<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/The-5-layer-framework-for-measuring-GEO-performance.png" class="attachment-large size-large wp-post-image" alt="The 5-layer framework for measuring GEO performance" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/05/The-5-layer-framework-for-measuring-GEO-performance.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/The-5-layer-framework-for-measuring-GEO-performance-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/The-5-layer-framework-for-measuring-GEO-performance-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/The-5-layer-framework-for-measuring-GEO-performance-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI visibility dashboards can’t answer the ROI question on their own. Here’s how to build a more credible GEO measurement model.

AI search measurement in 2026 looks a lot like paid media in 2008. Everyone can see the impressions. Almost nobody can defend the revenue. 
Agencies are slapping AI visibility dashboards onto retainers, clients are writing checks, and CFOs are starting to ask the question that always ends a hype cycle: Prove it.
Here’s the hard truth. Citation share, presence rate, and AI Overview appearance counts are the new domain authority. They look defensible in a slide. For 95% of the agencies selling them, they aren’t connected to pipelines in any rigorous way.
What I lay out below is a five-layer framework for measuring GEO performance that you can actually defend. None of the layers works alone. 
The goal isn’t a closed loop because the technology doesn’t yet allow one. The goal is triangulation: multiple imperfect signals that, when they move together, point to something real.
Layer 1: Direct attribution
This is the one step most agencies are already tracking, and I’m including it because it still matters. It’s the most direct evidence you can get of AI driving traffic to a site. A human saw an AI answer, clicked your link, and landed on the page. That’s a clean signal, and you should be capturing it.
The catch is that GA4 often misses it. Referrers from AI tools either get stripped or fall into Direct, so the sessions you can actually see are a small fraction of what’s happening. Loamly’s analysis of 446,405 visits in early 2026 found 70.6% of AI traffic in its dataset landed as Direct in GA4 by default.
Even with a clean setup, you’ll only see human clicks from AI tools. Anything an AI does on behalf of a user — browsing, fetching, or summarizing without sending a click — is invisible to GA4 entirely. And the human click rate is structurally getting smaller. 
Agentic browsers are making it worse: ChatGPT Atlas has been observed reporting as Chrome 141 in the user-agent string, making it indistinguishable from a regular Chrome session at the HTTP level. 
Other agentic browsers (e.g., Perplexity Comet) present similar challenges for traffic attribution. The traffic looks like a person on Chrome. The HTTP layer is silent about the AI driving the session.
Layer 1 is necessary, but it’s the tip of an iceberg that’s getting smaller every quarter. Build it because it’s the most direct signal you have, not because it’s the whole picture.
Takeaway




Rebuild GA4 channel grouping to capture referrers from chatgpt.com, chat.openai.com, perplexity.ai, gemini.google.com, copilot.microsoft.com, and claude.ai. 
Add a custom dimension for the full user agent.

  
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Layer 2: Crawl log diagnostics
Almost nobody is reading their access logs for AI activity. The data is sitting on every server, generated automatically, and the agencies I talk to aren’t parsing it. That’s a free signal layer being ignored, and it deserves to be treated as a signal source in its own right.
Three categories of bots show up in the logs, and they tell different stories. Don’t conflate them.
Training and model-improvement crawlers

GPTBot, ClaudeBot, anthropic-ai, CCBot, and Bytespider are infrastructure readiness signals, not demand signals. Their presence indicates that crawlers used for training and model improvement are requesting your content. 
It’s useful to know your site isn’t being ignored at the training layer. It’s not useful for measuring whether anyone is asking questions about your client today.
Search and indexing crawlers 

OAI-SearchBot, Claude-SearchBot, PerplexityBot, and DuckAssistBot index your content so it can surface in AI search features. They’re a leading indicator of eligibility for citation.
User-triggered fetchers

ChatGPT-User, Claude-User, Perplexity-User, and MistralAI-User are the closest things to real-time demand. 
When a user prompts an AI tool and the model needs to pull live information to answer, these are the user agents that appear in your logs.
A note on Google: Google-Agent and Google-NotebookLM are valid AI-specific user agents. Google-Agent powers products like Project Mariner, while Google-NotebookLM fetches URLs users provide as sources. 
The catch is that Google AI Mode and AI Overviews also rely on broader Google crawling infrastructure. In logs, you often can’t cleanly separate classic Search crawling from AI-related retrieval. Track these in aggregate, and don’t claim more precision than you have.
Here’s the scale of what gets missed by ignoring this layer. Cloudflare’s June 2025 data reported OpenAI’s crawl-to-referral ratio at 1,700:1 and Anthropic’s at 73,000:1, compared with Google at 14:1. 
Cloudflare’s year-end review showed Anthropic’s ratio ranged from roughly 25,000:1 to 100,000:1 after earlier volatility, with OpenAI reaching 3,700:1. SEOmator’s Q1 2026 analysis of Cloudflare Radar data reported ClaudeBot at 23,951:1 and GPTBot at 1,276:1.
In plain terms, for every visitor Anthropic sends, its bots have already read tens of thousands of your pages. That fetcher volume measures how often AI tools fetch your content, not how often a human ends up on your site. Read the trend as a signal of AI eligibility and demand pressure on a given URL, not as a stand-in for sessions.
The good news is you don’t need a custom log analysis pipeline to do this. Drop your weekly access logs into Claude or another LLM with a clear prompt: Separate the three bot categories, group hits by URL, and chart the change in fetcher volume per URL week over week. 
The model will return a structured table in minutes. This tells you which pages AI systems are fetching, whether fetch volume is rising or falling, and which tools are touching your content. It doesn’t prove the page was cited, summarized, or shown to a user. That’s a separate question for Layer 3.
Two things to keep in mind when reading the data:
Track the three categories separately. Training crawlers are infrastructure readiness, search indexers are eligibility, and user-triggered fetchers are in demand. Don’t average them, or you’ll lose all three signals.
Fetch traffic is spiky. A press mention, viral article, or backlink placement can spike one URL for a week. Smooth the data with a rolling weekly median so one anomalous spike doesn’t dominate the trend.
Takeaway




Parse access logs weekly using Claude or another LLM to separate the three bot categories and group hits by URL. 
Verify bot identity against vendor IP ranges. OpenAI publishes searchbot.json and chatgpt-user.json, while Anthropic and others publish similar ranges. 
Watch fetchers for demand signals, search indexers for eligibility, and training crawlers as a readiness check. Don’t sell any of them as a pipeline.
Layer 3a: Share of voice
This is what most agencies call “citation tracking.” The honest name for it is Share of Voice (SOV): the percentage of relevant AI answers in which your brand appears versus competitors.
SOV alone is a vanity metric. It tells you whether you’re appearing in answers, not whether anyone is buying anything as a result. To get past vanity, SOV has to be correlated against downstream demand signals like branded search and direct traffic over a meaningful window.
The data is straightforward to assemble: a time series of SOV, sourced from Profound, AthenaHQ, Peec, Semrush AI Visibility, or your own scripted prompt sampling against the OpenAI and Anthropic APIs, alongside branded search volume in GSC and direct traffic in GA4. Run it over a minimum 12-week window.
Three things to account for: 
This is correlation, not deterministic attribution. Brand growth has many causes. Frame the relationship as correlational evidence with stated confidence bands.
SOV is polling, not pageviews. The output has statistical limitations. You can see directional trends, but don’t oversell precision. Report ranges, not point estimates.
Vendors disagree. The same brand on the same day shows wildly different counts across Profound, AthenaHQ, Otterly, Semrush, and Ahrefs Brand Radar. Pick one tool, treat it as a trend instrument, and run your own scripted prompts when you need absolute counts.
The math, conceptually. You’re answering one question: When SOV goes up, does branded search follow, and by how much? Three concepts do the work:
Lag matters, and you have to find it. Don’t assume four weeks. The right lag depends on the buying cycle of the vertical. Run correlations at multiple weekly lags and use whichever one peaks.
Control for the underlying trend. Brands grow for non-AI reasons, too. Subtract the baseline organic momentum so your coefficient isn’t taking credit for PR, seasonality, or paid media.
Report a range, not a point estimate. “10-point SOV gain corresponded to X-Y% branded search lift” is defensible. “X%” alone is not.
If SOV goes up and branded search stays flat, the visibility is vanity. Say so out loud.
Takeaway




Pick one SOV vendor, treat it as a trend instrument, and run your own scripted prompts when you need absolute counts. 
Build the SOV-to-branded-search relationship with a lag test, a trend control, and a confidence range. 
Refresh quarterly, and don’t claim a win on SOV alone.
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Layer 3b: AI interrogation
SOV tells you whether your brand shows up. It doesn’t tell you what AI is actually saying when it does. That’s a separate question and, for brands that already show up a lot, arguably the more important one. The content of an AI answer determines whether you get qualified into a buyer’s shortlist or quietly disqualified from it.
Think of it this way: Imagine you sent a brand-new sales rep to a networking event with no briefing. They show up, get asked who you serve and what you do, and they fumble half the answers. 
You won’t hear about it, but you’ll lose deals from that event for months. AI is doing this on your behalf right now, at scale, in every conversation a buyer has with ChatGPT, Claude, Gemini, or Perplexity about your category. What it doesn’t know about you, you get silently disqualified for.
The interrogation layer is structured prompting designed to surface what AI knows, what it gets wrong, and where it’s getting its information. The exercise looks like SOV sampling, but the questions are different. Instead of “best [category] vendors,” you’re asking:
Who is the ideal customer for [your brand]?
What are [your brand]’s strengths and weaknesses?
What problems do [your brand]’s customers typically have?
Why would someone choose [your brand] over [top three competitors]?
What’s [your brand] known for in the [industry/vertical] space?
Run the same prompt set across multiple models on a regular cadence. Perplexity Enterprise has a feature that lets you query several models in one interface, which cuts the friction significantly. You can also script it against the OpenAI and Anthropic APIs directly if you want absolute control over the sampling.
What you’re looking for in the responses:
Factual accuracy: Is the AI correctly describing your products, services, and positioning?
ICP alignment: Does the AI describe a customer that actually matches your real ICP, or has it generalized you into a category you don’t serve?
Source attribution: Where is the AI getting its information? Your own site? Third-party reviews? A competitor’s comparison page? An outdated press mention? This tells you which content surfaces are contributing to AI’s knowledge of your brand, and which gaps are letting competitors or stale sources shape the narrative.
Weakness framing: When asked about your weaknesses or customer complaints, what surfaces? Real critiques you can address? Misinformation? Outdated issues you’ve already solved?
This is the layer that bridges brand reputation management and AI visibility. SOV asks whether you’re in the room. Interrogation asks whether what’s being said about you in the room would help you win.
Takeaway




Build a standing interrogation prompt set covering ICP, strengths, weaknesses, customer pain points, and competitive comparisons. 
Run it monthly across at least three models. Perplexity Enterprise consolidates this if you have access. Otherwise, script it. 
Track factual accuracy, ICP alignment, and source attribution over time.

When you find a source contributing to a wrong or weak narrative, that source becomes a content remediation target. 
When you find a gap — AI doesn’t know enough about you to answer a key question — that becomes a content production target.
Layer 4: Self-report
Pipeline tells the truth that dashboards can’t. Self-reported attribution from forms and sales conversations consistently surfaces double-digit percentages of pipeline as AI-influenced, even when CRM source attribution shows under 1%. That delta is the dark funnel made visible.
The signal is volunteered by motivated respondents at the bottom of the funnel, so don’t generalize to the full audience without sanity-checking. 
Cross-reference against Layer 3a. If branded search lift and self-reported AI attribution move together, you have triangulation. If they diverge, one of them is lying.
This layer takes time to bake in for industries where buyers don’t think of themselves as having “researched on AI.” The form data lags reality until the language catches up.
Takeaway




Add an explicit option to every “How did you find us” form — ChatGPT, Perplexity, Gemini, Claude, Copilot, or another AI tool — with an open-text field for the prompt or topic. 
Push the answer into your CRM as a custom property and roll it up to deal stage, closed-won value, and retention. 
Get the question into qualification scripts so SDRs ask when the form was skipped. 
Coach the sales team, and pilot the form copy before you trust the data.
Layer 5: Incrementality
You can’t run a geo-holdout on AI search the way you can on paid media. You can’t turn ChatGPT off in Cleveland. The closest substitute is a difference-in-differences analysis across a client portfolio: compare clients getting full GEO programs against matched clients getting little or none, and look for trajectory differences that aren’t explained by general market growth.
This is a benchmark study, not a clinical trial. PR, seasonality, product launches, leadership changes, and brand equity differences all bleed into the comparison. The control group is fuzzy by definition. The result is a best-effort macro view, not deterministic proof.
Two warnings:
Statistical power is real. Once you stratify by vertical and starting size, your effective sample per cell drops fast. That limits how small a lift you can credibly detect. State the minimum detectable effect when you publish, or restrict the analysis to your largest verticals.
Null results are real. A properly run benchmark can still show zero measurable lift. If your framework can’t survive a null result, it isn’t a framework.
Takeaway




Tag every client by GEO investment intensity — none, light, or full program — match on pre-treatment covariates (vertical, starting traffic, starting pipeline, and starting brand search volume), and add a buffer period before treatment. 
Track branded search and pipeline trajectories over six to 12 months. Run it as a portfolio benchmark and report what you find, including the negatives. Don’t oversell it as proof of ROI.
What the dashboard looks like
None of the layers individually proves AI search impact. Together, they build a defensible case. When the layers move together, the story is real. When they diverge, that’s where the diagnostic work lives.
Takeaway: Put seven things on one screen.
SOV and presence rate over time (Layer 3a input).
AI interrogation accuracy score and source attribution heatmap (Layer 3b output).
GA4 AI channel sessions and conversions (Layer 1).
Fitted SOV-to-branded-search relationship with confidence range (Layer 3a output).
Percent of closed-won pipeline self-reported as AI-influenced, broken out by tool (Layer 4).
12-month portfolio benchmark with minimum detectable effect (Layer 5).
Fetcher, indexer, and training crawler volume on top commercial URLs, weekly delta (Layer 2).

  
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How to operationalize GEO measurement
The temptation is to buy a vendor tool and call it done. The better move is to sequence the layers so each one starts producing signals before you commit to the next.
Takeaway




GA4 channel grouping rebuild and full user-agent capture (an afternoon).
Weekly log analysis through an LLM with the bot taxonomy above (under an hour to set up).
An SOV vendor with a 12-week observation window before publishing relationships to clients.
A standing interrogation prompt set run monthly across at least three models.
An AI source field on every lead form, with sales briefed on qualification language.
Portfolio tagging by GEO investment intensity to start the benchmark clock.
Agencies that build a transparent layered framework now will own credibility when the standards harden. The ones still selling citation count dashboards will get unwound by the first CFO who learns the difference between presence rate and a closed-won deal. 
The 2008 window is open. It’s the same one that produced every paid media agency still standing today.

AI visibility dashboards can’t answer the ROI question on their own. Here’s how to build a more credible GEO measurement model.

