<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/Video-is-the-canonical-source-of-truth-for-AI-–-and-your-brands-best-defense-.jpg" class="attachment-large size-large wp-post-image" alt="Video is the canonical source of truth for AI – and your brand&#039;s best defense" style="margin-bottom: 15px;" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Video-is-the-canonical-source-of-truth-for-AI-–-and-your-brands-best-defense-.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/02/Video-is-the-canonical-source-of-truth-for-AI-–-and-your-brands-best-defense--768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/02/Video-is-the-canonical-source-of-truth-for-AI-–-and-your-brands-best-defense--1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>A five-minute video provides more data for an LLM than most blog posts. Here’s how to maximize your brand’s surface area for AI retrieval.

The Wild West of web scraping is changing, due in large part to OpenAI’s deal with Disney. The deal allows OpenAI to train on high-fidelity, human-verified cinematic content – intended to combat AI slop fatigue. 

document.createElement('video');
https://beimpolite.com/media/Retterspitz/6_VIDEO_800X448.mp4
This is how most of us feel when dealing with AI slop. Video production by Impolite. 



This deal opens up new opportunities to reinforce your brand’s visibility and recall. AI models are hungry for high-quality data, and this shift turns video into an essential asset for your brand. 
Here’s a breakdown of why video is the new source of truth for AI and how you can use it to protect your brand’s identity.
How AI brand drift happens
When a large language model’s training set lacks data on a specific brand, the LLM doesn’t admit that it doesn’t know. Instead, it interpolates, filling the gaps in your brand’s story. It makes guesses about your brand identity based on patterns from similar brands or general industry information. 
This interpolation can lead to brand drift. Here’s what it looks like when an AI model narrates an inaccurate version of your business.
Say you represent a SaaS company. A user asks ChatGPT about one of your product’s features. But the model doesn’t have information about that specific feature.
So, the model constructs elaborate setup instructions, pricing tiers, and integration requirements for the phantom feature. 
This has surfaced for companies like Streamer.bot, where users regularly arrive with confidently wrong instructions generated by ChatGPT – forcing teams to correct misinformation that the product never published. 
A Streamer.bot team member describing how AI-generated setup instructions regularly misrepresent product behavior, creating confusion and additional support burden.
AI brand drift happens to local businesses, too. As one restaurant owner told Futurism, Google AI Overviews repeatedly shared false information about both specials and menu items.
To correct brand drift and prevent AI from distorting your brand message, your company must provide a canonical source of truth.

  
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Video as a source of truth
By producing authoritative videos (e.g., a demo that explicitly clarifies pricing), you provide strong semantic information through the transcript and visual proof. The video becomes the canonical source of truth that makes things clear, overriding opinions from Reddit and other sources.
In contrast, a text file contains low entropy. A statement like “50% off” is identical whether it was written in 2015 or 2025. Text often lacks the timestamp of reality, making it easy for AI to manipulate or lose the context of the real world.
To fix this, you need a medium with more data packed into every second. A five-minute video at 60 frames per second contains 18,000 frames of visual evidence, a nuanced audio track, and a text transcript.
Video enables LLMs to capture non-verbal, high-fidelity cues, creating a validation layer that preserves the visual evidence often flattened or lost in written content.
Creative studios like Berlin-based Impolite specialize in high-production-value video that provides the chaotic, non-repetitive entropy that AI needs to verify. The studio’s work for global brands serves as the high-density data source that prevents brand drift.
For example, Karman’s “The Space That Makes Us Human” project is a masterclass in creating a canonical source of truth, using high-fidelity, expert-led video to anchor brand identity.






Dig deeper: How to optimize video for AI-powered search
Authenticity as a signal
As deepfakes proliferate, authenticity is shifting from a vague moral concept to a hard technical signal. Search engines and AI agents need a way to verify the provenance.
Is this video real? Is it from the brand it claims to be?
For AI models, real-world human footage is the ultimate high-trust data source. It provides physical evidence, such as a person speaking, a product in motion, or a specific location. In contrast, AI-generated video often lacks the chaotic, non-repetitive entropy of real-world light and physics. 
The Coalition for Content Provenance and Authenticity (C2PA) is developing a new provenance standard to verify authenticity. The organization, which includes members such as Google, Adobe, Microsoft, and OpenAI, provides the technical specifications that enable this data to be cryptographically verifiable.
At the same time, the Content Authenticity Initiative (CAI), spearheaded by Adobe, drives the adoption of open-source tools for digital transparency. 
Together, the two organizations go beyond simple watermarking. They allow brands to sign videos the moment they begin recording, providing a signal that AI models can prioritize over unverified noise.
How media verification works: From lens to screen






Ever notice that tiny “CR” mark in the corner of certain media on LinkedIn? This label stands for content credentials. It appears on images and videos to indicate their origin and whether the creator used AI to produce or edit them. 
When you click or hover over the “CR” icon on a LinkedIn post, a sidebar or pop-up appears that shows:
The creator: The name of the person or organization that produced the media
The tools used: Which software (e.g., Adobe Photoshop) the creator used to edit or generate the media
AI disclosure: A specific note if the content was generated with AI
The process: A history of edits made to the file to ensure the image hasn’t been deceptively altered
Some creators are already looking to circumvent the icon. Some have shared tips to hide the tag.
While some call it LinkedIn shaming, its presence signals authority. It’s also gaining traction. 
Google has begun integrating C2PA signals into search and ads to help enforce policies regarding misrepresentation and AI disclosure. The search giant has also updated its documentation to explain how C2PA metadata is handled in Google Images.
Dig deeper: The SEO shift you can’t ignore: Video is becoming source material
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How verified media maintains its integrity
For content marketers, adopting C2PA is a defensive moat against misinformation and a proactive signal of quality.
If a bad actor deepfakes your CEO, the absence of your corporate cryptographic signature acts as a silent alarm. Platforms and AI agents will immediately detect that the content lacks a verified origin seal and de-prioritize it in favor of authenticated assets.
Here’s how it works in practice.
1. Capture: The hardware root of trust
Select Sony cameras use the brand’s camera authenticity solution to embed digital signatures in real time. The signature uses keys held in a secure hardware chipset. Sony uses 3D depth data alongside the C2PA manifest rather than a 2D screen or a projection to verify that a real 3D subject was filmed.
Similarly, select Qualcomm’s products support a cryptographic seal that proves the photo’s authenticity. In addition, apps like Truepic and ProofMode can sign footage on standard devices.
2. Edit: The editorial ledger
C2PA-aware software, such as Adobe Premiere Pro, integrates content credentials. This allows brands to embed a manifest listing the creator, edits, and software.
Think of it as a content ledger. Content credentials act as a digital paper trail, logging every hand that touches the file:
When an editor exports a video, the software preserves the original camera signature and appends a manifest of every cut and color grade.
If generative AI tools are used, relevant frames are tagged as AI-generated, preserving the integrity of the remaining human-verified footage.
3. Verify: Tamper-proof evidence in action
If the content is altered outside of a C2PA-compliant tool, the cryptographic link is severed. 
When an AI model performs an evidence-weighting calculation to decide which information to show a user, it will see this broken signature.
Dig deeper: How to dominate video-driven SERPs
The expert content workflow
Information overload is constant nowadays. Traditional gatekeepers are struggling because AI generates content faster than humans can verify it. Authenticity becomes scarce online as Audiences increasingly seek out authenticity and strive to distinguish signal from noise.
From LLMs to search engines like Google, AI systems struggle with the same challenge. Verified subject matter experts (SMEs) are emerging as critical differentiators and as guarantors of credibility and pertinence.
An SME is a human anchor point of credibility for both humans and machines. When brands pair expertise with verifiable video documentation, they create something AI can’t replicate: authentic authority that audiences can see, hear, and trust.
Why expert video should be the source material 

A video transcript of an expert explaining a complex topic often captures colloquial, nuanced details that polished, static blog posts miss. Here’s how to use expert-led videos as the starting point of your content flywheel: 
Text stream: Extract the transcript to create authoritative, long-form blogs, FAQs, and social captions. This provides the semantic foundation for text-based retrieval.
Visual stream: Pull high-quality frames for infographics and thumbnails. This provides visual proof that anchors the text.
Audio stream: Repurpose the audio for podcast distribution, capturing your expert’s tonal authority.
Discovery stream: Cut vertical TikTok and YouTube clips. These act as entry points that lead AI agents back to your canonical source.
By repurposing a single high-density video asset across these formats, you create a self-reinforcing loop of authority. 
This increases the probability that an AI model will encounter and index your brand’s expertise in the format that the model prefers. For example, Gemini might index the video, while Perplexity might index the transcript.
It doesn’t have to be fancy, as this clip from Search with Sean shows:







