<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns-.png" class="attachment-large size-large wp-post-image" alt="Click fraud in Google Ads: Where exposure rises and how to reduce it" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns-.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns--768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns--1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns--1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Advertisers and developers will soon have new tools to automate campaign management and targeting within existing DV360 workflows.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns-.png" class="attachment-large size-large wp-post-image" alt="Click fraud in Google Ads: Where exposure rises and how to reduce it" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns-.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns--768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns--1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/The-click-fraud-hierarchy-in-Google-Ads-Safest-to-riskiest-campaigns--1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Demand Gen campaigns are becoming more deeply integrated into Google&#8217;s advertising stack. Starting June 10th, Display &amp; Video 360 API users will gain the ability to manage Demand Gen resources directly through the API, bringing campaign automation and management workflows closer to parity with other DV360 inventory types.</p>



<p><strong>What&#8217;s happening</strong>. Google will begin rolling out Demand Gen resource support to Display &amp; Video 360 API partners on June 10, with full availability expected by June 24.</p>



<p>The update adds support for Demand Gen line items, ad groups and ad formats, allowing developers and advertisers to retrieve, create, update and delete Demand Gen resources through the API. Once enabled, Demand Gen resources will also appear in standard line item and ad group list responses alongside existing DV360 campaign objects.</p>



<p><strong>Between the lines</strong>. For advertisers and technology partners that rely on API integrations, the biggest immediate impact is that existing list queries may begin returning additional Demand Gen line items and ad groups. Google is advising developers to update integrations before June 10 to ensure systems can process the new resource types.</p>



<p><strong>Why we care</strong>. The update makes it easier to automate Demand Gen campaign management within existing DV360 workflows, reducing the need for separate tools or manual processes as advertisers expand into YouTube and other discovery-focused inventory.</p>



<p><strong>The bottom line</strong>. <a href="https://ads-developers.googleblog.com/2026/05/display-video-360-api-will-start.html">Demand Gen is moving from beta functionality to a standard part of the DV360 API</a>, giving advertisers and partners more flexibility to manage campaigns programmatically at scale.</p>


Demand Gen campaigns are becoming more deeply integrated into Google’s advertising stack. Starting June 10th, Display & Video 360 API users will gain the ability to manage Demand Gen resources directly through the API, bringing campaign automation and management workflows closer to parity with other DV360 inventory types.
What’s happening. Google will begin rolling out Demand Gen resource support to Display & Video 360 API partners on June 10, with full availability expected by June 24.
The update adds support for Demand Gen line items, ad groups and ad formats, allowing developers and advertisers to retrieve, create, update and delete Demand Gen resources through the API. Once enabled, Demand Gen resources will also appear in standard line item and ad group list responses alongside existing DV360 campaign objects.
Between the lines. For advertisers and technology partners that rely on API integrations, the biggest immediate impact is that existing list queries may begin returning additional Demand Gen line items and ad groups. Google is advising developers to update integrations before June 10 to ensure systems can process the new resource types.
Why we care. The update makes it easier to automate Demand Gen campaign management within existing DV360 workflows, reducing the need for separate tools or manual processes as advertisers expand into YouTube and other discovery-focused inventory.
The bottom line. Demand Gen is moving from beta functionality to a standard part of the DV360 API, giving advertisers and partners more flexibility to manage campaigns programmatically at scale.

Advertisers and developers will soon have new tools to automate campaign management and targeting within existing DV360 workflows.

DV360 API Adds Demand Gen Support

<div><img width="800" height="457" src="https://searchengineland.com/wp-content/seloads/2024/05/google-logo-leaking-data-1920-800x457.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/05/google-logo-leaking-data-1920-800x457.jpg 800w, https://searchengineland.com/wp-content/seloads/2024/05/google-logo-leaking-data-1920-592x338.jpg 592w, https://searchengineland.com/wp-content/seloads/2024/05/google-logo-leaking-data-1920-198x113.jpg 198w, https://searchengineland.com/wp-content/seloads/2024/05/google-logo-leaking-data-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2024/05/google-logo-leaking-data-1920-1536x878.jpg 1536w, https://searchengineland.com/wp-content/seloads/2024/05/google-logo-leaking-data-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Google is helping advertisers unify measurement workflows and boost audience match rates across Google's advertising ecosystem.

<div><img width="800" height="457" src="https://searchengineland.com/wp-content/seloads/2024/05/google-logo-leaking-data-1920-800x457.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/05/google-logo-leaking-data-1920-800x457.jpg 800w, https://searchengineland.com/wp-content/seloads/2024/05/google-logo-leaking-data-1920-592x338.jpg 592w, https://searchengineland.com/wp-content/seloads/2024/05/google-logo-leaking-data-1920-198x113.jpg 198w, https://searchengineland.com/wp-content/seloads/2024/05/google-logo-leaking-data-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2024/05/google-logo-leaking-data-1920-1536x878.jpg 1536w, https://searchengineland.com/wp-content/seloads/2024/05/google-logo-leaking-data-1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Google is consolidating measurement and audience activation workflows across its advertising platforms. New Data Manager API capabilities allow advertisers and partners to send offline conversion data to multiple Google Marketing Platform destinations and improve Customer Match performance with IP-based matching.</p>



<p><strong>What&#8217;s happening</strong>. The Data Manager API now supports offline conversion event uploads to Campaign Manager 360, Search Ads 360 and Display &amp; Video 360. The update expands the API&#8217;s role as a centralized data ingestion layer across Google&#8217;s advertising ecosystem.</p>



<p>Advertisers can now use a single schema to send conversion data across multiple Google products, replacing fragmented workflows that previously required separate integrations. The API also supports encrypted user identifiers, including email addresses and phone numbers, and allows events to be routed to multiple destinations in a single request.</p>



<p><strong>Between the lines:</strong> Google is encouraging advertisers that still rely on the Campaign Manager 360 API for conversion uploads to migrate to Data Manager API. The company says the newer framework simplifies implementation while providing greater flexibility for measurement and attribution use cases.</p>



<p>The update also introduces IP ingestion support for Google Ads Customer Match through a new CompositeData field. Advertisers can now upload IP addresses alongside traditional identifiers such as email addresses, phone numbers and postal addresses.</p>



<p>Beginning in Q3 2026, Google says including IP addresses with corresponding observation timestamps will help improve Customer Match rates, potentially increasing audience reach and match accuracy.</p>



<p><strong>Why we care</strong>. The changes make it easier to unify conversion measurement across Google&#8217;s advertising products while improving audience matching capabilities. For advertisers managing large-scale first-party data programs, higher match rates and a streamlined data ingestion workflow could translate into better attribution and audience activation.</p>



<p><strong>The bottom line</strong>. Google is positioning the <a href="https://ads-developers.googleblog.com/2026/05/data-manager-api-now-supports-sending.html">Data Manager API as the central hub for conversion and audience data</a>, giving advertisers a more unified way to manage measurement and Customer Match across its ad platforms.</p>






Google is consolidating measurement and audience activation workflows across its advertising platforms. New Data Manager API capabilities allow advertisers and partners to send offline conversion data to multiple Google Marketing Platform destinations and improve Customer Match performance with IP-based matching.
What’s happening. The Data Manager API now supports offline conversion event uploads to Campaign Manager 360, Search Ads 360 and Display & Video 360. The update expands the API’s role as a centralized data ingestion layer across Google’s advertising ecosystem.
Advertisers can now use a single schema to send conversion data across multiple Google products, replacing fragmented workflows that previously required separate integrations. The API also supports encrypted user identifiers, including email addresses and phone numbers, and allows events to be routed to multiple destinations in a single request.
Between the lines: Google is encouraging advertisers that still rely on the Campaign Manager 360 API for conversion uploads to migrate to Data Manager API. The company says the newer framework simplifies implementation while providing greater flexibility for measurement and attribution use cases.
The update also introduces IP ingestion support for Google Ads Customer Match through a new CompositeData field. Advertisers can now upload IP addresses alongside traditional identifiers such as email addresses, phone numbers and postal addresses.
Beginning in Q3 2026, Google says including IP addresses with corresponding observation timestamps will help improve Customer Match rates, potentially increasing audience reach and match accuracy.
Why we care. The changes make it easier to unify conversion measurement across Google’s advertising products while improving audience matching capabilities. For advertisers managing large-scale first-party data programs, higher match rates and a streamlined data ingestion workflow could translate into better attribution and audience activation.
The bottom line. Google is positioning the Data Manager API as the central hub for conversion and audience data, giving advertisers a more unified way to manage measurement and Customer Match across its ad platforms.

Google is helping advertisers unify measurement workflows and boost audience match rates across Google's advertising ecosystem.

Google expands Data Manager API with GMP event ingestion

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Commerce-media-expands-beyond-retail-sites-with-Demand-Gen-integration.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Commerce-media-expands-beyond-retail-sites-with-Demand-Gen-integration.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Commerce-media-expands-beyond-retail-sites-with-Demand-Gen-integration-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Commerce-media-expands-beyond-retail-sites-with-Demand-Gen-integration-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Commerce-media-expands-beyond-retail-sites-with-Demand-Gen-integration-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>The update now lets brands use retailer data to reach high-intent shoppers across YouTube, Discover and Gmail.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Commerce-media-expands-beyond-retail-sites-with-Demand-Gen-integration.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Commerce-media-expands-beyond-retail-sites-with-Demand-Gen-integration.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Commerce-media-expands-beyond-retail-sites-with-Demand-Gen-integration-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Commerce-media-expands-beyond-retail-sites-with-Demand-Gen-integration-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Commerce-media-expands-beyond-retail-sites-with-Demand-Gen-integration-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p>Brands can now tap retailer first-party data to run Demand Gen campaigns across YouTube, Discover and Gmail directly through Commerce Media Suite, expanding the reach of retail media beyond traditional onsite placements.</p>



<p><strong>What&#8217;s happening</strong>. Google is expanding Commerce Media Suite to support Demand Gen inventory, creating a new way for brands and retailers to collaborate using shared audience data. </p>



<p>The update allows advertisers to activate retailer audiences across Google&#8217;s visual and discovery-focused surfaces while maintaining access to the retailer insights that power retail media campaigns.</p>



<p><strong>Why we care. </strong>This update combines retailer first-party data with the scale of YouTube, Discover and Gmail, helping brands reach high-intent shoppers beyond retailer sites. It also provides better measurement by connecting ad exposure to actual sales.</p>



<p><strong>How it works</strong>. Retailers make their first-party audience data available through Commerce Media Suite, enabling brands to activate those audiences through Demand Gen campaigns across Google&#8217;s properties. </p>



<p>Google AI then optimizes campaign delivery to drive conversions and sales throughout the customer journey, while reporting capabilities connect ad exposure with purchase outcomes, providing advertisers with greater visibility into campaign performance and business impact.</p>



<p><strong>Key benefits:</strong>



<ul class="wp-block-list">
<li>Leverages retailer first-party data to reach relevant customers at scale.</li>



<li>Optimizes for conversions and sales using Google AI.</li>



<li>Simplifies campaign management through a shared data and activation framework.</li>



<li>Enhances reporting visibility by connecting digital engagement with final purchases.</li>
</ul>



<p><strong>The bottom line</strong>. <a href="https://business.google.com/us/accelerate/announcements/demand-gen-in-commerce-media-suite/">The addition of Demand Gen inventory marks the next phase of commerce media&#8217;s evolution</a>. As retail media networks look beyond owned-and-operated channels, brands are gaining new opportunities to combine retailer audience intelligence with Google&#8217;s reach across YouTube, Discover and Gmail.</p>


Brands can now tap retailer first-party data to run Demand Gen campaigns across YouTube, Discover and Gmail directly through Commerce Media Suite, expanding the reach of retail media beyond traditional onsite placements.
What’s happening. Google is expanding Commerce Media Suite to support Demand Gen inventory, creating a new way for brands and retailers to collaborate using shared audience data. 
The update allows advertisers to activate retailer audiences across Google’s visual and discovery-focused surfaces while maintaining access to the retailer insights that power retail media campaigns.
Why we care. This update combines retailer first-party data with the scale of YouTube, Discover and Gmail, helping brands reach high-intent shoppers beyond retailer sites. It also provides better measurement by connecting ad exposure to actual sales.
How it works. Retailers make their first-party audience data available through Commerce Media Suite, enabling brands to activate those audiences through Demand Gen campaigns across Google’s properties. 
Google AI then optimizes campaign delivery to drive conversions and sales throughout the customer journey, while reporting capabilities connect ad exposure with purchase outcomes, providing advertisers with greater visibility into campaign performance and business impact.
Key benefits:




Leverages retailer first-party data to reach relevant customers at scale.
Optimizes for conversions and sales using Google AI.
Simplifies campaign management through a shared data and activation framework.
Enhances reporting visibility by connecting digital engagement with final purchases.
The bottom line. The addition of Demand Gen inventory marks the next phase of commerce media’s evolution. As retail media networks look beyond owned-and-operated channels, brands are gaining new opportunities to combine retailer audience intelligence with Google’s reach across YouTube, Discover and Gmail.