The 5-layer framework for measuring GEO performance

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-fred-vallayes-cover.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-fred-vallayes-cover.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-fred-vallayes-cover-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-fred-vallayes-cover-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-fred-vallayes-cover-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Frederick Vallaeys reflects on how PPC evolved from scrappy arbitrage to AI-driven automation—and what marketers risk missing next.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-fred-vallayes-cover.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-fred-vallayes-cover.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-fred-vallayes-cover-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-fred-vallayes-cover-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-fred-vallayes-cover-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<p><a href="https://www.youtube.com/embed/8SHZiWNflOQ?feature=oembed" rel="noopener noreferrer">Watch this video on YouTube</a>
</div></figure>



<p>Frederick Vallaeys’ route into PPC started with a student side hustle, not a career plan.</p>



<p>While at Stanford in 1998, he spotted a resale opportunity in used Blockbuster video cassettes and needed a way to find buyers. That led him to GoTo, an early search engine where advertisers could bid on keywords &#8211; and gave him his first glimpse of the power of paid search.</p>



<p>More than 20 years later, Vallaeys has become one of the best-known voices in PPC, a former Googler, one of the early builders in the Google Ads ecosystem, and founder of Optmyzr.</p>



<p>In this interview, he looks back at the early days of Google Ads, how automation changed everything, and what marketers need to remember as search moves from keywords to prompts.</p>



<h2 class="wp-block-heading" id="h-paid-search-started-with-a-simple-idea">Paid search started with a simple idea</h2>



<p>Vallaeys’ first “aha” moment came before Google Ads became the giant it is today.</p>



<p>Goto showed him that you didn’t need a huge budget to reach an audience. You could buy a keyword, get traffic and test whether something worked.</p>



<p>That was a major shift from traditional advertising, where budgets were bigger and measurement was weaker.</p>



<p>Paid search made advertising accessible.</p>



<h2 class="wp-block-heading" id="h-google-ads-made-performance-measurable">Google Ads made performance measurable</h2>



<p>When Vallaeys joined Google in 2002, he helped launch Google Ads in Dutch, the sixth language supported by the platform.</p>



<p>At the time, a top-tier advertiser was spending around $30,000 a month — a number that feels modest today, but was significant then.</p>



<p>What made Google different was not just traffic. It was proof.</p>



<p>The acquisition of Urchin, which became Google Analytics, and the development of conversion tracking gave advertisers visibility into what happened after the click.</p>



<p>That changed paid search from something that seemed to work into something advertisers could prove worked.</p>



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



<p>By the time Search Engine Land launched in 2006, paid search had already become a serious advertising channel.</p>



<p>For Vallaeys, Search Engine Land became more than a news source. It became a place to share ideas, learn from others and build connections.</p>



<p>In fact, Optmyzr started because of a Search Engine Land article.</p>



<p>Vallaeys had written about quality score and shared a script to calculate account-level quality score. His future co-founders commented on the article, they connected, and within half an hour of talking, decided to build what became Optmyzr.</p>



<h2 class="wp-block-heading" id="h-quality-score-was-always-about-relevance">Quality score was always about relevance</h2>



<p>Quality score has been one of the defining features of Google Ads.</p>



<p>In the earliest days, Vallaeys said, it was essentially click-through rate. Google needed a way to make sure ads were not just high-bid, but relevant.</p>



<p>That balance between bid and quality became foundational to the auction.</p>



<p>Before machine learning handled much of that work, humans were heavily involved. Vallaeys even reviewed keywords himself and could disapprove them for relevance.</p>



<h2 class="wp-block-heading" id="h-search-has-always-moved-in-cycles">Search has always moved in cycles</h2>



<p>Vallaeys sees the history of paid search as cyclical.</p>



<p>At first, advertisers had very little data. Then Google gave them more visibility through analytics, conversion tracking and search query reports. Later, privacy changes reduced some of that visibility again.</p>



<p>Performance Max followed a similar pattern. It launched with limited controls, then Google gradually added more as advertisers asked for them.</p>



<p>The industry often treats each “black box” moment as new, but Vallaeys argues that paid search has always moved between simplicity, control, automation and transparency.</p>



<h2 class="wp-block-heading" id="h-smart-bidding-changed-the-business">Smart Bidding changed the business</h2>



<p>One of the biggest turning points, according to Vallaeys, was when Smart Bidding became good.</p>



<p>That changed how advertisers worked and forced tool providers like Optmyzr to rethink their value.</p>



<p>If Google could automate bidding well, advertisers no longer needed tools simply to adjust bids. They needed “PPC insurance” — ways to monitor automation, set guardrails and understand when systems went wrong.</p>



<p>That became a key part of Optmyzr’s role in an increasingly automated world.</p>



<h2 class="wp-block-heading" id="h-ai-is-the-next-existential-shift">AI is the next existential shift</h2>



<p>For Vallaeys, the next major shift began when ChatGPT launched publicly.</p>



<p>It pushed Google to accelerate Gemini and forced the industry to think beyond keyword-based advertising.</p>



<p>Google Ads was built on keywords. But users are increasingly searching through prompts, conversations and AI assistants.</p>



<p>That raises a bigger question: should the old Google Ads system be rebuilt for prompts, or should something entirely new replace it?</p>



<h2 class="wp-block-heading" id="h-ai-search-is-not-just-search-anymore">AI search is not just search anymore</h2>



<p>Vallaeys believes AI is blurring the line between searching and doing.</p>



<p>People no longer just ask for information. They ask AI tools to create spreadsheets, draft posts, build slides and solve problems.</p>



<p>That changes what advertisers are trying to interrupt or support.</p>



<p>The opportunity is no longer just matching one keyword to one ad. It is understanding the user’s broader goal and finding the right moment to be useful.</p>



<h2 class="wp-block-heading" id="h-marketers-need-to-give-ai-better-context">Marketers need to give AI better context</h2>



<p>One mistake Vallaeys sees is that people use AI like old search.</p>



<p>They ask a narrow question, get a weak answer and decide the tool is bad.</p>



<p>His advice: give AI the real goal.</p>



<p>If you want to be healthier, don’t just ask for the best mattress. Explain the broader problem and let AI help work through the options.</p>



<p>The same applies to marketing. If you want a LinkedIn post, explain whether the goal is leads, hiring, education or brand building.</p>



<h2 class="wp-block-heading" id="h-the-next-20-years-will-reward-problem-solvers">The next 20 years will reward problem solvers</h2>



<p>Vallaeys believes marketers need to stop defining themselves by old mechanics.</p>



<p>If your job was “keyword manager,” the future may feel threatening. But if your job is to find customers and solve business problems, the tools are simply changing.</p>



<p>The next phase of search will reward people who understand customers, communicate value and adapt to new ways people discover information.</p>



<h2 class="wp-block-heading" id="h-what-he-would-tell-his-younger-self">What he would tell his younger self</h2>



<p>His first answer was simple: buy more Google stock.</p>



<p>But beyond that, Vallaeys said he is happy with the path he took.</p>



<p>His advice is to be purposeful, think in systems and join communities that give you meaningful insight.</p>



<p>For him, Search Engine Land, SMX and Silicon Valley communities helped surface problems worth solving.</p>



<h2 class="wp-block-heading" id="h-what-he-is-proudest-of">What he is proudest of</h2>



<p>Vallaeys is proud of joining Google early and contributing to the infrastructure that helped shape modern digital life.</p>



<p>While he is also proud of Optmyzr, he sees Google’s impact as operating on a much larger scale — from Google Ads to Maps, Docs and Drive.</p>



<p>His work in monetization helped fund products that changed how people access information and manage everyday life.</p>



<h2 class="wp-block-heading" id="h-one-thing-ppc-marketers-won-t-admit">One thing PPC marketers won’t admit</h2>



<p>Vallaeys joked that PPC experts never admit they don’t know the answer.</p>



<p>Instead, they say: “It depends.”</p>



<p>And in fairness, that is often true.</p>



<p>Paid search has always been full of caveats, context and changing systems. That is what makes the industry challenging — and why people who keep learning tend to last.</p>


Watch this video on YouTube




Frederick Vallaeys’ route into PPC started with a student side hustle, not a career plan.
While at Stanford in 1998, he spotted a resale opportunity in used Blockbuster video cassettes and needed a way to find buyers. That led him to GoTo, an early search engine where advertisers could bid on keywords – and gave him his first glimpse of the power of paid search.
More than 20 years later, Vallaeys has become one of the best-known voices in PPC, a former Googler, one of the early builders in the Google Ads ecosystem, and founder of Optmyzr.
In this interview, he looks back at the early days of Google Ads, how automation changed everything, and what marketers need to remember as search moves from keywords to prompts.
Paid search started with a simple idea
Vallaeys’ first “aha” moment came before Google Ads became the giant it is today.
Goto showed him that you didn’t need a huge budget to reach an audience. You could buy a keyword, get traffic and test whether something worked.
That was a major shift from traditional advertising, where budgets were bigger and measurement was weaker.
Paid search made advertising accessible.
Google Ads made performance measurable
When Vallaeys joined Google in 2002, he helped launch Google Ads in Dutch, the sixth language supported by the platform.
At the time, a top-tier advertiser was spending around $30,000 a month — a number that feels modest today, but was significant then.
What made Google different was not just traffic. It was proof.
The acquisition of Urchin, which became Google Analytics, and the development of conversion tracking gave advertisers visibility into what happened after the click.
That changed paid search from something that seemed to work into something advertisers could prove worked.
Search Engine Land helped shape the industry
By the time Search Engine Land launched in 2006, paid search had already become a serious advertising channel.
For Vallaeys, Search Engine Land became more than a news source. It became a place to share ideas, learn from others and build connections.
In fact, Optmyzr started because of a Search Engine Land article.
Vallaeys had written about quality score and shared a script to calculate account-level quality score. His future co-founders commented on the article, they connected, and within half an hour of talking, decided to build what became Optmyzr.
Quality score was always about relevance
Quality score has been one of the defining features of Google Ads.
In the earliest days, Vallaeys said, it was essentially click-through rate. Google needed a way to make sure ads were not just high-bid, but relevant.
That balance between bid and quality became foundational to the auction.
Before machine learning handled much of that work, humans were heavily involved. Vallaeys even reviewed keywords himself and could disapprove them for relevance.
Search has always moved in cycles
Vallaeys sees the history of paid search as cyclical.
At first, advertisers had very little data. Then Google gave them more visibility through analytics, conversion tracking and search query reports. Later, privacy changes reduced some of that visibility again.
Performance Max followed a similar pattern. It launched with limited controls, then Google gradually added more as advertisers asked for them.
The industry often treats each “black box” moment as new, but Vallaeys argues that paid search has always moved between simplicity, control, automation and transparency.
Smart Bidding changed the business
One of the biggest turning points, according to Vallaeys, was when Smart Bidding became good.
That changed how advertisers worked and forced tool providers like Optmyzr to rethink their value.
If Google could automate bidding well, advertisers no longer needed tools simply to adjust bids. They needed “PPC insurance” — ways to monitor automation, set guardrails and understand when systems went wrong.
That became a key part of Optmyzr’s role in an increasingly automated world.
AI is the next existential shift
For Vallaeys, the next major shift began when ChatGPT launched publicly.
It pushed Google to accelerate Gemini and forced the industry to think beyond keyword-based advertising.
Google Ads was built on keywords. But users are increasingly searching through prompts, conversations and AI assistants.
That raises a bigger question: should the old Google Ads system be rebuilt for prompts, or should something entirely new replace it?
AI search is not just search anymore
Vallaeys believes AI is blurring the line between searching and doing.
People no longer just ask for information. They ask AI tools to create spreadsheets, draft posts, build slides and solve problems.
That changes what advertisers are trying to interrupt or support.
The opportunity is no longer just matching one keyword to one ad. It is understanding the user’s broader goal and finding the right moment to be useful.
Marketers need to give AI better context
One mistake Vallaeys sees is that people use AI like old search.
They ask a narrow question, get a weak answer and decide the tool is bad.
His advice: give AI the real goal.
If you want to be healthier, don’t just ask for the best mattress. Explain the broader problem and let AI help work through the options.
The same applies to marketing. If you want a LinkedIn post, explain whether the goal is leads, hiring, education or brand building.
The next 20 years will reward problem solvers
Vallaeys believes marketers need to stop defining themselves by old mechanics.
If your job was “keyword manager,” the future may feel threatening. But if your job is to find customers and solve business problems, the tools are simply changing.
The next phase of search will reward people who understand customers, communicate value and adapt to new ways people discover information.
What he would tell his younger self
His first answer was simple: buy more Google stock.
But beyond that, Vallaeys said he is happy with the path he took.
His advice is to be purposeful, think in systems and join communities that give you meaningful insight.
For him, Search Engine Land, SMX and Silicon Valley communities helped surface problems worth solving.
What he is proudest of
Vallaeys is proud of joining Google early and contributing to the infrastructure that helped shape modern digital life.
While he is also proud of Optmyzr, he sees Google’s impact as operating on a much larger scale — from Google Ads to Maps, Docs and Drive.
His work in monetization helped fund products that changed how people access information and manage everyday life.
One thing PPC marketers won’t admit
Vallaeys joked that PPC experts never admit they don’t know the answer.
Instead, they say: “It depends.”
And in fairness, that is often true.
Paid search has always been full of caveats, context and changing systems. That is what makes the industry challenging — and why people who keep learning tend to last.

Frederick Vallaeys reflects on how PPC evolved from scrappy arbitrage to AI-driven automation—and what marketers risk missing next.

From video tapes to AI: Frederick Vallaeys on the evolution of paid search

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/SEO-or-GEO-audits-fail-without-these-3-essentials.png" class="attachment-large size-large wp-post-image" alt="SEO or GEO audits fail without these 3 essentials" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/SEO-or-GEO-audits-fail-without-these-3-essentials.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/SEO-or-GEO-audits-fail-without-these-3-essentials-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/SEO-or-GEO-audits-fail-without-these-3-essentials-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/SEO-or-GEO-audits-fail-without-these-3-essentials-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI can scale SEO and GEO audits, but only if your system has the right data, methodology, and human oversight.

Running SEO and GEO/AEO audits is an excellent use case for AI, especially with recent models that have agentic capabilities. These models have extensive knowledge bases and can perform multistep processes, such as extracting webpages, reviewing data, and formulating recommendations.
But before running your SEO or GEO/AEO audit in Claude or ChatGPT, have you considered whether the model has everything it needs to provide a good response? 
You might be shocked to discover that state-of-the-art models provide detailed recommendations without the most basic information, including Google SERPs, keyword volumes, or even the ability to fetch URL content. For example:




As CEO of an SEO/GEO agency in the B2B tech space, I receive AI-generated audits from clients and prospects daily. I call them “naive audits.” They appear detailed and impressive, but they quickly fall apart when you ask basic questions:
What was this based on?
Where did the AI get this data?
What is the methodology?
I’m writing this article to help explain the gap between what you expect from an AI-based audit and what you get in practice. My goal isn’t to discourage you from using AI for these purposes — you definitely should. Rather, I propose a framework with three elements to help you get AI audits right, ensuring they’re rooted in reality and provide real value.
‘Naive’ SEO audits: What can go wrong?
Let’s take a simple example: generating SEO recommendations for an existing blog. This should be an easy task for an advanced language model.
I took a blog written by one of our clients about shortages in the flash storage industry. It’s a timely topic, and the article could probably attract traffic if optimized properly.
Here is the prompt I gave Claude Opus 4.7 (with Adaptive Reasoning).




Claude thinks for a bit and provides a detailed 1,600-word report with everything we should fix in the article. See the full response here. I’m showing the highlights below. Looks promising!












But here’s the first warning signal: Claude hints that it had to infer the current structure of the article. Let’s check that out. I asked whether it had actually read the article:




Surprise 1: Claude couldn’t actually read the article. It didn’t admit that until I asked. Instead, it relied on search snippets. The entire analysis was based on a few snippets rather than the full content, which means many of the findings were probably irrelevant.
Now let’s take it one step further. I asked Claude to come up with a main keyword, and it suggested [intelligent data tiering]. Does that keyword actually have search volume?




Surprise 2: Claude doesn’t have access to search volumes, and when thinking about it, it admits the keyword is unlikely to have volume. Here is what Semrush had to say.




So the results were based on “inferred” content of the page and a keyword that isn’t really searched by anyone.
But let’s take it one step further. Let’s say this is the right keyword. Did Claude actually have the top SERPs for this keyword?




Surprise 3: Even assuming the keyword is fine, Claude didn’t really have the top 10 results. Again, it “inferred” the top results from related searches.
I went out of my way to help the model. I grabbed the top 10 results and provided them. Then I asked whether it would actually read them.