  
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What to look out for
Before you hit record, identify where your brand is most vulnerable to AI drift. To maximize the surface area for AI retrieval, proceed this way: 
Identify the gap: Where is AI hallucinating elements of your story? Find the topics where your brand voice is missing or being misrepresented by outdated Reddit posts or competitor noise.
Anchor with verified experts: Use real people with verifiable credentials. AI agents now cross-reference experts against LinkedIn data and professional knowledge graphs to weigh the authority of the content.
Preserve the nuance: Marketing and legal departments often strip it from blog posts, making them generic. Video preserves the colloquial, detailed explanations that signal true expertise. 
Here’s a concrete example recorded with Semrush’s Brand Control Quadrant framework:






Dig deeper: The future of SEO content is video – here’s why
Context still beats compliance
With infinite, low-cost AI slop cropping up, it’s going to get harder and harder to fight deepfakes. But it’s harder for an AI to hallucinate a real physical event than a sentence.
The most valuable asset a brand owns is its verifiable expertise. By anchoring your brand in expert-led, multimodal video, you ensure that your identity remains consistent, protected, and prioritized.
A clear hierarchy of data is emerging: high-fidelity, cryptographically signed video is the premium currency. For every other brand, the mandate is simple: Record reality. If you don’t provide a signed, high-density video record of your business, the AI will hallucinate one for you.

A five-minute video provides more data for an LLM than most blog posts. Here’s how to maximize your brand’s surface area for AI retrieval.

Why video is the canonical source of truth for AI and your brand’s best defense

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2024/07/What-is-generative-engine-optimization-GEO-800x450.png" class="attachment-large size-large wp-post-image" alt="What is generative engine optimization (GEO)?" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/07/What-is-generative-engine-optimization-GEO-800x450.png 800w, https://searchengineland.com/wp-content/seloads/2024/07/What-is-generative-engine-optimization-GEO-600x338.png 600w, https://searchengineland.com/wp-content/seloads/2024/07/What-is-generative-engine-optimization-GEO-200x113.png 200w, https://searchengineland.com/wp-content/seloads/2024/07/What-is-generative-engine-optimization-GEO-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2024/07/What-is-generative-engine-optimization-GEO-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2024/07/What-is-generative-engine-optimization-GEO.png 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Understand what GEO is, how it's revolutionizing digital marketing and key strategies to optimize for AI-driven search.

Understand what GEO is, how it's revolutionizing digital marketing and key strategies to optimize for AI-driven search.

Generative Engine Optimization: The Patterns Behind AI Visibility

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/11/ai-content-crawlers-1920.png" class="attachment-large size-large wp-post-image" alt="AI content crawlers" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/11/ai-content-crawlers-1920.png 1920w, https://searchengineland.com/wp-content/seloads/2025/11/ai-content-crawlers-1920-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/11/ai-content-crawlers-1920-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google's WebMCP protocol lets AI agents execute structured actions on websites via browser APIs. Is this the next frontier of technical SEO?

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/11/ai-content-crawlers-1920.png" class="attachment-large size-large wp-post-image" alt="AI content crawlers" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/11/ai-content-crawlers-1920.png 1920w, https://searchengineland.com/wp-content/seloads/2025/11/ai-content-crawlers-1920-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/11/ai-content-crawlers-1920-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google today announced an early preview of WebMCP, a new protocol that defines how AI agents interact with websites. </p>



<ul class="wp-block-list">
<li>&#8220;WebMCP aims to provide a standard way for exposing structured tools, ensuring AI agents can perform actions on your side with increased speed, reliability, and precision,&#8221; <a href="https://developer.chrome.com/blog/webmcp-epp" target="_blank" rel="noopener">wrote</a> André Cipriani Bandarra from Google.</li>
</ul>



<p>WebMCP lets developers tell large language models exactly what each button or link on a website does. WebMCP allows websites to explicitly publish a clear “Tool Contract” that defines available actions.</p>



<p>It runs on a new browser API, <code>navigator.modelContext</code>. Through that API, the website shares a structured list of tools — such as <code>buyTicket(destination, date)</code>. The AI can then call those functions directly, making interactions faster, more accurate, and far more reliable.</p>



<p><strong>Structured interactions for the agentic web</strong>. WebMCP introduces two new APIs that let browser agents act on a user’s behalf:</p>



<ul class="wp-block-list">
<li><strong>Declarative API:</strong> Handles standard actions defined directly in HTML forms.</li>



<li><strong>Imperative API:</strong> Supports complex, dynamic interactions that require JavaScript execution.</li>
</ul>



<p>These APIs act as a bridge, making your website agent-ready. They enable faster, more reliable agent workflows than raw DOM manipulation.</p>



<p><strong>Use cases. </strong>Google shared use cases that show how an AI agent can handle complex tasks for your users with speed and confidence:</p>



<ul class="wp-block-list">
<li><strong>Travel:</strong> Users can get the exact flights they want. Agents can search, filter results, and complete bookings using structured data that delivers accurate results every time.</li>



<li><strong>Customer support:</strong> Users can create detailed support tickets faster. Agents can automatically fill in the required technical details.</li>



<li><strong>Ecommerce:</strong> Users can shop more efficiently. Agents can find products, configure options, and move through checkout with precision.</li>
</ul>



<p><strong>How to access the preview. </strong>You can apply for the preview to <a href="https://developer.chrome.com/docs/ai/join-epp" target="_blank" rel="noopener">WebMCP here</a>.</p>



<p><strong>Why we care. </strong>Agentic experiences are shaping the future of search—and possibly SEO. Dan Petrovic called it the <a href="https://dejan.ai/blog/webmcp/" target="_blank" rel="noopener">biggest shift in technical SEO since structured data</a>. Glenn Gabe called this a <a href="https://x.com/glenngabe/status/2021582770326159477" target="_blank" rel="noopener">big deal</a>. It’s worth exploring these new protocols now.</p>


Google today announced an early preview of WebMCP, a new protocol that defines how AI agents interact with websites. 
“WebMCP aims to provide a standard way for exposing structured tools, ensuring AI agents can perform actions on your side with increased speed, reliability, and precision,” wrote André Cipriani Bandarra from Google.
WebMCP lets developers tell large language models exactly what each button or link on a website does. WebMCP allows websites to explicitly publish a clear “Tool Contract” that defines available actions.
It runs on a new browser API, navigator.modelContext. Through that API, the website shares a structured list of tools — such as buyTicket(destination, date). The AI can then call those functions directly, making interactions faster, more accurate, and far more reliable.
Structured interactions for the agentic web. WebMCP introduces two new APIs that let browser agents act on a user’s behalf:
Declarative API: Handles standard actions defined directly in HTML forms.
Imperative API: Supports complex, dynamic interactions that require JavaScript execution.
These APIs act as a bridge, making your website agent-ready. They enable faster, more reliable agent workflows than raw DOM manipulation.
Use cases. Google shared use cases that show how an AI agent can handle complex tasks for your users with speed and confidence:
Travel: Users can get the exact flights they want. Agents can search, filter results, and complete bookings using structured data that delivers accurate results every time.
Customer support: Users can create detailed support tickets faster. Agents can automatically fill in the required technical details.
Ecommerce: Users can shop more efficiently. Agents can find products, configure options, and move through checkout with precision.
How to access the preview. You can apply for the preview to WebMCP here.
Why we care. Agentic experiences are shaping the future of search—and possibly SEO. Dan Petrovic called it the biggest shift in technical SEO since structured data. Glenn Gabe called this a big deal. It’s worth exploring these new protocols now.

Google's WebMCP protocol lets AI agents execute structured actions on websites via browser APIs. Is this the next frontier of technical SEO?

