become a signal of a demand gap. The capture still happens later, in Fight, once the person is ready to act. 
Generate demand fills the pool that Fight then fishes from. Measured only on last-click Search ROAS, this work always looks weak because its payoff appears as brand searches and better-qualified pipeline weeks later.

BucketThe jobThe paid response
FightClear purchase intent, where the click still feeds the sales pipeline directlyPay for absolute top when your brand isn’t already winning the answer box
InfluenceTop and mid-funnel, where the answer shapes considerationWin share of the answer through citation and SEO coordination, not raw CPC
Generate demandZero-click is winning and Search alone can’t rebuild the funnelBuild demand before the search, through Demand Gen, YouTube, and awareness






Dig deeper: What happens when AI Overviews contradict paid search ads?



Why one bid rule fails after AI Overviews
AI Overviews sit at the top of the page, sharing space that used to belong to ads and organic results. The answer often satisfies the user before they scroll, changing the economics of paid search in three ways.
1. Click-through falls on informational and mid-funnel queries
When users get a usable answer without leaving Google, both organic and paid lose clicks. The effect is stronger when your brand isn’t mentioned in the AI Overview. 
The clicks that remain tend to be better qualified, but the drop in volume usually exceeds the lift in quality, so total conversions decline.
2. Cost per click goes up
There are fewer premium positions at the top. More advertisers are shifting their budgets to paid search to offset lost visibility. 
The pressure is strongest in B2B and services, and on comparative queries such as “best X” or “X reviews.” Pure transactional queries hold up better because users still need to click through to buy or request a quote.
3. Position gets harder to read
There are often only a few useful slots above the AI Overview or AI Mode answer, and falling below the Overview can sharply reduce click-through, especially on mobile. Absolute Top impression share and impression share lost to rank become especially useful for understanding that pressure.
Underneath these changes is a shift in behavior. People can research and decide within a single answer experience, shortening the window to capture and persuade them. Intent still lives in your account, but the economics attached to each type of intent have changed. That’s why you should classify a query before you touch the bid.
Dig deeper: 4 strategic paid search pivots to survive Google’s AI Overviews




	
	
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
How to classify your terms in practice
The framework only pays off if your account can distinguish between valuable and low-value traffic, so two foundations come first.
One is semantic coherence: keyword, ad, and landing page as a single unit of meaning, with each ad group built around one clear concept. Mixing concepts teaches the system the wrong matches, and that gets worse the moment you hand control to broad match, Performance Max, or AI Max.
The other is conversion quality: primary conversions that reflect real business value, lead scoring, and offline or CRM signals wherever you can pass them back. Smart Bidding and the AI surfaces amplify whatever you give them, so thin data makes every bucket noisier.
With those in place, classification becomes a routine you can repeat:
Pull your search terms and map intent, separating clear commercial intent from research and from purely informational queries.
Use SEO data, Search Console, AI Overview Reports, plus manual SERP checks as a proxy for where Overviews appear and whether your brand is cited.
Sort each term into Fight, Influence, or Generate demand, and revisit the sorting regularly because the SERP keeps changing.

StepActionData SourcesOutput
1. Map intentPull search terms and classify by intent type.Google Ads keywords and search terms (+ Search Console).Terms grouped by: commercial, research, informational.
2. Identify AI presenceCheck where AI Overviews appear and whether your brand is cited.AI Overview reports, manual SERP checks.Map of AI Overview presence by term.
3. Sort into bucketsAssign each term to a strategic bucket.Intent mapping + AI presence data.Fight, Influence, or Generate demand list.
4. Revisit RegularlyRe-sort terms as SERP landscape changes.Ongoing SERP monitoring, AI Overview reports.Update bucket assignments and review budget assigned to each.