The update now lets brands use retailer data to reach high-intent shoppers across YouTube, Discover and Gmail.

Commerce media expands beyond retail sites with Demand Gen integration

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2023/06/Link-intent-How-to-combine-great-content-with-strategic-outreach.png" class="attachment-large size-large wp-post-image" alt="Link intent- How to combine great content with strategic outreach" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2023/06/Link-intent-How-to-combine-great-content-with-strategic-outreach.png 1920w, https://searchengineland.com/wp-content/seloads/2023/06/Link-intent-How-to-combine-great-content-with-strategic-outreach-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2023/06/Link-intent-How-to-combine-great-content-with-strategic-outreach-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2023/06/Link-intent-How-to-combine-great-content-with-strategic-outreach-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Link intent combines authoritative content with strategic outreach to build links, referral traffic, and AI search visibility.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2023/06/Link-intent-How-to-combine-great-content-with-strategic-outreach.png" class="attachment-large size-large wp-post-image" alt="Link intent- How to combine great content with strategic outreach" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2023/06/Link-intent-How-to-combine-great-content-with-strategic-outreach.png 1920w, https://searchengineland.com/wp-content/seloads/2023/06/Link-intent-How-to-combine-great-content-with-strategic-outreach-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2023/06/Link-intent-How-to-combine-great-content-with-strategic-outreach-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2023/06/Link-intent-How-to-combine-great-content-with-strategic-outreach-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>The importance of establishing <a href="https://searchengineland.com/links-brand-signals-seo-authority-model-475968">authority</a> through link building has only increased as the surface areas of search expand into LLMs.</p>



<p>Your content is now competing with more sources, including AI results in the SERP and AI-generated content from other publishers. </p>



<p>At the same time, <a href="https://searchengineland.com/backlinks-seo-importance-442529">backlinks</a> remain important signals of authority for both Google and LLMs, which treat those placements as indicators that your brand is trustworthy and relevant.</p>



<p>If you’ve been in SEO as long as I have, you probably still get daily LinkedIn messages from “link building agencies” promising a set number of links. That approach misses the point. </p>



<p>The most effective link building strategy is creating content people genuinely want to reference and share. That’s what I call writing content with link intent.</p>



<h2 class="wp-block-heading" id="h-the-philosophy-driving-content-with-link-intent">The philosophy driving content with link intent</h2>



<p>Link building and content creation should be part of the same process, though I’ve found that&#8217;s rare. Treating link building as a separate initiative increases the likelihood that you’ll <a href="https://searchengineland.com/measure-seo-effectiveness-388013">optimize for links alone</a> without considering down-funnel effects.</p>



<p>Instead, start by thinking about who in your community cares — or should care — about what you’re writing and why.</p>



<p>Content created from this mindset, rather than a quantity-driven “must get links” mentality, has a much better chance of passively earning links and building clout in both traditional and AI search over time.</p>



<p>When your content is genuinely useful and relevant, people naturally want to share and reference it without the need for spammy emails or InMails.</p>



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<h2 class="wp-block-heading" id="h-where-strategic-outreach-fits">Where strategic outreach fits</h2>



<p>Strategic outreach works best after the relevance work is done. That means identifying the writers, journalists, and creators already covering your topic and showing them why your perspective adds something timely, useful, or differentiated from other sources they could reference.</p>



<p>In many cases, the strongest opportunities come from content tied to reference-intent topics around statistics, benchmarks, reports, or highly relevant industry developments.</p>



<p>If you’re working in content and link building silos, your teams are probably focused on:</p>



<ul class="wp-block-list">
<li>Hitting a target number of links.</li>



<li>Requesting link swaps.</li>



<li>Promoting content without considering whether it’s actually useful or relevant.</li>
</ul>



<p>In my experience, that approach often ignores whether the content genuinely benefits your brand, which runs counter to what good content should accomplish.</p>



<p>Content that provides genuine value and enhances the user experience will naturally appeal to people seeking credible sources for their own work.</p>



<p>If you can produce content strong enough to contribute meaningfully to a topic’s discourse, it will attract links, and Google, ChatGPT, Claude, et al. will recognize its relevance. It’s a much deeper and more integrated approach than chasing raw link numbers.</p>



<p>From what we know about LLMs, they favor content that credible sources treat as the definitive reference on a topic. That means depth and concentrated authority matter more than volume. </p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/guide/digital-pr-campaign-examples">Digital PR examples: 13 powerful campaigns and strategies that work</a></em></strong>



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<h2 class="wp-block-heading" id="h-the-business-significance-of-effective-link-intent">The business significance of effective link intent</h2>



<p>If LLM visibility is your goal, focus your efforts on a smaller number of high-value, deeply authoritative pieces instead of casting a wide net.</p>



<p>I can confidently say I’ve won several clients for my agency because of the content I’ve written (thanks, Search Engine Land!). Many B2B businesses can probably say the same.</p>



<p>Content strong enough to generate passive links also has a strong chance of being shared and driving referral traffic, which remains undervalued in SEO. Valuable content produced with link intent naturally builds links and SEO/AEO equity over time, creating a built-in snowball effect.</p>



<p>Beyond reducing time spent on outreach, it can create a network of related sites and publishers that continue driving referral traffic and long-term value. Think of it as an organic version of <a href="https://searchengineland.com/search-misconceptions-affiliate-marketing-debunked-392756">affiliate marketing</a>, which continues to grow as a channel.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/discoverability-in-2026-how-digital-pr-and-social-search-work-together-467559">Discoverability in 2026: How digital PR and social search work together</a></em></strong>



<h2 class="wp-block-heading" id="h-considerations-for-content-that-builds-link-intent">Considerations for content that builds link intent</h2>



<p>There are good reasons to create content on news-related topics, such as offering a perspective on a new platform release or a product in your industry. </p>



<p>Newsjacking remains a proven PR tactic that can help you earn citations in relevant outlets. But if your content resources are limited, it’s useful to weigh the pros and cons of news-focused versus evergreen topics. News-focused content may generate clusters of links in the short term, but those topics also lose relevance more quickly over time. </p>



<p>Evergreen resources can continue accumulating citations and links long after the news cycle moves on, and that durability carries weight in both SEO and AEO because LLMs aren’t primarily trained on this week’s headlines.</p>



<p>Specificity and timing can increase a piece’s citation potential even when the topic itself is evergreen. Advice targeted to hay fever sufferers during a particularly severe pollen season, for example, is more likely to attract attention and references than generic sleep advice published without context.</p>



<p><strong><em>Dig deeper: </em></strong><a href="https://searchengineland.com/produce-content-build-aeo-clout-473487"><strong><em>How to produce content that naturally builds AEO clout</em></strong></a>



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<h2 class="wp-block-heading" id="h-honing-in-on-intent-driven-link-building">Honing in on intent-driven link building</h2>



<p>Todoist is a good example of this approach in practice. Its <a href="https://todoist.com/productivity-methods">unique presentation of productivity methods</a> has generated hundreds of referring domains — a number that’s grown 50% year over year and contributed meaningfully to the brand’s growth.</p>



<p>I talk with many SEOs these days who place less emphasis on link building than they did years ago.</p>



<p>In my opinion, that has less to do with links losing importance and more to do with outdated link building tactics becoming ineffective.</p>



<p>A link-intent approach that combines strong content with strategic outreach is more effective, evergreen, and efficient than siloed content and link initiatives.</p>



<p>It also strengthens your brand’s reputation while driving incremental traffic and improving the overall user experience.</p>

The importance of establishing authority through link building has only increased as the surface areas of search expand into LLMs.
Your content is now competing with more sources, including AI results in the SERP and AI-generated content from other publishers. 
At the same time, backlinks remain important signals of authority for both Google and LLMs, which treat those placements as indicators that your brand is trustworthy and relevant.
If you’ve been in SEO as long as I have, you probably still get daily LinkedIn messages from “link building agencies” promising a set number of links. That approach misses the point. 
The most effective link building strategy is creating content people genuinely want to reference and share. That’s what I call writing content with link intent.
The philosophy driving content with link intent
Link building and content creation should be part of the same process, though I’ve found that’s rare. Treating link building as a separate initiative increases the likelihood that you’ll optimize for links alone without considering down-funnel effects.
Instead, start by thinking about who in your community cares — or should care — about what you’re writing and why.
Content created from this mindset, rather than a quantity-driven “must get links” mentality, has a much better chance of passively earning links and building clout in both traditional and AI search over time.
When your content is genuinely useful and relevant, people naturally want to share and reference it without the need for spammy emails or InMails.
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Where strategic outreach fits
Strategic outreach works best after the relevance work is done. That means identifying the writers, journalists, and creators already covering your topic and showing them why your perspective adds something timely, useful, or differentiated from other sources they could reference.
In many cases, the strongest opportunities come from content tied to reference-intent topics around statistics, benchmarks, reports, or highly relevant industry developments.
If you’re working in content and link building silos, your teams are probably focused on:
Hitting a target number of links.
Requesting link swaps.
Promoting content without considering whether it’s actually useful or relevant.
In my experience, that approach often ignores whether the content genuinely benefits your brand, which runs counter to what good content should accomplish.
Content that provides genuine value and enhances the user experience will naturally appeal to people seeking credible sources for their own work.
If you can produce content strong enough to contribute meaningfully to a topic’s discourse, it will attract links, and Google, ChatGPT, Claude, et al. will recognize its relevance. It’s a much deeper and more integrated approach than chasing raw link numbers.
From what we know about LLMs, they favor content that credible sources treat as the definitive reference on a topic. That means depth and concentrated authority matter more than volume. 
Dig deeper: Digital PR examples: 13 powerful campaigns and strategies that work




	
	
