For most of the history of paid search, performance measurement followed a clear cause-and-effect relationship.

Advertisers controlled the inputs inside their campaigns like bid strategies, keyword and campaign structure, ad copy, and landing pages. All these factors contributed to conversion performance in some shape or form.

When performance changed, the explanation was usually traceable. For example, a new keyword theme improved conversion rates. Or, a bidding strategy increased efficiency.

That simple cause-and-effect framework is breaking down in real time, and has been for a while.

Over the past several months, Google has accelerated its transition toward AI-driven campaign types like Performance Max, Demand Gen, or assets inside those like AI Max or AI-driven ad creative components.

Not only do these change how campaigns are set up and managed, but they also change how performance must be measured.

Advertisers increasingly receive conversions from queries they did not explicitly target, from creative assets that are automatically assembled, and from placements distributed across multiple channels. In this environment, measuring performance by analyzing individual campaign inputs becomes less useful.

The real challenge is understanding how automated systems generate outcomes.

This article provides a measurement framework for that reality. It explains what has changed in advertising platforms, how PPC teams can evaluate performance when automation controls more of the auction, and how practitioners can communicate results clearly to leadership.

The Current Measurement Crisis In PPC

Right now, most discussions about AI in PPC tend to focus on automation features like campaign types, targeting capabilities, ad creative development, and bid strategy expansion.

But, there’s a deeper shift happening in measurement but not talked about as much.

Automation introduces a larger set of variables influencing each auction. When the platforms make targeting, bidding, placement decisions (and more) dynamically, isolating the impact of individual campaign inputs becomes difficult.

Recent platform updates have not only changed how campaigns are managed, but also how performance should be interpreted. The connection between action and outcome is less direct, and in many cases, partially obscured.

Several platform developments illustrate why traditional measurement methods are becoming less reliable.

AI Max Expands Queries Beyond Keyword Lists

In my opinion, AI Max represents Google’s most aggressive step toward intent-driven matching.

Instead of relying solely on advertiser-defined keywords, AI systems evaluate contextual signals, user behavior patterns, and historical performance data to match ads with queries that may not exist in the account.

Not only that, but AI Max goes beyond search terms. It also has the ability to change your ad assets for more tailored messaging when Google deems appropriate.

For PPC managers, this introduces a structural shift in how to measure performance. Conversions may originate from queries that were never explicitly targeted.

And we knew that something like this was coming. Back in 2023, Google first publicly used the word “keywordless” in communications when talking about Search and Performance Max.

For example, a retailer who bids on “trail running shoes” may now appear for search terms like:

• “best shoes for rocky terrain running”
• “ultra marathon footwear”
• “durable hiking running hybrids”

These queries reflect the same intent, but they don’t map cleanly back to the original keyword strategy.

Instead of trying to force these queries into keyword-level reporting, try analyzing performance by grouping into intent clusters. By evaluating conversion rate and revenue at the category level, teams can maintain strategic clarity even as query matching expands.

Google Ads already does a decent job of this in the Insights tab within the platform. They have a “Search terms insights” report that groups queries into “Search category,” where you can see conversions and search volume.

Performance Max Distributes Spend Across Multiple Channels

Performance Max can further complicate measurement by distributing budget across Search, YouTube, Display, Discover, Gmail, and Maps.

Up until last year, there was little-to-no transparency in how spend was allocated across those channels. Back in April 2025, Google launched the long-awaited feature of channel reporting to the PMax campaign type. It now shows channel-level reporting, better search terms data, and expanded asset performance metrics.

For example, say you have a $40,000 monthly PMax campaign budget and see this channel breakdown:

If Search drives the majority of conversions, but YouTube consumes a large portion of spend, PPC marketers could try the following:

• Test separating out branded search outside of PMax.
• Refine asset groups to improve search alignment.
• Run controlled experiments comparing PMax vs. Search.

Measurement becomes an exercise in interpreting how the system allocates spend rather than controlling each placement.

Ads Are Beginning To Appear Inside AI Conversations

Conversational search introduces an entirely new layer of complexity into PPC measurement.

Google is now testing shopping results embedded directly within AI Mode, allowing users to compare products without leaving the interface.

Google isn’t the only one doing this. ChatGPT announced on Jan. 16, 2026, that it would begin testing ads for its Free and Go users in the United States.

No matter which platform is running or testing ads in AI conversations, it’s clear that the measurement gap hasn’t been solved, and leaves many PPC managers with unanswered questions.

In my own recent search, I came across ads at the end of an AI Mode thread when I searched “noise cancelling headphones”:

So, if I were to click on one of those sponsored ads but convert at a later time, that attribution is unclear right now. Will my conversion be measured from the AI recommendation, the product listing click, or a later branded search?

These journeys challenge traditional attribution models, which were built around linear click paths rather than multi-step AI interactions.

Why Traditional PPC Metrics Are No Longer Enough

Many PPC reporting dashboards still rely on communicating metrics like impressions, clicks, conversion rate, and return on ad spend.

While some of those metrics remain useful, they no longer tell the full user story when bringing in automated and AI-driven environments.

These three shifts explain why.

1. Attribution Windows Are Expanding

AI-assisted search increases both the length and complexity of user journeys.

Research from Google and Boston Consulting Group show that “4S behaviors” (streaming, scrolling, searching, and shopping) have completely reshaped how users discover and engage with brands.

When AI introduces product recommendations earlier in a user’s journey, the time between initial interaction and conversion often grows. This could be because that user is still at the beginning of their research phase. Just because you’re introducing a product earlier, does not mean that they’ll be ready to purchase it any earlier.

So, what can marketers do about that gap now? Here are a few helpful tips to better understand how users are engaging with your business:

• Review conversion lag reports in Google Ads.
• Analyze time-to-conversion in GA4. Are there any differences or shifts in the last three, six, or nine months?
• Extend attribution windows to 60-90 days where appropriate.

This ensures automated systems receive more accurate feedback on what (and when they) drive conversions.

Organic Search Is Losing Click Share

Search results now include everything from AI Overviews, scrollable shopping modules at the top, and expanded ad placements across all devices.

Where does that leave organic listings?

A study conducted by SparkToro and Datos found that nearly 60% of Google searches end without a click.

This reduces organic traffic even more and shifts more demand capture towards paid media.

From a measurement standpoint, PPC should be evaluated alongside organic performance when possible.

Tracking blended search revenue provides a more accurate view of total search performance, rather than isolating paid channels.

AI Systems Optimize For Outcomes Rather Than Inputs

Traditional PPC management focused on inputs like keywords, bids, and ad copy to influence performance directly.

AI systems work differently. Instead of optimizing individual levers, they evaluate large sets of signals in real-time to determine which combinations are most likely to drive conversions.

This changes what measurement needs to do. Instead of asking which specific keyword or bid strategy adjustment improved performance, marketers need to evaluate whether the platform is producing the right business outcomes.

As platforms take over more of the execution, measurement has to focus less on the mechanics and more on whether automation is driving profitable, meaningful results.

The New Measurement Stack For AI-Driven PPC

If AI is now controlling more of the auction, then PPC teams need a different way to evaluate performance.

The old measurement stack was built around visibility into campaign inputs. You could look at keyword performance, search terms, ad copy, device segmentation, and bid adjustments to understand what was working. That model starts to fall apart when automation is making many of those decisions on your behalf.

The replacement becomes a new measurement stack that advertisers should look at in these four layers:

• Profitability.
• Incrementality.
• Blended acquisition efficiency.
• First-party conversion quality.

Together, these give marketers a more accurate picture of whether automation is actually helping the business grow.

Start With Profit, Not Just ROAS

ROAS still has value, but it should no longer be treated as the primary success metric in highly automated campaigns.

The problem is that AI-driven systems are often very good at capturing demand that already exists. That can make campaign efficiency look strong on paper, even if the business is not gaining much incremental value.

A campaign with a 700% ROAS may still be underperforming if it is primarily driving low-margin products, repeat purchasers, or orders that would have happened anyway.

That is why profitability should sit at the top of the measurement stack.

Instead of asking, “Did this campaign generate enough revenue?” marketers should be asking, “Did this campaign generate profitable revenue?”

For ecommerce brands, this could mean incorporating:

• Contribution margin.
• Product margin by category.
• Average order profitability.
• New customer revenue vs. returning customer revenue.

A simple starting point is to compare campaign revenue against both ad spend and cost of goods sold.

For lead gen advertisers, the same principle applies, just different incorporations:

• Qualified lead rate.
• Sales acceptance rate.
• Close rate by campaign.
• Revenue per opportunity.

If AI is optimizing toward cheap conversions that never turn into revenue, the system is learning the wrong lesson.

Add Incrementality To Separate Demand Capture From Demand Creation

The second layer of the stack is incrementality. This is where many PPC measurement frameworks still fall short.

Automation can be highly effective at finding conversions, but that does not automatically mean it is generating new business. In many cases, AI systems are simply getting better at intercepting users who were already on their way to converting.

If your campaign is mostly capturing existing demand, performance may look strong inside the ad platform while actual business lift remains modest.

This is why incrementality testing has become much more important in the AI era.

For PPC teams, this means at least part of measurement should be designed to answer: “Would this conversion have happened without the ad?”

You don’t need an enterprise-level media mix modeling to get started. A few practical approaches include:

• Geo holdout tests. Pause or reduce spend in a small set of markets while maintaining normal activity elsewhere.
• Use Google incrementality testing. Google reduced the minimum of testing incrementality in its platform to just $5,000, making it more affordable for many advertisers.
• Branded search suppression tests. In select markets or windows, test the impact of reducing branded spend where brand demand is already strong.

Answering this question does not mean automation is bad. It means PPC teams need a better way to distinguish between platform efficiency and true business lift.

Use Blended CAC To Measure Search More Realistically

The third layer of the new measurement stack is blended acquisition efficiency.

As AI Overviews, AI Mode, and other search changes continue to reduce traditional organic click opportunities, PPC should not be measured in a vacuum.

That is especially true for brands where paid and organic search are increasingly working together to capture the same demand.

A campaign may appear less efficient in-platform while still playing a critical role in maintaining total search visibility and revenue.

That is where blended customer acquisition cost (CAC) becomes useful.

Blended CAC looks at total acquisition spend across relevant channels and divides it by the total number of new customers acquired.

The formula for this is simple:

Total acquisition spend ÷ total new customers = blended CAC

This gives leadership a much more realistic picture of what it actually costs to grow the business.

It also helps PPC managers explain why paid search may need to carry more weight when organic search visibility declines due to AI-driven search features.

In other words, this metric helps move the conversation away from “Did Google Ads hit target ROAS?” and toward “What is it costing us to acquire a customer across modern search systems?”

Make First-Party Conversion Quality The Foundation

The final layer of the stack is first-party data quality. This is the part many advertisers still underestimate.

As platforms automate more of the targeting, bidding, and matching logic, the quality of the signals you send back becomes even more important. If the platform is deciding who to show ads to and which conversions to optimize toward, your job is to make sure it is learning from the right outcomes.

That means not all conversions should be treated equally.

If a lead form completion, low-value purchase, repeat customer order, and high-margin new customer sale are all fed back into the system the same way, automation will optimize toward volume, not value.

For PPC teams, that means the measurement stack should include a serious review of conversion quality inputs, including:

• Offline conversion imports.
• CRM-based revenue mapping.
• New vs. returning customer segmentation.
• Lead quality or opportunity-stage imports.
• Customer lifetime value indicators where available.