Google previews WebMCP, a new protocol for AI agent interactions

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google’s GM of Ads explains how AI and agentic commerce in 2026 will help advertisers create, target, and convert sales.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920.jpg" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/01/google-robot-laptop-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google is redesigning shopping and advertising around AI-powered, agent-driven experiences, and said speed and certainty will converge for consumers and brands in 2026.</p>



<p>In her third annual letter, Vidhya Srinivasan, Google’s VP and GM of Ads and Commerce, outlined how Search, YouTube, and its shopping infrastructure are being rebuilt for the agentic era — where AI doesn’t just surface information but actively assists, recommends, and completes transactions.</p>



<p><strong>Key trends</strong>. Google is redefining commercial intent across Search, YouTube, and AI interfaces. Ads are moving deeper into conversational experiences like AI Mode, creative production is becoming AI-native, and checkout is embedding directly into Search. Here are key takeaways from Srinivasan’s letter:</p>



<ul class="wp-block-list">
<li><strong>Creators to commerce:</strong> YouTube remains a discovery hub, with creators serving as trusted tastemakers. AI helps match brands with the right creators, turning influence into measurable business impact.</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" width="1200" height="1830" src="https://searchengineland.com/wp-content/seloads/2026/02/New-Shopping-Ad-Format_AI-Mode.gif" alt="" class="wp-image-468998"/></figure>



<ul class="wp-block-list">
<li><strong>Search ads evolve:</strong> As conversational and visual queries rise, AI Mode reimagines ads as part of the discovery journey. New formats (e.g., sponsored retail listings, Direct Offers), aim to help users find products and services while giving brands meaningful ways to convert interest into sales.</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" width="1080" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/Direct-Offers.gif" alt="" class="wp-image-468996"/></figure>



<ul class="wp-block-list">
<li><strong>Agentic commerce arrives:</strong> Google is standardizing AI-driven shopping through the Universal Commerce Protocol (UCP), enabling consumers to browse, pay, and complete purchases seamlessly in AI Mode. Early rollouts include Etsy and Wayfair, with Shopify, Target, and Walmart to follow.</li>



<li><strong>AI-powered creative and performance:</strong> Gemini 3 powers ad tools that automate creative production and campaign optimization. Generative tools like Nano Banana and Veo 3 help advertisers create studio-quality assets in minutes, while AI Max expands reach and drives performance.</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" width="960" height="540" src="https://searchengineland.com/wp-content/seloads/2026/02/Veo-Image-to-Video-in-Google-Ads.gif" alt="" class="wp-image-468997" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Veo-Image-to-Video-in-Google-Ads.gif 960w, https://searchengineland.com/wp-content/seloads/2026/02/Veo-Image-to-Video-in-Google-Ads-768x432.gif 768w" sizes="(max-width: 960px) 100vw, 960px" /></figure>



<p><strong>Why we care.</strong> Adapting to AI-mediated commerce is increasingly necessary to stay competitive. Buying decisions are shifting — more often happening inside AI-driven search, creator content, and agent-powered checkout flows that could reshape traffic and conversion paths. These changes may create new ways to reach high-intent shoppers, but they also signal growing platform control over discovery, measurement, and transactions, potentially affecting competition, costs, and brand visibility.</p>



<p><strong>Google&#8217;s blog post. </strong><a href="https://blog.google/products/ads-commerce/digital-advertising-commerce-2026/" target="_blank" rel="noopener">What to expect in digital advertising and commerce in 2026</a></p>


Google is redesigning shopping and advertising around AI-powered, agent-driven experiences, and said speed and certainty will converge for consumers and brands in 2026.
In her third annual letter, Vidhya Srinivasan, Google’s VP and GM of Ads and Commerce, outlined how Search, YouTube, and its shopping infrastructure are being rebuilt for the agentic era — where AI doesn’t just surface information but actively assists, recommends, and completes transactions.
Key trends. Google is redefining commercial intent across Search, YouTube, and AI interfaces. Ads are moving deeper into conversational experiences like AI Mode, creative production is becoming AI-native, and checkout is embedding directly into Search. Here are key takeaways from Srinivasan’s letter:
Creators to commerce: YouTube remains a discovery hub, with creators serving as trusted tastemakers. AI helps match brands with the right creators, turning influence into measurable business impact.





Search ads evolve: As conversational and visual queries rise, AI Mode reimagines ads as part of the discovery journey. New formats (e.g., sponsored retail listings, Direct Offers), aim to help users find products and services while giving brands meaningful ways to convert interest into sales.





Agentic commerce arrives: Google is standardizing AI-driven shopping through the Universal Commerce Protocol (UCP), enabling consumers to browse, pay, and complete purchases seamlessly in AI Mode. Early rollouts include Etsy and Wayfair, with Shopify, Target, and Walmart to follow.
AI-powered creative and performance: Gemini 3 powers ad tools that automate creative production and campaign optimization. Generative tools like Nano Banana and Veo 3 help advertisers create studio-quality assets in minutes, while AI Max expands reach and drives performance.




Why we care. Adapting to AI-mediated commerce is increasingly necessary to stay competitive. Buying decisions are shifting — more often happening inside AI-driven search, creator content, and agent-powered checkout flows that could reshape traffic and conversion paths. These changes may create new ways to reach high-intent shoppers, but they also signal growing platform control over discovery, measurement, and transactions, potentially affecting competition, costs, and brand visibility.
Google’s blog post. What to expect in digital advertising and commerce in 2026

Google’s GM of Ads explains how AI and agentic commerce in 2026 will help advertisers create, target, and convert sales.

Google outlines AI-powered, agent-driven future for shopping and ads in 2026

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/How-AI-driven-shopping-discovery-changes-product-page-optimization.png" class="attachment-large size-large wp-post-image" alt="How AI-driven shopping discovery changes product page optimization" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/How-AI-driven-shopping-discovery-changes-product-page-optimization.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/How-AI-driven-shopping-discovery-changes-product-page-optimization-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/How-AI-driven-shopping-discovery-changes-product-page-optimization-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Product pages supply the data AI uses to recommend products. Learn which details matter most for AI shopping discovery.

As consumers lean into AI search, the industry has focused on the technical “how” – tracking everything from Agentic Commerce Protocols (ACP) to ChatGPT’s latest shopping research tools. In doing so, it often misses the larger shift: conversational search, which is changing how visibility is earned.
There’s a common argument that big brands will always win in AI. I disagree. When you move beyond the “best running shoes” shorthand and look at the deep context users now provide, the playing field levels. AI is trying to match user needs to specific solutions, and it’s up to your brand to provide the details.
This article explains how conversational search changes product discovery and what ecommerce teams need to update on product detail pages (PDPs) to remain visible in AI-driven shopping experiences.
How conversational search builds on semantic search
While semantic search is critical for understanding the meaning and context of words, conversational search is the ability to maintain a back-and-forth dialogue with a user over time.
Semantic search is the foundation for conversational visibility. Think of it like a restaurant: If semantic search is the chef who knows exactly what you mean by “something light,” conversational search is the waiter who remembers that you’re ordering for dinner.

FeatureSemantic searchConversational search
GoalTo understand intent and contextTo handle a flow of questions
How it thinksIt knows “car” and “automobile” are the same thingIt knows that when you say “how much is it?”, “it” refers to the car you just mentioned
The interactionSearching with a phrase instead of keywordsHaving a chat where the computer remembers what you were asking about before
ExampleAsking “What is a healthy meal?” and getting results for “nutritious recipes.”Asking “What is a healthy meal?” followed by “give me a recipe for that.”





AI blends them together. It uses semantic understanding to decode your complex intent and conversational logic to keep the thread of the story moving. For brands, this means your content has to be clear enough for the “chef” to interpret and consistent enough for the “waiter” to follow.

  
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What conversational search and AI discovery mean for ecommerce
I recently shared how my mom was using ChatGPT to remodel her kitchen. She didn’t start by searching for “the best cabinets.” Instead, she leveraged ChatGPT as her pseudo-designer and contractor, using AI to solve specific problems.
Product discovery happened naturally through constraint-based queries:
“Find cabinets that fit these dimensions and match this specific wood type.”
“Are these cabinets easy for a DIY installation?”
Her conversations were piling up, allowing her to reach multiple solutions at once. Her discovery journey was layered. When ChatGPT recommended products to complete her tasks, she simply followed up with, “Where can I buy those?”
Brands and marketers need to stop optimizing for keywords and start optimizing for tasks. Identify the specific conversations where your product becomes the solution. If your data can’t answer the “Will this fit?” or “Is this easy?” questions, you won’t be part of the final recommendation.