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The business significance of effective link intent
If LLM visibility is your goal, focus your efforts on a smaller number of high-value, deeply authoritative pieces instead of casting a wide net.
I can confidently say I’ve won several clients for my agency because of the content I’ve written (thanks, Search Engine Land!). Many B2B businesses can probably say the same.
Content strong enough to generate passive links also has a strong chance of being shared and driving referral traffic, which remains undervalued in SEO. Valuable content produced with link intent naturally builds links and SEO/AEO equity over time, creating a built-in snowball effect.
Beyond reducing time spent on outreach, it can create a network of related sites and publishers that continue driving referral traffic and long-term value. Think of it as an organic version of affiliate marketing, which continues to grow as a channel.
Dig deeper: Discoverability in 2026: How digital PR and social search work together



Considerations for content that builds link intent
There are good reasons to create content on news-related topics, such as offering a perspective on a new platform release or a product in your industry. 
Newsjacking remains a proven PR tactic that can help you earn citations in relevant outlets. But if your content resources are limited, it’s useful to weigh the pros and cons of news-focused versus evergreen topics. News-focused content may generate clusters of links in the short term, but those topics also lose relevance more quickly over time. 
Evergreen resources can continue accumulating citations and links long after the news cycle moves on, and that durability carries weight in both SEO and AEO because LLMs aren’t primarily trained on this week’s headlines.
Specificity and timing can increase a piece’s citation potential even when the topic itself is evergreen. Advice targeted to hay fever sufferers during a particularly severe pollen season, for example, is more likely to attract attention and references than generic sleep advice published without context.
Dig deeper: How to produce content that naturally builds AEO clout




  
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Honing in on intent-driven link building
Todoist is a good example of this approach in practice. Its unique presentation of productivity methods has generated hundreds of referring domains — a number that’s grown 50% year over year and contributed meaningfully to the brand’s growth.
I talk with many SEOs these days who place less emphasis on link building than they did years ago.
In my opinion, that has less to do with links losing importance and more to do with outdated link building tactics becoming ineffective.
A link-intent approach that combines strong content with strategic outreach is more effective, evergreen, and efficient than siloed content and link initiatives.
It also strengthens your brand’s reputation while driving incremental traffic and improving the overall user experience.

Link intent combines authoritative content with strategic outreach to build links, referral traffic, and AI search visibility.

Link intent: How to combine great content with strategic outreach

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/How-a-client-brain-gives-AI-the-context-SEO-work-needs.png" class="attachment-large size-large wp-post-image" alt="How a client brain gives AI the context SEO work needs" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-a-client-brain-gives-AI-the-context-SEO-work-needs.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/How-a-client-brain-gives-AI-the-context-SEO-work-needs-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-a-client-brain-gives-AI-the-context-SEO-work-needs-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-a-client-brain-gives-AI-the-context-SEO-work-needs-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Structured client memory helps AI-generated work stay grounded in brand guidance, campaign history, and technical limitations.

Every SEO agency has a hidden context tax. It shows up when a strategist, content lead, or analyst opens Claude and starts rebuilding all the dos and don’ts for that particular account from memory: the brand voice, the keyword cluster killed last quarter, the CMS limitation, the founder’s rejected angle, the competitor the client doesn’t want mentioned.
That’s the part of AI adoption we’re still underestimating. LLMs can help with specific SEO tasks, but the problem with unleashing AI on more complex work is providing enough account context to make it useful without creating more review work.
One solution is a per-client memory system called a “client brain.” It gives account context a place to live, allowing AI to support the work without treating every task like it’s the first day on the account.
Context is the problem
Context is essential for any worker. A senior SEO account lead onboards human teammates onto client accounts by sharing the strategy, history, politics, preferences, constraints, client language, technical limitations, and all the “don’t do that again” lessons that never quite make it into the brief.
LLMs have inherited that same agency problem. The difference is that AI hits it every time it’s asked to support the work without knowing the account.
A lot of the AI conversation in SEO right now is about connecting data sources. Load GSC, GA4, Ads, crawl data, rank tracking, and maybe CRM data into one place, so that we can finally “chat” with the data.
That’s genuinely useful, especially with live alerts. But for agencies, analysis is just one part of the job. AI also needs account context to summarize a technical audit without recommending a fix the dev team already rejected or to write a brief that sounds like the client and fits the strategy.
That kind of work depends on institutional memory: the account knowledge that builds up after months of working with a client and its stakeholders.

  
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A client brain is the solution
A client brain gives that institutional memory a shared home. The team updates it as decisions are made, feedback comes in, and the account evolves. This isn’t a replacement for human judgment. It’s infrastructure that helps that judgment travel across workflows.
In an agency world, SEO work rarely belongs to one person. The strategist sets direction, the content lead builds the brief, the writer drafts, the analyst checks performance, and the technical SEO reviews implementation.
When context stays in people’s heads, every handoff creates drift. When it’s shared, the work stays aligned. A strategist ramps faster, a writer misses fewer client preferences, and the team spends less time re-explaining the account.
What a client brain is
A client brain is a structured, per-client knowledge base that AI reads before it starts the work. Think of it as the institutional memory of an SEO account, written in a way the machine can use.
Not all client knowledge behaves the same way. Some knowledge is stable: the brand, audience, positioning, voice, product, category, and lines the client doesn’t want to cross. Some knowledge is active: decisions, experiments, objections, failed angles, technical blockers, and lessons from client feedback.
Those two types of knowledge need different homes. A client brain splits them into two layers: the soul and the memory.
The soul is static, identity-level knowledge: Who the brand is, how they speak, who they serve, what they sell, and what “good” sounds like for them
The memory is dynamic, experience-level knowledge: What the team tried, what worked, what failed, what the client rejected, and what changed during the campaign
This split keeps the brain usable. If everything goes into one big file, brand principles get buried under meeting notes, and old keyword decisions start looking like the current strategy.
The technical anatomy of a brain
A client brain doesn’t need to be a complicated system. It is built as a simple folder of plain-text Markdown files. You don’t need special software, a database, or a custom interface.
Building core logic of the soul
To get started, go into your existing client project folder and create a sub-folder named brain, then create one more folder inside that named soul. This folder path (brain/soul/) is where the core logic of the system lives. It consists of five files, each doing one specific job:
brain/soul/
├── company-profile.md
├── style-guide.md
├── audience.md
├── keyword-map.md
└── never-do.md
company-profile.md 



This is the operating version of the client, not the polished marketing version. Who is this client? What do they really sell? Who do they compete with? Where do they win? Where are they not trying to play?
Six honest sentences usually beat a six-page deck because the AI doesn’t need the full brand story. It needs enough context to avoid bad adjacent decisions.
A real example, anonymized:
“[Client] is a DTC Japanese-style kitchen knife brand selling chef knives, paring knives, and care accessories. They serve home cooks who value craftsmanship over price, with an average order value around $180. Their differentiator is free in-house sharpening for life. They compete with Made In and Misen on the tier just below Shun and Global. They don’t sell to commercial kitchens or restaurant supply, those have separate procurement cycles. Their highest-converting traffic comes from long-form reviews and YouTube cooking channels, not paid social.”
That’s enough information for AI to make better SEO choices. It knows not to chase restaurant-supply keywords, not to position the brand as the cheap alternative to Shun, and to weight content toward reviews, comparisons, and care guides.
The point isn’t to sound impressive. The point is to be true.
style-guide.md 



This file is where most teams accidentally write something useless. “Warm but professional” doesn’t help AI much. Neither does “expert but accessible.” What works is concrete instruction: one paragraph on tone, a few examples that pass, and a few that fail.
audience.md 



The audience file is where the team stops writing for demographics and starts writing for people. “Small business owners aged 35 to 55” is a targeting box, not an audience. Useful audience context captures worries, objections, misconceptions, language, and what earns trust.
keyword-map.md 



You do not need to create a 500-row export from your keyword tool. Instead, capture how the brand thinks about the category: primary terms we own, secondary terms we want, competitor-owned terms we approach carefully, and terms we don’t want to touch.
never-do.md 



This is the file I wish I’d had years ago. It’s the list of things AI should never propose, never write, and never recommend. 
Some are brand-level: “Never describe the client as an industry leader.” 
Some are operational: “Don’t suggest content that requires legal approval unless the account lead confirms it first.” 
Some are strategic: “Don’t recommend State X landing pages. The client doesn’t serve that state yet.”
Every “we already discussed this and decided no” should eventually end up here. AI is very good at confidently resurfacing dead ideas. This file stops the team from having the same conversation every month.
Memory captures decisions, patterns, and logs
Memory lives in brain/memory/. It’s organized differently from the soul because it comes from doing the work.
brain/memory/
├── decisions/    — choices made and why
├── patterns/     — things that worked or didn’t, by task type
└── log/          — chronological notes by date
The decisions/ folder stores choices made and why. A memory entry looks like this:
# 2026-04-21 — Content brief for Q2 implant campaign

Decided NOT to target "dental implants near me" as the primary keyword.
Reason: Client doesn't accept Medicaid; the highest-volume "near me" searches in our markets skew Medicaid.
Pivot to "premium implants [city]" framing.
Source: Client strategy call notes, 2026-04-21.
Tags: client:[name], task:content_brief, type:decision
The reason matters more than the decision. If AI only knows “don’t target dental implants near me,” it may avoid that keyword forever, even when the context changes. If it knows why, it can make better adjacent decisions later.
The patterns/ folder 



This stores what the team learns across repeatable work. After enough AI visibility audits, for example, our system started building a pattern file around where those audits tend to break: changing DOM selectors, fabricated review counts, Cloudflare blocking direct fetches, and tools returning partial data without making the failure obvious.
The log/ folder 



Here is where you keep the running journal: meeting summaries (AI transcripts are great here), daily notes, client comments, and small updates that don’t yet deserve to become formal decisions. Most of it won’t be read again. But when something breaks two months later, the answer is often in the log.
One warning: A brain should capture operating knowledge, not raw sensitive data. Don’t turn it into a warehouse for exports, transcripts, credentials, private client documents, or anything the team wouldn’t want surfaced in the wrong context.
Store the lesson, not the raw data.
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Building the brain step-by-step
Step 1: Pick the right starting client
Don’t start with every client. Pick the account where context loss is already costing you time.
Usually, that means a long-running client with a strong brand voice, a history of rejected ideas, and multiple people touching the work each week.
Step 2: Block 90 minutes and write the soul together
Get the account lead and strategist in the same room or on the same call. Open the five soul files and write in plain sentences. Use real examples. Don’t try to make it perfect.
The goal isn’t to create a brand book. It’s to write down the context your best account person already carries around in their head.
Step 3: Decide where the brain lives
If you’re solo, a local folder may be enough. If you have a team, you need one shared source of truth.
Technical teams can use git: track the Markdown files, not raw client data. Non-technical teams can use Google Drive, Notion, or another shared workspace. The tool matters less than the rule: one client, one brain, one place everyone trusts.
Step 4: Set ownership rules
Soul changes need friction. That’s intentional. If every passing comment gets added to the soul, the brand layer gets polluted. The account lead should own it, review changes, and decide what becomes stable client truth.
Memory should be easier to update. Anyone working on the account should be able to add a sourced entry when a client rejects an angle, a tactic fails, a blocker appears, or the team learns something that shouldn’t be lost.
Step 5: Schedule maintenance
Memory gets messy if nobody owns it. Every couple of weeks, someone should clean the brain: consolidate duplicates, remove stale notes, surface conflicts, and check whether old decisions are still true.
Then schedule a quarterly soul review and ask one question: “Is anything here no longer true?” A stale brain is worse than no brain because the AI will sound confident while working from old context.
How AI agents read the brain
Once a brain exists, the question becomes operational: Which files should the AI agent read whenit starts a brief, audit, competitor analysis, or reporting summary?
This is where the brain proves its day-to-day value. A strategist, content lead, and analyst may all touch the same client in the same week. Without shared context, the brief drifts from the strategy, the content drifts from the brief, and the audit repeats what the team already knows.
The brain keeps that work aligned without turning every task into another meeting, Slack thread, re-explanation, or rewrite. There are three ways to handle this.
Version A: Load everything
The simplest version is to have the AI read every file in the brain folder before it starts: all soul files and the full memory folder.
For a new client, that might only be a few thousand tokens. For a client active for six months, it can become 30K to 50K tokens per session. That’s a real cost, but often still cheaper than the human time lost re-explaining the account every week.
Start here if you’re testing the idea. Run the same task twice: once with the brain loaded, once without it. Use something real, like a content brief, metadata rewrite, technical summary, or internal linking recommendation. If the brain-loaded version is more accurate, more on-brand, or avoids a mistake the team would normally catch manually, you’ve got your signal.
Version B: Route by task type
The next version is selective loading. Instead of asking AI to read everything, you give it a router file that tells it which parts of the brain to load based on the task.
For example:
# claude.md