This is where measurement and optimization start to overlap.

If the wrong conversions are being measured, the wrong outcomes will be optimized.

That is why first-party data is not just a reporting issue. It is the foundation of the entire AI-era measurement stack.

What To Show Your CMO Or Clients

One of the most difficult aspects of managing automated campaigns is explaining performance to leadership teams.

Executives often expect reporting frameworks built around the mechanics of traditional campaign management. In automated environments, those indicators tell only a small part of the story.

A more effective reporting structure focuses on three layers that connect advertising performance to business outcomes.

The first layer should always focus on the metrics that leadership teams care about most. Revenue growth, contribution margin, and customer acquisition cost provide a direct connection between marketing activity and company performance. These indicators allow executives to evaluate marketing investments in the same framework they use to evaluate other business decisions.

Instead of presenting keyword-level reports, PPC leaders should begin with a clear summary of how paid media contributed to revenue and profit during the reporting period. If revenue increased by 18% quarter over quarter while customer acquisition costs remained stable, that outcome provides a far more meaningful signal than any individual campaign metric.

The second layer of reporting should explain how paid media contributes to the broader acquisition ecosystem. As AI-driven search experiences reshape the visibility of organic results, paid media often carries a larger share of the responsibility for capturing demand.

Blended customer acquisition cost provides an effective way to communicate this relationship. By combining marketing spend across channels and dividing it by the total number of new customers acquired, organizations gain a clearer understanding of the overall efficiency of their acquisition strategy.

This approach also helps executives understand how paid search interacts with organic search, social advertising, and other marketing channels. Rather than evaluating PPC in isolation, leadership can see how the entire acquisition system performs.

The final layer of reporting should focus on experimentation and strategic insights. Automated systems constantly evolve, and the best way to evaluate them is through structured experimentation.

Reports should include summaries of campaign experiments, including:

• The hypotheses tested.
• The metrics evaluated.
• The outcomes observed.

For example, if enabling AI-driven query expansion increased conversion volume while maintaining acceptable acquisition costs, that result provides valuable guidance for future campaign structure decisions.

Equally important is identifying metrics that are becoming less relevant.

Keyword-level performance reports, average ad position, and manual bid adjustments were once central components of PPC reporting. In automated campaign environments, those metrics often provide little strategic value. Continuing to emphasize them can distract leadership from the outcomes that truly matter.

Effective reporting in the AI era should emphasize growth, profitability, and strategic learning rather than operational mechanics.

Measurement Gaps That Still Exist

Despite improvements in automation and reporting transparency, several emerging advertising experiences remain difficult to measure.

One example is the growing presence of personalized offers within AI-driven shopping experiences. Google’s Direct Offers feature allows retailers to surface dynamic discounts during AI-generated shopping recommendations. While the feature may influence purchase decisions, advertisers currently have limited visibility into how frequently those offers appear or how strongly they influence conversion behavior.

Without that visibility, marketers cannot easily determine whether the discounts are generating incremental revenue or simply reducing margins on purchases that would have occurred anyway.

Another emerging measurement challenge involves conversational commerce. Google has begun exploring “agentic commerce” systems where AI assistants help users research and purchase products across multiple retailers.

In these environments, the user journey may involve several conversational prompts before a purchase occurs. The traditional concept of an ad impression or click may become less meaningful when AI systems guide the user through a multi-step research process.

As these experiences evolve, marketers will need new attribution models capable of evaluating influence across conversational journeys rather than isolated interactions.

These developments highlight the importance of ongoing experimentation and advocacy from advertisers. Measurement frameworks will need to evolve alongside the platforms themselves.

The Future Of PPC Measurement

Automation has changed the mechanics of paid advertising, but it has not eliminated the need for strategic oversight.

If anything, the role of human expertise has become more important.

AI systems are extremely effective at executing campaigns across large datasets and complex auctions. What they cannot do on their own is define the business outcomes that matter most or interpret performance within the broader context of organizational growth.

The most effective PPC teams are adapting to this reality. Instead of focusing exclusively on the mechanics of campaign management, they are investing more effort in defining profitability metrics, designing incrementality tests, and building reporting frameworks that connect advertising performance to business outcomes.

Measurement in the AI era will look different from the measurement frameworks that defined the early years of paid search. The focus will shift away from controlling individual campaign inputs and toward understanding how automated systems generate value for the business.

For PPC practitioners and marketing leaders alike, that shift represents the next stage in the evolution of paid media strategy.

More Resources:

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Featured Image: Roman Samborskyi/Shutterstock

https://www.searchenginejournal.com/how-to-measure-ppc-performance-when-ai-controls-the-auction/570184/

Google’s Sundar Pichai recently said that the future of Search is agentic, but what does that really mean? A recent tweet from Google’s search product lead shows what the new kind of task-based search looks like. It’s increasingly apparent that the internet is transitioning to a model where every person has their own agent running tasks on their behalf, experiencing an increasingly personal internet.

Search Is Becoming Task-Oriented

The internet, with search as the gateway to it, is a model where websites are indexed, ranked, and served to users who basically use the exact same queries to retrieve virtually the same sets of web pages. AI is starting to break that model because users are transitioning to researching topics, where a link to a website does not provide the clear answers users are gradually becoming conditioned to ask for. The internet was built to serve websites that users could go to and read stuff and to connect with others via social media.

What’s changing is that now people can use that same search box to do things, exactly as Pichai described. For example, Google recently announced the worldwide rollout of the ability to describe the needs for a restaurant reservation, and AI agents go out and fetch the information, including booking information.

Google’s Search Product Lead Rose Yao tweeted:

“Date nights and big group dinners just got a lot easier.

We’re thrilled to expand agentic restaurant booking in Search globally, including the UK and India!

Tell AI Mode your group size, time, and vibe—it scans multiple platforms simultaneously to find real-time, bookable spots.

No more app-switching. No more hassle. Just great food.”

That’s not search, that’s task completion. What was not stated is that restaurants will need to be able to interact with these agents, to provide information like available reservation slots, menu choices that evening, and at some point those websites will need to be able to book a reservation with the AI agent. This is not something that’s coming in the near future, it’s here right now.

That is exactly what Pichai was talking about when he recently described the future of search:

“I feel like in search, with every shift, you’re able to do more with it.

…If I fast forward, a lot of what are just information seeking queries will be agentic search. You will be completing tasks, you have many threads running.”

When asked if search will still be around in ten years, Pichai answered:

“Search would be an agent manager, right, in which you’re doing a lot of things.

…And I can see search doing versions of those things, and you’re getting a bunch of stuff done.”

Everyone Has Their Own Personal Internet

Cloudflare recently published an article that says the internet was the first way for humans to interact with online content, and that cloud infrastructure was the second adaptation that emerged to serve the needs of mobile devices. The next adaptation is wild and has implications for SEO because it introduces a hyper-personalized version of the web that impacts local SEO, shopping, and information retrieval.

AI agents are currently forced to use an internet infrastructure that’s built to serve humans. That’s the part that Cloudflare says is changing. But the more profound insight is that the old way, where millions of people asked the same question and got the same indexed answer, is going away. What’s replacing it is a hyper-personal experience of the web, where every person can run their own agent.

Cloudflare explains:

“Unlike every application that came before them, agents are one-to-one. Each agent is a unique instance. Serving one user, running one task. Where a traditional application follows the same execution path regardless of who’s using it, an agent requires its own execution environment: one where the LLM dictates the code path, calls tools dynamically, adjusts its approach, and persists until the task is done.

Think of it as the difference between a restaurant and a personal chef. A restaurant has a menu — a fixed set of options — and a kitchen optimized to churn them out at volume. That’s most applications today. An agent is more like a personal chef who asks: what do you want to eat? They might need entirely different ingredients, utensils, or techniques each time. You can’t run a personal-chef service out of the same kitchen setup you’d use for a restaurant.”

Cloudflare’s angle is that they are providing the infrastructure to support the needs of billions of agents representing billions of humans. But that is not the part that concerns SEO. The part that concerns digital marketing is that the moment when search transforms into an “agent manager” is here, right now.

WordPress 7.0

Content management systems are rapidly adapting to this change. It’s very difficult to overstate the importance of the soon-to-be-released WordPress 7.0, as it is jam-packed with the capability to connect to AI systems that will enable the internet transition from a human-centered web to an increasingly agentic-centered web.

The current internet is built for human interaction. Agents are operating within that structure, but that’s going to change very fast. The search marketing community really needs to wrap its collective mind around this change and to really understand how content management systems fit into that picture.

What Sources Do The Agents Trust?

Search marketing professional Mike Stewart recently posted on Facebook about this change, reflecting on what it means to him.

He wrote:

“I let Claude take over my computer.
Not metaphorically — it moved my mouse, opened apps, and completed tasks on its own.
That’s when something clicked…
This isn’t just AI assisting anymore.
This is AI operating on your behalf.

Google’s CEO is already talking about “agentic search” — where AI doesn’t just return results, it manages the process.
So the real questions become:
👉 Who controls the journey?
👉 What sources does the agent trust?
👉 Where does your business show up in that decision layer?
Because you don’t get “agentic search” without the ecosystem feeding it — websites, content, businesses.

That part isn’t going away. But it is being abstracted.”

Task-Based Agentic Search

I think the part that I guess we need to wrap our heads around is that humans are still making the decision to click the “make the reservation” button, and at some point, at least at the B2B layer, making purchases will increasingly become automated.

I still have my doubts about the complete automation of shopping. It feels unnatural, but it’s easy to see that the day may rapidly be approaching when, instead of writing a shopping list, a person will just tell an AI agent to talk to the local grocery store AI agent to identify which one has the items in stock at the best price, dump it into a shopping cart, and show it to the human, who then approves it.

The big takeaway is that the web may be transitioning to the “everyone has a personal chef” model, and that’s a potentially scary level of personalization. How does an SEO optimize for that? I think that’s where WordPress 7.0 comes in, as well as any other content management systems that are agentic-web ready.

Featured Image by Shutterstock/Stock-Asso

https://www.searchenginejournal.com/googles-task-based-search/571800/

Ask ChatGPT or Claude to recommend a product in your market. If your brand does not appear, you have a problem that no amount of keyword optimization will fix.

Most SEO professionals, when faced with this, immediately think about content. More pages, more keywords, better on-page signals. But the reason your brand is absent from an AI recommendation may have nothing to do with pages or keywords. It has to do with something called relational knowledge, and a 2019 research paper that most marketers have never heard of.

The Paper Most Marketers Missed

In September 2019, Fabio Petroni and colleagues at Facebook AI Research and University College London published “Language Models as Knowledge Bases?” at EMNLP, one of the top conferences in natural language processing.

Their question was straightforward: Does a pretrained language model like BERT actually store factual knowledge in its weights? Not linguistic patterns or grammar rules, but facts about the world. Things like “Dante was born in Florence” or “iPod Touch is produced by Apple.”

To test this, they built a probe called LAMA (LAnguage Model Analysis). They took known facts, thousands of them drawn from Wikidata, ConceptNet, and SQuAD, and converted each one into a fill-in-the-blank statement. “Dante was born in ___.” Then they asked BERT to predict the missing word.

BERT, without any fine-tuning, recalled factual knowledge at a level competitive with a purpose-built knowledge base. That knowledge base had been constructed using a supervised relation extraction system with an oracle-based entity linker, meaning it had direct access to the sentences containing the answers. A language model that had simply read a lot of text performed nearly as well.