On Fight terms, accept a position premium only where absolute top still wins your incremental cost per acquisition.
On Influence terms, align ad copy with the question the Overview answers, coordinate with SEO on content and citation, and pull back paid pressure where organic already wins.
On Generate demand terms, move budget into YouTube, Demand Gen or content when the Search clicks are gone but the query still matters and measure through brand search and assisted pipeline.
AI Max and broad match sit on top of this classification. They don’t replace it. Google needs broad match or keywordless campaigns for your ads to appear around AI answer surfaces, which expands reach while reducing control over what you show. 
Use them where you already know the bucket and your tracking is clean, and introduce them through small, gradual tests.
Dig deeper: How to get your Google Ads seen in AI Overviews



  
a customer you lose.
      
See who’s stealing your traffic
    
Put the budget where the opportunity is
Accounts that apply one rule to every query will keep funding the wrong mechanism, seeing it as rising costs they can’t explain. Accounts that re-segment and move money between buckets and into channels outside the auction spend less time arguing with a results page that has already answered the user. 
In B2B and industrial search especially, shifting from optimizing bids to reallocating budget across the three buckets is the difference between defending last year’s setup and building for how people search now.

Classify queries by intent and AI visibility to decide where to bid, influence the answer, or shift budget to demand generation.

AI Overviews and paid search: When to fight, influence, or generate demand

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/Optmyzr-20260915-feature.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Optmyzr-20260915-feature.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/09/Optmyzr-20260915-feature-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/09/Optmyzr-20260915-feature-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Optmyzr-20260915-feature-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Automation layering techniques can be deployed to successfully make AI-driven PPC safer.

It’s been a pretty crazy few weeks in AI safety. We’ve watched agents go off the rails and do things nobody expected them to do, in production, on other people’s systems. So it’s a reasonable moment to ask how you use AI safely when the thing it’s touching is a live ad account with real money moving through it every hour.
Almost every conversation I have about agents in ad accounts opens the same way.
Do you trust the AI?
While it’s a good question, it’s often asked to shirk responsibility and conclude that AI shouldn’t be used. A better question is: How can we trust AI?
Then the conversation turns to what we can build around the AI model to make it safe for our business to use.
To explain, it’s always helpful to frame it the way you’re already evaluating collaborators. Nobody asks whether they trust a new PPC agency in the abstract. They ask what the team has access to, what they’re allowed to change without checking first, and who reviews the work. Three different questions with three different answers, and you’d never accept “I simply trust them” as a substitute.
Same three questions for an agent:
What can it see? An agent working off a thin data layer will give you a confident answer built on a third of your account. It won’t tell you it’s guessing when it doesn’t know it’s guessing.
What is it allowed to do? Not what you told it to do. What it is structurally prevented from doing, no matter what anyone tells it.
Who signs off? Not “we review the change history afterward.” Who has to say yes before?




Most agentic PPC setups I look at have a decent answer to the first question, but there’s a lot of benefit in becoming more stringent with the answers for the other two.
We’ve been thinking about safe AI for PPC a lot at Optmyzr, so let’s take a look at what we’ve learned so you can get yourself to safer AI faster.
Each layer pays for itself on its own
The reason I’d rather write this as three techniques than as one system: you don’t have to do all three to get value. Each one closes a different failure mode, and each one is useful the day you turn it on.
Layer 1: Better grounding means fewer confidently wrong answers. That’s worth having whether you ever let an agent write anything. Most people should start here.
Layer 2: A policy layer means the changes that do get made stay inside limits you set. That holds whether the change came from an agent, from a script, or from a person having a bad Tuesday.
Layer 3: A review step means nothing reaches the account without someone seeing it first. And as a nice side benefit, you end up with a record of why. That’s worth having even if your data layer is thin and you have no policies at all.
Then these layers compound, which is the part I find genuinely satisfying. Grounding makes the agent’s proposals worth reviewing, so the review step feels like leverage instead of homework. Policies filter the obvious non-starters before a human ever sees them, so the queue stays short enough that people keep opening it. Each layer makes the next one work better than it would on its own.
So, if you’re starting from zero, start with grounding. It has the fastest payoff and the least process to stand up. If you’re already letting an agent make changes, add policies this week. If you have both, the review queue is what turns it from something you use into something your team uses.
Order matters less than accumulation. Any one of these leaves you better off than you were yesterday, and each one you add compounds with the last.
Layer 1: Ground it
A blind agent is a dangerous agent.