At the start of every task, ALWAYS read:
- brain/soul/company-profile.md
- brain/soul/never-do.md

IF the task involves writing copy, ALSO read:
- brain/soul/style-guide.md
- brain/soul/audience.md

IF the task involves SEO content briefs, ALSO read:
- brain/soul/keyword-map.md
- brain/memory/decisions/ latest 5 entries
- brain/memory/patterns/content_briefs.md

IF the task involves debugging a tool failure, ALSO read:
- brain/memory/patterns/tool_failures.md
AI reads the instructions, decides which rules apply, and loads only the relevant files. Token cost drops. Context gets cleaner. This is where most agencies should stop for a while.
It’s still just Markdown. No database. No new platform. No complicated setup. The discipline is in writing useful files, keeping them current, and making sure AI reads them before doing the work.
Version C: Vector retrieval
The more advanced version is vector retrieval. If you’re managing 20 or more active clients, each with deep memory, you can tag entries with metadata, embed them into a vector store, and retrieve only the most relevant items at the start of each task. 
AI can also write back to memory, but this needs guardrails. Don’t ask it to summarize every session and dump the result into the brain. That creates noise fast. Write to memory only when something specific happens: a task fails, and the team finds a workaround, a client rejects an angle, the account lead corrects the AI on something client-specific, or a decision gets made that should affect future work.
Event-triggered writes are useful. Session-end summaries usually aren’t. And every write needs a source.
Using the brain across Claude Code, Chat, and Cowork
The surface matters less than the pattern. Whether the team is using Claude Code, Claude Chat, Cowork, or another AI workflow, the rule is the same: AI should read the client’s soul before doing anything important.
In Claude Code, place the brain folder at the root of your project and add a claude.md instruction telling it to read /brain/soul/ at the start of every task. Treat never-do.md as a hard constraint, not a suggestion.
In Claude Chat, create one project per client and upload the contents of brain/soul/ into Project Knowledge. Don’t share one project across clients. That’s how one client’s tone, rules, or constraints start bleeding into another.
In Claude Cowork, use a task template that attaches the brain folder at the start. For repeatable tasks like content briefs, SERP reviews, metadata refreshes, or AI visibility audits, build the brain attachment into the workflow.
You’re not just making AI faster. You’re making the starting context consistent.
Where this breaks (and how to fix it)
Once the brain starts shaping real work, a few failure modes show up quickly. Most aren’t technical problems. They’re maintenance problems, which means they’re fixable if someone owns the review process.
Drift: AI produces work that’s almost right, but slightly off. Usually, the style guide is too abstract. The fix isn’t more adjectives. It’s better examples: pass/fail pairs, before-and-after intros, weak and strong meta descriptions, or a sentence the client rewrote with a note explaining why.
Stale soul: The client repositions, changes their offer, shifts into a new market, drops a service, or changes how they want to talk about themselves. Nobody updates the soul, so AI keeps producing work from the old reality. The fix is a quarterly soul review. Ask: “Is anything here no longer true?”
Memory rot: Some memory entries were true when written, but stop being true later. A client rejected comparison content six months ago, then decided to test it. The fix is to date entries clearly, include the reason behind each decision, and remove or update entries when the account changes.
Fabrication: This is the failure mode to take seriously. AI can write false memory, not maliciously, but because it’s trying to be helpful. When a task fails or a source is incomplete, the model may still produce a clean-looking note that sounds plausible.
We’ve seen AI fabricate ChatGPT search queries, report review counts that weren’t tied to reality, and create explanations for tool failures that sounded reasonable but weren’t supported by the output. Memory compounds. One false entry can influence future briefs, audits, recommendations, and client-facing work.
The fix is provenance. Every factual memory entry needs a source: a meeting note, client quote, tool output, strategist correction, or linked deliverable. No source, no entry.
A brain is only useful if the team trusts it. Trust doesn’t come from the folder structure. It comes from knowing where the knowledge came from.
How to get started this week
You don’t need the full system to start. Start with one client, one 90-minute session, and one before-and-after test.
Pick one client. Choose the account where re-explaining context costs the most time.
Block 90 minutes this week. Write the five soul files with the account lead and strategist. Use plain sentences, real examples, and concrete corrections. Don’t let adjectives do all the work.
Add a router file. Keep it simple at first. At the project root, add one instruction: “At the start of every task, read everything in brain/soul/.”
Run a real SEO task twice. Use a content brief, keyword cluster, meta description rewrite, SERP analysis, internal linking recommendation, or audit summary. Run it once with the soul loaded and once without it. Compare the outputs honestly.
Start writing memory from the next session. When AI recommends a ruled-out keyword angle, a client pushes back on tone, or a technical recommendation gets blocked by the CMS, capture the lesson and the reason.

  
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AI works better when account knowledge survives
Most teams don’t have an AI intelligence problem. They have a context problem. They haven’t written down what their best account people already know, or separated stable client knowledge from working history. That’s what the client brain fixes.
The agencies that get the most from AI won’t just be the ones with better prompts, models, or automations. They’ll be the ones that preserve the context behind the work: the client history, rejected angles, technical constraints, tone corrections, and small decisions that make an account make sense.
Because speed without memory creates more review, more correction, and more “we already talked about this” moments.
The real opportunity isn’t using AI to push more SEO work through the system. It’s using AI to carry forward the context that makes the work better.

Structured client memory helps AI-generated work stay grounded in brand guidance, campaign history, and technical limitations.

How a ‘client brain’ gives AI the context SEO work needs

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/How-to-use-schema-markup-to-optimize-for-the-agentic-web.png" class="attachment-large size-large wp-post-image" alt="How to use schema markup to optimize for the agentic web" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-to-use-schema-markup-to-optimize-for-the-agentic-web.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-use-schema-markup-to-optimize-for-the-agentic-web-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-use-schema-markup-to-optimize-for-the-agentic-web-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-use-schema-markup-to-optimize-for-the-agentic-web-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI agents rely on structured data to understand and act on your content. Learn how schema markup supports the emerging agentic web.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/How-to-use-schema-markup-to-optimize-for-the-agentic-web.png" class="attachment-large size-large wp-post-image" alt="How to use schema markup to optimize for the agentic web" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-to-use-schema-markup-to-optimize-for-the-agentic-web.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-use-schema-markup-to-optimize-for-the-agentic-web-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-use-schema-markup-to-optimize-for-the-agentic-web-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-use-schema-markup-to-optimize-for-the-agentic-web-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Schema markup has earned its place at the center of the <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> and <a href="https://searchengineland.com/what-is-generative-engine-optimization-geo-444418">GEO</a> conversation. Google and Bing have confirmed they use <a href="https://searchengineland.com/structured-data-seo-what-you-need-to-know-447304">structured data</a> to power <a href="https://searchengineland.com/guide/how-to-optimize-for-ai-overviews">AI Overviews</a>, and <a href="https://searchengineland.com/what-is-chatgpt-and-why-seos-should-care-392165">ChatGPT</a> factors it into product recommendations.</p>



<p>Now, schema markup is becoming part of the infrastructure behind the agentic web, where AI systems increasingly interact directly with websites on behalf of users.</p>



<p>For AI agents, understanding content isn’t enough. They also need to interpret and act on it. Schema markup helps make that possible.</p>



<h2 class="wp-block-heading" id="h-the-role-of-schema-markup-in-the-agentic-web">The role of schema markup in the agentic web</h2>



<p>In traditional search, schema helps drive visibility by making content more eligible for SERP features and helping search engines better understand entities. That information supports the index and knowledge graph, influencing how results appear to users.</p>



<p>AI agents take this further. They use schema markup not only to identify entities, but also to understand relationships, relevance, and whether content is trustworthy and actionable enough to support recommendations or complete tasks.</p>



<p>Structured data also makes websites easier and cheaper for AI systems to process. Parsing unstructured HTML is computationally expensive compared to reading clean, structured data, especially as LLMs operate within finite context windows and growing inference costs.</p>



<p>As these systems scale, sites that make their content easier to interpret become the path of least resistance for AI agents.</p>



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<h2 class="wp-block-heading" id="h-nlweb-and-the-infrastructure-of-the-agentic-web">NLWeb and the infrastructure of the agentic web</h2>



<p>Schema markup is the foundation, and <a href="https://searchengineland.com/agentic-web-nlweb-schema-seo-asset-463778">NLWeb</a> is built on top of it. Understanding this connection is essential for anyone thinking ahead.</p>



<p>NLWeb, Microsoft&#8217;s open-source initiative, enables websites to easily add AI-powered conversational interfaces. It effectively turns any website into an AI app that lets users query content using natural language. </p>



<p><a href="https://www.schemaapp.com/schema-markup/nlweb-consuming-schema-markup-for-ai-applications/"></a>Think of it as the difference between a website a human browses and a website an AI agent can interrogate directly — asking questions, retrieving structured answers, and acting on them without any human in the loop.</p>



<p>To be truly agentic, a site must move beyond being “read” to being queryable. NLWeb is designed to help AI agents interact with websites through natural-language queries and structured responses. </p>



<p>While schema tells an agent what is on the page, NLWeb enables more direct interaction with that information in real time. It’s the difference between an agent reading a static menu and an agent asking, &#8220;Do you have a table for four at 7:00 PM tonight?&#8221; and receiving a deterministic, real-time answer.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1999" height="1125" src="https://searchengineland.com/wp-content/seloads/2026/06/How-an-NLWeb-query-works.png" alt="How an NLWeb query works" class="wp-image-479085" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-an-NLWeb-query-works.png 1999w, https://searchengineland.com/wp-content/seloads/2026/06/How-an-NLWeb-query-works-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-an-NLWeb-query-works-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-an-NLWeb-query-works-1200x675.png 1200w" sizes="(max-width: 1999px) 100vw, 1999px" /></figure>
</div>


<p>NLWeb was conceived and developed by R.V. Guha, who recently joined Microsoft as CVP and technical fellow. Guha is the creator of widely used web standards, including RSS, RDF, and Schema.org.</p>



<p>The same person who built the vocabulary that defines structured data on the web is now building the protocol that lets AI agents use it. That&#8217;s a through-line, not a coincidence.</p>