The model was not searching for answers. It had absorbed associations between entities and concepts during training, and those associations were retrievable. BERT had built an internal map of how things in the world relate to each other.

After this, the research community started taking seriously the idea that language models work as knowledge stores, not merely as pattern-matching engines.

What “Relational Knowledge” Means

Petroni tested what he and others called relational knowledge: facts expressed as a triple of subject, relation, and object. For example: (Dante, [born-in], Florence). (Kenya, [diplomatic-relations-with], Uganda). (iPod Touch, [produced-by], Apple).

What makes this interesting for brand visibility (and AIO) is that Petroni’s team discovered that the model’s ability to recall a fact depends heavily on the structural type of the relationship. They identified three types, and the accuracy differences between them were large.

1-To-1 Relations: One Subject, One Object

These are unambiguous facts. “The capital of Japan is ___.” There is one answer: Tokyo. Every time the model encountered Japan and capital in the training data, the same object appeared. The association built up cleanly over repeated exposure.

BERT got these right 74.5% of the time, which is high for a model that was never explicitly trained to answer factual questions.

N-To-1 Relations: Many Subjects, One Object

Here, many different subjects share the same object. “The official language of Mauritius is ___.” The answer is English, but English is also the answer for dozens of other countries. The model has seen the pattern (country → official language → English) many times, so it knows the shape of the answer well. But it sometimes defaults to the most statistically common object rather than the correct one for that specific subject.

Accuracy dropped to around 34%. The model knows the category but gets confused within it.

N-To-M Relations: Many Subjects, Many Objects

This is where things get messy. “Patrick Oboya plays in position ___.” A single footballer might play midfielder, forward, or winger depending on context. And many different footballers share each of those positions. The mapping is loose in both directions.

BERT’s accuracy here was only about 24%. The model typically predicts something of the correct type (it will say a position, not a city), but it cannot commit to a specific answer because the training data contains too many competing signals.

I find this super useful because it maps directly onto what happens when an AI tries to recommend a brand. Brands (without monopolies) operate in a “many-to-many” relationship. So “Recommend a [Brand] with a [feature]” is one of the hardest things for AI to “predict” with consistency. I will come back to that…

What Has Happened Since 2019

Petroni’s paper established that language models store relational knowledge. The obvious next question was: where, exactly?

In 2022, Damai Dai and colleagues at Microsoft Research published “Knowledge Neurons in Pretrained Transformers” at ACL. They introduced a method to locate specific neurons in BERT’s feed-forward layers that are responsible for expressing specific facts. When they activated these “knowledge neurons,” the model’s probability of producing the correct fact increased by an average of 31%. When they suppressed them, it dropped by 29%.

OMG! This is not a metaphor. Factual associations are encoded in identifiable neurons within the model. You can find them, and you can change them.

Later that year, Kevin Meng and colleagues at MIT published “Locating and Editing Factual Associations in GPT” at NeurIPS. This took the same ideas and applied them to GPT-style models, which is the architecture behind ChatGPT, Claude, and the AI assistants that buyers actually use when they ask for recommendations. Meng’s team found they could pinpoint the specific components inside GPT that activate when the model recalls a fact about a subject.

More importantly, they could change those facts. They could edit what the model “believes” about an entity without retraining the whole system.

That finding matters for SEOs. If the associations inside these models were fixed and permanent, there would be nothing to optimize for. But they are not fixed. They are shaped by what the model absorbed during training, and they shift when the model is retrained on new data. The web content, the technical documentation, the community discussions, the analyst reports that exist when the next training run happens will determine which brands the model associates with which topics.

So, the progress from 2019 to 2022 looks like this. Petroni showed that models store relational knowledge. Dai showed where it is stored. Meng showed it can be changed. That last point is the one that should matter most to anyone trying to influence how AI recommends brands.

What This Means For Brands In AI Search

Let me translate Petroni’s three relation types into brand positioning scenarios.

The 1-To-1 Brand: Tight Association

Think of Stripe and online payments. The association is specific and consistently reinforced across the web. Developer documentation, fintech discussions, startup advice columns, integration guides: They all connect Stripe to the same concept. When someone asks an AI, “What is the best payment processing platform for developers?” the model retrieves Stripe with high confidence, because the relational link is unambiguous.

This is Petroni’s 1-to-1 dynamic. Strong signal, no competing noise.

The N-To-1 Brand: Lost In The Category

Now consider being one of 15 cybersecurity vendors associated with “endpoint protection.” The model knows the category well. It has seen thousands of discussions about endpoint protection. But when asked to recommend a specific vendor, it defaults to whichever brand has the strongest association signal. Usually, that is the one most discussed in authoritative contexts: analyst reports, technical forums, standards documentation.

If your brand is present in the conversation but not differentiated, you are in an N-to-1 situation. The model might mention you occasionally, but it will tend to retrieve the brand with the strongest association instead.

The N-To-M Brand: Everywhere And Nowhere

This is the hardest position. A large enterprise software company operating across cloud infrastructure, consulting, databases, and hardware has associations with many topics, but each of those topics is also associated with many competitors. The associations are loose in both directions.

The result is what Petroni observed with N-to-M relations: The model produces something of the correct type but cannot commit to a specific answer. The brand appears occasionally in AI recommendations but never reliably for any specific query.

I see this pattern frequently when working with enterprise brands. They have invested heavily in content across many topics, but have not built the kind of concentrated, reinforced associations that the model needs to retrieve them with confidence for any single one.

Measuring The Gap

If you accept the premise, and the research supports it, that AI recommendations are driven by relational associations stored in the model’s weights, then the practical question is: Can you measure where your brand sits in that landscape?

AI Share of Voice is the metric most teams start with. It tells you how often your brand appears in AI-generated responses. That is useful, but it is a score without a diagnosis. Knowing your Share of Voice is 8% does not tell you why it is 8%, or which specific topics are keeping you out of the recommendations where you should appear.

Two brands can have identical Share of Voice scores for completely different structural reasons. One might be broadly associated with many topics but weakly on each. Another might be deeply associated with two topics but invisible everywhere else. These are different problems requiring different strategies.

This is the gap that a metric called AI Topical Presence, developed by Waikay, is designed to address. Rather than measuring whether you appear, it measures what the AI associates you with, and what it does not. [Disclosure: I am the CEO of Waikay]

The metric captures three dimensions. Depth measures how strongly the AI connects your brand to relevant topics, weighted by importance. Breadth measures how many of the core commercial topics in your market the AI associates with your brand. Concentration measures how evenly those associations are distributed, using a Herfindahl-Hirschman Index borrowed from competition economics.

A brand with high depth but low breadth is known well for a few things but invisible for many others. A brand with wide coverage but high concentration is fragile: One model update could change its visibility significantly. The component breakdown tells you which problem you have and which lever to pull.

In the chart above, we start to see how different brands are really competing with each other in a way we have not been able to see before. For example, Inlinks is competing much more closely with a product called Neuronwriter than previously understood. Neuronwriter has less share of voice (I probably helped them by writing this article… oops!), but they have a better topical presence around the prompt, “What are the best semantic SEO tools?” So all things being equal, a bit of marketing is all they need to take Inlinks. This, of course, assumes that Inlinks stands still. It won’t. By contrast, the threat of Ahrefs is ever-present, but by being a full-service offering, they have to spread their “share of voice” across all of their product offerings. So while their topical presence is high, the brand is not the natural choice for an LLM to choose for this prompt.

This connects back to Petroni’s framework. If your brand is in a 1-to-1 position for some topics but absent from others, topical presence shows you where the gaps are. If you are in an N-to-1 or N-to-M situation, it helps you identify which associations need strengthening and which topics competitors have already built dominant positions on.

From Ranking Pages To Building Associations

For 25 years, SEO has been about ranking pages. PageRank itself was a page-level algorithm; the clue was always in the name (IYKYK … No need to correct me…). Even as Google moved towards entities and knowledge graphs, the practical work of SEO remained rooted in keywords, links, and on-page optimization.

AI visibility requires something different. The models that generate brand recommendations are retrieving associations built during training, formed from patterns of co-occurrence across many contexts. A brand that publishes 500 blog posts about “zero trust” will not build the same association strength as a brand that appears in NIST documentation, peer discussions, analyst reports, and technical integrations.

This is fantastic news for brands that do good work in their markets. Content volume alone does not create strong relational associations. The model’s training process works as a quality filter: It learns from patterns across the entire corpus, not from any single page. A brand with real expertise, discussed across many contexts by many voices, will build stronger associations than a brand that simply publishes more.

The question to ask is not “Do we have a page about this topic?” It is: “If someone read everything the AI has absorbed about this topic, would our brand come across as a credible participant in the conversation?”

That is a harder question. But the research that began with Petroni’s fill-in-the-blank tests in 2019 has given us enough understanding of the mechanism to measure it. And what you can measure, you can improve.

More Resources:

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Fair to say the majority of evergreen content will not drive the value it did five years ago. Hell, even one or two years ago. What we have done for the last decade will not be as profitable.

AIOs have eroded clicks. Answer engines have given people options. And to be fair, people are bored of the +2,000-word article answering “What time does X start?” Or recipes where the ingredient list is hidden below 1,500 words about why daddy didn’t like me.

In response to this, publishers say it will be important to focus on more original investigations and less on things like evergreen content (-32 percentage points).

So, you’ve got to be smart. This has to be framed as a commercial decision. Content needs to drive real business value. You’ve got to be confident in it delivering.

That doesn’t mean every article, video, or podcast has to drive a subscription or direct conversion. But it needs to play a clear part in the user’s journey. You need to be able to argue for its inclusion:

• Is it a jumping-off point?
• Will it drive a registration?
• Or a free subscriber, save or follow on social

More commonly known as micro-conversions, these things really matter when it comes to cultivating and retaining an audience. People don’t want more bland, banal nonsense. They want something better.

The antithesis to AI slop will help your business be profitable.

Inherently, nothing. It’s a foundational part of the content pyramid.

In most cases, it’s been done to death, and AI is very effective at summarizing a lot of this bread-and-butter content.

Over the last 10 years, it’s been pretty easy to build a strategy around evergreen content, particularly if you go down the parasite SEO route. Remember Forbes’ Advisor and the great affiliate cull?

The epitome of quantity over quality; it worked and made a fortune.

But I digress.

An authoritative enough site has been able to drive clicks and follow-up value with sub-par content for decades. That is, slowly diminishing. Rightly or wrongly.

And not because of the Helpful Content stuff. Google nerfed all the small sites long before the goliaths. Now they’ve gone after the big fish.

We have to make commercial decisions that help businesses make the right choice. Concepts like E-E-A-T have had an impact on the quality of content (a good thing). It’s also had an impact on the cost of creating quality content.

• Working with experts.
• Unique imagery.
• Video.
• Product and development costs.
• Data.

This isn’t cheap. Once upon a time, we could generate value from authorless content full of stock images and no unique value. Unless you’re willing to bend the rules (which isn’t an option for most of us), you need an updated plan.

It depends.

You need to establish how much your content now costs to produce and the value it brings. Not everything is going to drive a significant conversion. That doesn’t mean you shouldn’t do it. It means you need to have a very clear reason for what you’re creating and why.

If particular topics are essential to your audience, service, and/or product, then they should at least be investigated.

One of the joys of creating evergreen content has always been that it adds value throughout the year(s). A couple of annual updates, even relatively light touch, could yield big results.

Commissioning something of quality in this space is likely more expensive. It needs to be worth it; it has to form part of your multi-channel experience to make it so.