Ask an agent connected to a thin data layer why your CPA went up last month. It will answer you. Fluently. Immediately. Based on whatever slice of your account it could actually reach.
What it couldn’t see might have been the entire answer. It’ll make do with what it has.
Realize that grounding is a safety feature, not a convenience feature. Every gap in what an agent can see is a place where it will make something up, and it will do it in exactly the same confident tone as everything else it says. There is no tonal tell.




So here’s what I think you should expect from an MCP, or any data layer, that you’re going to let an agent reason over for PPC.
The full query layer for Google Ads. Real GAQL. Any resource, field, segment or metric the API exposes, including the ones no packaged report covers. Not a curated summary of what a product manager thought was interesting. The moment your data layer is a curated subset, you’ve limited which questions the agent can answer well, and you haven’t told anyone which ones those are.
GA4 sitting alongside the ads data. So “what happened after the click” is part of the same question, instead of a second tool and a manual join. A lot of the diagnosis questions people actually ask are cross-boundary questions. If the boundary is still there, the agent guesses across it.
Complete change history, every actor. UI edits, scripts, Optmyzr, other tools. So “who made the change that moved our ROAS in March” is a question with an answer, rather than a group chat.
Negative keywords consolidated across all four levels. Account level, shared lists, campaign, ad group, plus a deterministic check of whether a given query is already blocked and by which negative, and a list of campaigns sitting with no negative protection at all. Negatives are a common place to watch an agent reason confidently and wrongly, because the true state is scattered across four places and nobody assembles it.
Auction Insights, with a drill-down into one competitor domain, shows your own performance on every keyword you share with them. Competitive questions are the ones where a hallucination is hardest to catch, because you have no independent read on the answer.
Vertical benchmarks. Your CTR, CPC, conversion rate, and impression share as a percentile against other accounts in your industry, rather than against a blog post average from three years ago that everyone quotes and nobody sources.
Multiple ad platforms. Google, Microsoft, Meta, Amazon, LinkedIn, OpenAI, TikTok, Yahoo. Budget questions are rarely single-platform questions, even when the person asking works mostly in one.
A stored profile of each account. Business model, economic posture, bid strategy mix, structure, budget behavior, what’s already been tried and what happened when you tried it. The agent reads this before it opens its mouth.
Optmyzr’s MCP has all of the above today, and it’s a one-click install from the Claude directory rather than an API console, a developer token, or an engineer on speed dial.
Grounding raises the quality of everything the agent says. The next layer decides what it’s allowed to do about it.
Layer 2: Gate it
How do you stop an AI from blowing through a month of ad budget by mistake?



Not by asking it nicely. Not in the prompt.
You use a policy layer that controls what an AI can and cannot do, and you separate it from the AI itself, so the AI can never change the rules.
Set the rules once. Enforce them everywhere, always.
This is automation layering, which I’ve been writing about across my books for years, applied to a new first layer. The original idea is simple: one system does the work, like Google’s own bidding and budget automations, and a second system, your own automations, scripts or rule engine strategies, validates that what the first system did actually makes sense for your business before it sticks.
AI just took over the first job. It’s now the thing making the recommendation. The second layer didn’t become less necessary; it became more necessary because the first layer is more creative and sometimes unpredictable.
Account policies are how you write down the “never do this.” Rules you set once, on the account itself, about what is allowed to happen there. No bid increase above 10% in a single move. No budget change beyond a set threshold. These campaigns don’t get touched. No competitor brand terms added. Whatever your version of “absolutely not” happens to be.
And the policy doesn’t care who’s asking.




An agent proposing a 20% bid increase gets blocked. A hallucination gets blocked. An instruction hidden in a document gets blocked. A junior with a misplaced decimal gets blocked. You, at 11pm on a Friday, in a hurry, on your phone, get blocked.
Same rule, same verdict, no exemption for good intentions or seniority.
Want it through anyway? Override it deliberately. It goes on the record with your name attached. An override you can perform without noticing is not a guardrail but a speed bump made of paint.
This structural detail matters: this isn’t a setting for the AI. It’s a setting on the account, and the AI is one more thing subject to it. Same as a script. Same as a person.
What PPC account policies look like. Screenshot from Optmyzr.com, September 2026.