<p>NLWeb leverages existing structured formats, such as Schema.org and RSS, and LLM-powered tools to create natural language interfaces usable by both humans and AI agents. </p>



<p>It<a href="https://yoast.com/structured-data-schema-ultimate-guide/"></a> isn&#8217;t asking you to rebuild your content infrastructure. It&#8217;s asking you to have your schema markup in order so it can take it from there.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1999" height="1125" src="https://searchengineland.com/wp-content/seloads/2026/06/Types-of-structured-data-used-in-NLWeb.png" alt="Types of structured data used in NLWeb" class="wp-image-479086" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Types-of-structured-data-used-in-NLWeb.png 1999w, https://searchengineland.com/wp-content/seloads/2026/06/Types-of-structured-data-used-in-NLWeb-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Types-of-structured-data-used-in-NLWeb-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Types-of-structured-data-used-in-NLWeb-1200x675.png 1200w" sizes="(max-width: 1999px) 100vw, 1999px" /></figure>
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<h2 class="wp-block-heading" id="h-5-tips-for-agentic-schema-optimization">5 tips for agentic schema optimization</h2>



<p>As a search marketer, you’ve probably been implementing schema markup for years. Here are some new considerations as you <a href="https://searchengineland.com/ai-optimization-how-to-optimize-your-content-for-ai-search-and-agents-451287">optimize for the agentic web</a>.</p>



<h3 class="wp-block-heading" id="h-1-prioritize-completeness-over-coverage">1. Prioritize completeness over coverage</h3>



<p>It&#8217;s better to have fully populated schema markup on your most important pages than thin markup spread across your entire site. AI agents prioritize properties that help them answer user queries directly.</p>



<p>For a product page, that means price, availability, ratings, and specifications, not just a product name. Incomplete schema signals uncertainty to agents, while complete schema signals reliability.</p>



<h3 class="wp-block-heading" id="h-2-automate-where-you-can">2. Automate where you can</h3>



<p>Manual schema management doesn&#8217;t scale, which is a challenge for teams without dedicated technical SEO resources. Some platforms can handle this automatically for key page types — like product pages, blog posts, events, bookings, and local business information — generating markup by default when content is created.&nbsp;</p>



<p>This baseline matters for both coverage and consistency. Stale or mismatched structured data actively works against you: If your schema says a product costs one price and your page displays another, agents will distrust both signals. Agents can also trust a signal more when it appears reliably across a site than when it appears sporadically.</p>



<h3 class="wp-block-heading" id="h-3-use-ai-to-scale-implementation">3. Use AI to scale implementation</h3>



<p>Platform automation handles the baseline — but AI can go further, analyzing your content to generate more specific and relevant markup. With AI, you can scale structured data generation, installation, and validation.</p>



<h3 class="wp-block-heading" id="h-4-use-json-ld">4. Use JSON-LD</h3>



<p>This isn&#8217;t new advice, but it&#8217;s more important than ever. JSON-LD is cleanly separated from your HTML, making it far easier for agents to parse programmatically. Google&#8217;s official guidance explicitly recommends JSON-LD for AI-optimized content.</p>



<h3 class="wp-block-heading" id="h-5-think-about-your-schema-as-a-site-level-graph">5. Think about your schema as a site-level graph</h3>



<p>Agents benefit from understanding how your content connects across your entire site: how articles relate to authors, how products relate to categories, how services relate to locations. This means you should periodically audit your structured data at scale. Take note of:</p>



<ul class="wp-block-list">
<li>Which page types have markup and which don&#8217;t.</li>



<li>Where entity definitions conflict across URLs.</li>



<li>Whether your Organization or Person markup is consistent.</li>
</ul>



<p>The goal is a coherent, connected picture of your site&#8217;s entities, one that an agent can trust regardless of which page it enters from.</p>



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<h2 class="wp-block-heading" id="h-the-window-for-early-mover-advantage">The window for early mover advantage</h2>



<p>AI systems increasingly prefer sources they have already indexed, validated, and found reliable in prior interactions. For agentic optimization, early adoption matters. Content that establishes itself as agent-friendly now builds compounding advantages as agents develop preference patterns.</p>



<p>Schema markup has always rewarded the teams that took it seriously. In the agentic web, the stakes of getting it right — and the cost of ignoring it — are substantially higher. The agents are already crawling. The question is what they find when they get to you.</p>

Schema markup has earned its place at the center of the SEO and GEO conversation. Google and Bing have confirmed they use structured data to power AI Overviews, and ChatGPT factors it into product recommendations.
Now, schema markup is becoming part of the infrastructure behind the agentic web, where AI systems increasingly interact directly with websites on behalf of users.
For AI agents, understanding content isn’t enough. They also need to interpret and act on it. Schema markup helps make that possible.
The role of schema markup in the agentic web
In traditional search, schema helps drive visibility by making content more eligible for SERP features and helping search engines better understand entities. That information supports the index and knowledge graph, influencing how results appear to users.
AI agents take this further. They use schema markup not only to identify entities, but also to understand relationships, relevance, and whether content is trustworthy and actionable enough to support recommendations or complete tasks.
Structured data also makes websites easier and cheaper for AI systems to process. Parsing unstructured HTML is computationally expensive compared to reading clean, structured data, especially as LLMs operate within finite context windows and growing inference costs.
As these systems scale, sites that make their content easier to interpret become the path of least resistance for AI agents.

  
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NLWeb and the infrastructure of the agentic web
Schema markup is the foundation, and NLWeb is built on top of it. Understanding this connection is essential for anyone thinking ahead.
NLWeb, Microsoft’s open-source initiative, enables websites to easily add AI-powered conversational interfaces. It effectively turns any website into an AI app that lets users query content using natural language. 
Think of it as the difference between a website a human browses and a website an AI agent can interrogate directly — asking questions, retrieving structured answers, and acting on them without any human in the loop.
To be truly agentic, a site must move beyond being “read” to being queryable. NLWeb is designed to help AI agents interact with websites through natural-language queries and structured responses. 
While schema tells an agent what is on the page, NLWeb enables more direct interaction with that information in real time. It’s the difference between an agent reading a static menu and an agent asking, “Do you have a table for four at 7:00 PM tonight?” and receiving a deterministic, real-time answer.




NLWeb was conceived and developed by R.V. Guha, who recently joined Microsoft as CVP and technical fellow. Guha is the creator of widely used web standards, including RSS, RDF, and Schema.org.
The same person who built the vocabulary that defines structured data on the web is now building the protocol that lets AI agents use it. That’s a through-line, not a coincidence.
NLWeb leverages existing structured formats, such as Schema.org and RSS, and LLM-powered tools to create natural language interfaces usable by both humans and AI agents. 
It isn’t asking you to rebuild your content infrastructure. It’s asking you to have your schema markup in order so it can take it from there.





	
	
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
5 tips for agentic schema optimization
As a search marketer, you’ve probably been implementing schema markup for years. Here are some new considerations as you optimize for the agentic web.
1. Prioritize completeness over coverage
It’s better to have fully populated schema markup on your most important pages than thin markup spread across your entire site. AI agents prioritize properties that help them answer user queries directly.
For a product page, that means price, availability, ratings, and specifications, not just a product name. Incomplete schema signals uncertainty to agents, while complete schema signals reliability.
2. Automate where you can
Manual schema management doesn’t scale, which is a challenge for teams without dedicated technical SEO resources. Some platforms can handle this automatically for key page types — like product pages, blog posts, events, bookings, and local business information — generating markup by default when content is created. 
This baseline matters for both coverage and consistency. Stale or mismatched structured data actively works against you: If your schema says a product costs one price and your page displays another, agents will distrust both signals. Agents can also trust a signal more when it appears reliably across a site than when it appears sporadically.
3. Use AI to scale implementation
Platform automation handles the baseline — but AI can go further, analyzing your content to generate more specific and relevant markup. With AI, you can scale structured data generation, installation, and validation.
4. Use JSON-LD
This isn’t new advice, but it’s more important than ever. JSON-LD is cleanly separated from your HTML, making it far easier for agents to parse programmatically. Google’s official guidance explicitly recommends JSON-LD for AI-optimized content.
5. Think about your schema as a site-level graph
Agents benefit from understanding how your content connects across your entire site: how articles relate to authors, how products relate to categories, how services relate to locations. This means you should periodically audit your structured data at scale. Take note of:
Which page types have markup and which don’t.
Where entity definitions conflict across URLs.
Whether your Organization or Person markup is consistent.
The goal is a coherent, connected picture of your site’s entities, one that an agent can trust regardless of which page it enters from.

  
complete picture of your search visibility.
      
Start Free Trial
    
Get started with
The window for early mover advantage
AI systems increasingly prefer sources they have already indexed, validated, and found reliable in prior interactions. For agentic optimization, early adoption matters. Content that establishes itself as agent-friendly now builds compounding advantages as agents develop preference patterns.
Schema markup has always rewarded the teams that took it seriously. In the agentic web, the stakes of getting it right — and the cost of ignoring it — are substantially higher. The agents are already crawling. The question is what they find when they get to you.

AI agents rely on structured data to understand and act on your content. Learn how schema markup supports the emerging agentic web.

How to use schema markup to optimize for the agentic web

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Optimove-20260601.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Optimove-20260601.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Optimove-20260601-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/Optimove-20260601-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Optimove-20260601-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI 1.0 saved time. AI 2.0 makes money. The marketers who win will be Positionless.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Optimove-20260601.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Optimove-20260601.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Optimove-20260601-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/Optimove-20260601-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Optimove-20260601-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div><div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Optimove-20260601.jpg" alt="" class="wp-image-478895" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Optimove-20260601.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Optimove-20260601-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/Optimove-20260601-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Optimove-20260601-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></figure>
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<p>Archilochus, the ancient Greek poet, wrote a line that has traveled through 28 centuries and now belongs to every Navy SEAL training manual and leadership keynote: <a href="https://www.goodreads.com/quotes/387614-we-don-t-rise-to-the-level-of-our-expectations-we" target="_blank" rel="noopener sponsored nofollow">We don&#8217;t rise to the level of our expectations. We fall to the level of our training.</a>



<p>That is where most marketers find themselves with AI right now.</p>



<p>The expectations are enormous. The training is thin. Every vendor has an AI feature. Every conference has an AI keynote. Every analyst has a framework. And every marketing team is being asked to deliver more growth, more personalization, and more efficiency with the same headcount.</p>



<p>The reality, according to Gartner, <a href="https://www.gartner.com/en/articles/ai-in-marketing" target="_blank" rel="noopener sponsored nofollow"><em>From Efficiency to Impact: How CMOs Can Achieve Real AI Value,</em></a> is that CMOs are now allocating an average of 15.3% of their marketing budgets to AI. Yet only 30% of marketing organizations report mature or fully developed AI readiness. The budget is there. The readiness is not.</p>



<p>This is the AI overwhelm that defines marketing in 2026. And it is the reason the most important question for marketing leaders right now is not “<em>what AI should we buy?”</em> It is “<em>are we actually capturing the value of what we have already bought?”</em>



<p>A May 2025 study commissioned by Optimove, <a href="https://www.optimove.com/resources/reports/accelerating-marketing-impact-through-ai-and-agile-workflows" target="_blank" rel="noopener nofollow">“Forrester Opportunity Snapshot AI: Accelerating Marketing Impact Through AI And Agile Workflows</a><em>,”</em> tells the same story. It found a clear gap between AI ambition and execution. Only 39% of marketers use AI for content creation, 37% for campaign workflows, and just 14% for building audience segments. It indicates that the highest-impact functions are the lowest-adoption.</p>