Surprise 4: Even given the top nine URLs, Claude was only able to read five of them. The rest were blocked and not available for retrieval.
Fun fact: In our testing, AI chatbots typically retrieved only 30% to 40% of the URLs we provided due to various technical issues. This is why it’s important to pre-scrape URLs or use a specialized library to access page content.

  
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What went wrong in this example?
Claude couldn’t fetch the page’s content. It generated a keyword with no search volume and based all the recommendations on it.
Even for this keyword, Claude didn’t have a good way to grab the top 10 SERPs. And even when I handed it the top 10 SERPs, it could only retrieve about half of them.
Two more points to think about:
Imagine the prompt wouldn’t mention a main and subkeyword. The audit would just be a list of generic SEO guidelines, like adding a title and meta description, without even attempting to focus the page on an actual query. This is why methodology matters.
The report is way too long and detailed. Even assuming it were valid, few writers would spend the time to read a 1,600-word analysis that’s longer than the post itself. Even if they read it, they probably wouldn’t have the time to implement it.
This example isn’t sensitive to the blog post or model used. Feel free to try it yourself on state-of-the-art models from OpenAI, Google, etc., with content from your own site. 
Ask for an audit and push back on the findings. You’ll be shocked at the responses.
Most importantly, this is a simplified example. Imagine what happens when you give AI an open-ended task to audit your entire site, review all the URLs, and help you grow traffic. I’ll leave it to your imagination.
How to do it right: Building a page audit agent
When we build an agent that provides SEO content recommendations, we build a self-sufficient agent that includes the following steps to make sure the output is robust:
Pre-scrape the content of the page for analysis and provide full HTML to the model.
Use a keyword research skill connected to a keyword tool to identify keywords with real volume related to the content, and allow the user to verify the keywords.
Pull the top 10 URLs for the selected query from the same keyword tool.
Pre-scrape the top 10 URLs and hand the full HTML to the model.
Use an outline skill to build an ideal outline of an SEO article for the keyword and compare it to the actual blog outline.
Instruct the model to provide minimal, bite-sized recommendations. Writers are busy, so we need to give them brief guidelines with minimal changes to align the post with the ideal structure.
Here’s the output of an agent we built for the same task:




This is accurate, insightful, and actionable for a human writer — only 350 words of concise, focused guidance.
A world of difference from the “naive audit,” which basically guessed its way through the task, provided invalid recommendations, and wrote a 1,600-word thesis explaining them.
‘Naive’ GEO/AEO audits: What can go right?
In the previous section, I showed how a simple SEO task, which should be easy for an AI model, can go horribly wrong. If this is the case with SEO, where there are established practices going back two decades and lots of authoritative material for AI to learn from, what would happen with a GEO/AEO task where there are no established practices?
Here are a few basic issues to consider when you ask a language model to audit your site for GEO/AEO:
What material do AI engines use to learn about GEO/AEO? There’s a deluge of speculative information by so-called experts and, worse, AI-generated articles with wildly hallucinated info. Lily Ray called this the AI slop loop — AI generating info on how to optimize for AI, which is then regurgitated by AI.
Are there studies based on data or experimentation? Most of the info about GEO/AEO, even content written by experts, isn’t backed by hard data, and there are very few recorded experiments. As an anecdote, I tried to find research proving the common notion that FAQs improve AI visibility. Guess what? There’s none.
Some best practices are actually harmful. Many of the so-called “best practices” in this field can be very harmful to your organic presence, again referencing Ray: “Your GEO strategy might be destroying your SEO.” Some of them could even hurt your AI presence. Following AI-generated recommendations in this field can be dangerous.
AI isn’t self-aware. It can’t tell you how to optimize for itself. This is a fallacy many people subscribe to: “I can ask Claude how to optimize my site for AI visibility, because it’s Claude, it knows what it likes.” But this is fundamentally wrong. AI engines aren’t self-aware. Claude needs a “Claude capabilities” skill because it doesn’t even know its basic features, not to mention its inner workings and how to optimize for it.
Everything these engines say is based on their world knowledge, which, in the case of GEO/AEO, is extremely lacking. If you’re going to the moon, ask someone who has actually been there, not someone who has read AI-generated content about it.
People like me, who are actively working on GEO/AEO projects with clients, are engaged in active experimentation and are learning by doing — what works and what doesn’t. We didn’t learn how to do GEO/AEO from blogs. Many of our unique learnings aren’t written anywhere, so AI engines don’t know them. You really need to become an expert, or engage with an expert, to succeed in this field.
Does this mean you can’t do GEO/AEO audits with AI?
You can. But you need to have a solid understanding of what actually works in GEO/AEO and what to avoid. 
It’s simply dangerous to blindly follow AI-generated guidance without a strong understanding of the field. Once you have a sound methodology, you can use it to build an agent that performs effective audits at the page or site level.
AI is a great execution tool, but please don’t use it to learn how to optimize for AI engines.
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The CaML framework: 3 things AI needs to provide a useful audit




Meet CaML, my new recruit. He’s a friendly AI agent that gets a lot of stuff done for us. But the important thing is he has everything he needs to succeed, abbreviated as C, M, and L:
Context/da.
Methodology.
Human in the loop.
I think the camel metaphor is fitting because a camel is self-sufficient — you can send it into the desert for weeks, and it has everything it needs to survive. Send a donkey into the desert and it will die.
A naive audit is like a donkey — it fails because it doesn’t have the basic things it needs for its desert mission.
A properly built AI agent is like a camel, which is completely self-sufficient and can survive and thrive on the hard road.
C: Context/data
The first thing your SEO/GEO agent needs is context and data to make the right decisions. Most of the problems in our example fell into this category.
Make sure your agent has:
The basic info required to analyze: Typically, crawl data and webpage content. Don’t ask the agent to get this stuff on its own. Prepare it yourself and provide it, or build an MCP server that lets the agent grab exactly what it needs.
Relevant SEO metrics: SERPs, keyword volumes, keyword ranks, clicks, impressions, sessions — anything relevant to the task at hand. Connect the relevant tooling to your AI via MCP (quite easy nowadays) and instruct it to use relevant tools for each task.
Relevant GEO/AEO metrics: Solutions like Profound, Semrush AIO, or Ahrefs Brand Radar provide critical info like visibility of your brand and competitors for important prompts.
Operational data: Do you have an SEO/GEO audit task board or ticketing system? Let the AI agent access it. This way it will know what you’re already working on, which fixes you were unable to perform for whatever reason, and avoid suggesting the same tasks again.
Business context: It’s useful to tell the agent, as part of its instructions, any important context you would share with a junior employee. For example, the size of your organization, approval process for changes, or technical infrastructure of your website.
M: Methodology
There are many ways to do SEO, and even more approaches to GEO/AEO. Don’t let the agent randomly pick its methodology. Make sure you have a solid understanding of how to perform the task at hand.
If you’re not an xEO expert, follow real experts online, read their stuff, and understand the best approach to carrying it out. If you’re just getting started, take a good SEO course or read one of the classic books (my favorite is “The Art of SEO”) to get your bearings.
Once you have your methodology down, guide the agent on how to do the work:
Define the work process: For example, for a page audit, read the content, find a main keyword, approve it with the user, read the top 10 results, then proceed with recommendations.
Specify data sources and decision support: Tell the agent specifically what data it should use and how to use it to make decisions. For example, in our page audit agent we tell the agent to first brainstorm keywords on its own, then check search volumes, then find more variations (same process a human SEO would follow).
Think about the end user: An AI agent doesn’t operate in a vacuum. It typically provides recommendations a human will need to review, approve, and implement. Think about these humans — what would make the recommendations useful and actionable for them?
Update the process as algorithms change: SEO, and especially GEO/AEO, aren’t static. Whenever Google releases a Core Update or a new AI model launches, learn about the changes, experiment if possible, and update your agent’s technique. Otherwise, it will quickly become irrelevant.
Create guardrails: Agents can go berserk. Carefully limit the agent’s operation so it can’t do any damage. I recommend never letting the agent update your site. An SEO agent should generate recommendations, and web or content teams are then responsible for implementing them. If they use AI for implementation, fine, but this is their separate responsibility. Also, think about token usage — an improperly designed agent could run up thousands of dollars in costs.
L: Human in the loop
This is possibly the most important element. Even with today’s super-advanced models, you can’t trust them to make the right decision every time. They will make mistakes, miss relevant context, hallucinate, and often run into technical issues. You need a human in the loop (HITL) to validate every agent decision
What is needed for human in the loop:
Make your agent explainable: The agent should not only spit out recommendations. It should explain to the user how it arrived at those recommendations. The explanation must be brief — this is critical, otherwise humans will ignore it and move on. Agents can churn out tasks at scale, and no one has time to read pages of analysis.
Create a process for reviewing tasks at scale: For example, we have a central recommendations board in Asana. Agents create tasks directly on this board, they are reviewed by an expert, escalated to a senior expert if needed, and only then handed over to customers.
The human must have relevant expertise: If the task is related to content, the person reviewing it should be an editor or writer. If it’s related to SEO, it probably should be an SEO expert. If you’re a small operation or a one-person team, make sure the people reviewing tasks are reasonably knowledgeable about what the agents are doing.
Use the feedback loop to improve the agent: When you review tasks, you will see recurring issues or edge cases where the agent fails or gets things wrong. Go back to the agent’s instructions and tweak them to solve the problem. It’s important to do this carefully. If you overadjust, you might create other side effects.
Tip: I recommend this prompt engineering course by Andrew Ng (old but still relevant), which covers the basics of tuning AI engine instructions.
Beyond the basics: What SEO pros can add to an AI-generated audit
Let’s finally address the elephant in the room: If you can run complex SEO/GEO/AIO audits with AI, why do you need an SEO professional on your team, or why should you hire a consultant or agency?
In light of the previous discussion, I think it should be obvious that SEO pros can add tremendous value in an agentic AI environment.
Strategy, direction, and guidance
It starts with building the agent and reviewing its output, but goes far beyond that.
Which agents should you build? What should you be focusing on? What are the main problems preventing your site from growing in traffic or AI visibility?
Having an expert on your project can guide these decisions and help you understand what can move the needle. Then AI can help execute.
Think of an SEO expert as the North Star of AI workflows: identifying growth issues, devising solutions, and designing AI systems that can execute them.
Unique analysis
SEO and GEO/AEO are highly dynamic and getting exponentially more complex. Google updates its algorithms, and AI systems launch and update models daily.
As an agency, we have to constantly innovate and find new techniques to help our clients. This means reading studies, carrying out our own studies based on customer data, and performing experiments to see what works.
This is where real SEO professionals shine. The cliché that says “AI will free up humans to do more strategic work” is really true in this case.
Instead of crunching spreadsheets and reviewing URLs until their eyes bleed, SEO experts can come up with new breakthrough techniques that will help you lead your industry.
For example, based on the results of the last Google Core Update, we’re developing a new AI Overview optimization technique. When we have it, we’ll build it into our next generation of agents, use them to optimize our clients’ content at scale, and help them get more traffic from AI Overview citations. No AI system can provide this value, at least for now. 



Measuring results and updating AI workflows
The most important part of search engine optimization, and the same holds true for GEO/AEO, is measurement and analytics.
After you’ve done all the things — written content, made technical fixes, published links, or brand mentions — you need to measure whether it actually works. Did those tactics generate traffic or visibility? What worked and what didn’t?
AI can help with this analysis. But anyone with experience in this field knows that analytics and measurement are hard. It’s difficult to get the right data, make sense of it, and draw conclusions.
Most organizations have “dashboard blindness” — they stare at graphs and make decisions without really understanding what the data means and whether it’s even valid at all.
Going forward, we will need SEO experts to measure results: to collect the right data, analyze it, present it, and make the hard call. Did it work or not? What should we do differently?
Then they can build those hard-earned lessons into AI workflows to ensure they really provide value.

  
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Building an agent-first SEO organization
As I write these words, our SEO/GEO agency is undergoing a major transformation into an agent-first enterprise. We’re building a platform of 60-plus AI agents that perform all major SEO/GEO tasks.
Our team will use these agents to provide more value to clients, and we’ll hand them over to clients so they can do their own analysis and optimization.
I’m confident this is the future of SEO agencies. Our role in providing strategy and expert guidance hasn’t changed.
But instead of doing the heavy lifting and manual work, we’ll take over the complex job of building AI systems that can do the work at scale. We’ll review their output, maintain and optimize it, and make sure they’re really delivering results for clients.
Hit me up if you want to join us on this exciting journey.

AI can scale SEO and GEO audits, but only if your system has the right data, methodology, and human oversight.

SEO/GEO audits with AI fail without these 3 essentials

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/4-ways-to-strengthen-buy-in-for-technical-SEO-work.png" class="attachment-large size-large wp-post-image" alt="4 ways to strengthen buy-in for technical SEO work" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/4-ways-to-strengthen-buy-in-for-technical-SEO-work.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/4-ways-to-strengthen-buy-in-for-technical-SEO-work-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/4-ways-to-strengthen-buy-in-for-technical-SEO-work-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/4-ways-to-strengthen-buy-in-for-technical-SEO-work-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Technical SEO projects gain traction faster when you communicate their value in terms executives and developers understand.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/4-ways-to-strengthen-buy-in-for-technical-SEO-work.png" class="attachment-large size-large wp-post-image" alt="4 ways to strengthen buy-in for technical SEO work" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/4-ways-to-strengthen-buy-in-for-technical-SEO-work.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/4-ways-to-strengthen-buy-in-for-technical-SEO-work-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/4-ways-to-strengthen-buy-in-for-technical-SEO-work-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/4-ways-to-strengthen-buy-in-for-technical-SEO-work-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>What differentiates a great <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> professional from a good one is often their ability to manage stakeholders effectively. This is especially true in <a href="https://searchengineland.com/guide/what-is-technical-seo">technical SEO</a>, where projects often depend on multiple teams, and it can be difficult to communicate the need for implementation.</p>



<p>Stakeholder management typically comes down to one thing: the perceived value of the work. With technical SEO, that can be hard to establish. Non-SEOs are unlikely to inherently understand the value of improving a website’s internal linking architecture or implementing schema markup.</p>



<p>The most effective technical SEO initiatives aren’t framed only as SEO improvements. They’re directly tied to business outcomes such as revenue growth, conversion improvements, and operational efficiency. The stronger the connection, the easier it becomes to secure stakeholder support and demonstrate long-term value.</p>



<h2 class="wp-block-heading" id="h-why-you-need-to-align-technical-seo-work-with-business-impact">Why you need to align technical SEO work with business impact</h2>



<p>Technical SEO work is rarely a top priority for executives or development teams. That’s why technical SEO recommendations need to be tied directly to business goals and measurable outcomes.</p>



<p>For example, when a company changes the CMS used for its website, the SEO implications of changing the site’s underlying code can be overlooked. On a project manager’s long list of priorities, SEO mitigations often rank low. It’s usually not until the risks and their potential impact on the migration&#8217;s success are clearly demonstrated that SEO is properly considered.</p>



<p>Technical SEO projects are also often complex workstreams. They require a strong understanding of your company’s systems and teams, along with strong communication and management skills. </p>



<p>Although you may view this work as critical to the website&#8217;s SEO health, non-SEOs may struggle to appreciate its value if they’re only hearing about crawl budget or index management. Measuring success against core business goals makes your work easier to understand and value.</p>



<p>The best way to secure buy-in for technical SEO initiatives and demonstrate their impact is by aligning them with business performance and goals.</p>



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<h2 class="wp-block-heading" id="h-the-business-outcomes-that-drive-seo-buy-in">The business outcomes that drive SEO buy-in</h2>



<p>To show how technical SEO work is likely to impact business performance, it’s critical to understand the metrics and goals that matter to your business.</p>



<p>Most companies set corporate goals each year, whether that’s growing reach, increasing revenue, or entering new markets. These goals will vary by organization, but they typically align with a few core focus areas.</p>