• Unique data and visuals that can be shared on socials.
• Building campaigns around it (or it’s part of a campaign).
• You can even build authors and your brand around it.
• And if it resonates, you can rinse and repeat year after year.

And this type of content or campaign can increase demand for a topic. You can become a thought leader by shifting the tide of public opinion.

For publishers and content creators, that is foundational.

Two broadly rhetorical questions:

1. Do you think in a world of zero click searches, clicks and reach are sensible tier one goals?
2. Do you want to be targeted against a metric that is very likely to go down each year?

I don’t – on both counts. We should want to be targeted on driving real value for the business.

Something like:

1. Tier 1: Value – core, revenue, and value-driving conversions.
2. Tier 2: Registrations (and things that help you build your owned properties), links, shares, and comments.
3. Tier 3: Page views, returning visits, and engagement metrics.

Micro-conversions over clicks. We’re focusing on registrations, free or lower-value subscriptions. Whatever gets the user into the ecosystem and one step closer to a genuinely valuable conversion.

Now, could a click be a micro-conversion? If you know that someone who reads a secondary article (by clicking a follow-up link) is 10x more likely to register, that follow-up click could be a sensible micro-conversion.

This type of conversion may not directly drive your bottom line. But it forces you and your team to focus on behaviors that are more likely to lead to a valuable conversion.

That is the point of a micro-conversion. It changes behaviors.

You can tweak the above tiers to better suit your content offering. Not all content is going to drive direct tier one or even two value. You just need to have a very clear idea of its purpose in the customer journey.

If what you’re creating already exists, you’d better make sure you add something extra. You’ve got to force your way into the conversation, and unless you can offer something unique, you’re (almost certainly) wasting your time IMO.

I’ll break all of these down, but I think (in order of importance):

1. Writing content for people.
2. Information gain.
3. Getting it found.
4. Creating it at the right time.
5. Structuring it for bots.

Everyone is obsessed with getting cited or being visible in AI.

I think this is completely the wrong way of framing this new era. Getting cited there, or being visible, is a happy byproduct of building a quality brand with an efficient, joined-up approach to marketing.

The more you understand your audience, the more likely you will be to create high-quality, relevant content that gets cited.

If you know your audience really cares about a topic, that’s step one taken care of. If you know where they spend time and how they’re influenced, that’s step two. And if you know how to cut through the noise, that’s step three.

Really, this is an evolution in SEO and the internet at large.

• Invest in and create content that will resonate with your audience.
• Create a cross-channel marketing strategy that will genuinely reach and influence them.
• Share, share, share. Be impactful. Get out there.
• Make sure it’s easy to read, share, and consume.

Your content still needs to reach and be remembered by the right people. Do that better than anybody else, and wider visibility will come.

In SEO, we have a different definition of information gain than more traditional information retrieval mechanics. I don’t know if that’s because we’re wrong (probably), or that we have a valid reason…

Maybe someone can enlighten me?

In more traditional machine learning, information gain measures how much uncertainty is reduced after observing new data. That uncertainty is captured by entropy, which is a way of quantifying how unpredictable a variable is based on its probability distribution.

Events with low probability are more surprising and therefore carry more information. High probability events are less surprising and novel. Therefore, entropy reflects the overall level of disorder and unpredictability across all possible outcomes.

Information gain, then, tells us how much that unpredictability drops when we split or segment the data. A higher information gain means the data has become more ordered and less uncertain – in other words, we’ve learned something useful.

To us in SEO, information gain means the addition of new, relevant information. Beyond what is already out there in the wider corpus.

Google wants to reduce uncertainty. Reduce ambiguity. Content with a higher level of information gain isn’t only different, it elevates a user’s understanding. It raises the bar by answering the question(s) and topic more effectively than anyone else.

So, try something different, novel even, and watch Google test your content higher up in the SERPs to see if it satisfies a user.

This is such an important concept for evergreen content because so many of these queries have well-established answers. If you’re just parroting these answers because your competitors do it, you’re not forcing Google’s hand.

Particularly if you’re still just copying headers and FAQs from the top three results. Audiences are not arriving at publisher destinations through direct navigation at the same scale. They encounter journalism incidentally, through social feeds, not through habitual site visits.

You’ve got to meet them there and force their hand.

According to this patent – contextual estimation of link information gain – Google scores documents based on the additional information they offer to a user, considering what the user has already seen.

Bots, like people, need structure to properly “understand” content.

Elements like headings (h1 – h6), semantic HTML, and linking effectively between articles help search engines (and other forms of information retrieval) understand what content you deem important.

While the majority of semi-literates “understand” content, bots don’t. They fake it. They use engagement signals, NLP, and the vector model space to map your document against others.

They can only do this effectively if you understand how to structure a page.

• Frontloading key information.
• Effectively targeting highly relevant queries.
• Using structured data formats like lists and tables, where appropriate (these are more cost-effective forms of tokenization).
• Internal and external links.
• Increasing contextual knowledge gain with multimedia (yes, Google can interpret them).

The more clearly a page communicates its topic, subtopics, and relationships, the more likely it is to be consistently retrieved and reused across search and AI surfaces. This has a compounding effect.

Rank more effectively (great for RAG, obviously) – feature more heavily in versions of the internet – force your way into model training data.

If you need to get development work put through, frame it through the lens of assistive technology. Can people with specific needs fully access your pages?

As up to 20% need some kind of digital assistive technology, this becomes a ‘ranking factor’ of sorts.

I won’t go through this in much detail, as I’ve written a really detailed post on it. Basically:

• Track and pay very close attention to spikes in demand (Google Trends API being a very obvious option here).
• Make sure you’re adding something of value to the wider corpus.
• If quality content is already out there and you have nothing extra to add, consider whether it’s worth spending money on (SEO is not free).

While this is primarily for news, you can apply a similar logic to evergreen content if you zoom out and follow macro trends.

Evergreen content still spikes at different times throughout the year. Take Spain as an example. There’s much more limited interest in going to Spain in the Winter months from the UK. But January (holiday planning or weekend breaks) and summer (more immediate holiday-ing with the kids) provide better opportunities to generate traffic.

You’re capturing the spike in demand by updating content at the right time. Particularly if you understand the difference in user needs when this spike in demand happens.

• In January, get your holiday planning content ready.
• In the summer, get your family-friendly and last-minute holiday content up and running.

Demand for evergreen topics can be cyclical. In this example, you would want to capture the spike(s) with carefully planned updates, so you have up-to-date content when a user is really searching for that product, service, or information.

Well, what matters to your brand and your users? Have you asked them?

By the very nature of new and evolving topics and concepts, not everything “evergreen” has been done.

New topics rise. Old ones fall. Some are cyclical.

My rule(s) of thumb would be to establish:

• Is the topic foundational to your product and service?
• Does your current (and potential) audience demand it?
• Do you have something new to add to the wider corpus of information?

If the answer to those three is a broad variation of yes, it’s almost certainly a good bet. Then, I would consider topic search volume, cross-platform demand, and whether the topic is trending up or down in popularity.

There are some things you should be doing “just for SEO.” Content isn’t one of them. You can yell topical authority until you’re blue in the face. If you’re creating stuff just for SEO – kill it.

IMO, these plays have been dead or dying for some time. The modern-day version of the internet (in particular search) demands disambiguation. It demands accuracy. Verification that you are an expert. Otherwise, you’re competing with those who have a level of legitimacy that you do not.

Social profiles, newsletters, real people sharing stories. You’re competing with people who aren’t polishing turds.

If all you’re thinking about is search volume or clicks, I don’t think it’s worth it.

YouTube and TikTok are flying. The young mind cannot escape big tech’s immeasurable evil.

They’re bored with reading the news, but they really, really like video. They will watch it.

The good news for you (and me) is that platforms like YouTube are still very viable opportunities to build something brilliant. Memorable even. They’re also far more AI-resilient – even if Google desperately tries to summarize everything with AI.

And this brings me nicely onto rented land. Platforms you don’t own.

We’ve spent years creating assets (your websites) to deliver value in search. Owning all of your assets and prioritizing your site above all else. But that is changing. In many cases, people don’t reach your website until they’ve already made a purchasing decision.

So, you have to get your stuff out there. Create large, unique studies. Cut them into snippets and short-form videos. Use your individual platform to boost your profile and the content’s chances of soaring.

This is, IMO, particularly prescient for publishers. You’ve got to get out there. You’ve got to share and reuse your content. To make the most of what you’ve created.

Sweat your assets. Even if senior figures aren’t comfortable with this, you need to make it happen.

People have been espousing how important it is to feature as part of the answer. And that may be true. But you’re going to have to be good at selling your projects in if there’s no clear attribution or value.

It might not have the spikes of news, but evergreen interest still spikes at certain times in the year.

Get people – real people – to share it. To have their spin on it.

Outperform the expected early stage engagement and maximize your chance of appearing in platforms like Discover with wider platform engagement.

You have to work harder than before.

I shared an example of this around a year ago, but to revisit it, I now have 11 recommendations from other Substacks.

They have accounted for over 40% of my total subscribers. Admittedly, mainly from Barry, Shelby, and Jessie. But they are, if I may be so bold, superhumans.

And when our main driver of evergreen traffic to the site (Google) has really leaned into the evil that surrounds big tech, we’ve got to be cannier. We have to find ways to get people to share our content.

Even evergreen content.

If we’re being honest, a lot of SEO content has been rubbish. Churned out muck.

People are still churning out muck at an incredible rate. When what you’ve got is crap, more crap isn’t the answer. I think people are turned off. They’re tuning out of things at an alarming rate, especially young people.

It is all about getting the right people into the system. Evergreen content is still foundational here. You just have to make it work harder. Be more interesting. Be shareable.

Hopefully, this makes decisions over what we should and shouldn’t create easier.

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AI and automation in ad platforms are well established. Google Ads and Microsoft Advertising are heavily invested in automated features, and the technical barrier to entry has never been lower. However, that accessibility comes with a tradeoff.

Two common challenges surface when bringing a PPC team in-house:

1. Campaigns are easier to launch than they are to explain and analyze.
2. Machine-driven decisions risk going unquestioned without an outside perspective.

Those challenges point to something CMOs probably already know: Automation doesn’t eliminate the need for human judgment. It raises the requirements for it. Even with strong AI tools in place, experienced PPC practitioners are still writing strategy, creating ad copy, and manually updating targeting.

This article covers two structural paths for managing that reality.

1. All in-house means your internal team manages PPC end-to-end, with no agency or external consultant involved.
2. Hybrid means your internal team handles day-to-day execution and internal oversight while an external specialist or consultant provides strategy, auditing, and a second set of eyes.

Both models can work. The goal is to match machine automation with human accountability and independent performance checks. Without that structure, an in-house team can end up in a bubble where the ad platform’s suggestions dictate all of the optimization decisions.

Is Your Organization Ready? What To Assess Before You Hire

Before you post a job description, determine whether your company is ready to manage the technical work that comes with modern PPC search ads. Hiring an internal team is a long-term commitment.

The Shift In Daily Tasks

The role of the search marketer is shifting from manual campaign creation to evaluating and guiding automated systems. The human role is increasingly about checking what the AI creates and stepping in to do the work the ad platform can’t do well on its own.

That last part matters so much more than most job descriptions reflect. In my experience, AI-generated ad copy is often not platform-ready, and strategy still requires a human who understands the brand, the profit model, and the customer. If your candidates are only talking about managing manual bids and features, they may not be ready for the current landscape. You need people who can navigate automated systems and know when to override them.

