Google has quietly updated its list of user-triggered fetchers with new documentation for Google NotebookLM. The importance of this seemingly minor change is that it’s clear that Google NotebookLM will not obey robots.txt.
Google NotebookLM
NotebookLM is an AI research and writing tool that enables users to add a web page URL, which will process the content and then enable them to ask a range of questions and generate summaries based on the content.
Google’s tool can automatically create an interactive mind map that organizes topics from a website and extracts takeaways from it.
User-Triggered Fetchers Ignore Robots.txt
Google User-Triggered Fetchers are web agents that are triggered by users and by default ignore the robots.txt protocol.
According to Google’s User-Triggered Fetchers documentation:
“Because the fetch was requested by a user, these fetchers generally ignore robots.txt rules.”
Google-NotebookLM Ignores Robots.txt
The purpose of robots.txt is to give publishers control over bots that index web pages. But agents like the Google-NotebookLM fetcher aren’t indexing web content, they’re acting on behalf of users who are interacting with the website content through Google’s NotebookLM.
How To Block NotebookLM
Google uses the Google-NotebookLM user agent when extracting website content. So, it’s possible for publishers wishing to block users from accessing their content could create rules that automatically block that user agent. For example, a simple solution for WordPress publishers is to use Wordfence to create a custom rule to block all website visitors that are using the Google-NotebookLM user agent.
Another way to do it is with .htaccess using the following rule:
RewriteEngine On
RewriteCond %{HTTP_USER_AGENT} Google-NotebookLM [NC]
RewriteRule .* - [F,L]
The rise of AI technologies has brought a level of panic to the publishing industry not seen since the birth of the world wide web. We all know AI is changing how people engage with websites, but we’re not sure yet what these changes will lead to.
The fact that AI is just past its peak in the hype cycle isn’t helping us form any clarity. The bubble is about to burst, but things will not go back to “normal” – whatever that normal may have been pre-AI.
There will be a new post-bubble status quo that will eventually materialize. I strongly believe publishing as a whole will end up in a healthier state, but also that some individual publishers will suffer – and may even cease to exist.
In this article, I’ll outline what I believe are several key survival strategies that online publishers can adopt to help them weather the storm and emerge stronger and more resilient.
1. Search Lives On
Despite the bleatings of a number of misguided LinkedIn influencers who I mercifully shall not name, there is no evidence that search as a channel is dying. Google is still by far the largest driver of traffic to websites, and news is no exception.
What we do need to realize is that “Peak Search” has already happened, and the total number of clicks that Google sends to the web will not substantially grow.
The Google traffic curve has flattened (Image Credit: Barry Adams)
Publishers that are what I call “SEO mature,” with strong SEO and audience growth tactics in place for many years, will not be able to grow their readership through search alone. For these publishers, search traffic is – at best – leveling off.
That doesn’t mean search cannot be a growth channel. Many publishers are nowhere near “SEO mature” and have plenty of scope for growth in search clicks. In fact, I daresay most of the publishers I work with have not yet maximized their search potential, and can achieve strong gains from improved editorial and technical SEO.
But search as a whole is now a flat channel. We cannot expect to see the same consistent increase in search clicks that have been foundational to many publishers’ growth strategies for the last two decades.
Search is now a zero-sum game. When you get a click, a competitor isn’t. The pie has ceased to grow, which means we have to fight harder for our slice. Good SEO is even more crucial. You cannot rely on Google’s own growth to get your share; you need to wrestle it out of the hands of your competitors.
AI Is An Accelerant
While publishers are rightfully skeptical of Google’s claims about the impact of AI on traffic, the flattening of the search traffic curve began long before AI appeared on the scene.
There have been many warning signs that the endless growth of Google clicks wasn’t endless after all. Zero-click search was a concept years before ChatGPT launched.
But most publishers failed to heed these warnings and continued to rely on Google as their primary growth channel, taking no efforts to diversify their audience strategies.
AI didn’t cause the search curve to flatten, but it did serve as a bucket of gasoline on that particular fire. AI has accelerated zero-click, and sped up the rising discomfort many publishers were already experiencing.
Featured snippets and other intrusive search elements, fragmented online user behavior, algorithm updates, audience fatigue, bad user experiences, and many more factors formed the foundation of zero-click. AI merely hastened the trend by offering a channel for users to engage with the web’s content without the friction imposed by visiting a website.
Maximizing Search
Apparently, realizing its precarious position as the gateway to the web and direct responsibility for the health of the publishing ecosystem, Google is throwing us a few bones to help us build audience loyalty.
One of these is “Preferred sources.” This is a new feature in Top Stories on Google’s results that allows a user to set preferred news sources.
When a Top Stories box is shown on a Google search page, if the user’s preferred sources have a relevant article, Google will give that source a spot in Top Stories.
Top Stories on Google.com for the ‘fernando alonso’ query with GPFans.com as a preferred source (Image Credit: Barry Adams)
Setting a preferred source can be done by clicking the relevant icon at the top of a Top Stories box and searching for your preferred publication, or directly with a link:
You can encourage your readers to add your site as a preferred source with a call to action. Google even provides an image for this that you can add to your website:
(Image credit: Barry Adams)
This new feature does raise some concerns about filter bubbles and echo chambers. But that’s a station the Google train long since passed, especially with the fully personalized and filter-bubbled content feed that billions around the world engage with daily.
2. Discover Is Growing
Where search is flattening, Discover is on the rise. Most publishers I work with see good numbers coming from the Discover feed. For many, Discover is growing to such an extent that it more than compensates for diminished search traffic.