<h3 class="wp-block-heading" id="h-revenue">Revenue</h3>



<p>The bottom line for most businesses, whether they’re charities, ecommerce brands, multinationals, or SMEs, is revenue.</p>



<p>Linking technical SEO work directly to revenue growth is a clear way to secure buy-in and demonstrate its value.</p>



<h3 class="wp-block-heading" id="h-conversion">Conversion</h3>



<p>Another way to demonstrate the value of technical SEO is by connecting it to conversion optimization.</p>



<p>For example, <a href="https://searchengineland.com/guide/page-speed-seo-to-boost-website-performance#:~:text=Effect%20on%20conversion,your%20bottom%20line.">numerous studies</a> show that a one-second delay in page load speed can reduce conversions by up to 7%. You may want to improve page load speed because it’s hurting your Core Web Vitals scores, but in practice, it could have a much larger impact on conversion rates.</p>



<p>“We stand to see a 7% increase in conversions for every 1 second we save on load speed” is a much stronger justification for investing development resources in page speed improvements than “I want all our Core Web Vitals scores to be green.”</p>



<h3 class="wp-block-heading" id="h-cost-reduction">Cost reduction</h3>



<p>Another business priority SEOs often overlook is cost reduction. You might wonder how SEO contributes to cost savings beyond canceling a few tool subscriptions.</p>



<p>However, SEO can contribute to unnecessary website costs. For example, you may not realize how much each visit to your website costs your business.&nbsp;</p>



<p>On a small scale, the cost may seem negligible, but hosting, infrastructure, and security expenses add up quickly, especially for large websites or sites with heavy traffic. Every visit or bot hit to your site costs money.</p>



<p>Linking your technical SEO work to reducing unnecessary spend is key.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/prioritize-technical-seo-fixes-business-impact-477385">How to prioritize technical SEO fixes by business impact</a></em></strong>



<h2 class="wp-block-heading" id="h-how-to-strengthen-buy-in-for-technical-seo-work">How to strengthen buy-in for technical SEO work</h2>



<p>These four approaches can help stakeholders better understand, support, and prioritize technical SEO work.</p>



<h3 class="wp-block-heading" id="h-1-determine-the-value-of-the-work">1. Determine the value of the work</h3>



<p>Never assume a technical SEO activity is worthwhile simply because it’s considered “best practice.” Everything you work on should have a demonstrable business benefit. When planning work, discussing resources, or reporting on outcomes, always tie it back to a core organic KPI.&nbsp;</p>



<p>For example, direct organic revenue, assisted revenue through multi-touch journeys involving organic, increased visibility in traditional and LLM search platforms, increased qualified traffic, or improved conversion rates.</p>



<p>The work may not immediately generate new revenue, but it should at least support revenue growth, conversion improvements, or cost savings.</p>



<p>For example, you may want to review cannibalization across product pages. By implementing canonical tags and reviewing the internal linking structure to reinforce which pages should be canonical, you’re not just helping Google better understand your site. This work should reduce cannibalization, improve rankings, and increase organic traffic.</p>



<p>Conservatively, if you have 10,000 organic visitors per month, an average conversion rate of 3%, and an average order value of $15, increasing organic traffic by just 5% could generate an additional $7,500 per month.</p>



<p>There’s no guarantee that traffic will increase the moment canonical tags go live. However, you can model the expected gradual traffic growth over the next 12 months and estimate how revenue may be affected.</p>



<p>By doing this, you shift the conversation from “this is a good technical SEO activity for improving search visibility” to “this is a good technical SEO activity for driving revenue growth.”</p>



<h3 class="wp-block-heading" id="h-2-identify-how-the-work-will-impact-company-goals">2. Identify how the work will impact company goals</h3>



<p>Once you understand the value of your technical SEO activities, you can connect them to your company or project goals.</p>



<p>For example, let’s say a global ecommerce company has a goal of “increasing profitability in the LATAM region over the next 12 to 24 months through increased website traffic from LATAM countries and a 50% increase in spend from LATAM consumers.”</p>



<p>The company’s SEO team wants to review its hreflang implementation, but it has struggled to secure developer resources to support the work.</p>



<p>Rather than explaining the importance of the x-default tag or reciprocal references to engineering leadership, the SEO team should focus on how the work supports the company’s business goals. For example:</p>



<ul class="wp-block-list">
<li>“We’ve identified significant traffic from the LATAM region being misdirected to the U.S. website because Google is surfacing the U.S. version instead of the appropriate localized version. Visitors who land on the correct regional and language version of the site convert at a significantly higher rate than those who land on the wrong version. By reviewing our hreflang tags, we expect to improve search engines’ understanding of our international website. That should increase the number of LATAM visitors landing on the correct version of the site by Y%, leading to higher conversion rates. Based on our current traffic levels, we estimate that improved conversion rates could generate $$$$ in additional revenue at the cost of one engineer for half a sprint.”</li>
</ul>



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<h3 class="wp-block-heading" id="h-3-communicate-effectively">3. Communicate effectively</h3>



<p>One challenge with technical SEO work is that senior stakeholders don’t always understand its practical implications. When discussing the business impact of SEO work, it’s important to break it down into the core components stakeholders need to understand: who, what, where, why, when, and how.</p>



<p><strong>Who</strong>



<p>What resources will this work require? For example, will it involve only the SEO team, or will it require two developer sprints for implementation? Will the entire SEO team need to support the project, or can it be handled by a single account manager?</p>



<p><strong>What</strong>



<p>What exactly are you planning to do? Communicate this in a “wide-to-narrow” format. Start with an explanation suited for a broader, nontechnical audience, then add more specific details as the communication progresses.</p>



<p>This approach allows stakeholders who want a quick overview to get an executive summary upfront, while those interested in the finer details can continue reading.</p>



<p><strong>Where</strong>



<p>This doesn’t necessarily mean listing every page affected by the work. Instead, focus on the products, regions, or business areas involved.</p>



<p>For example, does the product owner responsible for the LATAM webpages need detailed information about hreflang tag updates? Probably not. However, they’ll likely want to know that technical SEO work is being carried out on their section of the site and that it will be completed within two weeks.</p>



<p><strong>Why</strong>



<p>What is the intended impact of this work? Explain why it matters to existing business priorities and communicate its level of importance.</p>



<p>If you’re seeking stakeholder sign-off or buy-in, this is often your strongest opportunity to secure it. Clearly explaining the “why” in the context of projects and goals stakeholders already support will help them understand the value of the proposed work.</p>



<p><strong>When</strong>



<p>Be specific about timelines. Whenever possible, estimate when the work is likely to affect the business. Even if the impact may take several months, setting expectations gives stakeholders a useful frame of reference.</p>



<p>You should also include project milestones that stakeholders can track. This demonstrates progress before the work is completed and before search engines fully process the changes.</p>



<p>For example, an hreflang review project may involve several phases: reviewing existing hreflang tags, making recommendations, creating developer tickets, and implementing updates. Rather than saying the project will take several months, break the timeline into milestones.</p>



<p>This gives stakeholders regular visibility into progress and can also highlight where a project may slow down because of resource constraints or competing priorities from other teams.</p>



<p><strong>How</strong>



<p>The way you communicate technical SEO work can strongly influence how stakeholders understand its business impact. Instead of reporting only on reduced crawl errors or improved Core Web Vitals scores, make sure reports for non-SEO stakeholders connect the work back to business outcomes.</p>



<p>For example, monthly reports should include details about technical SEO improvements, but more importantly, they should highlight:</p>



<ul class="wp-block-list">
<li>Organic revenue.</li>



<li>Incremental revenue from SEO.</li>



<li>Organic sessions (qualified traffic only).</li>



<li>Share of voice.</li>



<li>Organic conversion rate.</li>



<li>Technical health score.</li>
</ul>



<p>When discussing technical SEO activities, always explain how they affected one or more of these KPIs. Even if the explanation becomes technical, anchoring the discussion in familiar business metrics helps stakeholders understand and appreciate the impact.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/technical-seo-focus-471824">Where to focus technical SEO when you can’t do it all</a></em></strong>



<h3 class="wp-block-heading" id="h-4-prove-the-impact-over-time">4. Prove the impact over time</h3>



<p>The best way for SEOs and stakeholders to align technical SEO work with business goals is by demonstrating results over time.</p>



<p>This is an effective way to keep your technical SEO strategy aligned with business goals. It also makes future stakeholder conversations much easier when they can see how your recommendations consistently benefit the business.</p>



<p>When you complete a technical SEO implementation, don’t immediately move on to the next project. Regularly review performance over the following months to understand how the work is paying off. This also helps you better estimate time-to-impact in future projects.</p>



<p>For example, if you’ve completed a website rearchitecture project to improve topical relevance, don’t just wait to see whether rankings improve.</p>



<p>Instead, review log files each month to see how quickly Googlebot identifies changes in the site architecture. How soon after new pages launched were they crawled?</p>



<p>If you don’t have access to log files, you can map crawl rates in Google Search Console against technical SEO fixes to see how quickly Googlebot is responding and increasing crawl activity in the affected section of the site.</p>



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<h2 class="wp-block-heading" id="h-business-impact-matters-more-than-technical-best-practices">Business impact matters more than technical best practices</h2>



<p>Sometimes your best assumption about what would move the needle turns out to be wrong. In many cases, it may take several months before that becomes clear. If you don’t revisit previous implementations, you won’t know what actually worked.&nbsp;</p>



<p>Just because something is considered “best practice” doesn’t mean it will work for your site. Always check whether your technical SEO implementations are delivering the intended outcomes, even months after deployment.</p>



<p>Don’t keep these learnings to yourself. Include them in future updates and proposals. Doing so makes it much easier to demonstrate the value of your technical SEO recommendations.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/guide/advanced-technical-seo-tips">Advanced technical SEO tips: 14 technical SEO issues you’re missing</a></em></strong>

What differentiates a great SEO professional from a good one is often their ability to manage stakeholders effectively. This is especially true in technical SEO, where projects often depend on multiple teams, and it can be difficult to communicate the need for implementation.
Stakeholder management typically comes down to one thing: the perceived value of the work. With technical SEO, that can be hard to establish. Non-SEOs are unlikely to inherently understand the value of improving a website’s internal linking architecture or implementing schema markup.
The most effective technical SEO initiatives aren’t framed only as SEO improvements. They’re directly tied to business outcomes such as revenue growth, conversion improvements, and operational efficiency. The stronger the connection, the easier it becomes to secure stakeholder support and demonstrate long-term value.
Why you need to align technical SEO work with business impact
Technical SEO work is rarely a top priority for executives or development teams. That’s why technical SEO recommendations need to be tied directly to business goals and measurable outcomes.
For example, when a company changes the CMS used for its website, the SEO implications of changing the site’s underlying code can be overlooked. On a project manager’s long list of priorities, SEO mitigations often rank low. It’s usually not until the risks and their potential impact on the migration’s success are clearly demonstrated that SEO is properly considered.
Technical SEO projects are also often complex workstreams. They require a strong understanding of your company’s systems and teams, along with strong communication and management skills. 
Although you may view this work as critical to the website’s SEO health, non-SEOs may struggle to appreciate its value if they’re only hearing about crawl budget or index management. Measuring success against core business goals makes your work easier to understand and value.
The best way to secure buy-in for technical SEO initiatives and demonstrate their impact is by aligning them with business performance and goals.

  
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The business outcomes that drive SEO buy-in
To show how technical SEO work is likely to impact business performance, it’s critical to understand the metrics and goals that matter to your business.
Most companies set corporate goals each year, whether that’s growing reach, increasing revenue, or entering new markets. These goals will vary by organization, but they typically align with a few core focus areas.
Revenue
The bottom line for most businesses, whether they’re charities, ecommerce brands, multinationals, or SMEs, is revenue.
Linking technical SEO work directly to revenue growth is a clear way to secure buy-in and demonstrate its value.
Conversion
Another way to demonstrate the value of technical SEO is by connecting it to conversion optimization.
For example, numerous studies show that a one-second delay in page load speed can reduce conversions by up to 7%. You may want to improve page load speed because it’s hurting your Core Web Vitals scores, but in practice, it could have a much larger impact on conversion rates.
“We stand to see a 7% increase in conversions for every 1 second we save on load speed” is a much stronger justification for investing development resources in page speed improvements than “I want all our Core Web Vitals scores to be green.”
Cost reduction
Another business priority SEOs often overlook is cost reduction. You might wonder how SEO contributes to cost savings beyond canceling a few tool subscriptions.
However, SEO can contribute to unnecessary website costs. For example, you may not realize how much each visit to your website costs your business. 
On a small scale, the cost may seem negligible, but hosting, infrastructure, and security expenses add up quickly, especially for large websites or sites with heavy traffic. Every visit or bot hit to your site costs money.
Linking your technical SEO work to reducing unnecessary spend is key.
Dig deeper: How to prioritize technical SEO fixes by business impact



How to strengthen buy-in for technical SEO work
These four approaches can help stakeholders better understand, support, and prioritize technical SEO work.
1. Determine the value of the work
Never assume a technical SEO activity is worthwhile simply because it’s considered “best practice.” Everything you work on should have a demonstrable business benefit. When planning work, discussing resources, or reporting on outcomes, always tie it back to a core organic KPI. 
For example, direct organic revenue, assisted revenue through multi-touch journeys involving organic, increased visibility in traditional and LLM search platforms, increased qualified traffic, or improved conversion rates.
The work may not immediately generate new revenue, but it should at least support revenue growth, conversion improvements, or cost savings.
For example, you may want to review cannibalization across product pages. By implementing canonical tags and reviewing the internal linking structure to reinforce which pages should be canonical, you’re not just helping Google better understand your site. This work should reduce cannibalization, improve rankings, and increase organic traffic.
Conservatively, if you have 10,000 organic visitors per month, an average conversion rate of 3%, and an average order value of $15, increasing organic traffic by just 5% could generate an additional $7,500 per month.
There’s no guarantee that traffic will increase the moment canonical tags go live. However, you can model the expected gradual traffic growth over the next 12 months and estimate how revenue may be affected.
By doing this, you shift the conversation from “this is a good technical SEO activity for improving search visibility” to “this is a good technical SEO activity for driving revenue growth.”
2. Identify how the work will impact company goals
Once you understand the value of your technical SEO activities, you can connect them to your company or project goals.
For example, let’s say a global ecommerce company has a goal of “increasing profitability in the LATAM region over the next 12 to 24 months through increased website traffic from LATAM countries and a 50% increase in spend from LATAM consumers.”
The company’s SEO team wants to review its hreflang implementation, but it has struggled to secure developer resources to support the work.
Rather than explaining the importance of the x-default tag or reciprocal references to engineering leadership, the SEO team should focus on how the work supports the company’s business goals. For example:
“We’ve identified significant traffic from the LATAM region being misdirected to the U.S. website because Google is surfacing the U.S. version instead of the appropriate localized version. Visitors who land on the correct regional and language version of the site convert at a significantly higher rate than those who land on the wrong version. By reviewing our hreflang tags, we expect to improve search engines’ understanding of our international website. That should increase the number of LATAM visitors landing on the correct version of the site by Y%, leading to higher conversion rates. Based on our current traffic levels, we estimate that improved conversion rates could generate $$$$ in additional revenue at the cost of one engineer for half a sprint.”
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See terms.
		