<h3 class="wp-block-heading" id="h-the-mckinsey-diagnosis">The McKinsey diagnosis</h3>



<p>In the newly revised book, <a href="https://www.amazon.com/Rewired-McKinseys-Playbook-Companies-Technology-ebook/dp/B0GHMMLK5J/?_encoding=UTF8&amp;pd_rd_w=uT5Ki&amp;content-id=amzn1.sym.f8e88413-4697-42ea-9bf7-b28eb886330d&amp;pf_rd_p=f8e88413-4697-42ea-9bf7-b28eb886330d&amp;pf_rd_r=134-2786774-6693613&amp;pd_rd_wg=q5UlC&amp;pd_rd_r=647342fa-7293-48dd-b34f-e9846e945e1e" target="_blank" rel="noopener sponsored nofollow">“Rewired: How Leading Companies Win with Technology and AI</a>,” the authors from McKinsey make a sharp argument that applies directly to marketing leaders. Most companies are doing AI wrong. They are chasing isolated pilots. They are confusing experimentation with transformation. They are not capturing measurable value because they have not rewired the way their organization actually operates.</p>



<p>Are you doing this right?</p>



<p>McKinsey identifies six capabilities that distinguish companies that capture value from companies that just spend money on AI:</p>



<p><strong>Transformation roadmap.</strong> Move beyond isolated pilots. Tie every digital and AI initiative to concrete financial value and strategic business goals. If you cannot draw a line from an AI tool to a P&amp;L outcome, the tool is not earning its place.</p>



<p><strong>Talent bench.</strong> Train the business leaders you have, in tech and AI. Stop outsourcing your core capabilities. The companies winning are building internal talent, not renting it.</p>



<p><strong>Operating model.</strong> Break the waterfall. Move to product and platform-based operating models where multidisciplinary teams of technologists and business operators work together as a unit, not as a relay race.</p>



<p><strong>Distributed technology environment.</strong> Decompose monolithic IT systems into modular, API-enabled architectures. The point is not the architecture itself. The point is that individual teams can innovate without waiting for a centralized bottleneck to clear.</p>



<p><strong>Data everywhere.</strong> Give hundreds of distributed teams easy access to high-quality data products, governed federally. The companies winning at AI have already solved data accessibility. The companies losing are still emailing CSVs (data files) to each other.</p>



<p><strong>User adoption and enterprise scaling.</strong> This is where most AI initiatives die. Solve the adoption hurdle by changing how employees actually work. Deliberate change management. End-to-end process transformation. Not just a training video and a Slack announcement.</p>



<p>If your marketing organization is honest, you will recognize gaps in at least three of these six. That is not a failure. That is the starting point.</p>



<h3 class="wp-block-heading" id="h-from-ai-1-0-to-ai-2-0"><strong>From AI 1.0 to AI 2.0</strong></h3>



<p>AI 1.0 was the productivity era. Tools that wrote faster, generated faster, summarized faster, executed faster. For the teams that did it well, the productivity translated into real business results. Campaigns shipped at the speed of the customer. Messages landed at the right moment.</p>



<p><strong>AI 2.0 is the business outcomes era</strong>. It builds on what AI 1.0 made possible, but it measures success differently. Not by time saved. By revenue gained, conversion lifted, retention earned, customer relationships deepened.</p>



<p>Gartner&#8217;s data is unambiguous. Only one in three CMOs are seeing the returns they expect from AI investments. Most focus on efficiency. They measure time saved and speed. High-performing CMOs take a next-step approach. They prioritize business outcomes, not just productivity. They measure conversion rates, customer satisfaction, retention, and revenue impact.</p>



<p>Organizations automating more of their marketing work are twice as likely to see ROI from AI. Yet short-term productivity gains rarely translate into meaningful business results unless you intentionally measure and optimize for impact.</p>



<p>By 2028, Gartner predicts that only 10% of CMOs who focus on time savings over business outcomes will secure the budget needed to meet strategic goals. That is the wake-up call. The CMOs still measuring AI by hours saved will lose the budget argument to the CMOs measuring AI by revenue gained.</p>



<p>The companies further along understand this. Gartner found that the most AI-ready marketing leaders allocate 21.3% of their marketing budgets to AI, compared with 15.3% for the average. Investment scales with readiness. Readiness scales with the discipline to measure outcomes.</p>



<h3 class="wp-block-heading" id="h-what-this-looks-like-in-practice">What this looks like in practice</h3>



<p>We have seen what this transition looks like for marketing teams that have done the rewiring. A leading iGaming operator is one of the most telling examples. The team cut campaign execution time from five days to five minutes by combining a unified data foundation with agentic AI for decisioning and orchestration. That was a real productivity gain. And it translated directly into business results, because the team could deliver the right message to the right customer at the right time.</p>



<p>Not to be discounted, that is AI 1.0. Real efficiency, with real customer-facing impact, the foundation for the next horizon.<br><br>AI 1.0 built the capability. AI 2.0 builds on it.</p>



<h3 class="wp-block-heading" id="h-the-positionless-future">The Positionless future</h3>



<p>The marketing teams winning in AI 2.0 are Positionless. They are not be locked into rigid roles where the data analyst hands off to the campaign manager who hands off to the creative who hands off to the optimization specialist. They are teams where any marketer can do any task, supported by AI that meets them wherever they are working.</p>



<p>That is the rewiring that matters for marketing.</p>



<p>This is why we integrated AI inside, outside, and on top of the platform. It assures marketing teams are rewired and realize the power of Positionless Marketing. AI inside the platform through Native AI. AI extending into the external tools marketers already use through MCPs. AI-powered custom applications on top of the platform for client-specific business needs.</p>



<p>Three pillars, one execution layer. The marketer chooses where to start. The platform holds the work together.</p>



<p>The marketers who will capture value from AI are not the ones with the most tools. They are the ones with the right operating model, the right data foundation, the right talent, and the right platform to make all of it work together.</p>



<p>The question is not whether your company will be rewired for AI. The question is whether you will rewire it on purpose, or wait for the market to do it for you.</p>



<p>Archilochus knew the answer twenty-eight centuries ago. We do not rise to our expectations. We fall to our training.</p>



<p>It is time to train.</p>



<hr class="wp-block-separator has-alpha-channel-opacity"/>



<p><strong>Written by:<br></strong><em>By Pini Yakuel, CEO, Optimove</em>


Archilochus, the ancient Greek poet, wrote a line that has traveled through 28 centuries and now belongs to every Navy SEAL training manual and leadership keynote: We don’t rise to the level of our expectations. We fall to the level of our training.



That is where most marketers find themselves with AI right now.
The expectations are enormous. The training is thin. Every vendor has an AI feature. Every conference has an AI keynote. Every analyst has a framework. And every marketing team is being asked to deliver more growth, more personalization, and more efficiency with the same headcount.
The reality, according to Gartner, From Efficiency to Impact: How CMOs Can Achieve Real AI Value, is that CMOs are now allocating an average of 15.3% of their marketing budgets to AI. Yet only 30% of marketing organizations report mature or fully developed AI readiness. The budget is there. The readiness is not.
This is the AI overwhelm that defines marketing in 2026. And it is the reason the most important question for marketing leaders right now is not “what AI should we buy?” It is “are we actually capturing the value of what we have already bought?”



A May 2025 study commissioned by Optimove, “Forrester Opportunity Snapshot AI: Accelerating Marketing Impact Through AI And Agile Workflows,” tells the same story. It found a clear gap between AI ambition and execution. Only 39% of marketers use AI for content creation, 37% for campaign workflows, and just 14% for building audience segments. It indicates that the highest-impact functions are the lowest-adoption.
The McKinsey diagnosis
In the newly revised book, “Rewired: How Leading Companies Win with Technology and AI,” the authors from McKinsey make a sharp argument that applies directly to marketing leaders. Most companies are doing AI wrong. They are chasing isolated pilots. They are confusing experimentation with transformation. They are not capturing measurable value because they have not rewired the way their organization actually operates.
Are you doing this right?
McKinsey identifies six capabilities that distinguish companies that capture value from companies that just spend money on AI:
Transformation roadmap. Move beyond isolated pilots. Tie every digital and AI initiative to concrete financial value and strategic business goals. If you cannot draw a line from an AI tool to a P&L outcome, the tool is not earning its place.
Talent bench. Train the business leaders you have, in tech and AI. Stop outsourcing your core capabilities. The companies winning are building internal talent, not renting it.
Operating model. Break the waterfall. Move to product and platform-based operating models where multidisciplinary teams of technologists and business operators work together as a unit, not as a relay race.
Distributed technology environment. Decompose monolithic IT systems into modular, API-enabled architectures. The point is not the architecture itself. The point is that individual teams can innovate without waiting for a centralized bottleneck to clear.
Data everywhere. Give hundreds of distributed teams easy access to high-quality data products, governed federally. The companies winning at AI have already solved data accessibility. The companies losing are still emailing CSVs (data files) to each other.
User adoption and enterprise scaling. This is where most AI initiatives die. Solve the adoption hurdle by changing how employees actually work. Deliberate change management. End-to-end process transformation. Not just a training video and a Slack announcement.
If your marketing organization is honest, you will recognize gaps in at least three of these six. That is not a failure. That is the starting point.
From AI 1.0 to AI 2.0
AI 1.0 was the productivity era. Tools that wrote faster, generated faster, summarized faster, executed faster. For the teams that did it well, the productivity translated into real business results. Campaigns shipped at the speed of the customer. Messages landed at the right moment.
AI 2.0 is the business outcomes era. It builds on what AI 1.0 made possible, but it measures success differently. Not by time saved. By revenue gained, conversion lifted, retention earned, customer relationships deepened.
Gartner’s data is unambiguous. Only one in three CMOs are seeing the returns they expect from AI investments. Most focus on efficiency. They measure time saved and speed. High-performing CMOs take a next-step approach. They prioritize business outcomes, not just productivity. They measure conversion rates, customer satisfaction, retention, and revenue impact.
Organizations automating more of their marketing work are twice as likely to see ROI from AI. Yet short-term productivity gains rarely translate into meaningful business results unless you intentionally measure and optimize for impact.
By 2028, Gartner predicts that only 10% of CMOs who focus on time savings over business outcomes will secure the budget needed to meet strategic goals. That is the wake-up call. The CMOs still measuring AI by hours saved will lose the budget argument to the CMOs measuring AI by revenue gained.
The companies further along understand this. Gartner found that the most AI-ready marketing leaders allocate 21.3% of their marketing budgets to AI, compared with 15.3% for the average. Investment scales with readiness. Readiness scales with the discipline to measure outcomes.
What this looks like in practice
We have seen what this transition looks like for marketing teams that have done the rewiring. A leading iGaming operator is one of the most telling examples. The team cut campaign execution time from five days to five minutes by combining a unified data foundation with agentic AI for decisioning and orchestration. That was a real productivity gain. And it translated directly into business results, because the team could deliver the right message to the right customer at the right time.
Not to be discounted, that is AI 1.0. Real efficiency, with real customer-facing impact, the foundation for the next horizon.
AI 1.0 built the capability. AI 2.0 builds on it.
The Positionless future
The marketing teams winning in AI 2.0 are Positionless. They are not be locked into rigid roles where the data analyst hands off to the campaign manager who hands off to the creative who hands off to the optimization specialist. They are teams where any marketer can do any task, supported by AI that meets them wherever they are working.
That is the rewiring that matters for marketing.
This is why we integrated AI inside, outside, and on top of the platform. It assures marketing teams are rewired and realize the power of Positionless Marketing. AI inside the platform through Native AI. AI extending into the external tools marketers already use through MCPs. AI-powered custom applications on top of the platform for client-specific business needs.
Three pillars, one execution layer. The marketer chooses where to start. The platform holds the work together.
The marketers who will capture value from AI are not the ones with the most tools. They are the ones with the right operating model, the right data foundation, the right talent, and the right platform to make all of it work together.
The question is not whether your company will be rewired for AI. The question is whether you will rewire it on purpose, or wait for the market to do it for you.
Archilochus knew the answer twenty-eight centuries ago. We do not rise to our expectations. We fall to our training.
It is time to train.
Written by:
By Pini Yakuel, CEO, Optimove

AI 1.0 saved time. AI 2.0 makes money. The marketers who win will be Positionless.