“Recommend products” is the top task users trust AI to handle, highlighting a clear opportunity for brands, according to Tinuiti’s 2026 AI Trends Study. (Disclosure: I am the Sr. Director of AI SEO Innovation at Tinuiti.) 
For your brand to be the one recommended, your PDPs must provide the “ground truth” details these assistants need to make a confident selection.  
Dig deeper: How to make ecommerce product pages work in an AI-first world
What to do before you start changing every PDP
Step away from the keyword research tools and stop asking for “prompt volumes.” In an AI-driven world, intent is more important than volume. Before changing a single page, you need to understand the high-intent journeys your personas are actually taking.
To identify your high-intent semantic opportunities:
Audit your personas: Who is your buyer, and what are their non-negotiable questions? If you haven’t mapped these lately, start there.
Bridge the team gap: Talk to your product and sales teams. They know the specific attributes and “deal-breaker” details that actually drive conversions.
Listen to the market: Use sentiment analysis and social listening to find hidden use cases or brand problems. How are people actually using, or struggling with, your product in ways your brand team hasn’t considered?
Map constraints, not keywords: Identify the specific constraints (size, compatibility, budget) that AI agents use to filter recommendations.
How to build PDPs for AI search with decision support
Your PDP should operate like a product knowledge document and be optimized for natural language. This helps an AI system decide whether to recommend the product for a specific situation.
Name your ideal buyer and edge cases
Content should support better decision-making. Audit your PDPs to determine whether they provide enough detail on who the product is best for – and not for. Does the page explicitly name your ideal buyer, their skill level, lifestyle constraints, and deal-breakers?
AI shopping queries often include exclusions, and clearly outlining the important parts of your user search journey will help you understand where your products fit best.
Cover compatibility and product specifications
Compatibility feels synonymous with electronics (e.g., “Will my headphones connect to this computer?”). But think beyond one-to-one compatibility and expand into lifestyle compatibility:
Is this laptop bag waterproof enough for a 20-minute bike ride in the rain, and does it have a clip for a taillight?
Can I fit a Kindle and a book in this purse?
Will this detergent work with my HE washer?
Will this carry-on suitcase fit in the overhead compartment on every airline?
Is this “family-sized” cutting board actually small enough to fit inside a standard dishwasher?
People are searching for how products fit into their lifestyle needs. Highlight and emphasize the features that make your products compatible with their lifestyle.
Dig deeper: How to make products machine-readable for multimodal AI search
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See terms.
Provide vertical-specific product guidance
Breaking down your customer search journey and listening to your customers’ concerns, either through AI sentiment analysis, social listening, or product reviews, will help you understand what you need to be specific about.
Apparel brands should add sizing and fit guidance. Maybe you’re comparing your size 10 jeans to competitors’ sizing, or considering sizing changes based on the cut or style of your other jeans.
Beauty or skincare brands need ingredient combination details. Is this product compatible with other common formulas? Can I layer it over a vitamin C serum?
Toy brands could include important details for parents. Does your product need to be assembled, and how long will it take? Can they assemble it the night before Christmas?
If your biggest customer complaint is understanding when and how to use your products, you’re likely not making it easy enough for them to buy. Better defining your product attributes helps users and LLMs alike better understand your products.
Write for constraint matching instead of browsing
AI shopping discovery is driven by constraints instead of keywords. Shoppers aren’t asking for “the best laptop bag.” They’re asking for a bag that fits under an airplane seat, survives a rainy commute, and still looks professional in a meeting.
PDPs should be written to reflect that reality. Audit your product pages to see whether they answer common “Can I …?” and “Will this work if …?” questions in plain language. These details often live in reviews, FAQs, or support tickets, but rarely surface in core product copy where AI systems are most likely to pull from.
Here’s what transforming your content can look like:
Traditional PDP copy
Laptop backpack

Water-resistant polyester exterior.
Fits laptops up to 15″.
Multiple interior compartments.
Lightweight design.
USB charging port.
PDP copy written for constraints
Laptop backpack

Best for: Daily commuters, frequent flyers, and students who need to carry tech in unpredictable weather.
Not ideal for: Extended outdoor exposure or laptops larger than 15.6″.
Weather readiness: Water-resistant coating protects electronics during short walks or bike commutes in light rain, but is not designed for heavy downpours.
Travel compatibility: Fits comfortably under most airplane seats and in overhead bins on domestic flights.
Capacity and layout: Holds a 15-15.6″ laptop, charger, and tablet, with room for a book or light jacket – but not bulky items.
Lifestyle considerations: Integrated USB port supports charging on the go (power bank not included).
LLMs evaluate how well a product satisfies specific constraints in conversational queries or based on predetermined user preference information. 
PDPs that clearly articulate those constraints are more likely to be selected, summarized, and recommended. This type of copy should also help your on-site customers better understand your products.
Dig deeper: Why ecommerce SEO audits fail – and what actually works in 30 days
Technical foundations still matter for ecommerce
Just because search platforms change doesn’t mean we should abandon everything we’ve learned in traditional optimization.
Technical SEO fundamentals still heavily apply in AI search:
Can crawlers access and index your site?
Are your product listing pages (PLPs) and PDPs clearly linked and structured?
Do pages load quickly enough for crawlers and users?
Is your most critical content accessible?
In conversational shopping, structured data is playing a different role than it did in traditional SEO strategies. In conversational shopping, it’s about verification. 
AI systems use your schema to validate facts before they risk reusing them in an answer. If the AI can’t verify your price, availability, or shipping details through a merchant feed or structured data, it won’t risk recommending you.
Variant clarity is just as important. When differences like size, color, or configuration aren’t clearly defined, AI systems may treat variants as separate products or merge them incorrectly. The result is inaccurate pricing, incompatible recommendations, or missed visibility.
Most importantly, structured data must match what’s visibly true on the page. When schema contradicts on-page content, AI systems avoid recommending uncertain information.
Dig deeper: How SEO leaders can explain agentic AI to ecommerce executives

  
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Owning the digital shelf in 2026
Success on the digital shelf has moved beyond high-volume keywords. In this new era, your visibility depends on how well you satisfy the complex constraints users can provide in a single search. AI models are scanning your pages to see if you meet specific, nuanced requirements, like “gluten-free,” “easy to install,” or “fits a 30-inch window.”
The shift to conversational discovery means your product data must be ready to sustain a dialogue. The goal is simple: provide the density of information necessary for an AI to confidently transact on a user’s behalf. Those who build for these multi-layered journeys will own the future of discovery.

Product pages supply the data AI uses to recommend products. Learn which details matter most for AI shopping discovery.

How AI-driven shopping discovery changes product page optimization

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app-800x450.jpg" class="attachment-large size-large wp-post-image" alt="OpenAI ChatGPT iOS app" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>OpenAI’s Assad Awan says ChatGPT ads let advertisers reach Free and Go users safely, with relevance and privacy built in.

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app-800x450.jpg" class="attachment-large size-large wp-post-image" alt="OpenAI ChatGPT iOS app" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2023/05/openai-chatgpt-ios-app.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>In a conversation on the OpenAI podcast, host Andrew Maine spoke with OpenAI executive Assad Awan, who detailed <a href="https://searchengineland.com/openai-starts-testing-chatgpt-ads-468593">how ads will roll out in ChatGPT</a>, who will see them and how the company plans to protect user trust.</p>



<p><strong>Who will see ads:</strong></p>



<ul class="wp-block-list">
<li>Ads will appear for Free and Go tier users</li>



<li>Plus, Pro and Enterprise subscribers won’t see ads</li>



<li>Enterprise workspaces will remain fully ad-free</li>
</ul>



<p><strong>The guardrails:</strong> Awan emphasized that OpenAI is structuring ads around strict trust principles:</p>



<ul class="wp-block-list">
<li><strong>Separation:</strong> Ads are visually and technically separate from model answers</li>



<li><strong>Privacy:</strong> Conversations aren’t shared with advertisers</li>



<li><strong>Sensitive topics:</strong> Health, politics and other sensitive chats won’t show ads</li>



<li><strong>Controls:</strong> Users can adjust or turn off personalization — or upgrade to remove ads</li>
</ul>



<p>According to Awan, the model itself doesn’t know when ads are present and can’t reference them unless a user explicitly asks about one.</p>



<p><strong>Zoom in</strong>. OpenAI internally prioritizes user trust over user value, advertiser value and revenue, Awan said — a framework meant to prevent ads from shaping how the model responds.</p>



<p><strong>For small businesses.</strong> Awan described a future where AI acts as an advertising agent, helping small businesses run campaigns by describing goals in plain language rather than managing complex dashboards.</p>



<p><strong>Why we care.</strong> ChatGPT ads could open a new, high-intent channel where businesses reach users during active conversations and decision-making moments. The platform’s focus on relevance, AI-driven matching and agent-style campaign tools could lower the barrier to entry for small and midsize advertisers while improving performance for larger brands. </p>



<p>If OpenAI succeeds in building a trusted ad environment, it may reshape how advertisers think about discovery and customer engagement in AI-driven interfaces.</p>



<p><strong>What’s next.</strong> Early ad tests will be conservative, focusing on usefulness and relevance over volume as OpenAI refines formats and placement.</p>