3. Communicate effectively
One challenge with technical SEO work is that senior stakeholders don’t always understand its practical implications. When discussing the business impact of SEO work, it’s important to break it down into the core components stakeholders need to understand: who, what, where, why, when, and how.
Who



What resources will this work require? For example, will it involve only the SEO team, or will it require two developer sprints for implementation? Will the entire SEO team need to support the project, or can it be handled by a single account manager?
What



What exactly are you planning to do? Communicate this in a “wide-to-narrow” format. Start with an explanation suited for a broader, nontechnical audience, then add more specific details as the communication progresses.
This approach allows stakeholders who want a quick overview to get an executive summary upfront, while those interested in the finer details can continue reading.
Where



This doesn’t necessarily mean listing every page affected by the work. Instead, focus on the products, regions, or business areas involved.
For example, does the product owner responsible for the LATAM webpages need detailed information about hreflang tag updates? Probably not. However, they’ll likely want to know that technical SEO work is being carried out on their section of the site and that it will be completed within two weeks.
Why



What is the intended impact of this work? Explain why it matters to existing business priorities and communicate its level of importance.
If you’re seeking stakeholder sign-off or buy-in, this is often your strongest opportunity to secure it. Clearly explaining the “why” in the context of projects and goals stakeholders already support will help them understand the value of the proposed work.
When



Be specific about timelines. Whenever possible, estimate when the work is likely to affect the business. Even if the impact may take several months, setting expectations gives stakeholders a useful frame of reference.
You should also include project milestones that stakeholders can track. This demonstrates progress before the work is completed and before search engines fully process the changes.
For example, an hreflang review project may involve several phases: reviewing existing hreflang tags, making recommendations, creating developer tickets, and implementing updates. Rather than saying the project will take several months, break the timeline into milestones.
This gives stakeholders regular visibility into progress and can also highlight where a project may slow down because of resource constraints or competing priorities from other teams.
How



The way you communicate technical SEO work can strongly influence how stakeholders understand its business impact. Instead of reporting only on reduced crawl errors or improved Core Web Vitals scores, make sure reports for non-SEO stakeholders connect the work back to business outcomes.
For example, monthly reports should include details about technical SEO improvements, but more importantly, they should highlight:
Organic revenue.
Incremental revenue from SEO.
Organic sessions (qualified traffic only).
Share of voice.
Organic conversion rate.
Technical health score.
When discussing technical SEO activities, always explain how they affected one or more of these KPIs. Even if the explanation becomes technical, anchoring the discussion in familiar business metrics helps stakeholders understand and appreciate the impact.
Dig deeper: Where to focus technical SEO when you can’t do it all



4. Prove the impact over time
The best way for SEOs and stakeholders to align technical SEO work with business goals is by demonstrating results over time.
This is an effective way to keep your technical SEO strategy aligned with business goals. It also makes future stakeholder conversations much easier when they can see how your recommendations consistently benefit the business.
When you complete a technical SEO implementation, don’t immediately move on to the next project. Regularly review performance over the following months to understand how the work is paying off. This also helps you better estimate time-to-impact in future projects.
For example, if you’ve completed a website rearchitecture project to improve topical relevance, don’t just wait to see whether rankings improve.
Instead, review log files each month to see how quickly Googlebot identifies changes in the site architecture. How soon after new pages launched were they crawled?
If you don’t have access to log files, you can map crawl rates in Google Search Console against technical SEO fixes to see how quickly Googlebot is responding and increasing crawl activity in the affected section of the site.

  
complete picture of your search visibility.
      
Start Free Trial
    
Get started with
Business impact matters more than technical best practices
Sometimes your best assumption about what would move the needle turns out to be wrong. In many cases, it may take several months before that becomes clear. If you don’t revisit previous implementations, you won’t know what actually worked. 
Just because something is considered “best practice” doesn’t mean it will work for your site. Always check whether your technical SEO implementations are delivering the intended outcomes, even months after deployment.
Don’t keep these learnings to yourself. Include them in future updates and proposals. Doing so makes it much easier to demonstrate the value of your technical SEO recommendations.
Dig deeper: Advanced technical SEO tips: 14 technical SEO issues you’re missing

Technical SEO projects gain traction faster when you communicate their value in terms executives and developers understand.

4 ways to strengthen buy-in for technical SEO work

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/05/When-did-25-efficiency-become-not-enough.png" class="attachment-large size-large wp-post-image" alt="When did 25% efficiency become not enough" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/When-did-25-efficiency-become-not-enough.png 1672w, https://searchengineland.com/wp-content/seloads/2026/05/When-did-25-efficiency-become-not-enough-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/When-did-25-efficiency-become-not-enough-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/When-did-25-efficiency-become-not-enough-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>AI hype has created unrealistic expectations around automation, making meaningful productivity gains feel like failure.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/05/When-did-25-efficiency-become-not-enough.png" class="attachment-large size-large wp-post-image" alt="When did 25% efficiency become not enough" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/When-did-25-efficiency-become-not-enough.png 1672w, https://searchengineland.com/wp-content/seloads/2026/05/When-did-25-efficiency-become-not-enough-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/When-did-25-efficiency-become-not-enough-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/When-did-25-efficiency-become-not-enough-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p>Anyone who’s been following me on LinkedIn or X has seen my smartass responses to people making some of the dumbest claims imaginable.</p>



<ul class="wp-block-list">
<li>“100% automate your SEO.”</li>



<li>“Fully automate your content calendar.”</li>



<li>“Run your business with AI while you sleep.”</li>
</ul>



<p>At this point, I’m just waiting for AI to start doing my dishes. <em>(Please be true.)</em>



<p>But jokes aside, there’s something deeper going on here that’s worth talking about.</p>



<p><a href="https://www.seoforlunch.com/embed" rel="noopener noreferrer">View embedded content</a>



<h2 class="wp-block-heading" id="h-llm-looting-lying-manipulating">LLM: Looting, lying, manipulating</h2>



<p>I was talking with a good buddy, <a href="https://www.linkedin.com/in/robwormley/" target="_blank" rel="noopener">Rob Wormley</a>, a great GTM consultant, about the state of AI.</p>



<p>Like most conversations right now, it started with a discussion of the cool things we’re both using AI for. And honestly, there’s a lot to like.</p>



<p>It’s fast, it’s useful, and when used correctly, it can absolutely make you better at your job.</p>



<p>But pretty quickly, the conversation shifted. Across very different projects, we kept landing on the same three realities:</p>



<ul class="wp-block-list">
<li>If you’re not using AI at all, you’re missing obvious efficiency gains.</li>



<li>If you’re relying entirely on AI, you’re walking straight into quality issues and <a href="https://www.seoforlunch.com/p/ai-jail" target="_blank" rel="noopener">potential legal headaches</a>.</li>



<li>The only version that consistently works is somewhere in the middle. <strong>Human-led AI</strong>. Not flashy, not easy to sell, but it holds up.</li>
</ul>



<h2 class="wp-block-heading" id="h-what-s-wrong-with-all-in-ai">What’s wrong with all-in AI?</h2>



<p>I’ve said it before, and I’ll say it again. “<a href="https://www.seoforlunch.com/p/ai-guarantees" target="_blank" rel="noopener">AI optimization is the biggest scam of 2026.</a>” Not because AI itself is a problem, but because of how it’s being sold. Open any social platform, and you’ll see the same recycled pitch. Some Rolex-wearing, Lamborghini-driving, broccoli-haired bro telling you how to automate your entire business with a single prompt. Just comment “win,” and your problems disappear.</p>



<p><strong>Don’t believe me?<br></strong>Hold your breath and hit play below. (fair warning: cringe alert!)</p>



<p><blockquote class="instagram-media" data-instgrm-captioned="" data-instgrm-permalink="https://www.instagram.com/reel/DWKBoOVD2x0/?utm_source=ig_embed&amp;utm_campaign=loading" data-instgrm-version="14"><div> <a href="https://www.instagram.com/reel/DWKBoOVD2x0/?utm_source=ig_embed&amp;utm_campaign=loading" target="_blank"> <div> <div></div> <div> <div></div> <div></div></div></div><div></div> <div><svg width="50px" height="50px" viewBox="0 0 60 60" version="1.1" xmlns="https://www.w3.org/2000/svg" xmlns:xlink="https://www.w3.org/1999/xlink"><g stroke="none" stroke-width="1" fill="none" fill-rule="evenodd"><g transform="translate(-511.000000, -20.000000)" fill="#000000"><g><path d="M556.869,30.41 C554.814,30.41 553.148,32.076 553.148,34.131 C553.148,36.186 554.814,37.852 556.869,37.852 C558.924,37.852 560.59,36.186 560.59,34.131 C560.59,32.076 558.924,30.41 556.869,30.41 M541,60.657 C535.114,60.657 530.342,55.887 530.342,50 C530.342,44.114 535.114,39.342 541,39.342 C546.887,39.342 551.658,44.114 551.658,50 C551.658,55.887 546.887,60.657 541,60.657 M541,33.886 C532.1,33.886 524.886,41.1 524.886,50 C524.886,58.899 532.1,66.113 541,66.113 C549.9,66.113 557.115,58.899 557.115,50 C557.115,41.1 549.9,33.886 541,33.886 M565.378,62.101 C565.244,65.022 564.756,66.606 564.346,67.663 C563.803,69.06 563.154,70.057 562.106,71.106 C561.058,72.155 560.06,72.803 558.662,73.347 C557.607,73.757 556.021,74.244 553.102,74.378 C549.944,74.521 548.997,74.552 541,74.552 C533.003,74.552 532.056,74.521 528.898,74.378 C525.979,74.244 524.393,73.757 523.338,73.347 C521.94,72.803 520.942,72.155 519.894,71.106 C518.846,70.057 518.197,69.06 517.654,67.663 C517.244,66.606 516.755,65.022 516.623,62.101 C516.479,58.943 516.448,57.996 516.448,50 C516.448,42.003 516.479,41.056 516.623,37.899 C516.755,34.978 517.244,33.391 517.654,32.338 C518.197,30.938 518.846,29.942 519.894,28.894 C520.942,27.846 521.94,27.196 523.338,26.654 C524.393,26.244 525.979,25.756 528.898,25.623 C532.057,25.479 533.004,25.448 541,25.448 C548.997,25.448 549.943,25.479 553.102,25.623 C556.021,25.756 557.607,26.244 558.662,26.654 C560.06,27.196 561.058,27.846 562.106,28.894 C563.154,29.942 563.803,30.938 564.346,32.338 C564.756,33.391 565.244,34.978 565.378,37.899 C565.522,41.056 565.552,42.003 565.552,50 C565.552,57.996 565.522,58.943 565.378,62.101 M570.82,37.631 C570.674,34.438 570.167,32.258 569.425,30.349 C568.659,28.377 567.633,26.702 565.965,25.035 C564.297,23.368 562.623,22.342 560.652,21.575 C558.743,20.834 556.562,20.326 553.369,20.18 C550.169,20.033 549.148,20 541,20 C532.853,20 531.831,20.033 528.631,20.18 C525.438,20.326 523.257,20.834 521.349,21.575 C519.376,22.342 517.703,23.368 516.035,25.035 C514.368,26.702 513.342,28.377 512.574,30.349 C511.834,32.258 511.326,34.438 511.181,37.631 C511.035,40.831 511,41.851 511,50 C511,58.147 511.035,59.17 511.181,62.369 C511.326,65.562 511.834,67.743 512.574,69.651 C513.342,71.625 514.368,73.296 516.035,74.965 C517.703,76.634 519.376,77.658 521.349,78.425 C523.257,79.167 525.438,79.673 528.631,79.82 C531.831,79.965 532.853,80.001 541,80.001 C549.148,80.001 550.169,79.965 553.369,79.82 C556.562,79.673 558.743,79.167 560.652,78.425 C562.623,77.658 564.297,76.634 565.965,74.965 C567.633,73.296 568.659,71.625 569.425,69.651 C570.167,67.743 570.674,65.562 570.82,62.369 C570.966,59.17 571,58.147 571,50 C571,41.851 570.966,40.831 570.82,37.631"></path></g></g></g></svg></div><div> <div>View this post on Instagram</div></div><div></div> <div><div> <div></div> <div></div> <div></div></div><div> <div></div> <div></div></div><div> <div></div> <div></div> <div></div></div></div> <div> <div></div> <div></div></div></a><p><a href="https://www.instagram.com/reel/DWKBoOVD2x0/?utm_source=ig_embed&amp;utm_campaign=loading" target="_blank">A post shared by Carson Reed (@carsonreed)</a></div></blockquote>



<p>Ugh. Sorry. If your brain is still operational, please stay with me. If you contributed to the comments to receive their solution to becoming rich overnight. <em>Please unsubscribe.</em>



<h2 class="wp-block-heading" id="h-the-real-problem-isn-t-ai">The real problem isn’t AI</h2>



<p>Again, let me reiterate. This is not an anti-AI rant. This is an anti-broken-expectations <s>rant</s> post.</p>



<p>Somewhere along the way, “this made me 25% more efficient” stopped being a win and became a failure.</p>



<h2 class="wp-block-heading" id="h-we-ve-completely-lost-the-plot">We’ve completely lost the plot</h2>



<p>Take something like a redirect mapping project. You take a task that used to require three hours of tedious, manual work and cut it down to 30 minutes with AI support. That’s not just a win, that’s a massive gain in productivity. But now? <strong>It gets side-eyed because it wasn’t fully automated.</strong>



<p>Or content. Writing something thoughtful, original, maybe even a <a href="https://www.seoforlunch.com/p/ice" target="_blank" rel="noopener">little vulnerable</a>, used to take hours. Now you can use AI to structure, outline, and accelerate the process, getting it down to an hour with better clarity and direction. Still requires skill. Still requires a point of view. <a href="https://www.seoforlunch.com/p/good-enough" target="_blank" rel="noopener">Still delivers a better end product.</a>



<p><strong>And somehow… still not good enough.</strong>



<p>Then you get the real insanity. Instead of investing in a skilled and or credentialed person to QA, guide, and maximize what AI can do, the instinct is to cut that role entirely and tell a tool like Claude to “just handle it.”</p>



<p><strong>No oversight. No accountability. Just vibes and prompts.</strong>



<p>We’ve gone from using tools to improve output… to expecting tools to replace responsibility entirely.</p>



<p>And anything short of that gets labeled as failure.</p>



<h2 class="wp-block-heading" id="h-why-the-easy-button-sells">Why the ‘easy button’ sells</h2>



<p>The reality is AI has never been an all-or-nothing decision. It’s a spectrum. But the way it’s being marketed right now? You’d think anything short of full automation means you’re already behind.</p>



<p>That’s where the pressure kicks in. Not from the technology, but from the noise around it. Every scroll comes with another reminder that you’re “doing it wrong,” that you’re “missing out,” that you’re about to become irrelevant if you don’t flip the switch immediately.</p>



<p>“SEO is dead, GEO now! Adapt or get left behind.”</p>



<p>It’s the same playbook every time. Manufacture urgency, create a false binary, and push people to react instead of think.</p>



<p>And the delivery doesn’t help. You watch enough of these reels, and it starts to feel less like insight and more like a used car pitch. High energy, big promises, zero substance. “Trust me, this one prompt replaces your entire team.”</p>



<p>It works, though. Not because it’s credible, but <strong>because it taps into something real</strong>.</p>



<p>People don’t just want to improve their workflow. They want out of the hard parts entirely.</p>



<p>And when someone shows up selling that fantasy with confidence, a lot of people stop asking questions.</p>



<p>Yeah, buddy. Throw in the Lambo for free and consider me all-in. <img src="https://s.w.org/images/core/emoji/17.0.2/72x72/1f4b0.png" alt="💰" class="wp-smiley" /><img src="https://s.w.org/images/core/emoji/17.0.2/72x72/1f911.png" alt="🤑" class="wp-smiley" /> <img src="https://s.w.org/images/core/emoji/17.0.2/72x72/1f644.png" alt="🙄" class="wp-smiley" />



<h2 class="wp-block-heading" id="h-the-middle-ground-nobody-wants">The middle ground nobody wants</h2>



<p>Here’s the part nobody wants to hear.</p>



<p>The version of AI that actually works still requires you to think.</p>



<p>It requires you to guide it, challenge it, edit it, and take responsibility for what gets published or implemented. It’s not passive. It’s not hands-off. It doesn’t magically remove the need for experience or judgment. If anything, it puts more pressure on those things.</p>