Input And Data Quality

Because AI success depends on signal strength, an in-house PPC team’s value is directly tied to their ability to connect and maintain clean data. Ad platforms rely on:

• Conversion tracking.
• CRM integration.
• Audience modeling.
• Bidding inputs.

Tools such as Google Ads Data Manager (connecting external products inside Google Ads) and offline conversion uploads mean managing data should be a core responsibility of in-house PPC specialists.

Poorly configured conversion tracking or incomplete data signals can lead automated bidding to optimize toward low-value actions, if the data isn’t managed effectively in-house. You can’t expect a machine to give you good results if you’re feeding it bad information.

If You Are Hiring, Look For These Skills

If you’ve decided to build fully in-house, hiring criteria should shift toward business data management and the ability to work alongside AI without taking every single suggestion.

1. Understanding Business Margins

Most PPC managers haven’t had to think in depth about COGS (Cost of Goods Sold) or return rates, but that’s changing.

The bar is rising for in-house hires. A team that can connect ad spend to net profit, not just revenue, is far better positioned to make smart decisions as automation takes over the mechanical work.

2. Owning The Post-Click Experience

The PPC team must care about what happens after the user lands on the site. Creative quality and landing page performance are directly tied to conversions and what the algorithm learns over time.

AI-driven traffic efficiency can be thrown off by a poor landing page experience. Your internal hires should have a working knowledge of landing page testing and website user experience.

3. Ad Copy And Strategic Judgment

AI can generate ad copy, but it can create variations that are missing marketing strategy or brand-ready messaging. Your team needs to evaluate, rewrite, and at times reject what the ad platform produces.

The same applies to strategy. Automated systems optimize toward the goals you set, but setting the right goals and interpreting performance still require a skilled human. Hire for that judgment, not just ad platform knowledge.

4. Technical Data Strategy

Your team needs to know how to build and maintain first-party data connections, such as CRM data and customer match uploads.

Your team’s job is to ensure the right signals are flowing to the right campaigns at the right time. Technical data competency should be a core requirement for the job.

Why A Hybrid Model May Work Better

Even when hiring and data processes are going well, blind spots can happen inside fully internal teams. Three issues can show up:

• Brand blindness from working primarily inside a single account.
• Lack of independent auditing on spend and profit.
• Difficulty pushing back on ad platform pressure.

An external perspective adds accountability that internal teams can have trouble providing for themselves. In an environment where so many features are automated, that accountability matters more because teams don’t tend to deep dive into the automations.

1. The Problem With Brand Blindness

Internal teams are focused on one brand. That focus builds deep expertise, but it can limit perspective. For example, when performance changes, it’s difficult to determine whether the change reflects a platform-wide trend, an industry shift, or a campaign-specific issue.

Working across many industries gives specialist consultants a reference point that internal teams may not have. They can tell you if a performance drop is happening to everyone in the industry or just to you.

2. The Need For Independent Auditing

An external partner acts as an independent auditor for your search spend. They can help confirm that internal goals line up with actual business profit rather than ad platform metrics.

It’s easy for internal teams to grow comfortable and focus on vanity metrics like ROAS (Return on Ad Spend). An objective third party can help show you exactly how much actual profit your search spend is generating.

3. Managing Ad Platform Pressure

Internal teams are the primary target for PPC ad platform representatives. These reps frequently push recommendations such that are auto-applied and display network serving that eat up budgets and prioritize the platform’s revenue over your business.

Independent experts are less likely to follow these suggestions without questioning them. They provide the pushback needed to ensure spend is justified by performance, not the platform’s optimization score.

Structuring The Partnership For Success

Consider a division of labor that draws on internal brand knowledge and external expertise. This hybrid approach offers the most protection for your ad spend.

What The In-House Team Should Own

• Data Ownership: Managing the privacy and quality of your customer signals.
• Creative Guidance: Ensuring brand voice stays consistent across AI-generated ads.
• Ad Copy and Strategy: Writing, evaluating, and refining what the ad platform produces.
• Sales Coordination: Connecting PPC spend with internal inventory levels and sales cycles.

What The External Specialist Should Own

• Strategic Roadmap: Providing a long-term view of where the search industry is heading.
• Advanced Analysis: Proving the true value of your spend through profit-based measurement.
• Objective Auditing: Serving as an independent check against ad platform recommendations.

Successful PPC teams in an AI-first search environment won’t be worried about who automated the fastest. They’ll be more thoughtful and strategic about defining what the machine does and what a human approves.

Matching Structure To Accountability

The decision to go fully in-house or hybrid isn’t permanent. What matters is that your structure matches the level of accountability your ad spend requires.

If your team has clean data, strong hiring, and the ability to question what the ad platform suggests, a fully in-house model can work. But if no one is challenging the machine’s recommendations, you have a gap that’s hard to fix from the inside.

A hybrid model doesn’t mean your internal team isn’t capable. It means you’re building in a check that protects your budget from blind spots.

Whatever you choose, the people managing your PPC need to understand your business at the profit level, not just the platform level. Automation handles the mechanics. Your team handles the judgment.

More Resources:

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Enterprise SEO doesn’t fail because teams don’t care, lack expertise, or miss tactics. It fails because ownership is fractured.

In most large organizations, everyone controls a piece of SEO, yet no single group owns the outcome. Visibility, traffic, and discoverability depend on dozens of upstream decisions made across engineering, content, product, UX, legal, and local markets. SEO is measured on the result, but it does not control the system that produces it.

In smaller organizations, this problem is manageable. SEO teams can directly influence content, technical decisions, and site structure. In the enterprise, that control dissolves. Incentives diverge. Workflows fragment. Coordination becomes optional.

SEO success requires alignment, but enterprise structures reward isolation. That mismatch creates what I call the accountability gap – the silent failure mode behind most large-scale SEO underperformance.

SEO Is Measured By The Team That Doesn’t Control It

SEO is the only business function I am aware of that, judged by performance, cannot be delivered independently. This is especially true in the enterprise, where SEO performance is evaluated using familiar metrics: visibility, traffic, engagement, and increasingly AI-driven exposure. The irony is that the SEO function rarely controls the systems that generate those outcomes.

SEO depends on all of these departments to do their job in an SEO-friendly manner for it to have a remote chance of success. This makes SEO unusual among business functions. It is judged by performance, yet it cannot deliver that performance independently. And because SEO typically sits downstream in the organization, it must request changes rather than direct them.

That structural imbalance is not a process issue. It is an ownership problem.

The Accountability Gap Explained

The accountability gap appears whenever a business-critical outcome depends on multiple teams, but no single team is accountable for the result.

SEO is a textbook example as fundamental search success requires development to implement correctly, content to align with demand, product teams to structure information coherently, markets to maintain consistency, and legal to permit eligibility-supporting claims. Failure occurs when even one link breaks.

Inside the enterprise, each of those teams is measured on its own key performance indicators. Development is rewarded for shipping. Content is rewarded for brand alignment. Product is rewarded for features. Legal is rewarded for risk avoidance. Markets are rewarded for local revenue. SEO lives in the cracks between them.

No one is incentivized to fix a problem that primarily benefits another department’s metrics. So issues persist, not because they are invisible, but because resolving them offers no local reward.

KPI Structures Encourage Metric Shielding

This is where enterprise SEO collides head-on with organizational design.

In practice, resistance to SEO rarely looks like resistance. No one says, “We don’t care about search.” Instead, objections arrive wrapped in perfectly reasonable justifications, each grounded in a different team’s success metrics.

Engineering teams explain that template changes would disrupt sprint commitments. Localization teams point to budgets that were never allocated for rewriting content. Product teams note that naming decisions are locked for brand consistency. Legal teams flag risk exposure in expanded explanations. And once something has launched, the implicit assumption is that SEO can address any fallout afterward.

Each of these responses makes sense on its own. None are malicious. But together, they form a pattern where protecting local KPIs takes precedence over shared outcomes.

This is what I refer to as metric shielding: the quiet use of internal performance measures to avoid cross-functional work. It’s not a refusal to help; it’s a rational response to how teams are evaluated. Fixing an SEO issue rarely improves the metric a given department is rewarded for, even if it materially improves enterprise visibility.

Over time, this behavior compounds. Problems persist not because they are unsolvable, but because solving them benefits someone else’s scorecard. SEO becomes the connective tissue between teams, yet no one is incentivized to strengthen it.

This dynamic is part of a broader organizational failure mode I call the KPI trap, where teams optimize for local success while undermining shared results. In enterprise SEO, the consequences surface quickly and visibly. In other parts of the organization, the damage often stays hidden until performance breaks somewhere far downstream.

The Myth: “SEO Is Marketing’s Job”

To simplify ownership, enterprises often default to a convenient fiction: SEO belongs to marketing.

On the surface, that assumption feels logical. SEO is commonly associated with organic traffic, and organic traffic is typically tracked as a marketing KPI. When visibility is measured in visits, conversions, or demand generation, it’s easy to conclude that SEO is simply another marketing lever.

In practice, that logic collapses almost immediately. Marketing may influence messaging and campaigns, but it does not control the systems that determine discoverability. It does not own templates, rendering logic, taxonomy, structured data pipelines, localization standards, release timing, or engineering priorities. Those decisions live elsewhere, often far upstream from where SEO performance is measured.

As a result, marketing ends up owning SEO on the organizational chart, while other teams own SEO in reality. This creates a familiar enterprise paradox. One group is held accountable for outcomes, while other groups control the inputs that shape those outcomes. Accountability without authority is not ownership. It is a guaranteed failure pattern.

The Core Reality

At its core, enterprise SEO failures are rarely tactical. They are structural, driven by accountability without authority across systems SEO does not control.

Search performance is created upstream through platform decisions, information architecture, content governance, and release processes. Yet SEO is almost always measured downstream, after those decisions are already locked. That separation creates the accountability gap.

SEO becomes responsible for outcomes shaped by systems it doesn’t control, priorities it can’t override, and tradeoffs it isn’t empowered to resolve. When success requires multiple departments to change, and no one owns the outcome, performance stalls by design.

Why This Breaks Faster In AI Search

In traditional SEO, the accountability gap usually expressed itself as volatility. Rankings moved. Traffic dipped. Teams debated causes, made adjustments, and over time, many issues could be corrected. Search engines recalculated signals, pages were reindexed, and recovery, while frustrating, was often possible. AI-driven search behaves differently because the evaluation model has changed.

AI systems are not simply ranking pages against each other. They are deciding which sources are eligible to be retrieved, synthesized, and represented at all. That decision depends on whether the system can form a coherent, trustworthy understanding of a brand across structure, entities, relationships, and coverage. Those signals must align across platforms, templates, content, and governance.

This is where the accountability gap becomes fatal. When even one department blocks or weakens those elements – by fragmenting entities, constraining content, breaking templates, or enforcing inconsistent standards – the system doesn’t partially reward the brand. It fails to form a stable representation. And when representation fails, exclusion follows. Visibility doesn’t gradually decline. It disappears.

AI systems default to sources that are structurally coherent and consistently reinforced. Competitors with cleaner governance and clearer ownership become the reference point, even if their content is not objectively better. Once those narratives are established, they persist. AI systems are far less forgiving than traditional rankings, and far slower to revise once an interpretation hardens.

This is why the accountability gap now manifests as a visibility gap. What used to be recoverable through iteration is now lost through omission. And the longer ownership remains fragmented, the harder that loss is to reverse.