<p><strong>The big picture</strong>. Through advertising, OpenAI is aiming to scale ChatGPT access while maintaining a trust-first design — a balance the company says is central to its long-term strategy.</p>



<p><strong>Dig deeper.</strong> <a href="https://www.youtube.com/watch?v=2agJo3Jf_O4&amp;t=1233s">Watch full interview with Assad Awan</a></p>


In a conversation on the OpenAI podcast, host Andrew Maine spoke with OpenAI executive Assad Awan, who detailed how ads will roll out in ChatGPT, who will see them and how the company plans to protect user trust.
Who will see ads:
Ads will appear for Free and Go tier users
Plus, Pro and Enterprise subscribers won’t see ads
Enterprise workspaces will remain fully ad-free
The guardrails: Awan emphasized that OpenAI is structuring ads around strict trust principles:
Separation: Ads are visually and technically separate from model answers
Privacy: Conversations aren’t shared with advertisers
Sensitive topics: Health, politics and other sensitive chats won’t show ads
Controls: Users can adjust or turn off personalization — or upgrade to remove ads
According to Awan, the model itself doesn’t know when ads are present and can’t reference them unless a user explicitly asks about one.
Zoom in. OpenAI internally prioritizes user trust over user value, advertiser value and revenue, Awan said — a framework meant to prevent ads from shaping how the model responds.
For small businesses. Awan described a future where AI acts as an advertising agent, helping small businesses run campaigns by describing goals in plain language rather than managing complex dashboards.
Why we care. ChatGPT ads could open a new, high-intent channel where businesses reach users during active conversations and decision-making moments. The platform’s focus on relevance, AI-driven matching and agent-style campaign tools could lower the barrier to entry for small and midsize advertisers while improving performance for larger brands. 
If OpenAI succeeds in building a trusted ad environment, it may reshape how advertisers think about discovery and customer engagement in AI-driven interfaces.
What’s next. Early ad tests will be conservative, focusing on usefulness and relevance over volume as OpenAI refines formats and placement.
The big picture. Through advertising, OpenAI is aiming to scale ChatGPT access while maintaining a trust-first design — a balance the company says is central to its long-term strategy.
Dig deeper. Watch full interview with Assad Awan

OpenAI’s Assad Awan says ChatGPT ads let advertisers reach Free and Go users safely, with relevance and privacy built in.

OpenAI details how ads will work in ChatGPT

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/09/Trusting-Google-Ads-AI.jpg" class="attachment-large size-large wp-post-image" alt="Trusting Google Ads AI" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/09/Trusting-Google-Ads-AI.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/09/Trusting-Google-Ads-AI-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/09/Trusting-Google-Ads-AI-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google Ads now suggests ready-to-run experiments, helping advertisers test optimizations faster and improve performance with less setup.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/09/Trusting-Google-Ads-AI.jpg" class="attachment-large size-large wp-post-image" alt="Trusting Google Ads AI" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/09/Trusting-Google-Ads-AI.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/09/Trusting-Google-Ads-AI-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/09/Trusting-Google-Ads-AI-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google Ads is rolling out recommended experiments on the Experiments page, surfacing test ideas based on an account’s setup and performance data.</p>



<p><strong>How it works:</strong> The platform suggests experiment opportunities — such as testing bidding strategies, creative variations, or new campaign features — and presents them directly inside the Experiments dashboard.</p>



<ul class="wp-block-list">
<li>Each recommendation includes a preconfigured experiment setup</li>



<li>Advertisers can launch immediately or customize settings</li>



<li>Suggestions appear alongside the standard Create Experiment workflow</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2026/02/recommended-experiments-1.png" alt="" class="wp-image-468902" srcset="https://searchengineland.com/wp-content/seloads/2026/02/recommended-experiments-1.png 1280w, https://searchengineland.com/wp-content/seloads/2026/02/recommended-experiments-1-768x432.png 768w" sizes="(max-width: 1280px) 100vw, 1280px" /></figure>



<p><strong>Why we care.</strong> By removing the need to build tests from scratch, Google is lowering the barrier to experimentation. Advertisers can act on optimization ideas faster and more consistently. However, advertisers should still ensure that the right tests/configurations are being launched to avoid wasted time and budget. </p>



<p><strong>Zoom in.</strong> Example prompts include suggestions like enabling <strong>final URL expansion</strong> to improve campaign performance, displayed through in-dashboard popups tied to the Experiments interface.</p>



<p><strong>The big picture</strong>. Google is increasingly embedding automated guidance into Ads workflows, nudging advertisers toward continuous testing and data-driven optimization.</p>



<p><strong>First seen.</strong> <a href="https://chatgpt.com/c/698c9b5c-02e4-8331-9a4c-a72feb1156b5">This update</a> was spotted by PPC News Feed owner, Hana Kobzová.</p>


Google Ads is rolling out recommended experiments on the Experiments page, surfacing test ideas based on an account’s setup and performance data.
How it works: The platform suggests experiment opportunities — such as testing bidding strategies, creative variations, or new campaign features — and presents them directly inside the Experiments dashboard.
Each recommendation includes a preconfigured experiment setup
Advertisers can launch immediately or customize settings
Suggestions appear alongside the standard Create Experiment workflow




Why we care. By removing the need to build tests from scratch, Google is lowering the barrier to experimentation. Advertisers can act on optimization ideas faster and more consistently. However, advertisers should still ensure that the right tests/configurations are being launched to avoid wasted time and budget. 
Zoom in. Example prompts include suggestions like enabling final URL expansion to improve campaign performance, displayed through in-dashboard popups tied to the Experiments interface.
The big picture. Google is increasingly embedding automated guidance into Ads workflows, nudging advertisers toward continuous testing and data-driven optimization.
First seen. This update was spotted by PPC News Feed owner, Hana Kobzová.

Google Ads now suggests ready-to-run experiments, helping advertisers test optimizations faster and improve performance with less setup.

Google Ads shows recommended experiments

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/07/How-to-write-high-performing-Google-Ads-copy-with-generative-AI.png" class="attachment-large size-large wp-post-image" alt="How to write high-performing Google Ads copy with generative AI" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/07/How-to-write-high-performing-Google-Ads-copy-with-generative-AI.png 1920w, https://searchengineland.com/wp-content/seloads/2025/07/How-to-write-high-performing-Google-Ads-copy-with-generative-AI-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/07/How-to-write-high-performing-Google-Ads-copy-with-generative-AI-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google Ads now shows per-product campaign eligibility, making gaps and overlaps easier to spot.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/07/How-to-write-high-performing-Google-Ads-copy-with-generative-AI.png" class="attachment-large size-large wp-post-image" alt="How to write high-performing Google Ads copy with generative AI" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/07/How-to-write-high-performing-Google-Ads-copy-with-generative-AI.png 1920w, https://searchengineland.com/wp-content/seloads/2025/07/How-to-write-high-performing-Google-Ads-copy-with-generative-AI-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/07/How-to-write-high-performing-Google-Ads-copy-with-generative-AI-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google Ads rolled out a new feature that shows advertisers which campaigns their products are eligible for, directly in the Products section.</p>



<p><strong>How it works.</strong> A new dashboard in the Products section includes:</p>



<ul class="wp-block-list">
<li>A table showing product details, status, issues, and priority flags</li>



<li>A line graph summarizing campaign status trends</li>



<li>Filters to segment eligibility views</li>



<li>A pop-up panel that lists “Eligible” and “Not eligible” campaigns per product</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2026/02/products-campaign-assignment.jpg" alt="" class="wp-image-468882" srcset="https://searchengineland.com/wp-content/seloads/2026/02/products-campaign-assignment.jpg 1280w, https://searchengineland.com/wp-content/seloads/2026/02/products-campaign-assignment-768x432.jpg 768w" sizes="(max-width: 1280px) 100vw, 1280px" /></figure>



<p><strong>Why we care.</strong> dvertisers can now quickly identify products that are missing from key campaigns or unintentionally overlapping across Shopping and Performance Max. The added visibility reduces the need to jump between campaign views to diagnose eligibility gaps.</p>



<p><strong>The big picture:</strong> The changes help advertisers quickly identify products that aren&#8217;t running in expected campaigns, spot campaign overlap before it becomes a budget problem and save time troubleshooting product-level issues.</p>



<p><strong>Between the lines</strong>. This is Google&#8217;s latest move to give advertisers more granular control over Shopping campaigns, where product-level optimization can make or break profitability.</p>



<p><strong>When</strong>. Available now in Google Ads.</p>



<p><strong>First seen.</strong> <a href="https://ppcnewsfeed.com/ppc-news/2026-02/product-eligibility-per-campaign-now-live-in-products-section/">This update</a> was spotted by PPC News Feed owner Hana Kobzová.</p>


Google Ads rolled out a new feature that shows advertisers which campaigns their products are eligible for, directly in the Products section.
How it works. A new dashboard in the Products section includes:
A table showing product details, status, issues, and priority flags
A line graph summarizing campaign status trends
Filters to segment eligibility views
A pop-up panel that lists “Eligible” and “Not eligible” campaigns per product




Why we care. dvertisers can now quickly identify products that are missing from key campaigns or unintentionally overlapping across Shopping and Performance Max. The added visibility reduces the need to jump between campaign views to diagnose eligibility gaps.
The big picture: The changes help advertisers quickly identify products that aren’t running in expected campaigns, spot campaign overlap before it becomes a budget problem and save time troubleshooting product-level issues.
Between the lines. This is Google’s latest move to give advertisers more granular control over Shopping campaigns, where product-level optimization can make or break profitability.
When. Available now in Google Ads.
First seen. This update was spotted by PPC News Feed owner Hana Kobzová.