McKinsey frames AI 2.0; Positionless Marketing delivers it by Optimove

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-800x450.jpg" class="attachment-large size-large wp-post-image" alt="Search marketing jobs" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-550x310.jpg 550w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Land your next job in SEO or PPC. These brands and agencies are hiring to fill open search marketing positions right now.

Looking to take the next step in your search marketing career? 
Below, you will find the latest SEO, PPC, and digital marketing jobs at brands and agencies. We also include positions from previous weeks that are still open.
Newest SEO Jobs 
(Provided to Search Engine Land by SEOjobs.com) 



SEO / WordPress Analyst (LATAM)
June 1, 2026 
SEO/AEO Analyst with strong technical experience (WordPress) Fully Remote in Latam Do you get excited when your site shows up in position 1? Are you comfortable with WordPress? This is a WordPress Developer/SEO hybrid position. We are looking for a very detailed focus person who can optimize our sites for organic conversions. You’ll work with […]

Associate, SEO Strategy
May 31, 2026 
WHY DEPT®? We are a Growth Invention company built to help the world’s most ambitious brands grow faster. Operating at the intersection of technology and marketing, we create what is next by pioneering ideas, acting fast, and moving further because standing still just is not in our DNA. We are drawn to people who stay […]

Senior Lead, SEO & Answer Engine Optimization (AEO)
May 31, 2026 
Job Description Welcome to AMN Healthcare — Where Talent Meets Purpose Ever wondered what it takes to build one of the largest and most respected healthcare staffing and total talent solutions companies? It takes trailblazers, innovators, and exceptional people like you. At AMN Healthcare, we don’t just offer jobs — we build careers that make […]

Senior SEO Strategist
May 30, 2026 
Location: Remote, North America Job Type: Full-Time Reports To: SEO Director Growth Track: SEO Director About Break The Web Break The Web is a boutique SEO agency built for in-house marketing teams at ambitious eCommerce and DTC brands. We help make SEO less annoying by bringing strategic thinking, clear communication, measurable progress, and real partnership […]

Marketing Lead
May 29, 2026 
We’re seeking an entrepreneurial, execution-focused marketer to own and grow our marketing function with help from AI agents. The Mission. Blissbook helps HR teams deliver premium employee handbook and policy experiences. We have hundreds of paying, happy customers (including names you’d recognize) and a product they love. We’re bootstrapped, profitable, and growing, but our growth […]

SEO, GEO & Content Strategist
May 28, 2026 
Do you want to help transform B2B brands? Do you want to work for a company that encourages strategic thinking? Do you want your ideas not only heard but also acted upon? If so, let’s talk! You’ll fit in if you… Are insatiably curious Strive to innovate Embrace collaboration Are ego-free Like to laugh Want […]

Growth SEO Associate
May 26, 2026 
About Solace Healthcare in the U.S. is fundamentally broken. The system is so complex that 88% of U.S. adults do not have the health literacy necessary to navigate it without help. Solace cuts through the red tape of healthcare by pairing patients with expert advocates and giving them the tools to make better decisions—and get […]

SEO Content Writer – Property Restoration & Disaster Recovery
May 25, 2026 
Overview This role is ideal for a writer with experience in the property restoration and disaster recovery industry (or closely adjacent fields) who understands how local SEO, on-page optimization, and service-based content work together. The writer must be able to create authoritative, technically accurate content that reflects real-world restoration workflows, terminology, regulations, and customer concerns, […]

Director, Earned and Owned Strategy (SEO and Content)
May 24, 2026 
The Role We are looking for a Director of Earned & Owned Strategy to lead enterprise-level organic growth initiatives across a portfolio of brands, with a strong focus on local strategy and multi-location businesses. Reporting to the Senior Director, Earned & Owned Strategy, this individual contributor role will oversee integrated Earned & Owned programs spanning […]

Director, Organic Growth (SEO & GEO)
May 22, 2026 
SKIMS is a solutions-oriented brand creating the next generation of underwear, loungewear, shapewear, and essentials. We are setting new standards by delivering technically innovative, comfort-driven products with elevated design for everybody. At the intersection of culture, innovation, and commerce, SKIMS is a global brand redefining modern apparel. Our teams are built on creativity, collaboration, and […]

Newest PPC and paid media jobs
(Provided to Search Engine Land by PPCjobs.com) 







Digital Marketing Specialist II — Paid Media & Campaigns
June 1, 2026 
Brookshire Grocery is seeking a Digital Marketing Specialist to develop and implement content across various electronic platforms. The role involves collaborating with ad agencies to optimize paid digital advertisements and ensuring brand standards on all digital platforms. The ideal candidate will have a Bachelor's degree and at least two years of related experience, applying their […]

Employer Brand & Growth Marketing Lead
June 1, 2026 
Role Overview The Marketing Manager’s initial focus will be on developing and executing an employer branding strategy to support organizational growth and technical recruitment. Over time, the scope of the role could expand to include broader corporate branding, digital strategy, and long-term market positioning. Reporting to the Director of Business Development, this individual will lead […]

Global Growth Marketing Lead
June 1, 2026 
Global Marketing Manager We’re looking for a commercially minded marketing operator to help drive growth across our international automotive wholesale business. This role is ideal for someone who enjoys working across strategy and execution—building campaigns, launching new initiatives, and continuously improving performance and developing professionally. The primary objective of this role is to support the […]

Head of Product-Led Growth Marketing (Remote)
June 1, 2026 
United Cerebral Palsy of Georgia is seeking a Head of Product Led Growth Marketing to lead a team that defines and optimizes the product-led growth strategy. The role involves collaboration across departments to enhance customer conversion and maximize revenue impact. As the ideal candidate, you will have over 8 years of experience in growth marketing, […]

Growth Marketing Campaign Manager
June 1, 2026 
About Us We're TD SYNNEX (NYSE: SNX), a $60B+ global distributor and aggregator of technology solutions, helping our partners and ISVs scale with speed, precision, and trust. We believe in operational excellence, GTM innovation, and a partner-first culture that drives measurable outcomes. About The Role Location: Remote (United States) We're looking for a Growth Marketing […]

Other roles you may be interested in
Search Marketing Manager- 3598321, AMS Staffing Inc. (Remote)
Salary: $110,000 – $125,000
Execute the overall search marketing strategy aligned to business growth and performance goals.
Lead planning, execution, and optimization of complex SEM programs across Google Ads, Microsoft Ads, Search Ads 360, and related platforms.
Paid Media Manager, Clients Blackbox, Inc. (Remote)
Salary: $90,000 – $125,000
Build, launch, and optimize Meta advertising campaigns focused on lead generation and appointment booking
Manage campaign budgets, audience targeting, bid strategies, and creative testing
Senior Manager, Paid Search, Talkiatry (Hybrid, New York, NY)
Salary: $150,000 – $180,000
Own and scale Talkiatry’s paid search program end-to-end, including forecasting, budgeting, pacing, bidding strategies, account structure, and creative testing for Google, Bing, and ZocDoc.
Develop and execute a rigorous testing roadmap, including ad copy, keyword strategy, landing page variants, and automation/algorithmic controls; quantify impact using sound experimental design.
Paid Digital Marketing Manager, Pei Wei (Irving, Texas)
Salary: $75,000 – $110,000
Develop and manage paid digital marketing strategies across multiple channels, including: Paid Search, Performance Max (PMAX), Meta Ads (Facebook & Instagram)
Monitor, optimize, and scale campaigns based on performance KPIs including ROAS,CPA, conversions, traffic, impressions, engagement, and sales.
Senior Manager, SEO/AEO, ActiveCampaign (Remote)
Salary: $140,500 – $193,200
Identify opportunities for technical improvements across the ActiveCampaign website, prioritize them based on their potential business impact, and collaborate with cross-functional stakeholders to implement them.
Pioneer LLM optimization and Answer Engine Optimization (AEO) by developing content strategies that ensure ActiveCampaign is the authoritative source material used by LLMs. 
SEO Marketing Manager, Care.com (Hybrid, Dallas, TX)
Salary: $85,000 – $95,000
Organic Growth: Build and execute the SEO roadmap across technical, content, and off-page. Own the numbers: traffic, rankings, conversions. No handoffs, no excuses.
AI-Optimized Search (AIO): Define and drive CARE.com’s strategy for visibility in AI-generated results — Google AI Overviews, ChatGPT, Perplexity, and whatever comes next. Optimize entity coverage, content structure, and schema to ensure we’re the answer, not just a result.
SEO Manager, Veracity Insurance Solutions, LLC, (Remote)
Salary: $100,000 – $135,000
Lead, coach, and develop a high-performing team of SEO Specialists
Set clear expectations, quality standards, workflows, and growth paths across the team
Marketing, Social Media & PR Manager, PARTNERS Staffing (Fort Myers, FL)
Salary: $75,000 – $85,000
Develop and execute integrated marketing campaigns for shows, content releases, events, and brand initiatives
Identify target audiences and create strategies to grow reach and engagement
Senior Paid Media Manager, Brightly Media Lab (Remote)
Salary: $70,000 – $100,000
Directly build, manage, and optimize campaigns within Google Ads, Microsoft Ads, and Facebook Ads (Meta).
Serve as the lead point of contact for your book of clients, taking full ownership of their success and growth.
Paid Search Specialist, Maui Jim Sunglasses (Peoria, IL)
Salary: $65,000 – $70,000
Plan, set up, and manage paid search, display, and shopping campaigns on Google Ads.
Manage and optimize advertising budgets to achieve revenue and efficiency targets.
Note: We update this post weekly. So make sure to bookmark this page and check back.

Land your next job in SEO or PPC. These brands and agencies are hiring to fill open search marketing positions right now.