<p><strong>Human-led AI</strong> is where the real gains are. You move faster, you cut down the busywork, you get to better outputs more efficiently. But you’re still in the driver’s seat. You’re still making decisions. You’re still accountable when something goes wrong.</p>



<p><strong>And that’s exactly why it doesn’t sell.</strong>



<p>There’s no fantasy in it. No “set it and forget it.” No promise that you can replace thinking with prompting and call it a strategy. It’s just better work, done faster, by someone who knows what they’re doing. This is much harder to fake.</p>



<p>You can’t hide behind automation when the output still depends on your judgment. You can’t outsource responsibility to a tool and pretend it’s a strategy.</p>



<p>So most people skip it. Not because it doesn’t work, but <strong>because it still requires effort.</strong>



<h2 class="wp-block-heading" id="h-this-isn-t-an-ai-problem-it-s-a-human-one">This isn’t an AI problem. It’s a human one.</h2>



<p>This isn’t an anti-AI rant. It’s an anti-human rant. More specifically, it’s about the expectation that anything short of full automation somehow isn’t good enough.</p>



<p>If it doesn’t replace your job, run your business, and print money while you sleep, it gets dismissed. Not evaluated. Not tested. Just written off.</p>



<p>That’s the problem.</p>



<p>Because that mindset guarantees misuse. It pushes people to skip the parts that actually matter. Thinking, judgment, accountability. It turns a powerful tool into a shortcut, and then into a crutch.</p>



<p>AI didn’t create this behavior. It exposed it.</p>



<p>And until that expectation shifts, most people won’t get the upside they think they’re chasing.</p>


<div class="wp-block-image">
<figure class="aligncenter"><a href="https://seoforlunch.com/?utm_source=searchengineland.com&amp;utm_medium=social"><img decoding="async" width="1774" height="887" src="https://searchengineland.com/wp-content/seloads/2026/05/leroy2.jpg.webp" alt="Leroy2" class="wp-image-476987" srcset="https://searchengineland.com/wp-content/seloads/2026/05/leroy2.jpg 1774w, https://searchengineland.com/wp-content/seloads/2026/05/leroy2-768x384.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/leroy2-1536x768.jpg 1536w" sizes="(max-width: 1774px) 100vw, 1774px" /></a></figure>
</div>


<h2 class="wp-block-heading" id="h-so-what-actually-matters">So what actually matters?</h2>



<p>AI is a multiplier, <a href="https://www.seoforlunch.com/p/ai-shortcut" target="_blank" rel="noopener">not a replacement</a>.</p>



<p>The people winning aren’t avoiding the work. They’re using AI to move faster while still thinking and making decisions.</p>



<p>A 25% gain isn’t failure. It’s an edge, if you actually use it.</p>



<p>Human-led AI… or the scam artists selling you the automated solution to “building the future.” Your call.</p>



<p id="h-this-post-first-appeared-on-the-author-s-website-and-is-republished-here-with-permission"><em>This post first appeared on the author’s website and is republished here with permission.</em>


Anyone who’s been following me on LinkedIn or X has seen my smartass responses to people making some of the dumbest claims imaginable.
“100% automate your SEO.”
“Fully automate your content calendar.”
“Run your business with AI while you sleep.”
At this point, I’m just waiting for AI to start doing my dishes. (Please be true.)



But jokes aside, there’s something deeper going on here that’s worth talking about.
View embedded content



LLM: Looting, lying, manipulating
I was talking with a good buddy, Rob Wormley, a great GTM consultant, about the state of AI.
Like most conversations right now, it started with a discussion of the cool things we’re both using AI for. And honestly, there’s a lot to like.
It’s fast, it’s useful, and when used correctly, it can absolutely make you better at your job.
But pretty quickly, the conversation shifted. Across very different projects, we kept landing on the same three realities:
If you’re not using AI at all, you’re missing obvious efficiency gains.
If you’re relying entirely on AI, you’re walking straight into quality issues and potential legal headaches.
The only version that consistently works is somewhere in the middle. Human-led AI. Not flashy, not easy to sell, but it holds up.
What’s wrong with all-in AI?
I’ve said it before, and I’ll say it again. “AI optimization is the biggest scam of 2026.” Not because AI itself is a problem, but because of how it’s being sold. Open any social platform, and you’ll see the same recycled pitch. Some Rolex-wearing, Lamborghini-driving, broccoli-haired bro telling you how to automate your entire business with a single prompt. Just comment “win,” and your problems disappear.
Don’t believe me?
Hold your breath and hit play below. (fair warning: cringe alert!)

  
 
  
 


 


 
View this post on Instagram

 
 
 
 

 
 

 
 
 

 
 
 

A post shared by Carson Reed (@carsonreed)




Ugh. Sorry. If your brain is still operational, please stay with me. If you contributed to the comments to receive their solution to becoming rich overnight. Please unsubscribe.



The real problem isn’t AI
Again, let me reiterate. This is not an anti-AI rant. This is an anti-broken-expectations rant post.
Somewhere along the way, “this made me 25% more efficient” stopped being a win and became a failure.
We’ve completely lost the plot
Take something like a redirect mapping project. You take a task that used to require three hours of tedious, manual work and cut it down to 30 minutes with AI support. That’s not just a win, that’s a massive gain in productivity. But now? It gets side-eyed because it wasn’t fully automated.



Or content. Writing something thoughtful, original, maybe even a little vulnerable, used to take hours. Now you can use AI to structure, outline, and accelerate the process, getting it down to an hour with better clarity and direction. Still requires skill. Still requires a point of view. Still delivers a better end product.



And somehow… still not good enough.



Then you get the real insanity. Instead of investing in a skilled and or credentialed person to QA, guide, and maximize what AI can do, the instinct is to cut that role entirely and tell a tool like Claude to “just handle it.”
No oversight. No accountability. Just vibes and prompts.



We’ve gone from using tools to improve output… to expecting tools to replace responsibility entirely.
And anything short of that gets labeled as failure.
Why the ‘easy button’ sells
The reality is AI has never been an all-or-nothing decision. It’s a spectrum. But the way it’s being marketed right now? You’d think anything short of full automation means you’re already behind.
That’s where the pressure kicks in. Not from the technology, but from the noise around it. Every scroll comes with another reminder that you’re “doing it wrong,” that you’re “missing out,” that you’re about to become irrelevant if you don’t flip the switch immediately.
“SEO is dead, GEO now! Adapt or get left behind.”
It’s the same playbook every time. Manufacture urgency, create a false binary, and push people to react instead of think.
And the delivery doesn’t help. You watch enough of these reels, and it starts to feel less like insight and more like a used car pitch. High energy, big promises, zero substance. “Trust me, this one prompt replaces your entire team.”
It works, though. Not because it’s credible, but because it taps into something real.
People don’t just want to improve their workflow. They want out of the hard parts entirely.
And when someone shows up selling that fantasy with confidence, a lot of people stop asking questions.
Yeah, buddy. Throw in the Lambo for free and consider me all-in.  



The middle ground nobody wants
Here’s the part nobody wants to hear.
The version of AI that actually works still requires you to think.
It requires you to guide it, challenge it, edit it, and take responsibility for what gets published or implemented. It’s not passive. It’s not hands-off. It doesn’t magically remove the need for experience or judgment. If anything, it puts more pressure on those things.
Human-led AI is where the real gains are. You move faster, you cut down the busywork, you get to better outputs more efficiently. But you’re still in the driver’s seat. You’re still making decisions. You’re still accountable when something goes wrong.
And that’s exactly why it doesn’t sell.



There’s no fantasy in it. No “set it and forget it.” No promise that you can replace thinking with prompting and call it a strategy. It’s just better work, done faster, by someone who knows what they’re doing. This is much harder to fake.
You can’t hide behind automation when the output still depends on your judgment. You can’t outsource responsibility to a tool and pretend it’s a strategy.
So most people skip it. Not because it doesn’t work, but because it still requires effort.



This isn’t an AI problem. It’s a human one.
This isn’t an anti-AI rant. It’s an anti-human rant. More specifically, it’s about the expectation that anything short of full automation somehow isn’t good enough.
If it doesn’t replace your job, run your business, and print money while you sleep, it gets dismissed. Not evaluated. Not tested. Just written off.
That’s the problem.
Because that mindset guarantees misuse. It pushes people to skip the parts that actually matter. Thinking, judgment, accountability. It turns a powerful tool into a shortcut, and then into a crutch.
AI didn’t create this behavior. It exposed it.
And until that expectation shifts, most people won’t get the upside they think they’re chasing.




So what actually matters?
AI is a multiplier, not a replacement.
The people winning aren’t avoiding the work. They’re using AI to move faster while still thinking and making decisions.
A 25% gain isn’t failure. It’s an edge, if you actually use it.
Human-led AI… or the scam artists selling you the automated solution to “building the future.” Your call.
This post first appeared on the author’s website and is republished here with permission.

AI hype has created unrealistic expectations around automation, making meaningful productivity gains feel like failure.

When did 25% efficiency become not enough?

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/AtData-article-20260518-1.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/AtData-article-20260518-1.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/AtData-article-20260518-1-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/AtData-article-20260518-1-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/AtData-article-20260518-1-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Adapting to customer journeys that increasingly resemble competing AI systems interpreting trust, risk, intent, and identity in real time.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/AtData-article-20260518-1.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/AtData-article-20260518-1.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/AtData-article-20260518-1-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/AtData-article-20260518-1-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/AtData-article-20260518-1-1200x675.png 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/2026/05/AtData-article-20260518-1.png" alt="" class="wp-image-477243" srcset="https://searchengineland.com/wp-content/seloads/2026/05/AtData-article-20260518-1.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/AtData-article-20260518-1-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/AtData-article-20260518-1-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/AtData-article-20260518-1-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></figure>
</div>


<p>For most of modern marketing history, the operating assumption was surprisingly theatrical. Brands performed. Consumers watched. Channels existed primarily to distribute persuasion more efficiently than the next company.</p>



<p>Even performance marketing, for all its mathematical confidence, still revolved around a fundamentally human premise: somewhere on the other side of the screen sat a person making a series of reasonably linear decisions.</p>



<p>That model is beginning to fracture.</p>



<p>Not because consumers disappeared. Because software started participating in the decision-making process and now marketers need to take notice.</p>



<p>Recommendation systems already shape discovery more aggressively than most creative campaigns. Fraud models silently determine who gets trusted. Identity systems decide which experiences persist across channels. Inbox providers filter commercial visibility before the first pixel renders. Algorithms increasingly negotiate attention long before a consumer consciously exercises preference.</p>



<h2 class="wp-block-heading" id="h-now-layer-autonomous-agents-into-that-environment">Now layer autonomous agents into that environment.</h2>



<p>The industry likes to discuss AI as though it were another productivity layer strapped onto existing workflows. Faster segmentation. Faster content generation. Faster optimization. The framing is comforting because it preserves familiar power structures. Humans remain pilots. AI becomes copilots.</p>



<p>That interpretation will age poorly.</p>



<h2 class="wp-block-heading" id="h-the-rise-of-machine-coordination">The rise of machine coordination</h2>



<p>What is emerging looks less like workflow automation and more like distributed machine coordination, where marketing becomes the orchestration layer sitting above thousands of semi-independent systems continuously interpreting intent, trust, risk, relevance, identity, and value in parallel.</p>



<p>Air traffic control is the more accurate analogy than broadcasting.</p>



<p>Not because marketers suddenly gain more control. Quite the opposite.</p>



<p>Air traffic controllers do not fly the planes. They govern dynamic systems they cannot fully see, predict, or command directly. Their value comes from maintaining harmony under conditions of partial visibility, compressed decision windows, and escalating complexity.</p>



<p>Modern marketing is drifting toward the same operational reality.</p>



<p>A customer journey no longer resembles a funnel so much as a negotiation between competing models. One system predicts purchase intent. Another scores fraud risk. Another suppresses outreach frequency. Another determines deliverability. Another rewrites creative dynamically. Another optimizes toward revenue. Another optimizes toward retention.</p>



<p>Increasingly, those systems are not sequential. They are simultaneous. And occasionally adversarial.</p>



<p>The uncomfortable truth is that many organizations already have machine ecosystems making contradictory decisions about the same customer at the same time. One model flags a user as high value while another quietly suppresses them as suspicious. One system personalizes aggressively while another strips identifiers for compliance reasons. One platform optimizes for engagement while another inadvertently rewards synthetic behavior because the metrics still look healthy on dashboards presented during quarterly business reviews with reassuring shades of green.</p>



<p>The machines are not aligned because the organization itself is not aligned.</p>



<p>AI simply exposes the inconsistency faster.</p>



<h2 class="wp-block-heading" id="h-why-identity-infrastructure-is-moving-back-to-the-center">Why identity infrastructure is moving back to the center</h2>



<p>This is partly why <a href="https://atdata.com/identity-infrastructure/?utm_source=martech&amp;utm_medium=sponsored-content&amp;utm_campaign=2026-05-atdata-identity-infrastructure-lp" target="_blank" rel="noopener sponsored nofollow">identity infrastructure</a> is becoming strategically central again after years of being treated as plumbing. The market spent the better part of a decade obsessing over activation while quietly underinvesting in signal integrity. That was manageable when humans remained the dominant interpreters inside the system. Humans compensate for ambiguity surprisingly well. Autonomous systems do not. They operationalize it.</p>



<p>An inaccurate identity layer inside a partially automated environment behaves less like a data quality issue and more like corrupted air traffic telemetry.</p>



<p>Small inconsistencies compound. Routing errors multiply. Trust deteriorates asymmetrically.</p>



<p>And unlike human teams, machine systems rarely announce confusion elegantly. They simply optimize into distortion.</p>



<p>This creates a strange inversion inside marketing leadership. Creativity still matters enormously, but increasingly at the architectural level rather than the asset level. The future advantage may belong less to organizations producing the highest volume of content and more to those capable of designing stable coordination systems between intelligence layers operating at machine speed.</p>



<p>In practical terms, this changes the strategic role of signal networks.</p>



<p>Historically, identity verification, email intelligence, engagement activity, and fraud prevention were often treated as supporting functions orbiting around “core” marketing execution. Useful. Necessary. Operational.</p>



<p>That hierarchy is beginning to reverse.</p>



<h2 class="wp-block-heading" id="h-the-dangerous-illusion-of-good-enough-signals">The dangerous illusion of ‘good enough’ signals</h2>



<p>In environments driven by autonomous decisioning, the quality of orchestration becomes inseparable from the quality of underlying identity confidence. Systems cannot coordinate effectively if they cannot reliably distinguish between persistence and noise, trust and mimicry, engagement and manufactured activity.</p>



<p>Which introduces a slightly uncomfortable possibility the industry has not fully metabolized yet.</p>



<p>Many companies may discover they do not actually know how much of their current performance is being generated by real human value versus increasingly sophisticated synthetic behavior patterns that merely resemble value convincingly enough to pass thresholds.</p>



<p>Because AI systems do not inherently optimize for truth. They optimize for measurable success criteria. If synthetic engagement produces downstream metrics that resemble commercial performance, large portions of the ecosystem may continue rewarding it until economic consequences surface somewhere else entirely. Usually finance. Eventually legal. Occasionally regulatory testimony delivered in rooms with very expensive wood paneling and unusually tense water pitchers.</p>



<p>This is where the industry’s fixation on personalization starts looking slightly outdated.</p>



<p>The emerging challenge is not simply predicting what customers want. It is maintaining stable <a href="https://atdata.com/trust-signals/?utm_source=martech&amp;utm_medium=sponsored-content&amp;utm_campaign=2026-05-atdata-trust-signals-lp" target="_blank" rel="noopener sponsored nofollow">trust frameworks</a> across environments where humans, agents, synthetic actors, fraud systems, and optimization engines increasingly interact with each other continuously and often invisibly.</p>