Google Ads now shows per-product campaign eligibility, making gaps and overlaps easier to spot.

Google Ads simplifies product campaign tracking

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/What-4-AI-search-experiments-reveal-about-attribution-and-buying-decisions.png" class="attachment-large size-large wp-post-image" alt="What 4 AI search experiments reveal about attribution and buying decisions" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/What-4-AI-search-experiments-reveal-about-attribution-and-buying-decisions.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/What-4-AI-search-experiments-reveal-about-attribution-and-buying-decisions-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/What-4-AI-search-experiments-reveal-about-attribution-and-buying-decisions-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI search rarely appears in analytics, yet it shapes trust, shortlists, and sales velocity. These experiments show where measurement breaks.

AI search influence didn’t show up in our SEO reports or AI prompt tracking tools. It showed up in sales calls.
“Found you via Grok, actually,” a new lead said.
That comment stopped us cold. We hadn’t tried to rank in Grok. We weren’t tracking it. Yet it was influencing how buyers discovered and evaluated us.
That disconnect kept appearing in client conversations, too. Everyone was curious about AI search, but no one trusted the data. 
Teams wanted visibility in ChatGPT and other AI tools, then asked the same question: “Why invest in a channel that doesn’t show up cleanly in attribution?”

To answer that, we ran controlled experiments using assets we could fully control – an agency website, personal sites, an ecommerce brand, and purpose-built test domains.
The goal wasn’t to win AI rankings. It was to understand what still matters once AI enters the decision process:
Does AI search change what people buy, or just where brands appear?
Can something influence revenue without ever appearing in analytics?
Does AI recommendation affect performance across other channels?
Why we ran the experiments
Most AI search conversations fixate on visibility signals like brand mentions, citations, or visibility screenshots from AI prompt tracking tools.
Search has always had one job: help people make a decision.
We wanted to know if AI search performed the same job and actually changed commercial outcomes.
AI systems now operate at the stage where buyers compare options, shortlist providers, and reduce risk.
If AI mattered, it had to show up at the moment of decision.
On measurement limits: 
We didn’t rely on API data because API responses often differ from what real users see. Instead, we observed live interfaces across ChatGPT, Perplexity, Gemini, and Google AI Overviews. 
We used prompt tracking to spot patterns, not to declare absolute wins.

  
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Experiment 1: Self-promotional ‘best of’ lists on your own website
A simple tactic became popular over the past year:
Create a “best X” list on your site.
Put yourself at the top.
Let AI systems pick up the list.
I’ve seen agencies do this locally and felt conflicted about it.
It wasn’t spam. But it relied on a blind spot – LLMs struggle to separate independent rankings from self-written ones.
Around the same time, Ahrefs published a large study that helped explain why this works. Glen Allsopp analyzed ChatGPT responses across hundreds of “best X”-style prompts and found that “best” list posts were the most commonly cited page type.
Two things from the study stood out:
Format: This included cases where brands ranked themselves first
Freshness: Most cited lists had been updated recently
I could have tested these observations on StudioHawk. Instead, I did it on my personal brand website to manage the risk. 
I published a list of the “Best SEO agencies in Sydney” and included my own website among the entries to test whether AI would “take the bait,” so to speak.
Within two weeks, LawrenceHitches.com appeared across AI tools for “best SEO agency Sydney” style searches:

The speed was surprising – traditional SEO rarely moves that fast.
If visibility appears this easily, then visibility alone can’t mean much, so I tested it again.
Experiment 2: Self-promotion of a fake business
Initially, I could have been piggybacking off the already established StudioHawk brand, so I decided to run a self-promotion test on a fake website
We used a basic landscaping site built only for SEO and AI testing and published the same type of page, a “best X” list.
This time, the topic was “best landscapers in Melbourne”:

Within two weeks, the list appeared in AI responses again. The result repeated almost exactly.
If a brand-new test site can surface this fast, then “appeared in AI” doesn’t mean much on its own.
Visibility vs. trust
These two experiments showed one thing clearly: LLMs are still easy to influence at the surface level.
I ran these tests back in August 2025, but the same pattern still appears today.
A “best SEO agency Sydney” search run in January 2026 shows the same list-driven results:


This creates a real conflict for brands.
On one side, the data says yes – the Ahrefs research shows “Best X” pages attract citations. Large brands like Shopify, Slack, and HubSpot publish self-ranked lists without obvious damage to rankings or AI visibility.
On the other side is buyer trust.
As Wil Reynolds put it, listing yourself first on your own site doesn’t build confidence with buyers. That’s the tension.
When bullish founders ask for the secret sauce to appear in ChatGPT, I’m blunt. List-based “best of X” pages that rank the author first have been a fast way to surface in some AI results.
That doesn’t work everywhere, and it’s unlikely to hold up long term.
Dig deeper: Google may be cracking down on self-promotional ‘best of’ listicles
If a landscaping site with no reputation can surface this quickly, then appearing in AI means very little on its own.
Why prompt tracking can’t be a success metric
A lot of money is flowing into AI prompt tracking tools. Clients ask for them constantly. We use them too, but with a clear warning.
I wouldn’t make major decisions based on screenshots or Reddit threads about where a brand appears in ChatGPT.
Brand overlap between API outputs and real user sessions was as low as 24%, according to recent research from Surfer SEO comparing tracking APIs with scraped user experiences.
That means three times out of four, what the API told you was happening wasn’t what the user was actually seeing.
If a brand can appear in a screenshot but disappear in a real user session, then appearance alone isn’t a metric.
We stopped asking if we showed up.
Instead, we started asking, “Did this change how buyers behaved?”
Did leads reference AI tools without prompting?
Did sales calls skip education?
Did the speed of buying change?
Did price resistance soften?
These signals were harder to collect.
Dig deeper: 7 hard truths about measuring AI visibility and GEO performance
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Experiment 3: Kadi and the limits of digital PR alone
Kadi, an ecommerce brand we invested in that sells luggage, provided insight into our questions about whether AI results were affecting buyer behavior.
Running tests on Kadi has been an eye-opening experience for two reasons: 
It’s the difference between running an agency and running ecommerce.
It forced us to become our own client.
To move fast, we led with digital PR.
Kadi’s SEO foundation was solid but not perfect. We wanted to see how far off-site mentions could push SEO and AI visibility without heavy technical work or a polished site structure.
We conducted a large number of creative data campaigns and product placements, including:
Travel data studies: “Over-touristed destinations,” “Hidden fees,” “Best time to fly,” and “Happy Hour at 30,000 ft.”
Advisory pieces: “Airport cybersecurity” and “duty-free shopping” guides
Product and feature focus: “Kadi kids carry-on adventure,” “cloud check-in features,” and inclusions in “best suitcase round-ups.”

			
				

It worked:
Coverage landed.
Authority grew without the need for “traditional SEO.”
We saw temporary keyword spikes and traffic boosts.