The latest jobs in search marketing

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/10/AI-Max-for-Search-Everything-you-need-to-know.jpg" class="attachment-large size-large wp-post-image" alt="AI Max for Search: Everything you need to know" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/10/AI-Max-for-Search-Everything-you-need-to-know.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/10/AI-Max-for-Search-Everything-you-need-to-know-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/10/AI-Max-for-Search-Everything-you-need-to-know-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google Ads is showing AI Max branded search controls that could help advertisers separate branded/non-branded traffic more effectively.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/10/AI-Max-for-Search-Everything-you-need-to-know.jpg" class="attachment-large size-large wp-post-image" alt="AI Max for Search: Everything you need to know" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/10/AI-Max-for-Search-Everything-you-need-to-know.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/10/AI-Max-for-Search-Everything-you-need-to-know-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/10/AI-Max-for-Search-Everything-you-need-to-know-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p id="h-advertisers-may-soon-have-more-control-over-how-ai-max-campaigns-participate-in-branded-searches-addressing-one-of-the-biggest-concerns-marketers-have-raised-since-the-campaign-type-launched">Advertisers may soon have more control over how AI Max campaigns participate in branded searches — addressing <a href="https://searchengineland.com/google-ai-max-pitch-advertisers-462128?utm_source=chatgpt.com">a main concern marketers have raised</a> since the campaign type launched.</p>



<p id="h-what-s-happening"><strong>What&#8217;s happening.</strong> Some advertisers are reporting a new &#8220;Branded Searches&#8221; control inside AI Max campaigns that lets them choose how the campaign handles brand-related queries.</p>



<p>The setting appears to offer three options:</p>



<ul class="wp-block-list">
<li>Show ads on all relevant searches (default behavior)</li>



<li>Control branded searches using brand inclusions and exclusions</li>



<li>Show ads only on unbranded searches</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" width="535" height="414" src="https://searchengineland.com/wp-content/seloads/2026/05/AI-Max-Branded-control.png" alt="" class="wp-image-479094"/></figure>



<p id="h-why-marketers-care"><strong>Why we care</strong>. One of the most common criticisms of AI Max is the potential for campaigns to capture branded traffic that advertisers are already targeting through dedicated brand campaigns.</p>



<p>That can create several challenges:</p>



<ul class="wp-block-list">
<li>Higher costs for traffic that was already likely to convert</li>



<li>Attribution complications across campaign types</li>



<li>Reduced visibility into incremental performance</li>



<li>Concerns about AI Max cannibalizing branded search activity</li>
</ul>



<p>The newly spotted &#8220;unbranded only&#8221; option could give advertisers a more direct way to keep AI Max focused on net-new demand and prospecting opportunities.</p>



<p id="h-between-the-lines"><strong>Between the lines. </strong>Until now, advertisers looking to prevent AI Max from serving on branded queries largely relied on brand exclusion lists.</p>



<p>A native campaign-level setting would represent a more straightforward approach and potentially provide greater transparency over how AI Max handles brand intent.</p>



<p id="h-the-big-picture"><strong>The big picture</strong>. Google has steadily added more controls to automated campaign types as advertisers push for greater visibility and governance over AI-driven systems.</p>



<p>If rolled out broadly, this feature would signal that Google is responding to advertiser concerns around brand traffic management while continuing to expand AI-powered campaign automation.</p>



<p id="h-what-to-watch"><strong>What to watch.</strong> It&#8217;s unclear whether this is an official launch, a limited beta, or an experiment being tested in select accounts.</p>



<p>Advertisers should monitor their AI Max campaign settings and watch for announcements from Google about expanded branded search controls.</p>



<p><strong>Bottom line</strong>. A new branded search setting spotted in AI Max could give advertisers one of their most requested controls — the ability to separate branded and non-branded traffic more cleanly. For now, however, it appears to be an early sighting rather than a confirmed global rollout.</p>



<p><strong>First spotted. </strong>This update was spotted by Paid Search specialist Thomas Eccel, who shared spotting it on <a href="https://www.linkedin.com/feed/update/urn:li:activity:7466065830204645376/">LinkedIn</a>. </p>


Advertisers may soon have more control over how AI Max campaigns participate in branded searches — addressing a main concern marketers have raised since the campaign type launched.
What’s happening. Some advertisers are reporting a new “Branded Searches” control inside AI Max campaigns that lets them choose how the campaign handles brand-related queries.
The setting appears to offer three options:
Show ads on all relevant searches (default behavior)
Control branded searches using brand inclusions and exclusions
Show ads only on unbranded searches




Why we care. One of the most common criticisms of AI Max is the potential for campaigns to capture branded traffic that advertisers are already targeting through dedicated brand campaigns.
That can create several challenges:
Higher costs for traffic that was already likely to convert
Attribution complications across campaign types
Reduced visibility into incremental performance
Concerns about AI Max cannibalizing branded search activity
The newly spotted “unbranded only” option could give advertisers a more direct way to keep AI Max focused on net-new demand and prospecting opportunities.
Between the lines. Until now, advertisers looking to prevent AI Max from serving on branded queries largely relied on brand exclusion lists.
A native campaign-level setting would represent a more straightforward approach and potentially provide greater transparency over how AI Max handles brand intent.
The big picture. Google has steadily added more controls to automated campaign types as advertisers push for greater visibility and governance over AI-driven systems.
If rolled out broadly, this feature would signal that Google is responding to advertiser concerns around brand traffic management while continuing to expand AI-powered campaign automation.
What to watch. It’s unclear whether this is an official launch, a limited beta, or an experiment being tested in select accounts.
Advertisers should monitor their AI Max campaign settings and watch for announcements from Google about expanded branded search controls.
Bottom line. A new branded search setting spotted in AI Max could give advertisers one of their most requested controls — the ability to separate branded and non-branded traffic more cleanly. For now, however, it appears to be an early sighting rather than a confirmed global rollout.
First spotted. This update was spotted by Paid Search specialist Thomas Eccel, who shared spotting it on LinkedIn.

Google Ads is showing AI Max branded search controls that could help advertisers separate branded/non-branded traffic more effectively.

Google appears to be testing new branded search controls in AI Max campaigns

<div><img width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2026/05/matt-mcgee-2020.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/matt-mcgee-2020.jpg 1280w, https://searchengineland.com/wp-content/seloads/2026/05/matt-mcgee-2020-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/matt-mcgee-2020-1200x675.jpg 1200w" sizes="(max-width: 1280px) 100vw, 1280px" /></div>Matt was also the former Editor-in-Chief at Search Engine Land, so this interview is full of great SEO topics.

<div><img width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2026/05/matt-mcgee-2020.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/matt-mcgee-2020.jpg 1280w, https://searchengineland.com/wp-content/seloads/2026/05/matt-mcgee-2020-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/matt-mcgee-2020-1200x675.jpg 1200w" sizes="(max-width: 1280px) 100vw, 1280px" /></div>
<p>Matt McGee was the former Editor-in-Chief at Search Engine Land, technically my boss at this site, for a period of time. I had the honor of interviewing Matt McGee, where we spoke about the early days of SEO, something he called the &#8220;Wild West&#8221; days of SEO, where you&#8217;d do keyword stuffing, cloaking and all sorts of what you would call today &#8220;black hat&#8221; tactics, to rank your content.</p>



<p>Times have changed but those old days were pretty fun and we spent some time reminiscing about the old days of SEO.  But we also spoke about how SEO has changed over the years, including will AI kill SEO, and much more.</p>



<p>So check out the interview below:</p>



<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/HmER5ITSd9c?feature=oembed" rel="noopener noreferrer">Watch this video on YouTube</a>
</div></figure>



<p>Here is an outline of what we spoke about:</p>



<ul class="wp-block-list">
<li>Discovering search marketing in the late 90s.</li>



<li>The era of self-taught SEO navigating the early web without established guides, leading to the discovery of Danny Sullivan’s early resources.</li>



<li>The wild west: Keyword stuffing and early tactics reflecting on the conservative vs. aggressive tactics of the late 90s, including keyword stuffing and early link networks. </li>



<li>Pre-Google search engines: A look back at ranking on Excite, AltaVista, and Northern Light before the checklist era of SEO. </li>



<li>Founding small business SEM: Starting a blog in 2004 to translate high-level SEO concepts for small business owners. </li>



<li>Meeting Danny Sullivan and joining Search Engine Land; the chance lobby encounter that led to a small business column and eventually a career as editor-in-chief. </li>



<li>The most important eras in search history: Analyzing the impact of Panda, Penguin, and the public expose of declining search results. </li>



<li>Will AI kill SEO? A candid discussion on whether AI-generated answers without citations could eventually end the industry. </li>



<li>Behind the scenes: Google PR and Street View scandals. The challenges of reporting on the Google Street View wi-fi data collection controversy and dealing with corporate PR. </li>



<li>Myths, click data, and the DOJ trials; debunking long-standing myths and reflecting on what the DOJ trials revealed about click data usage. </li>



<li>Ridiculous historical tactics. Remembering the days of debating keyword capitalization and the slow realization that the user matters. </li>



<li>Unsung heroes of search recognizing figures like Andy Hagans and Todd Malicoat who early on advocated for following the user.</li>



<li>Advice to a younger self: The importance of networking and building a community presence within the industry. </li>



<li>Proudest moments: Launching Marketing Land and MarTech, reflecting on the expansion of search coverage and the first interview with a Google Search Quality Rater. </li>



<li>The vital role of Search Engine Land: How the publication serves as the voice for the on-the-ground SEO to the world’s major search engines.</li>
</ul>



<p>To learn more about Matt McGee check out <a href="https://www.seosavvyagent.com/">seosavvyagent.com</a>.</p>


Matt McGee was the former Editor-in-Chief at Search Engine Land, technically my boss at this site, for a period of time. I had the honor of interviewing Matt McGee, where we spoke about the early days of SEO, something he called the “Wild West” days of SEO, where you’d do keyword stuffing, cloaking and all sorts of what you would call today “black hat” tactics, to rank your content.
Times have changed but those old days were pretty fun and we spent some time reminiscing about the old days of SEO.  But we also spoke about how SEO has changed over the years, including will AI kill SEO, and much more.
So check out the interview below:

Watch this video on YouTube




Here is an outline of what we spoke about:
Discovering search marketing in the late 90s.
The era of self-taught SEO navigating the early web without established guides, leading to the discovery of Danny Sullivan’s early resources.
The wild west: Keyword stuffing and early tactics reflecting on the conservative vs. aggressive tactics of the late 90s, including keyword stuffing and early link networks. 
Pre-Google search engines: A look back at ranking on Excite, AltaVista, and Northern Light before the checklist era of SEO. 
Founding small business SEM: Starting a blog in 2004 to translate high-level SEO concepts for small business owners. 
Meeting Danny Sullivan and joining Search Engine Land; the chance lobby encounter that led to a small business column and eventually a career as editor-in-chief. 
The most important eras in search history: Analyzing the impact of Panda, Penguin, and the public expose of declining search results. 
Will AI kill SEO? A candid discussion on whether AI-generated answers without citations could eventually end the industry. 
Behind the scenes: Google PR and Street View scandals. The challenges of reporting on the Google Street View wi-fi data collection controversy and dealing with corporate PR. 
Myths, click data, and the DOJ trials; debunking long-standing myths and reflecting on what the DOJ trials revealed about click data usage. 
Ridiculous historical tactics. Remembering the days of debating keyword capitalization and the slow realization that the user matters. 
Unsung heroes of search recognizing figures like Andy Hagans and Todd Malicoat who early on advocated for following the user.
Advice to a younger self: The importance of networking and building a community presence within the industry. 
Proudest moments: Launching Marketing Land and MarTech, reflecting on the expansion of search coverage and the first interview with a Google Search Quality Rater. 
The vital role of Search Engine Land: How the publication serves as the voice for the on-the-ground SEO to the world’s major search engines.
To learn more about Matt McGee check out seosavvyagent.com.

Matt was also the former Editor-in-Chief at Search Engine Land, so this interview is full of great SEO topics.

All About
Digital Marketing

Read the Latest Trends and Headlines in Digital Marketing.

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All About Digital Marketing

Read the Latest Trends and Headlines in Digital Marketing.

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

Free Consultation

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

All About
Digital Marketing