<p>That is a fundamentally different operating environment.</p>



<h2 class="wp-block-heading" id="h-the-new-competitive-advantage">The new competitive advantage</h2>



<p>It also explains why resilient signal infrastructure is becoming more valuable than isolated data abundance. Volume alone becomes dangerous when orchestration complexity rises faster than governance maturity. More signals do not necessarily create more clarity. Sometimes they create atmospheric interference.</p>



<p>Experienced pilots know this instinctively. During periods of turbulence, the problem is rarely lack of instrumentation. It is determining which instruments remain trustworthy under pressure.</p>



<p>The same principle is beginning to apply across marketing ecosystems.</p>



<p>This is partly why <a href="https://atdata.com/email-anchored-activity-backed-identity/?utm_source=martech&amp;utm_medium=sponsored-content&amp;utm_campaign=2026-05-atdata-email-anchored-activity-backed-lp" target="_blank" rel="noopener sponsored nofollow">activity-based intelligence</a> is gaining strategic importance beyond traditional campaign optimization. Persistent behavioral validation, identity confidence, deliverability integrity, fraud detection, and cross-channel trust signals increasingly function less like marketing enhancements and more like stabilization infrastructure for autonomous ecosystems.</p>



<p>Quietly, the center of gravity is shifting.</p>



<p>Not toward companies with the loudest AI messaging. Toward organizations capable of maintaining operational trust while automation scales. Toward signal networks built from continuous real-world activity rather than static assumptions. Toward systems designed to evaluate identity dynamically because static identity itself is becoming less economically useful in machine-mediated environments.</p>



<p>The irony is difficult to miss.</p>



<p>For years, marketing departments were told to become more scientific. More automated. More data-driven. Now many are discovering that scaling intelligence without scaling signal integrity resembles building faster aircraft while neglecting radar calibration.</p>



<p>Impressive right up until visibility disappears.</p>



<p>And visibility is about to become the defining constraint of the next decade.</p>



<p>Not visibility into consumers. <a href="https://atdata.com/identity-infrastructure-powering-the-customer-journey/?utm_source=martech&amp;utm_medium=sponsored-content&amp;utm_campaign=2026-05-atdata-identity-infrastructure-powering-the-customer-journey-lp" target="_blank" rel="noopener sponsored nofollow">Visibility into the behavior</a> of the systems acting on their behalf.</p>






For most of modern marketing history, the operating assumption was surprisingly theatrical. Brands performed. Consumers watched. Channels existed primarily to distribute persuasion more efficiently than the next company.
Even performance marketing, for all its mathematical confidence, still revolved around a fundamentally human premise: somewhere on the other side of the screen sat a person making a series of reasonably linear decisions.
That model is beginning to fracture.
Not because consumers disappeared. Because software started participating in the decision-making process and now marketers need to take notice.
Recommendation systems already shape discovery more aggressively than most creative campaigns. Fraud models silently determine who gets trusted. Identity systems decide which experiences persist across channels. Inbox providers filter commercial visibility before the first pixel renders. Algorithms increasingly negotiate attention long before a consumer consciously exercises preference.
Now layer autonomous agents into that environment.
The industry likes to discuss AI as though it were another productivity layer strapped onto existing workflows. Faster segmentation. Faster content generation. Faster optimization. The framing is comforting because it preserves familiar power structures. Humans remain pilots. AI becomes copilots.
That interpretation will age poorly.
The rise of machine coordination
What is emerging looks less like workflow automation and more like distributed machine coordination, where marketing becomes the orchestration layer sitting above thousands of semi-independent systems continuously interpreting intent, trust, risk, relevance, identity, and value in parallel.
Air traffic control is the more accurate analogy than broadcasting.
Not because marketers suddenly gain more control. Quite the opposite.
Air traffic controllers do not fly the planes. They govern dynamic systems they cannot fully see, predict, or command directly. Their value comes from maintaining harmony under conditions of partial visibility, compressed decision windows, and escalating complexity.
Modern marketing is drifting toward the same operational reality.
A customer journey no longer resembles a funnel so much as a negotiation between competing models. One system predicts purchase intent. Another scores fraud risk. Another suppresses outreach frequency. Another determines deliverability. Another rewrites creative dynamically. Another optimizes toward revenue. Another optimizes toward retention.
Increasingly, those systems are not sequential. They are simultaneous. And occasionally adversarial.
The uncomfortable truth is that many organizations already have machine ecosystems making contradictory decisions about the same customer at the same time. One model flags a user as high value while another quietly suppresses them as suspicious. One system personalizes aggressively while another strips identifiers for compliance reasons. One platform optimizes for engagement while another inadvertently rewards synthetic behavior because the metrics still look healthy on dashboards presented during quarterly business reviews with reassuring shades of green.
The machines are not aligned because the organization itself is not aligned.
AI simply exposes the inconsistency faster.
Why identity infrastructure is moving back to the center
This is partly why identity infrastructure is becoming strategically central again after years of being treated as plumbing. The market spent the better part of a decade obsessing over activation while quietly underinvesting in signal integrity. That was manageable when humans remained the dominant interpreters inside the system. Humans compensate for ambiguity surprisingly well. Autonomous systems do not. They operationalize it.
An inaccurate identity layer inside a partially automated environment behaves less like a data quality issue and more like corrupted air traffic telemetry.
Small inconsistencies compound. Routing errors multiply. Trust deteriorates asymmetrically.
And unlike human teams, machine systems rarely announce confusion elegantly. They simply optimize into distortion.
This creates a strange inversion inside marketing leadership. Creativity still matters enormously, but increasingly at the architectural level rather than the asset level. The future advantage may belong less to organizations producing the highest volume of content and more to those capable of designing stable coordination systems between intelligence layers operating at machine speed.
In practical terms, this changes the strategic role of signal networks.
Historically, identity verification, email intelligence, engagement activity, and fraud prevention were often treated as supporting functions orbiting around “core” marketing execution. Useful. Necessary. Operational.
That hierarchy is beginning to reverse.
The dangerous illusion of ‘good enough’ signals
In environments driven by autonomous decisioning, the quality of orchestration becomes inseparable from the quality of underlying identity confidence. Systems cannot coordinate effectively if they cannot reliably distinguish between persistence and noise, trust and mimicry, engagement and manufactured activity.
Which introduces a slightly uncomfortable possibility the industry has not fully metabolized yet.
Many companies may discover they do not actually know how much of their current performance is being generated by real human value versus increasingly sophisticated synthetic behavior patterns that merely resemble value convincingly enough to pass thresholds.
Because AI systems do not inherently optimize for truth. They optimize for measurable success criteria. If synthetic engagement produces downstream metrics that resemble commercial performance, large portions of the ecosystem may continue rewarding it until economic consequences surface somewhere else entirely. Usually finance. Eventually legal. Occasionally regulatory testimony delivered in rooms with very expensive wood paneling and unusually tense water pitchers.
This is where the industry’s fixation on personalization starts looking slightly outdated.
The emerging challenge is not simply predicting what customers want. It is maintaining stable trust frameworks across environments where humans, agents, synthetic actors, fraud systems, and optimization engines increasingly interact with each other continuously and often invisibly.
That is a fundamentally different operating environment.
The new competitive advantage
It also explains why resilient signal infrastructure is becoming more valuable than isolated data abundance. Volume alone becomes dangerous when orchestration complexity rises faster than governance maturity. More signals do not necessarily create more clarity. Sometimes they create atmospheric interference.
Experienced pilots know this instinctively. During periods of turbulence, the problem is rarely lack of instrumentation. It is determining which instruments remain trustworthy under pressure.
The same principle is beginning to apply across marketing ecosystems.
This is partly why activity-based intelligence is gaining strategic importance beyond traditional campaign optimization. Persistent behavioral validation, identity confidence, deliverability integrity, fraud detection, and cross-channel trust signals increasingly function less like marketing enhancements and more like stabilization infrastructure for autonomous ecosystems.
Quietly, the center of gravity is shifting.
Not toward companies with the loudest AI messaging. Toward organizations capable of maintaining operational trust while automation scales. Toward signal networks built from continuous real-world activity rather than static assumptions. Toward systems designed to evaluate identity dynamically because static identity itself is becoming less economically useful in machine-mediated environments.
The irony is difficult to miss.
For years, marketing departments were told to become more scientific. More automated. More data-driven. Now many are discovering that scaling intelligence without scaling signal integrity resembles building faster aircraft while neglecting radar calibration.
Impressive right up until visibility disappears.
And visibility is about to become the defining constraint of the next decade.
Not visibility into consumers. Visibility into the behavior of the systems acting on their behalf.

Adapting to customer journeys that increasingly resemble competing AI systems interpreting trust, risk, intent, and identity in real time.

Marketing is entering its ‘air traffic control’ era by AtData

<div><img width="1600" height="900" src="https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13.png 1600w, https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13-1200x675.png 1200w" sizes="(max-width: 1600px) 100vw, 1600px" /></div>Dean Kadi shared a real-world PPC story about the consequences of ignoring performance data and overriding a winning ad strategy.

<div><img width="1600" height="900" src="https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13.png 1600w, https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13-1200x675.png 1200w" sizes="(max-width: 1600px) 100vw, 1600px" /></div>
<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<p><a href="https://www.youtube.com/embed/EB14i8NCEuE?feature=oembed" rel="noopener noreferrer">Watch this video on YouTube</a>
</div></figure>



<p>In a recent PPC Live podcast conversation, Dean Kadi, Head of Paid Growth at OneLink Media, shared a real-world agency experience where a client insisted on replacing high-performing Meta ads with heavily branded creative — despite clear evidence that the existing strategy was delivering strong results. The discussion highlights the tension agencies often face between expertise and client preference, while offering valuable lessons on communication, testing, tracking, and why data should always lead decision-making in PPC.</p>



<h2 class="wp-block-heading" id="h-the-campaign-was-performing-exceptionally-well">The campaign was performing exceptionally well</h2>



<p>Dean Kadi and his team at One Link Media built a highly successful Meta advertising strategy for premium woodworking brand Rubio Monocoat using user-generated content (UGC). By testing multiple creators, hooks, formats, and messaging angles, they improved the account’s ROAS from around 2.1x to consistently between 3x and 4x. Their testing revealed that the biggest purchase driver was not the product’s variety of colours, but the fact that customers only needed one coat of product, saving significant time and effort.</p>



<h2 class="wp-block-heading" id="h-the-client-wanted-to-pause-all-winning-ads">The client wanted to pause all winning ads</h2>



<p>Despite strong performance, the client unexpectedly requested that all successful UGC ads be paused in favour of heavily branded static and video creatives. The new ads looked polished but failed to feel native to the Meta platform, which is often critical for engagement and conversion. The decision wasn’t based on performance issues but on the client’s preference for more traditional branding.</p>



<h2 class="wp-block-heading" id="h-the-dangerous-assumption-behind-the-new-strategy">The dangerous assumption behind the new strategy</h2>



<p>The client based their new creative direction on a survey suggesting customers liked the brand’s colour range, assuming this was the primary reason people purchased. However, the agency’s testing data had already proven otherwise. This highlights a common mistake in marketing where internal assumptions or isolated feedback override broader performance data and real-world customer behaviour.</p>



<h2 class="wp-block-heading" id="h-we-d-prefer-this-to-be-a-winner">“We’d prefer this to be a winner”</h2>



<p>One of the most telling moments in the discussion came when the client admitted they simply wanted the new creative approach to succeed. Dean pointed out that paid media doesn’t work based on preference or hope — audiences decide what resonates. No matter how strongly stakeholders feel about a campaign direction, performance data ultimately determines success.</p>



<h2 class="wp-block-heading" id="h-what-agencies-should-do-in-situations-like-this">What agencies should do in situations like this</h2>



<p>Dean advised agencies to stay calm, professional, and evidence-led when disagreements with clients arise. Rather than arguing emotionally, marketers should clearly communicate risks, explain their reasoning, and document recommendations in writing. By maintaining professionalism and allowing the data to speak for itself, agencies can protect relationships while still standing behind their expertise.</p>



<h2 class="wp-block-heading" id="h-the-results-tanked-exactly-as-expected">The results tanked — exactly as expected</h2>



<p>The new branded creatives quickly underperformed, with rising acquisition costs and declining efficiency across Meta campaigns. Although the agency continued testing audiences and optimisation strategies, the core issue remained the creative itself. After approximately eight weeks of poor results, it became clear that the client’s new direction was not working.</p>



<h2 class="wp-block-heading" id="h-returning-to-ugc-restored-performance">Returning to UGC restored performance</h2>



<p>Once the client agreed to reintroduce the original UGC ads, campaign performance improved rapidly within just a couple of weeks. The return of native-looking content and proven messaging angles restored the account’s efficiency and validated the agency’s original strategy. Interestingly, Google Ads performance remained relatively stable because those campaigns relied more heavily on branded search activity.</p>



<h2 class="wp-block-heading" id="h-the-bigger-lesson-let-data-tell-the-story">The bigger lesson: let data tell the story</h2>



<p>Dean’s biggest takeaway was that agencies should rely on data rather than emotion when navigating difficult client situations. Sometimes clients need to see underperformance firsthand before accepting recommendations. By consistently presenting clear reporting and measurable outcomes, marketers can use evidence to guide conversations and rebuild trust.</p>



<h2 class="wp-block-heading" id="h-common-ppc-mistakes-agencies-still-encounter">Common PPC mistakes agencies still encounter</h2>



<p>Beyond this client story, Dean highlighted poor tracking setup as one of the most common mistakes still seen in PPC accounts today. Missing server-side tracking, incorrect event configurations, and weak conversion tracking setups can severely impact optimisation and reporting. Even the strongest campaigns struggle if the underlying data infrastructure is flawed.</p>



<h2 class="wp-block-heading" id="h-ai-won-t-fix-a-bad-strategy">AI won’t fix a bad strategy</h2>



<p>Dean also warned against overreliance on AI tools in marketing. While AI can improve efficiency and speed up workflows, it cannot compensate for weak strategy or poor thinking. Marketers still need to critically evaluate outputs, refine prompts, and apply human judgment, because clients ultimately hold people accountable — not AI systems.</p>



<h2 class="wp-block-heading" id="h-final-thoughts">Final thoughts</h2>



<p>This story serves as a reminder that successful PPC campaigns rely on testing, data, and strategic discipline rather than internal opinions or branding preferences alone. Agencies must balance professionalism with confidence in their expertise, document their recommendations carefully, and trust performance metrics over assumptions. In the end, audiences decide what works — and the data almost always reveals the truth.</p>