A rule that lives in the prompt is a rule the model can be talked out of by a clever user, by an injected instruction sitting in a document it was asked to read, or by its own drift over a long session. A rule that lives on the account holds under every path into the account.
Layer 3: Keep a human in the loop
Everyone says they’re in the loop.
Few people can tell you which screen, which person, or which queue enforces being in the loop. Ask, and you usually get “we check the change history afterward,” which means nobody’s checking.
And good luck getting a quick answer when someone asks you to produce the reasoning behind an AI-assisted decision from three months ago.
So when we built the write path into our MCP, we didn’t leave the loop to good intentions. There is no route from the agent to your ad account that doesn’t first stop at a human. 
We took a pattern from engineering, where it’s been settled practice for decades: nobody pushes code to production without a change request that another person reviews. Why the account where you spend six figures a month deserves less process than a CSS tweak is a question our industry has never really answered.
Here’s the actual sequence.





The agent proposes. Every write, whether a bid, a budget, a paused campaign, or a new negative, becomes a draft change request. Nothing reaches the ad platform yet. The agent’s job ends at “here’s what I think you should do, and why.”
Policies evaluate it. Each row is checked against your account policies and carries its verdict with it, so a blocked row shows up as blocked, with the reason attached rather than in a log somewhere.
A human opens the review. The rows, the stated reasoning for each one, the policy warnings, a timeline of what happened when, and the list of people eligible to approve it.
You preview exactly what would go live. The exact deterministic changes, like your target ROAS for “Brand Campaign” will go from 200% to 220%, rather than a natural-language paraphrase of what the agent believes it’s about to do.
You confirm. Only then does anything change in the ads account.
What change requests look like in a PPC account. Screenshot from Optmyzr.com, September 2026.



Whether it’s an AI or a colleague suggesting the change, everything goes through the same pipe. In the case of a colleague, the change request is a second pair of eyes. In the case of an AI, it’s the first pair.
An audit trail for AI in PPC
The change request queue turned out to be more valuable than just the safety it was built for.
Because it’s not just a list you go to in order to be the human in the loop. It’s a complete record of intent.
When a client asks in November why their target CPA was moved in March, you have the proposal, the rationale, the policy verdicts, who approved it, and when. Was it a human? Was it an AI? What was the data behind the recommendation? Who made the final call?
Try assembling that from a chat transcript six months later. Try assembling it from change history, which tells you what changed but never why.
We built this for safety, and it turned into the best account documentation we’ve ever had. If you’re an agency, that’s a credibility argument as much as a safety one.
What good looks like
Put the three layers together, and you get something I’d describe, approvingly, as boring.
Not underpowered. Boring. 
As in: you know what it can see, you know what it structurally cannot do, and you know nothing reaches the account without you. The excitement belongs in the findings, not in wondering what it got up to while you were at lunch.
That’s the bar I’d hold any agentic PPC setup to, ours included, and it’s a bar you climb one rung at a time:
It sees the whole account because gaps in what an agent can see are where it starts inventing, confidently.
It’s bounded by rules you wrote, which live on the account rather than in the prompt, and which apply to everyone identically.
It can’t act alone, because every write becomes a change request with policy verdicts attached, reviewers named, and a confirmation step no agent can fabricate.
You don’t have to arrive there in one move. Pick the layer that closes your biggest gap, ship it, then add the next one.
We’ve been building this against real accounts and genuinely stupid edge cases since well before MCP was a term marketers used. It’s a one-click install from the Claude directory now.
If you tried agentic PPC once, got a confidently wrong answer, and quietly shelved it, that’s the failure I’d most like you to come back and retest.
Having safe AI for our ad accounts can’t be something we expect to get just from picking the right model. It’s something we achieve by layering in processes and technologies we control.
Try Optmyzr’s safe AI for PPC free for 14 days.

Automation layering techniques can be deployed to successfully make AI-driven PPC safer.

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