A Note On GEO, AIO, And The Labeling Distraction

Much of the current conversation reframes these challenges under new labels GEO, AIO, AI SEO, generative optimization. The terminology isn’t wrong. It’s just incomplete.

These labels describe where visibility appears, not why it succeeds or fails. Whether the surface is a ranking, an AI Overview, or a synthesized answer, the underlying requirements remain unchanged: structural clarity, entity consistency, governed content, trustworthy signals, and cross-functional execution.

Renaming the outcome does not change the operating model required to achieve it.

Organizations don’t fail in AI search because they picked the wrong acronym. They fail because the same accountability gap persists, with faster and less forgiving consequences.

The Enterprise SEO Ownership Paradox

At its core, enterprise SEO operates under a paradox that most organizations never explicitly confront.

SEO is inherently cross-functional. Its performance depends on systems, processes, platforms, and decisions that span development, content, product, legal, localization, and governance. It behaves like infrastructure, not a channel. And yet, it is still managed as if it were a marketing function, a reporting line, or a service desk that reacts to requests.

That mismatch explains why even well-funded SEO teams struggle. They are held responsible for outcomes created by systems they do not control, processes they cannot enforce, and decisions they are rarely empowered to shape.

This paradox stays abstract until it’s reduced to a single, uncomfortable question:

Who is accountable when SEO success requires coordinated changes across three departments?

In most enterprises, the honest answer is simple. No one.

And when no one owns cross-functional success, initiatives stall by design. SEO becomes everyone’s dependency and no one’s priority. Work continues, meetings multiply, and reports are produced – but the underlying system never changes.

That is not a failure of execution. It is a failure of ownership.

What Real Ownership Looks Like

Organizations that win redefine SEO ownership as an operational capability, not a departmental role.

They establish executive sponsorship for search visibility, shared accountability across development, content, and product, and mandatory requirements embedded into platforms and workflows. Governance replaces persuasion. Standards are enforced before launch, not debated afterward.

SEO shifts from requesting fixes to defining requirements teams must follow. Ownership becomes structural, not symbolic.

The Final Reality

This perspective isn’t theoretical. It’s grounded in my nearly 30 years of direct experience designing, repairing, and operating enterprise website search programs across large organizations, regulated industries, complex platforms, and multi-market deployments.

I’ve sat in escalation meetings where launches were declared successful internally, only for visibility to quietly erode once systems and signals reached the outside world. I’ve watched SEO teams inherit outcomes created months earlier by decisions they were never part of. And more recently, I’ve worked with leadership teams who didn’t realize they had a search problem until AI-driven systems stopped citing them altogether. These are not edge cases. They are repeatable organizational failure modes.

What ultimately separated failure from recovery was never better tactics, better tools, or better acronyms. It was ownership. Specifically, whether the organization recognized search as a shared system-level responsibility and structured itself accordingly.

Enterprise SEO doesn’t break because teams aren’t trying hard enough. It breaks when accountability is assigned without authority, and when no one owns the outcomes that require coordination across the organization.

That is the problem modern search exposes. And ownership is the only durable fix.

Coming Next

The Modern SEO Center Of Excellence: Governance, Not Guidelines

We’ll close the loop by showing how enterprises institutionalize ownership through a Center of Excellence that governs standards, enforcement, entity governance, and cross-market consistency, the missing layer that prevents the accountability gap from recurring.

More Resources:

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Featured Image: ImageFlow/Shutterstock

https://www.searchenginejournal.com/who-owns-seo-in-the-enterprise-the-accountability-gap-that-kills-performance/566095/

Google’s John Mueller responded to a question about whether core updates roll out in stages or follow a fixed sequence. His answer offers some clarity about how core updates are rolled out and also about what some core updates actually are.

Question About Core Update Timing And Volatility

An SEO asked on Bluesky whether core updates behave like a single rollout that is then refined over time or if the different parts being updated are rolled out at different stages.

The question reflects a common observation that rankings tend to shift in waves during a rollout period, often lasting several weeks. This has led to speculation that updates may be deployed incrementally rather than all at once.

They asked:

“Given the timing, I want to ask a core update related question. Usually, we see waves of volatility throughout the 2-3 weeks of a rollout. Broadly, are different parts of core updated at different times? Or is it all reset at the beginning then iterated depending on the results?”

Core Updates Can Require Step-By-Step Deployment

Mueller explained that Google does not formally define or announce stages for core updates. He noted that these updates involve broad changes across multiple systems, which can require a step-by-step rollout rather than a single deployment.

He responded:

“We generally don’t announce “stages” of core updates.. Since these are significant, broad changes to our search algorithms and systems, sometimes they have to work step-by-step, rather than all at one time. (It’s also why they can take a while to be fully live.)”

Updates Depend On Systems And Teams Involved

Mueller next added that there is no single mechanism that governs how all core updates are released. Instead, updates reflect the work of different teams and systems, which can vary from one update to another.

He explained:

“I guess in short there’s not a single “core update machine” that’s clicked on (every update has the same flow), but rather we make the changes based on what the teams have been working on, and those systems & components can change from time to time.”

Core Updates May Roll Out Incrementally Rather Than All At Once

Mueller’s explanation suggests that the waves of volatility observed during core updates may correspond to incremental changes across different systems rather than a single reset followed by adjustments. Because updates are tied to multiple components, the rollout may progress in parts as those systems are updated and brought fully live.

This reflects a process where some changes are complex and require a more nuanced step-by-step rollout, rather than being released all at once, which may explain why ranking shifts can appear uneven during the rollout period.

Connection To Google’s Spam Update?

I don’t think that it was a coincidence that the March Core update followed closely after the recent March 2026 Spam Update. The reason I think that is because it’s logical for spam fighting to be a part of the bundle of changes made in a core algorithm update. That’s why Googlers sometimes say that a core update should surface more relevant content and less of the content that’s low quality.

So when Google announces a Spam Update, that stands out because either Google is making a major change to the infrastructure that Google’s core algorithm runs on or the spam update is meant to weed out specific forms of spam prior to rolling out a core algorithm update, to clear the table, so to speak. And that is what appears to have happened with the recent spam and core algorithm updates.

Comparison With Early Google Updates

Way back in the early days, around 25 years ago, Google used to have an update every month, offering a chance to see if new pages are indexed and ranked as well as seeing how existing pages are doing. The initial first days of the update saw widescale fluctuations which we (the members of WebmasterWorld forum) called the Google Dance.

Back then, it felt like updates were just Google adding more pages and re-ranking them. Then around the 2003 Florida update it became apparent that the actual ranking systems were being changed and the fluctuations could go on for months. That was probably the first time the SEO community noticed a different kind of update that was probably closer a core algorithm update.

In my opinion, one way to think of it is that Google’s indexing and ranking algorithms are like software. And then, there’s also hardware and software that are a part of the infrastructure that the indexing and ranking algorithms run on (like the operating system and hardware of your desktop or laptop).

That’s an oversimplification but it’s useful to me for visualizing what a core algorithm update might be. Most, if not all of it, is related to the indexing and ranking part. But I think sometimes there’s infrastructure-type changes going on that improve the indexing and ranking part.

Featured Image by Shutterstock/A9 STUDIO

https://www.searchenginejournal.com/google-answers-why-core-updates-can-roll-out-in-stages/571003/

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In “The Science Of How AI Pays Attention,” I analyzed 1.2 million ChatGPT responses to understand exactly how AI reads a page. In “The Science Of How AI Picks Its Sources,” I analyzed 98,000 citation rows to understand which pages make it into the reading pool at all.

This is Part 3.

Where Part 1 told you where on a page AI looks, and Part 2 told you which pages AI routinely considers, this one tells you what AI actually rewards inside the content it reads.

The data clarifies:

• Most AI SEO writing advice doesn’t hold at scale. There is no universal “write like this to get cited” formula – the signals that lift one industry’s citation rates can actively hurt another.
• The entity types that predict citation are not the ones being targeted. DATE and NUMBER are universal positives. PRICE suppresses citation in five of six verticals, and KG-verified entities are a negative signal.
• The one writing signal that holds across all seven verticals: Declarative language in your intro, +14% aggregate lift.
• Heading structure is binary. Commit to the right number for your vertical or use none. Three to four headings are worse than zero in every vertical.
• Corporate content dominates. Reddit doesn’t. AI citation behavior does not mirror what happened to organic search in 2023-2024.

1. Specific Writing Signals Influence Citation, While Others Harm It

While “The Science Of How AI Pays Attention” covers parts of the page and types of writing that influence ChatGPT visibility, I wanted to understand which writing-level signals – word count, structure, language style – predict higher AI citation rates across verticals.

Approach

1. I compared high-cited pages (more than three unique prompt citations) vs. low-cited across seven writing metrics: word count, definitive language, hedging, list items, named entity density, and intro-specific signals.
2. I analyzed the first 1,000 words for list item count, named entity density, intro definitive language token density, and intro number count.

Results: Across all verticals, definitive phrasing and including relevant entities matter. But most signals are flat.

What The Industry Patterns Showed

When splitting the data up by vertical, we suddenly see preferences:

• Total word count was strongest in CRM/SaaS (1.59x).
• Finance was an anomaly with word count: Shorter pages win (0.86x word count).
• Definitive phrases in the first 1,000 characters were positive for most verticals.
• Education is a signal void. Writing style explains almost nothing about citation likelihood there.

Top Takeaways

1. There is no universal “write like this to get cited” formula. For example, the signals that lift CRM/SaaS citation rates actively hurt Finance. Instead, match content format to vertical norms.

2. The one universal rule: open with a direct declarative statement. Not a question, not context-setting, not preamble. The form is “[X] is [Y]” or “[X] does [Z].” This is the only writing instruction that holds regardless of vertical, content type, or length.

3. LLMs “penalize” hedging in your intro. “This may help teams understand” performs worse than “Teams that do X see Y.” Remove qualifiers from your opening paragraph before any other optimization.

2. The Entity Types That Predict Citation Are Not The Ones Being Targeted

Most AEO advice focuses on named entities as a category: Pack in more known brand names, tool names, numbers. The cross-vertical entity type analysis below tells a more specific (and more useful) story.

Approach

1. Ran Google’s Natural Language API on the first 1,000 characters (about 200-250 words) of each unique URL.
2. Computed lift per entity type: % of high-cited pages with that type / % of low-cited pages.
3. Analyzed 5,000 pages across seven verticals.

* A quick note on terminology: Google NLP classifies software products, apps, and SaaS tools as CONSUMER_GOOD, a legacy label from when the API was built for physical retail. Throughout this analysis, CONSUMER_GOOD means software/product entities.

Results: DATE and NUMBER are the most universal positive signals. Interestingly, PRICE is the strongest universal negative.

What The Industry Patterns Showed

• DATE is the most universal positive signal, with the exception of Finance (0.65x).
• NUMBER is the second most universal. Specific counts, metrics, and statistics in the intro consistently predict higher citation rates. Finance (0.98x) and Product Analytics (1.10x) mark the floor and ceiling of that range.
• PRICE is the strongest universal negative. Pages that open with pricing signal commercial intent. Finance is the sole exception at 1.16x, likely because price here means fee percentages and rate comparisons, which are the actual reference data financial queries are looking for.
• CONSUMER_GOOD (software/product entities) is mixed. In Healthcare, product entities signal established brands and tools. In Crypto, naming specific protocols and products is core to answering technical queries.
• PHONE_NUMBER is a positive signal in Healthcare (1.41x) and Education (1.40x). In both cases, it is almost certainly a proxy for established brands/institutions/providers with real physical presence, not a literal signal to add phone numbers to your pages.