Watch this video on YouTube




In a recent PPC Live podcast conversation, Dean Kadi, Head of Paid Growth at OneLink Media, shared a real-world agency experience where a client insisted on replacing high-performing Meta ads with heavily branded creative — despite clear evidence that the existing strategy was delivering strong results. The discussion highlights the tension agencies often face between expertise and client preference, while offering valuable lessons on communication, testing, tracking, and why data should always lead decision-making in PPC.
The campaign was performing exceptionally well
Dean Kadi and his team at One Link Media built a highly successful Meta advertising strategy for premium woodworking brand Rubio Monocoat using user-generated content (UGC). By testing multiple creators, hooks, formats, and messaging angles, they improved the account’s ROAS from around 2.1x to consistently between 3x and 4x. Their testing revealed that the biggest purchase driver was not the product’s variety of colours, but the fact that customers only needed one coat of product, saving significant time and effort.
The client wanted to pause all winning ads
Despite strong performance, the client unexpectedly requested that all successful UGC ads be paused in favour of heavily branded static and video creatives. The new ads looked polished but failed to feel native to the Meta platform, which is often critical for engagement and conversion. The decision wasn’t based on performance issues but on the client’s preference for more traditional branding.
The dangerous assumption behind the new strategy
The client based their new creative direction on a survey suggesting customers liked the brand’s colour range, assuming this was the primary reason people purchased. However, the agency’s testing data had already proven otherwise. This highlights a common mistake in marketing where internal assumptions or isolated feedback override broader performance data and real-world customer behaviour.
“We’d prefer this to be a winner”
One of the most telling moments in the discussion came when the client admitted they simply wanted the new creative approach to succeed. Dean pointed out that paid media doesn’t work based on preference or hope — audiences decide what resonates. No matter how strongly stakeholders feel about a campaign direction, performance data ultimately determines success.
What agencies should do in situations like this
Dean advised agencies to stay calm, professional, and evidence-led when disagreements with clients arise. Rather than arguing emotionally, marketers should clearly communicate risks, explain their reasoning, and document recommendations in writing. By maintaining professionalism and allowing the data to speak for itself, agencies can protect relationships while still standing behind their expertise.
The results tanked — exactly as expected
The new branded creatives quickly underperformed, with rising acquisition costs and declining efficiency across Meta campaigns. Although the agency continued testing audiences and optimisation strategies, the core issue remained the creative itself. After approximately eight weeks of poor results, it became clear that the client’s new direction was not working.
Returning to UGC restored performance
Once the client agreed to reintroduce the original UGC ads, campaign performance improved rapidly within just a couple of weeks. The return of native-looking content and proven messaging angles restored the account’s efficiency and validated the agency’s original strategy. Interestingly, Google Ads performance remained relatively stable because those campaigns relied more heavily on branded search activity.
The bigger lesson: let data tell the story
Dean’s biggest takeaway was that agencies should rely on data rather than emotion when navigating difficult client situations. Sometimes clients need to see underperformance firsthand before accepting recommendations. By consistently presenting clear reporting and measurable outcomes, marketers can use evidence to guide conversations and rebuild trust.
Common PPC mistakes agencies still encounter
Beyond this client story, Dean highlighted poor tracking setup as one of the most common mistakes still seen in PPC accounts today. Missing server-side tracking, incorrect event configurations, and weak conversion tracking setups can severely impact optimisation and reporting. Even the strongest campaigns struggle if the underlying data infrastructure is flawed.
AI won’t fix a bad strategy
Dean also warned against overreliance on AI tools in marketing. While AI can improve efficiency and speed up workflows, it cannot compensate for weak strategy or poor thinking. Marketers still need to critically evaluate outputs, refine prompts, and apply human judgment, because clients ultimately hold people accountable — not AI systems.
Final thoughts
This story serves as a reminder that successful PPC campaigns rely on testing, data, and strategic discipline rather than internal opinions or branding preferences alone. Agencies must balance professionalism with confidence in their expertise, document their recommendations carefully, and trust performance metrics over assumptions. In the end, audiences decide what works — and the data almost always reveals the truth.

Dean Kadi shared a real-world PPC story about the consequences of ignoring performance data and overriding a winning ad strategy.

Dean Kadi talks clients ignoring performance data

<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.
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(Provided to Search Engine Land by SEOjobs.com) 



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At NerdWallet, we’re on a mission to bring clarity to all of life’s financial decisions and every great mission needs a team of exceptional Nerds. We’ve built an inclusive, flexible, and candid culture where you’re empowered to grow, take smart risks, and be unapologetically yourself (cape optional). Whether remote or in-office, we support how you […]

Digital Marketing Manager (SEO/SEM)
April 28, 2026 
Job Description Attention: Kapitus is aware that individuals posing as recruiters may be communicating with job seekers about supposed positions with Kapitus. Kapitus has received reports that the content and method of communication can vary, but messages may contain requests for payment (e.g., fees for equipment or training) and/or for sensitive financial information. Kapitus will […]

SEO Account Manager
April 24, 2026 
Remote (Canada-wide) · Full-time · $75,000–$90,000 CAD About Webserv Webserv is a digital marketing agency that helps mission-driven businesses — particularly in behavioral health — grow through SEO, paid media, and conversion-focused web strategy. We’re a tight-knit team that values curiosity, ownership, and the kind of work that actually moves the needle for our clients. […]

Sr. SEO/AEO Manager (B2B SaaS – US or Canada Based)
April 21, 2026 
The Basics: Growth Plays is hiring a Senior SEO/AEO Manager based in the US, Canada or LATAM, to support and manage ongoing customer engagements and relationships. You’ll act as the main point of contact for your clients, and focus on building relationships and trust while driving strategy-aligned growth for the long term. This role is […]

Intermediate Search Specialist
April 17, 2026 
We are seeking an intermediate-level SEO Specialist for Hive Digital, a cutting-edge and award-winning agency that prides itself on helping change the world for the better. We offer a highly collaborative team that works together to deliver the best possible outcomes for our clients in a fast-paced, fun work environment. Are you ready to bring […]

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







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The Amazon Digital Advertising team (ADA) focuses on leveraging Amazon's Advertising DSP to support the digital marketing initiatives of Amazon's own strategic businesses. Our stakeholders are the internal marketing teams across Amazon and their agencies of record. Our mission is to develop better digital advertising programs for those businesses. We operate across marketing, program management, […]

Growth Marketing Lead — Campaigns & Projects
May 18, 2026 
Intercontinental Exchange (ICE) is seeking a Senior Growth Marketing Associate in New York. This role involves overseeing marketing projects, managing multi-channel campaigns, and ensuring project completion aligns with business goals. Required qualifications include a Bachelor’s degree and strong project management skills. The expected base salary ranges from $100,000 to $120,000 USD, alongside comprehensive employee benefits […]

Growth Marketing Director – Kings League USA
May 18, 2026 
In order to be considered for this role, after clicking "Apply Now" above and being redirected, you must fully complete the application process on the follow-up screen. About the Kings League Created and organized by Kosmos, the Kings League has become a global phenomenon that has revolutionized the way we understand football and sports. A […]

Growth Marketing Executive – Demand Gen & Campaigns
May 18, 2026 
PDMS Ltd is seeking a Marketing Executive in Douglas, Arizona, to support growth plans by generating leads, shaping campaigns, and collaborating with sales. Key duties include developing marketing strategies, managing social media, and organizing events. Ideal candidates will have experience in marketing, familiarity with CRM tools, and strong organizational skills. Benefits include competitive salaries, healthcare, […]

Assistant Manager, Digital Advertising (SEO/SEM)
May 18, 2026 
Job Description Add Flavor to Your Career – Explore New Opportunities at Lee Kum Kee! Ready to bring fresh energy to your career? At Lee Kum Kee, we’re committed to your growth, offering a place where creativity and innovation thrive. If you’re eager to make an impact and advance your career, apply today and start […]

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Marketing Specialist, The Bradford group (Hybrid, The Greater Chicago area)
Salary: $60,000 – $62,000
Launch and manage paid social campaigns primarily on Meta platforms.
Oversee daily budgets and performance optimizations against revenue and ROI goals, using data-driven insights to continuously improve results.
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.
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<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>This new guide talks about the differences, if any, between SEO, AEO, GEO and so on and touches on commodity content, AI agents and much more.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google has published a new guide on how to optimize for Google&#8217;s generative AI features, like AI Mode, AI Overviews and more. A lot of this content is put together from previous communication from Google but all in a neat new help document named <a href="https://developers.google.com/search/docs/fundamentals/ai-optimization-guide">Optimizing your website for generative AI features on Google Search</a>.</p>



<p><strong>What&#8217;s in the document. </strong>The document covers the following topics:</p>



<ul class="wp-block-list">
<li>SEO is relevant for generative AI search, follow Google&#8217;s best practices for SEO.</li>



<li>Create valuable, non-commodity content for your audience
<ul class="wp-block-list">
<li>Provide a unique point of view</li>



<li>Create non-commodity content that&#8217;s helpful, reliable and people-first</li>



<li>Organize content in a way that helps your readers</li>



<li>Add high-quality images and video</li>



<li>Focus on what your users want, and avoid overdoing it</li>



<li>Make sure your AI tools you use follow Google&#8217;s guidelines and best practices</li>
</ul>
</li>



<li>Build and maintain a clear technical structure
<ul class="wp-block-list">
<li>Meet the search technical requirements</li>



<li>Follow crawling best practices</li>



<li>When it comes to semantic HTML, focus on human readability, not code</li>



<li>JavaScript should follow Google&#8217;s best practices as well</li>



<li>Provide a good page experience</li>



<li>Reduce duplicate content</li>
</ul>
</li>



<li>Optimize your local business and ecommerce details</li>



<li>Mythbusting &#8211; what you don&#8217;t need to do
<ul class="wp-block-list">
<li>You don&#8217;t need LLMS.txt files</li>



<li>You don&#8217;t need other special markup</li>



<li>You don&#8217;t need to &#8220;Chunk&#8221; content</li>



<li>You don&#8217;t need to rewrite content for AI systems</li>



<li>You don&#8217;t need to seek inauthentic mentions</li>



<li>You shouldn&#8217;t overfocus on structured data</li>
</ul>
</li>



<li>Explore agentic experiences</li>



<li>Next steps</li>
</ul>



<p><strong>Why we care. </strong>This document sums up a lot of the advice Google has posted on its blogs, videos, spoken about at in-person events and more. It is a good document to see how Google thinks site owners should optimize their sites for AI-generative engines.</p>



<p>Check out the <a href="https://developers.google.com/search/docs/fundamentals/ai-optimization-guide">document over here</a>.</p>


Google has published a new guide on how to optimize for Google’s generative AI features, like AI Mode, AI Overviews and more. A lot of this content is put together from previous communication from Google but all in a neat new help document named Optimizing your website for generative AI features on Google Search.
What’s in the document. The document covers the following topics:
SEO is relevant for generative AI search, follow Google’s best practices for SEO.
Create valuable, non-commodity content for your audience

Provide a unique point of view
Create non-commodity content that’s helpful, reliable and people-first
Organize content in a way that helps your readers
Add high-quality images and video
Focus on what your users want, and avoid overdoing it
Make sure your AI tools you use follow Google’s guidelines and best practices
Build and maintain a clear technical structure

Meet the search technical requirements
Follow crawling best practices
When it comes to semantic HTML, focus on human readability, not code
JavaScript should follow Google’s best practices as well
Provide a good page experience
Reduce duplicate content
Optimize your local business and ecommerce details
Mythbusting – what you don’t need to do

You don’t need LLMS.txt files
You don’t need other special markup
You don’t need to “Chunk” content
You don’t need to rewrite content for AI systems
You don’t need to seek inauthentic mentions
You shouldn’t overfocus on structured data
Explore agentic experiences
Next steps
Why we care. This document sums up a lot of the advice Google has posted on its blogs, videos, spoken about at in-person events and more. It is a good document to see how Google thinks site owners should optimize their sites for AI-generative engines.
Check out the document over here.

This new guide talks about the differences, if any, between SEO, AEO, GEO and so on and touches on commodity content, AI agents and much more.

Google publishes guide on optimizing for generative AI features

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-800x450.jpg" class="attachment-large size-large wp-post-image" alt="Google Ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Google is pushing developers to move offline conversion imports from the Google Ads API to the Data Manager API by June.

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-800x450.jpg" class="attachment-large size-large wp-post-image" alt="Google Ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Google is phasing out offline conversion imports through the Google Ads API for some developers starting June 15th.</p>



<p>Advertisers and martech providers that rely on offline conversion imports, including enhanced conversions for leads, will need to migrate workflows to the Data Manager API to avoid disruptions.</p>



<p id="h-what-s-happening"><strong>Details. </strong>Google notified developers that offline conversion imports using the <code>UploadClickConversions</code> request will stop working after June 15, for accounts that have not used the functionality in the last 180 days.</p>



<p>The change applies to offline conversion imports and enhanced conversions for leads. Other Google Ads API operations will continue functioning normally.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="733" height="1472" src="https://searchengineland.com/wp-content/seloads/2026/05/Offline-conversion-imports.png" alt="" class="wp-image-477670"/></figure>



<p>Google says affected developers should move those workflows to the Data Manager API going forward.</p>



<p><strong>Why we care.</strong> Offline conversion imports are critical for measuring leads, sales, and other actions that happen outside the web journey. If integrations are not migrated in time, conversion data could stop flowing into Google Ads, which may impact reporting, attribution, and automated bidding performance. The update also signals Google’s continued shift toward centralized, AI-driven data infrastructure tied more closely to first-party data and automation.</p>



<p id="h-the-bigger-picture"><strong>The bigger picture.</strong> The move is part of Google’s broader push toward centralized data ingestion and automated measurement infrastructure.</p>



<p>Google describes the Data Manager API as a unified ingestion system for sending advertiser data into Google Ads, including Customer Match and conversion imports. The company also says the newer API offers a better developer experience and additional functionality not available in the existing workflow.</p>



<p id="h-between-the-lines"><strong>Between the lines.</strong> As attribution becomes more privacy-focused and increasingly dependent on first-party data, Google continues consolidating advertiser tools into fewer systems that are more tightly connected to automation and AI-driven campaign products.</p>



<p>For developers and platforms, the migration may require updating integrations, rebuilding import processes, and testing new ingestion workflows ahead of the deadline.</p>



<p id="h-what-s-next"><strong>What’s next. </strong>Developers can continue using the Google Ads API for all non-offline conversion operations.</p>



<p>However, any workflows involving offline conversion imports should be migrated to the Data Manager API before June 15th, when the existing functionality begins failing for affected accounts.</p>



<p><strong>First spotted. </strong>This update was spotted by PPC Specialist Arpan Banerjee who shared the comms he received on <a href="https://www.linkedin.com/posts/arpan301_googleads-ppc-sem-share-7461049492927176704-h9MQ?utm_source=social_share_send&amp;utm_medium=member_desktop_web&amp;rcm=ACoAAAJTV04Bw5M-1qqJtnImkpsEOFw9bHvfSSs">LinkedIn.</a>


Google is phasing out offline conversion imports through the Google Ads API for some developers starting June 15th.
Advertisers and martech providers that rely on offline conversion imports, including enhanced conversions for leads, will need to migrate workflows to the Data Manager API to avoid disruptions.
Details. Google notified developers that offline conversion imports using the UploadClickConversions request will stop working after June 15, for accounts that have not used the functionality in the last 180 days.
The change applies to offline conversion imports and enhanced conversions for leads. Other Google Ads API operations will continue functioning normally.




Google says affected developers should move those workflows to the Data Manager API going forward.
Why we care. Offline conversion imports are critical for measuring leads, sales, and other actions that happen outside the web journey. If integrations are not migrated in time, conversion data could stop flowing into Google Ads, which may impact reporting, attribution, and automated bidding performance. The update also signals Google’s continued shift toward centralized, AI-driven data infrastructure tied more closely to first-party data and automation.
The bigger picture. The move is part of Google’s broader push toward centralized data ingestion and automated measurement infrastructure.
Google describes the Data Manager API as a unified ingestion system for sending advertiser data into Google Ads, including Customer Match and conversion imports. The company also says the newer API offers a better developer experience and additional functionality not available in the existing workflow.
Between the lines. As attribution becomes more privacy-focused and increasingly dependent on first-party data, Google continues consolidating advertiser tools into fewer systems that are more tightly connected to automation and AI-driven campaign products.
For developers and platforms, the migration may require updating integrations, rebuilding import processes, and testing new ingestion workflows ahead of the deadline.
What’s next. Developers can continue using the Google Ads API for all non-offline conversion operations.
However, any workflows involving offline conversion imports should be migrated to the Data Manager API before June 15th, when the existing functionality begins failing for affected accounts.
First spotted. This update was spotted by PPC Specialist Arpan Banerjee who shared the comms he received on LinkedIn.

Google is pushing developers to move offline conversion imports from the Google Ads API to the Data Manager API by June.

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Read the Latest Trends and Headlines in Digital Marketing.

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

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