But there was a catch: Digital PR alone wasn’t enough to close the gap with competitors. It created quick traction in search results, but it didn’t resolve the underlying issues.
After launch, SEO foundation work became the priority.
Then, Black Friday made the reality obvious. A customer found Kadi through ChatGPT on a “kids carry-on” query.
We saw this happen on the day of the query and showed the pathway: 
They didn’t buy immediately.
They checked the shipping policy.
They browsed the range.
They added three additional products.
They debated colour (olive over pink).
Attribution later showed Instagram as the source.
That order was the largest of the Black Friday period.
On paper, AI did nothing. In reality, it was part of shaping the decision. 
Digital PR can get you visibility spikes, but it doesn’t address the whole picture. 
While AI traffic does convert, the attribution is inconsistent.
Experiment 4: StudioHawk 
Across 2024 and 2025, StudioHawk underwent a full website rebrand and migration from WordPress to HubSpot.
Our own site sat at the bottom of the priority list for years. It was always the project we would get to later. 
Finally, we paused other priorities and rebuilt the entire site.
The work started in 2023, before terms like “GEO” existed. We were focused only on rebuilding service pages, social proof, and user experience end to end.
After launch, rankings improved and continue to grow.

In 2025, SEO became the agency’s strongest channel by efficiency. It drove 65% of inbound leads and close to 60% of new revenue.

Between July and December 2025, AI search leads began to appear more often:

Initially, these were “Oh, cool, we got a lead from AI” moments around the office.
Sales calls started skipping early education. New leads arrived aligned based on fit and expectations.
Over time, we saw that:
SEO inbound leads: Averaged 29 days to close.
AI search leads: Closed in roughly 18 days.

That 10-day gap mattered.
It meant less time educating, fewer scope objections, lower price sensitivity, and higher confidence earlier in the process.
Within the first year, AI-influenced conversations contributed over $100,000 in closed revenue from 20+ leads, including deals with direct attribution from tools like ChatGPT, Perplexity, and Grok.
The blind spot remains attribution paths such as Instagram, direct, or organic, where AI influenced the decision but didn’t appear in reporting (as seen in the Kadi example).
Where direct AI attribution existed, buyers were more prepared. That preparedness shortened sales cycles and lifted revenue.
AI compresses consideration
We started by asking where people would search next.
Our key finding? AI search doesn’t replace discovery. It compresses the consideration phase.

Consideration is that messy middle where buyers reduce risk, shortlist vendors, compare tradeoffs, and ask, “Who should I trust?”
They answer these questions before a buyer ever clicks a link. 
It means your website no longer carries the full load – AI summaries and third-party mentions do the pre-selling for you.
This is the shift we now describe as the new consideration era.
As the map illustrates, we’ve moved from a straight funnel to a complex, AI-influenced pathway where consensus is key:

			
				

Because this happens off-site, last-click attribution is broken. 
A buyer might use ChatGPT to create a shortlist but convert later via direct search.
Where traditional SEO still fits
Strong SEO metrics were a core across all our experiments, but we’ve stopped viewing them as the primary driver of value:
Keyword rankings confirm search engines understand your entity.
However, those high rankings don’t guarantee effective pre-selling.
Traditional SEO became a supporting signal – proof that the foundation is sound, rather than the end goal.
What this means for brands
After running a variety of AI search experiments, here’s what I think brands should focus on.
1. Measure where AI influence actually lands
Stop obsessing over prompt appearances (e.g., citations, mentions). These are shiny objects, but they fluctuate too easily. 
Instead, measure:
Sales velocity (Did deals close faster?)
Quality of the lead (Did they ask fewer questions to learn?)
Value per lead (Did price friction ease?)
2. Make clarity more important than creativity
AI hates vagueness. Making pages that make it clear what you do and who it’s for.
Pay attention to content that answers questions about risk, comparison, and price.
3. Change the content to help people decide what to buy
Focus on content that answers comparison, risk, and pricing questions. This makes a bigger difference than general category explanations.
4. Make entity consistency a crucial factor
Lack of consistency makes people doubt themselves. Conversely, consistency boosts confidence.
Check to see that your website, reviews, and digital PR all talk about your brand in the same way.

  
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AI search compresses consideration, not discovery
In the end, the results were the same across all experiments. What we got from our sales pipeline was typical: 
Clear intent.
Tight positioning.
Consistent signals of authority.
AI search isn’t replacing basic SEO. Instead, it shows weak positioning more quickly than traditional search ever did. 
What does that mean? 
Simply put, AI speeds up decisions that were already forming.
Dig deeper: From searching to delegating: Adapting to AI-first search behavior

AI search rarely appears in analytics, yet it shapes trust, shortlists, and sales velocity. These experiments show where measurement breaks.

What 4 AI search experiments reveal about attribution and buying decisions

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/How-to-reduce-low-quality-leads-from-Performance-Max-campaigns.png" class="attachment-large size-large wp-post-image" alt="How to reduce low-quality leads from Performance Max campaigns" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/How-to-reduce-low-quality-leads-from-Performance-Max-campaigns.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/How-to-reduce-low-quality-leads-from-Performance-Max-campaigns-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/How-to-reduce-low-quality-leads-from-Performance-Max-campaigns-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Performance Max often optimizes for cheap conversions by default. Learn how guardrails and better signals can improve lead quality.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/How-to-reduce-low-quality-leads-from-Performance-Max-campaigns.png" class="attachment-large size-large wp-post-image" alt="How to reduce low-quality leads from Performance Max campaigns" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/How-to-reduce-low-quality-leads-from-Performance-Max-campaigns.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/How-to-reduce-low-quality-leads-from-Performance-Max-campaigns-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/How-to-reduce-low-quality-leads-from-Performance-Max-campaigns-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>When left to its own devices, there are a couple of things <a href="https://searchengineland.com/google-performance-max-431821">Performance Max</a> is absolutely great at doing for lead gen campaigns:</p>



<ul class="wp-block-list">
<li>Driving volume.</li>



<li>Finding the lowest-quality leads it possibly can.</li>
</ul>



<p>It’s not inherently surprising that Google is doing what’s best for Google – that is, lining its own pockets – by heavily optimizing toward the cheapest, path-of-least-resistance conversion events.</p>



<p>From experience with campaigns we inherit from new clients, this performance often catches brands off guard – especially those who take Google sales reps’ “helpful advice” at face value. </p>



<p>It can take time for those brands to look past PMax’s shiny, low CPAs and realize the truth: those leads do little to nothing for real pipeline or revenue.</p>



<p>However, Performance Max, when given the proper guardrails, can be a good source of incremental, quality leads – but the trick is in building those guardrails.&nbsp;</p>



<p>This article covers lead quality tactics that work and how to execute them, tactics that don’t work, and important differences between Performance Max campaigns in Google and Bing.&nbsp;</p>



<h2 class="wp-block-heading" id="h-how-to-improve-lead-quality-in-pmax-campaigns">How to improve lead quality in PMax campaigns</h2>



<p>These are the specific levers that consistently influence lead quality in Performance Max.</p>



<ul class="wp-block-list">
<li><strong>Use conversion goals focused on metrics that indicate a higher quality lead than just form fills.</strong>
<ul class="wp-block-list">
<li>Depending on your data density, this could mean closed-won leads, opportunities, or (if you need to go up the funnel to get enough volume) sales-qualified leads. </li>



<li>It’s important to note that the effectiveness of this tactic depends on good offline conversion tracking implementation and a clean CRM instance, so don’t turn on PMax lead gen campaigns until you’re confident in your HubSpot or Salesforce integrity.</li>
</ul>
</li>



<li><strong>Use high-value lists for audience signals.</strong> This can be based on a certain activity, like “booked a meeting,” instead of simply including all converters.</li>



<li><strong>Keep the focus on the right audiences.</strong> Exclude irrelevant ones and upload Customer Match lists to help Google’s algorithm find similar users.</li>



<li><strong>Be smart with your campaign settings.</strong>
<ul class="wp-block-list">
<li>Use brand exclusions to ensure you’re not letting PMax cannibalize your brand traffic. </li>



<li>Restrict your location targeting to high-performing geos.</li>



<li>Set strategic scheduling, such as excluding early-morning hours if those conversions tend to be lower quality.</li>



<li>Evaluate search themes and placements, and be aggressive about negative keywords and placement exclusions.</li>



<li>Use sitelinks to steer traffic to pages with full, detailed forms.</li>
</ul>
</li>



<li><strong>Refine the forms themselves.</strong>
<ul class="wp-block-list">
<li>Implement reCAPTCHA or honeypot fields in forms that keep bots from “converting.”</li>



<li>Use field validation:
<ul class="wp-block-list">
<li>Block disposable domains.</li>



<li>Block freemails.</li>
</ul>
</li>



<li>Add freeform or disqualifying questions.
<ul class="wp-block-list">
<li>“How did you hear about us?”</li>



<li>“Do you have a budget for [solution]?”</li>



<li>“How many employees are in your organization?”</li>
</ul>
</li>
</ul>
</li>
</ul>