The Knowledge Graph inversion deserves its own note here:

• The data showed that high-cited pages average 1.42 KG-verified entities vs. 1.75 for low-cited pages (lift: 0.81x).
• Pages built around well-known, KG-verified entities (major brands, institutions, famous people) tend toward generic coverage, which isn’t preferred by ChatGPT.
• High-cited pages are dense with specific, niche entities: a particular methodology, a precise statistic, a named comparison. Many of those niche entities have no KG entries at all. That specificity is what AI reaches for.

Top Takeaways

1. Add the publish date to your pages and aim to use at least one specific number in your content. That combination is the closest thing to a universal AI citation signal this dataset produced. But Finance gets there through price data and location specificity instead.

2. Avoid opening with pricing in non-finance verticals. Price-dominant intros correlate with lower citation rates.

3. KG presence and brand authority do not translate to an AI citation advantage. Chasing Wikipedia entries, brand panels, or KG verification is the wrong lever. Specific, niche entities (even ones without KG entries) outperform famous ones.

3. Heading Structure: Commit To One Or Don’t Bother

We know headings matter for citations from the previous two analyses. Next, I wanted to understand whether heading count predicts citation rates and whether the optimal structure varies by vertical.

Approach

1. Counted total headings per page (H1+H2+H3) across all cited URLs.
2. Grouped pages into 7 heading-count buckets: 0, 1-2, 3-4, 5-9, 10-19, 20-49, 50+.
3. Computed high-cited rate (% of URLs that are high-cited) per bucket per vertical.

Results: Including more headings in your content is not universally better. The sweet spot depends on vertical and content type. One finding holds everywhere: Strangely, 3-4 headings are worse than zero.

What The Industry Patterns Showed

• CRM/SaaS is the only vertical where the 20+ heading lift is confirmed: 12.7% high-cited rate at 20-49 headings vs. a 5.9% baseline. The 50+ bucket reaches 18.2%. Long structured reference pages and comparison guides with one section per tool outperform everything else here.
• Healthcare inverts most sharply. The high-cited rate drops from 15.1% at zero headings to 2.5% at 20-49 headings. A page with 30 H2s on telehealth topics signals optimization intent, not clinical authority.
• Finance peaks at 10-19 headings (29.4% high-cited rate). Structured but not exhaustive: think rate tables, regulatory breakdowns, and advisor comparison pages with moderate heading depth.
• Crypto peaks at five to nine headings (34.7% high-cited rate). Technical documentation in this vertical tends toward dense prose with moderate navigation structure. Over-structuring breaks up the technical depth.
• Education is flat across all heading counts, which is consistent with the writing signals finding. Heading structure explains almost nothing about citation likelihood in education content.
• The three to four heading dead zone holds across every vertical without exception. Partial structure confuses AI navigation without providing the full benefit of a committed hierarchy.

Top Takeaways

1. The 20+ heading finding from Part 1 is a CRM/SaaS finding, not a universal one. Applying it to healthcare, education, or finance could actively suppress citation rates in those verticals.

2. The principle that holds everywhere: Commit to structure or don’t use it. The middle ground costs you in every vertical. A fully-structured page with the right heading depth outperforms a half-structured page in every vertical.

3. Use the optimal heading range for your vertical. Crypto: 5-9. Finance and Education: 10-19. CRM/SaaS: 20+ (with H3s). Healthcare: 0 or 5-9 at most. Long CRM reference pages with 50+ sections are the one case where maximum heading depth pays off.

4. UGC Doesn’t Dominate

The “Reddit effect” reshaped organic search between 2024 and 2025. I wanted to understand whether ChatGPT cites user-generated content (Reddit, forums, reviews) at meaningful rates or whether corporate/editorial content dominates.

The common industry assumption – that AI also preferentially cites community voices – is not what we found in the data.

Approach

1. Classified these cited URLs as (1) UGC: Reddit, Quora, Stack Overflow, forum subdomains, Medium, Substack, Product Hunt, Tumblr, or (2) community/forum prefixes or corporate/editorial by domain.
2. Computed citation share per category per vertical.
3. Dataset: 98,217 citations across 7 verticals.

Results: Corporate content accounts for 94.7% of all citations. UGC is nearly invisible.

What The Industry Patterns Showed

• Finance is the most corporate-locked vertical at 0.5% UGC. YMYL (Your Money, Your Life) content appears to systematically suppress citations to community opinion.
• Healthcare sits at 1.8% UGC for the same structural reason. Clinical, telehealth, and HIPAA content draws almost exclusively from institutional sources.
• Crypto has the highest UGC penetration in the dataset at 9.2%. Community-generated content (Reddit technical threads, Medium tutorials, developer forum posts) answers a meaningful proportion of analyzed queries. In a fast-moving technical niche where official documentation consistently lags, community posts fill the gap.
• Product Analytics and HR Tech sit at 6.9% and 5.8% UGC. Both are verticals where Reddit comparison threads and product review communities provide genuine signal alongside corporate content.

Top Takeaways

1. The “Reddit effect” in SEO has not translated proportionally to AI citations. In most verticals, reddit.com captures 2-5% of total citations. This finding is in line with other industry research, including this report from Profound.

2. For finance and healthcare: UGC has near-zero AI citation value. Invest in structured, authoritative corporate content with clear sourcing. Community engagement may matter for other reasons, but it does not contribute meaningfully to AI citation share in these verticals.

3. For crypto, product analytics, and HR tech: Community presence has measurable citation value. Detailed Reddit comparison threads, technical Medium posts, and structured developer forum answers can supplement corporate content reach.

What This Means For How You Strategize For LLM Visibility

Across all three parts of this study, the consistent finding is that AI citation is not primarily a writing quality problem.

Part 2 showed it is a content architecture problem: Thin single-intent pages are structurally locked out regardless of how well they’re written. This piece shows the same logic applies inside the content itself.

The aggregate writing signals table is the most important chart in this analysis. Not because it shows you what to do, but because it shows how much of what the AI SEO/GEO/AEO industry is telling you doesn’t survive cross-vertical scrutiny. Word count, list density, named entity counts … all flat or negative at the aggregate. The signals that work are vertical-specific and smaller than our industry’s consensus implies.

The meta-lesson from this analysis is that findings are vertical (and probably topic) specific, which is no different in SEO.

This part concludes the Science of AI – for now. Because the AI ecosystem is constantly changing.

Methodology

We analyzed ~98,000 ChatGPT citation rows pulled from approximately 1.2 million ChatGPT responses from Gauge.

Because AI behaves differently depending on the topic, we isolated the data across seven distinct, verified verticals to ensure the findings weren’t skewed by one specific industry.

Analyzed verticals:

• B2B SaaS
• Finance
• Healthcare
• Education
• Crypto
• HR Tech
• Product Analytics

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Featured Image: CoreDESIGN/Shutterstock; Paulo Bobita/Search Engine Journal

https://www.searchenginejournal.com/the-science-of-what-ai-actually-rewards/570849/

We’ve all seen it. Brands with healthy websites and excellent content have been watching their organic traffic from Google’s SERP erode for years. In a recent webinar hosted by Search Engine Journal, guest speaker Nikhil Lai, principal analyst of Performance Marketing for Forrester Research, estimated his clients are losing between 10 and 40% of organic and direct traffic year-over-year.

However, a stunning bright spot is this: Lai said referral traffic from answer engines is growing 40% month over month. Visitors arriving from those engines convert at two to four times the rate of traditional search visitors, spend three times as long on site, and arrive with queries averaging 23 words, compared to the three or four words that defined the last decade of search.

Lai asserted that the channel driving this shift deserves a seat at the CMO’s table. Answer engines influence brand perception before purchase intent forms, which makes answer engine optimization (AEO) a brand investment, and puts budget and measurement decisions at the CMO level.

Here is the strategic roadmap Lai laid out at SEJ Live. He highlighted the decisions, org structures, and measurement frameworks that will move AEO from a search team initiative to a C-suite priority.

Answer Engines Build Demand Before Buyers Know What They Want

Classic search captures intent that already exists. A user types “running shoes,” clicks a result, and evaluates options. Answer engines operate earlier and differently: users hold extended conversations with large datasets, rarely click through, and leave those sessions with specific brand associations formed across multiple follow-up questions.

A user who once searched “running shoes” now asks ChatGPT, “What’s the best shoe for overpronation with wide feet in cold weather on pavement?” They exit that conversation with a brand name in mind and search for it directly. Your brand appeared in an AI conversation before the user ever reached your site. Every day, demand generation is created from users’ research sessions.

The Forrester data Lai presented reinforces the quality of that exposure: Sessions on answer engines average 23 minutes, with users asking five to eight follow-up questions per session. Each turn is another brand impression. The click-through rate stays low; the conversion rate on the traffic that does arrive runs two to four times higher than search-sourced traffic, with stronger average order value and lifetime value.

Brand familiarity is built in answer engines before purchase intent crystallizes in the user’s mind.

SEO Is The Foundation Of AEO

The brands pulling back on SEO investment in response to AEO are making a costly mistake. Lai put it directly: 85 to 90% of current SEO best practices remain fully valid for answer engine visibility.

Google’s E-E-A-T framework (experience, expertise, authoritativeness, trustworthiness) still governs how quality is evaluated across every index. Site architecture, mobile load speed, structured data, and indexation hygiene all strengthen performance across every engine. Every alternative index (Bing’s, Brave’s) is benchmarked against Google’s for completeness. Every bot (GPTBot, Claudebot, Perplexitybot) is benchmarked against Googlebot for sophistication.

SEO is the infrastructure on which AEO runs. The shift is an expansion of scope and emphasis, but AEO is not a replacement of SEO fundamentals.

What changes is where additional effort goes: natural-language FAQ optimization, off-site authority building, pre-rendering for less sophisticated bots, and a measurement framework built around share of voice rather than click volume.

Bing Is Now Your Distribution Network For Every Non-Google Engine

Most answer engines outside Google draw primarily from Bing’s index.

Bing evaluates credibility by weighting what others say about your brand more heavily than what your own site claims. This explains why Reddit threads, Quora answers, Wikipedia entries, G2 reviews, YouTube videos, and Trustpilot pages dominate AI-generated answers. The off-site web has become the primary source of record for how AI describes your brand.

The immediate tactical implication: Push every sitemap update directly to Bing via the IndexNow protocol. This triggers Bingbot to crawl fresh content and feeds that content into Perplexity, ChatGPT, and the broader answer engine ecosystem faster than waiting for organic discovery.

Bing’s index remains the fastest route to non-Google answer engine visibility. Perplexity is building its own index (Sonar), and OpenAI has signaled plans to build or acquire one, but Bing is the distribution network that matters today.

AEO Requires Cross-Functional Ownership

AEO arguably spans more functions than SEO, with these three in common with SEO: content, web development, and paid search. AEO also more strongly interfaces with PR, brand marketing, and social media.

PR earns a seat because off-site authority outweighs on-site signals in AEO. Brand mentions in publications, influencer mentions, and third-party reviews all directly shape how answer engines describe your brand.

Social belongs in the room because Reddit threads and Facebook group discussions show up in AI-generated answers. Community management and reputation management, previously handled separately from SEO, are now integral to AEO. When your social listening data reaches content teams before they draft, the content responds to the questions buyers are actually asking. When it doesn’t, you’re optimizing for questions nobody asked.