<p><strong><em>Dig deeper: </em></strong><a href="https://searchengineland.com/top-performance-max-optimization-tips-461913"><strong><em>Top Performance Max optimization tips for 2026</em></strong></a></p>



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<h2 class="wp-block-heading" id="h-tactics-that-won-t-affect-lead-quality">Tactics that won’t affect lead quality</h2>



<p>On the other hand, some of the usual campaign optimization strategies won’t do much to move the needle on PMax lead quality. If that’s your sole focus, you can de-prioritize:</p>



<ul class="wp-block-list">
<li>Switching <a href="https://searchengineland.com/google-ads-bid-strategy-campaign-success-437448">bid strategies</a> (e.g., switching from Max Conversions to tCPA helps a little but doesn’t fix everything).</li>



<li>Adding more assets.</li>



<li>Adding more budget.</li>



<li>Asking Google support (something I’d just stay away from in general these days).</li>
</ul>



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<h2 class="wp-block-heading" id="h-important-and-subtle-differences-to-know-between-google-and-bing-pmax-campaigns">Important (and subtle) differences to know between Google and Bing PMax campaigns</h2>



<p>Both Google and Bing have Performance Max campaigns, but there are differences in their offerings.</p>



<p>Google’s Performance Max network spans search, display, YouTube, discovery campaigns, and Gmail. It’s an absolutely huge amount of inventory – especially display and YouTube, which can be huge spam drivers if left unchecked.</p>



<p>Microsoft has far less video and display inventory. Their PMax campaigns primarily include Bing search, syndicated search, and the Microsoft audience network (which spans display, Outlook, and MSN).&nbsp;</p>



<p>When comparing performance between the two, we haven’t seen any notable differences, but it’s worth monitoring updates to each platform’s reporting and inventory going forward.</p>



<p><strong><em>Dig deeper: </em></strong><a href="https://searchengineland.com/google-microsoft-performance-max-approaches-464964"><strong><em>Google and Microsoft: How their Performance Max approaches align and diverge</em></strong></a></p>



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<h2 class="wp-block-heading" id="h-performance-max-isn-t-broken-but-it-needs-control">Performance Max isn’t broken, but it needs control</h2>



<p>If you’re considering running PMax for lead gen, you should approach it with a healthy dose of skepticism. </p>



<p>While PMax has been effective at driving scalable revenue for ecommerce,those campaigns need considerable guidelines to maintain lead quality.&nbsp;</p>



<p>For instance, preventing a high-end shoe retailer from racking up tons of conversions on things like replacement laces and shoe polish will require that the campaign develop sufficient PMax guardrails.</p>



<p>Considering how Google is moving toward additional automation and AI in campaigns, it’s important to keep testing and experimenting to gain an understanding of the tools available to analyze and shape PMax campaigns.&nbsp;</p>



<p>Google has issued some new releases to help as of late, including <a href="https://searchengineland.com/google-ads-expands-pmax-channel-reporting-to-account-level-461218">channel-level reporting</a>, <a href="https://searchengineland.com/google-ads-rolls-out-account-level-placement-exclusions-467569">more options for exclusions</a>, and <a href="https://searchengineland.com/google-ads-expands-negative-keyword-limits-pmax-453154">campaign-level negative keywords</a>.)&nbsp;</p>



<p>There <strong>is</strong><em> </em>a lead scale out there that can provide a healthy ROAS if you’re willing and able to wrestle the algorithm into submission.&nbsp;</p>



<p>If you’ve tested Performance Max campaigns for lead gen but paused them once it was clear they weren’t driving revenue, do a quick post-mortem on your past efforts. You might find there’s room to whip Google into shape to do better this time around. </p>



<p>Take note of the tactics you haven’t yet implemented and prioritize putting them in place before you waste another dollar of your 2026 budget on poor-quality leads that just junk up your CRM.</p>

When left to its own devices, there are a couple of things Performance Max is absolutely great at doing for lead gen campaigns:
Driving volume.
Finding the lowest-quality leads it possibly can.
It’s not inherently surprising that Google is doing what’s best for Google – that is, lining its own pockets – by heavily optimizing toward the cheapest, path-of-least-resistance conversion events.
From experience with campaigns we inherit from new clients, this performance often catches brands off guard – especially those who take Google sales reps’ “helpful advice” at face value. 
It can take time for those brands to look past PMax’s shiny, low CPAs and realize the truth: those leads do little to nothing for real pipeline or revenue.
However, Performance Max, when given the proper guardrails, can be a good source of incremental, quality leads – but the trick is in building those guardrails. 
This article covers lead quality tactics that work and how to execute them, tactics that don’t work, and important differences between Performance Max campaigns in Google and Bing. 
How to improve lead quality in PMax campaigns
These are the specific levers that consistently influence lead quality in Performance Max.
Use conversion goals focused on metrics that indicate a higher quality lead than just form fills.

Depending on your data density, this could mean closed-won leads, opportunities, or (if you need to go up the funnel to get enough volume) sales-qualified leads. 
It’s important to note that the effectiveness of this tactic depends on good offline conversion tracking implementation and a clean CRM instance, so don’t turn on PMax lead gen campaigns until you’re confident in your HubSpot or Salesforce integrity.
Use high-value lists for audience signals. This can be based on a certain activity, like “booked a meeting,” instead of simply including all converters.
Keep the focus on the right audiences. Exclude irrelevant ones and upload Customer Match lists to help Google’s algorithm find similar users.
Be smart with your campaign settings.

Use brand exclusions to ensure you’re not letting PMax cannibalize your brand traffic. 
Restrict your location targeting to high-performing geos.
Set strategic scheduling, such as excluding early-morning hours if those conversions tend to be lower quality.
Evaluate search themes and placements, and be aggressive about negative keywords and placement exclusions.
Use sitelinks to steer traffic to pages with full, detailed forms.
Refine the forms themselves.

Implement reCAPTCHA or honeypot fields in forms that keep bots from “converting.”
Use field validation:

Block disposable domains.
Block freemails.
Add freeform or disqualifying questions.

“How did you hear about us?”
“Do you have a budget for [solution]?”
“How many employees are in your organization?”
Dig deeper: Top Performance Max optimization tips for 2026
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Tactics that won’t affect lead quality
On the other hand, some of the usual campaign optimization strategies won’t do much to move the needle on PMax lead quality. If that’s your sole focus, you can de-prioritize:
Switching bid strategies (e.g., switching from Max Conversions to tCPA helps a little but doesn’t fix everything).
Adding more assets.
Adding more budget.
Asking Google support (something I’d just stay away from in general these days).
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Important (and subtle) differences to know between Google and Bing PMax campaigns
Both Google and Bing have Performance Max campaigns, but there are differences in their offerings.
Google’s Performance Max network spans search, display, YouTube, discovery campaigns, and Gmail. It’s an absolutely huge amount of inventory – especially display and YouTube, which can be huge spam drivers if left unchecked.
Microsoft has far less video and display inventory. Their PMax campaigns primarily include Bing search, syndicated search, and the Microsoft audience network (which spans display, Outlook, and MSN). 
When comparing performance between the two, we haven’t seen any notable differences, but it’s worth monitoring updates to each platform’s reporting and inventory going forward.
Dig deeper: Google and Microsoft: How their Performance Max approaches align and diverge
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Performance Max isn’t broken, but it needs control
If you’re considering running PMax for lead gen, you should approach it with a healthy dose of skepticism. 
While PMax has been effective at driving scalable revenue for ecommerce,those campaigns need considerable guidelines to maintain lead quality. 
For instance, preventing a high-end shoe retailer from racking up tons of conversions on things like replacement laces and shoe polish will require that the campaign develop sufficient PMax guardrails.
Considering how Google is moving toward additional automation and AI in campaigns, it’s important to keep testing and experimenting to gain an understanding of the tools available to analyze and shape PMax campaigns. 
Google has issued some new releases to help as of late, including channel-level reporting, more options for exclusions, and campaign-level negative keywords.) 
There is a lead scale out there that can provide a healthy ROAS if you’re willing and able to wrestle the algorithm into submission. 
If you’ve tested Performance Max campaigns for lead gen but paused them once it was clear they weren’t driving revenue, do a quick post-mortem on your past efforts. You might find there’s room to whip Google into shape to do better this time around. 
Take note of the tactics you haven’t yet implemented and prioritize putting them in place before you waste another dollar of your 2026 budget on poor-quality leads that just junk up your CRM.

Performance Max often optimizes for cheap conversions by default. Learn how guardrails and better signals can improve lead quality.

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