Lai proposed two organizational models that work to capture the opportunities inherent in AEO:

1. Center of Excellence: A senior SEO specialist evolves into an AEO evangelist, runs a COE, and publishes cross-functional standards: clear rules like “every piece of content must answer these five questions” or “every page must include author schema.”
2. AI Orchestrator: A dedicated hire who builds agents to handle repeatable AEO tasks (schema implementation, JavaScript reduction, FAQ content creation) and governs the cross-functional workflow with published guidelines for all stakeholders.

The CMO’s decision is which model fits the organization’s scale, and whether to build it internally or partner with an agency that has already built the infrastructure.

The Content Strategy That Wins In AI Responses

Long-form skyscraper content is an ancient relic. Answer engines reward precise, specific answers to real questions, delivered succinctly and across multiple formats. Lai framed this as Forrester’s question-to-content framework: Every piece of content maps directly to a FAQ being asked on answer engines, including the follow-up questions that emerge within a single session.

Five content moves that produce results:

1. Build surround-sound FAQ coverage. Create glossaries, FAQ pages, videos, and blog posts that address the same topic cluster from different angles. When Claudebot crawls 38,000 pages for every referred page visit (per Cloudflare data), each page it indexes is an opportunity to signal topical authority. Volume and variety matter.
2. Publish direct competitor comparisons. Users ask answer engines to compare brands. Brands that create honest, data-backed comparison guides are gaining prominent visibility, because they directly answer the queries being asked that pit a brand against its competitors. This was once a taboo content format; it has become a competitive requirement.
3. Treat off-site syndication as the new backlinking. Hosting AMAs on Reddit, answering questions on Quora, and contributing to industry publications that rank in AI responses all earn the off-site authority that answer engines weigh most heavily. Give third-party voices data and perspective they couldn’t generate themselves, and they will produce mentions that shape how AI describes your brand.
4. Pre-render pages for bot access. The bots crawling your site lack the compute budget to render JavaScript-heavy pages. Claudebot’s 38,000:1 crawl-to-referral ratio compared to Googlebot’s 5:1 ratio reflects this sophistication gap. Pre-rendering a JavaScript-free version for bots while serving the full experience to human visitors ensures your content gets indexed across every engine. Over time, limit the amount of JavaScript on site. Have content directly in HTML so bots can understand your content, and index it more often. The more you’re crawled and indexed, the more visible you become.
5. Create unique content. Lai said, “Being distinctive, differentiated, and unique will help your brand stand out in a sea of sameness. Implicit in all this is that you need a lot more content, greater content velocity and diversity, which means you can use AI to create content. Google won’t automatically penalize AI-created content unless it lacks the watermarks of human authorship. The syntax and diction have to be natural. Use AI to create content, but don’t make it seem AI-generated. Get down into the details. It’s not enough to say your product is great. Explain why in different temperatures, conditions, the thickness, and so on, to satisfy long-tail intent.”

Replace Legacy KPIs With Metrics That Predict Market Share

The internal conversation, Lai said, he hears most from Forrester clients: “The hardest part of this transition from SEO to AEO has been trying to convince management to not focus as much on CTR and traffic. Those were indicators of organic authority. They are no longer reliable indicators.

“The new KPIs to focus on are visibility and share of voice. Share of voice can be measured in many ways. The most common are citation share: how often is my brand cited, how often is my content linked, of the opportunities I have to be cited; and mention share: how often is my brand mentioned of the opportunities I have to be mentioned. I’m also seeing more clients look into citation attempts: how often is ChatGPT trying to cite my content, and are there things I can do on the back end of my site to make that citation attempt score go up? Those are the new indicators of authority,” said Lai.

These metrics connect directly to branded search volume, which Lai called “the single strongest leading indicator of market share growth.” The chain of logic to present to the board: higher citation and mention share drives more branded searches, which converts at higher rates, which compounds into measurable market share gains against competitors.

Lai said he expects Google to add citation metrics to Search Console once AI Max adoption reaches critical mass, and an OpenAI Analytics product before year-end.

For now, Lai suggested, the best course of action is to establish a baseline with your current SEO platform and track the directional trend. Lai contended that, to address concerns of accuracy within today’s popular SEO tools of answer engine mentions, even imperfect measurement reveals which content clusters are earning citations and which need rebuilding.

The Agentic Phase Starts The Clock On B2B Urgency

Answer engines are moving from conversation to action. The current phase, characterized by extended back-and-forth with large datasets, is the warm-up. The agentic phase is defined by engines’ booking, filing, researching, and purchasing on users’ behalf. This will mean fewer clicks, longer sessions, and richer intent signals available to advertisers.

For B2B CMOs, the urgency is immediate. Forrester research shows GenAI has already become the number one source of information for business buyers evaluating purchases of $1 million or more, coming in ahead of customer references, vendor websites, and social media. Your largest deals are being influenced by AI conversations before your sales team enters the picture.

AEO visibility in B2B is a current-pipeline variable that requires immediate attention.

The brands building complete search strategies now, covering answer engines, on-site conversational search, and structured data across every indexed channel, will own discovery and have greater control over brand perception in the next phase of buying behavior.

The window to gain an early-mover competitive advantage is shrinking, before AEO visibility becomes just another standard expectation everyone has to meet.

Key Takeaways For CMOs

• Reframe the traffic story. Lower overall traffic volume paired with two-to-four-times higher conversion rates is a net performance gain. Build that case proactively before your CEO draws the wrong conclusion from a falling traffic chart.
• Fund AEO as an upper-funnel brand channel. That means applying the same budget logic, measurement frameworks, and executive ownership you would bring to any major brand awareness investment, where success is measured in visibility, perception, and long-term share of voice rather than clicks and conversions.
• Move to share-of-voice KPIs. Citation share and mention share drive branded search volume, which drives market share. Make that causal chain visible to your leadership team.
• Assign cross-functional ownership with clear governance. Choose between a center of excellence or an AI orchestrator model and make that structural decision this quarter.
• Prioritize off-site authority as a content strategy responsibility. Reddit, Quora, third-party publications, and YouTube shape AI’s perception of your brand. PR and social teams own the channels that matter most for AEO.
• Push every sitemap update to Bing via IndexNow. Bing’s index feeds most non-Google answer engines. This is a 15-minute technical change with compounding distribution benefits.
• Use AI to help with content, but always apply human editing for authority. Content that reads as machine-generated loses trust across every engine, including Google.

What Does A Smart CMO Do Next?

Start with a 90-day experiment using some or all of these strategies.

Audit your current citation and mention share in one category using your existing SEO platform. Identify three high-intent FAQ clusters where your brand should be visible and build surround-sound content for each: a dedicated FAQ page, a comparison guide, and one off-site piece in a publication that appears in AI responses. Push fresh sitemaps to Bing. Track citation share and branded search volume at 30, 60, and 90 days.

The data may make the investment case for broader rollout. If not, tweak your approach. The brands moving first will capture the highest-quality traffic at the lowest incremental cost, and set the citation baseline that becomes progressively harder for competitors to close.

The full webinar is available on demand.

More Resources:

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Featured Image: Dmitry Demidovich/Shutterstock

https://www.searchenginejournal.com/so-your-traffic-tanked-what-smart-cmos-do-next/570708/

WordPress 7.0, previously scheduled for an April 9th release, will be delayed in order to stabilize the Real-Time Collaboration feature and assure that the release, a major milestone, will “target extreme stability.” Much is riding on WordPress 7.0 as it will ship with features that will usher in the age of AI-driven content management systems.

Prioritization Of Stability

Matt Mullenweg, co-founder of WordPress, commenting in the official Making WordPress Slack workspace, said the release should step back from its current trajectory and prioritize stability, calling for a longer pre-release phase to get the real-time collaboration (RTC) feature working correctly. The delay is expected to last weeks, not days, and is described as a one-off deviation from WordPress’s planned date-driven schedule.

Mullenweg posted:

“Given the scope and status of 7.0, I think we should go back to beta releases, get the new tables right, lock in everything we want for 7.0, and then start RCs again. Date-driven is still our default, but for this milestone release we want to target extreme stability and exciting updates, especially as AI-accelerated development is increasing people’s expectations for software.

This is a one-off, I think for future we should get back on the scheduled train, with an aim for 4-a-year in 2027, to hopefully reflect our AI-enabled ability to move faster.”

Extended Release Candidate Phase Replaces Beta Reversion

To avoid technical compatibility issues, the project will remain in the release candidate phase, extending the testing period through additional RC builds as needed.

The proposal to return to beta releases was rejected because it would break PHP version comparison behavior, plugin update logic, and tooling that depends on standard version sequencing. Continuing with RC builds preserves compatibility while allowing more time for testing and fixes.

Real-Time Collaboration

The delay is largely due to the Real-Time Collaboration feature, which introduces new database tables and changes how WordPress handles editing sessions. Contributors identified risks related to performance, data handling, and interactions with existing systems.

A primary concern is that real-time editing currently disables persistent post caches during active sessions, a performance issue the team is working to resolve before the final release.

Database Design Raises Performance Concerns

A key part of the discussion focused on how to structure the database for Real-Time Collaboration (RTC).  A proposed single RTC table would support 1. real-time editing updates and 2. synchronization. But some contributors noted that the workloads for real-time editing and synchronization are fundamentally different.

Real-time collaboration generates high-frequency, bursty writes that require low latency (meaning updates happen with very little delay).

While synchronization between environments involves slower, structured updates that may include full-table scans.

Combining both patterns within one table risks performance issues and added complexity. Contributors discussed separating these workloads into separate tables optimized for each use case, but no decision has been made.

Gap In Release Candidate Testing Raises Concern

The discussion in the WordPress Slack workspace also raised concern over whether there was enough real-world release candidate testing, and database schema changes increase the risk of failures during upgrades. The solution of using the Gutenberg plugin for testing was rejected because database changes could affect production sites and require complex migration logic. Instead, the project will use an extended RC phase to increase testing exposure and gather feedback from a wider group of users.

Versioning Constraints

The proposal to delay version 7.0 led to additional issues. PHP version comparison rules and related tooling complicated returning to beta versions. It was agreed that staying within the release candidate sequence (ergo RC1, RC2, RC3) avoids these issues while allowing continued iteration, so it was decided to continue with release candidates.

Future Release Cadence Remains

The delay is described as a temporary exception. Matt Mullenweg said the project intends to return to a regular release schedule, with a goal of delivering roughly four releases per year by 2027 as development speeds increase with AI-assisted workflows.

Implications For Developers And Users

Developers should expect continued changes to the Real-Time Collaboration feature and its supporting database structures during the extended release candidate phase. The longer testing period provides more time to identify issues before release. For site owners and hosts, the delay shows that WordPress is prioritizing stability over schedule while introducing more complex real-time and synchronization features.

Impact Of RTC On Hosting Environments

Something that wasn’t discussed but is a real issue is how real-time collaboration might affect web hosting providers. They need to test that feature to see if it introduces issues on shared hosting environments. While RTC will be shipping with the feature turned off by default, the impact of it being used by customers in a shared hosting environment is currently unknown. A spokesperson for managed WordPress hosting provider Kinsta told Search Engine Journal they are still testing. Given how the feature is still evolving, Kinsta and other web hosts will have to continue testing the upcoming WordPress release candidates.

I think most people will agree that the decision to delay the release of WordPress 7.0 is the right call.

https://www.searchenginejournal.com/wordpress-delays-release-of-version-7-0-to-focus-on-stability/570944/