Despite this, I’m not a fan of building an audience strategy around Discover. There are several reasons for this, some of which I’ve outlined in a piece on Press Gazette and others which are detailed by David Buttle (also on Press Gazette).
Summarizing those objections here:
Discover strategies encourage bad habits; Clickbait, churnalism, sensationalism, and low information gain.
Discover traffic is volatile, unlikely to generate a consistent traffic profile, and highly susceptible to algorithm updates.
Discover is not a core service that Google offers, and they can kill it without any meaningful loss for them.
Reliance on Discover gives Google enormous power over publishers.
Discover lacks the regulatory scrutiny that search is subjected to.
Yet I cannot deny the reality that Discover is a huge source of traffic, and publishers need to optimize for it to some degree.
In addition to the known Discover optimization strategies, which I will cover in an upcoming newsletter, Google has also given us a new feature:
Follow Publishers In Discover
Similar to setting preferred sources in Top Stories, the new Follow feature in Discover allows publishers to encourage audience loyalty. With the follow feature, a user will see more content from followed publishers in their Discover feed.
A user can click on a publisher name in their Discover feed and end up on that publisher’s dedicated Discover page. Tapping the “Follow on Google” button there will add the publisher to the user’s followed list, and ensure more articles from that publisher will be shown in the user’s Discover feed.
The follow feature is not yet rolled out globally. As I’m on an iPhone and in the UK, I haven’t yet been able to see it for myself. So, here is a screenshot from the Discover page for Barry Schwartz’s Search Engine Roundtable:
SERoundtable.com’s Discover page with Follow feature (Image credit: Barry Adams)
I’ll be dedicating an upcoming newsletter to Discover optimisation strategies, and include what I’ll learn about the Follow feature there.
In the same announcement where this Follow feature was introduced, Google also said that Discover will start showing more social media content like YouTube videos, Instagram posts, and even X posts.
This brings me to the third strategy for publishers to embrace:
3. Multimedia Content
Online news hasn’t been consumed in written form only for many years. It should come as no surprise that your audience wants to engage with your news in many different formats on different platforms.
As Discover is now integrating social posts into its feed, this presents additional opportunities for publishers to create content in various formats and publish these on popular platforms.
So, you should be doing YouTube videos, especially Shorts. And Instagram posts and videos. Though I cannot recommend you stay active on X (Twitter) – I personally have gotten up from that table, and you should too.
Podcasts are another obvious format that enjoys great popularity. News podcasts dominate the top rankings on most podcast platforms, and news publishers are especially well-placed to carve out audiences for themselves there.
Email newsletters are enjoying a resurgence in popularity (one that I have taken advantage of myself, as you can see), though I would argue that email has never really gone out of fashion. It just lost the spotlight for a while, but always kept on delivering for those that do it right.
It’s never too late to start experimenting with multimodal content. If you have a great piece of journalism, it doesn’t take much to turn that into a podcast. That podcast should be recorded with a camera, and voila, you have a YouTube video. You can then turn that video into a series of YouTube shorts, which can also populate your Instagram feed, etc.
(Image credit: Barry Adams)
The barrier to entry is low, and you won’t need a massive audience to make your multimodal adventures pay off.
At the upcoming NESS conference, a few sessions will dig into channel diversification. I’m especially looking forward to Steve Wilson-Beales’s session on cracking the YouTube algorithm.
However, none of the above is going to save your publishing site in the long term, if you don’t do the most important thing:
4. Become Unforgettable
This has been a bit of a mantra for me for a while now. I strongly believe that if your news website is interchangeable with others in your topical area, you are going to have a Very Bad Time in the next few years.
It’s a tough reality to face for many websites, and I see the ostrich approach all too often. Many publishers are incapable of being honest with themselves and seeing the truth of their commoditization, clinging to some vague perception of uniqueness and value add.
But the fact is that probably about half of all news websites are perfectly forgettable. They don’t have anything that makes them sufficiently distinct. These publishers don’t have loyal audiences; they have a cohort of habitual readers that can just as easily switch to a competing website.
The reason many publishers don’t understand their own place in their market is because they don’t really understand their readers.
Such a simple thing, yet so very rarely done. When is the last time you talked to your readers? Asked them what they liked about your website, and what they didn’t? Ask them what they wanted to see more of, and what other things you could be doing?
That’s an exercise you should regularly be engaging in. I can guarantee you, your own perception and your audience’s perception of your site will be very different indeed.
When you talk to your audience and understand what they want from you, it allows you to make the right decisions for your publication’s long-term health. You’ll know where your real value-add is, and whether or not that’s worth a subscription fee (and how much you can charge).
It enables you to find the most popular channels and platforms your audience uses, so you can post there too. It tells you which creators’ content they enjoy, so you can reach out to them for partnerships. (Sparktoro is a great audience research tool that can help with this.)
There will be so much you will learn from just talking to your audience that it’s hard to overstate its importance. I know “marketing” is a dirty word for many publishers, but it genuinely is the critical ingredient.
Most importantly, it’ll give you the insights you need to really nail down your publication’s USP and deliver the kind of value to your visitors that transforms them from casual readers into a loyal audience.
That’s where the key to survival lies. A loyal audience immunizes you from whatever the ketamine-addled Silicon Valley tech bros next dream up. It ensures your continued success, independent of platforms and algorithms.
And that’s something worth striving for.
What About AEO/GEO/LLMO?
You’ll have noticed I didn’t mention optimizing for AI Search as a survival tactic. That’s because it’s not. LLMs are great at many things, but generating traffic for websites isn’t one of them.
For websites that have a transaction pipeline driven by search traffic, such as ecommerce or travel booking sites, optimizing for LLMs has some added value. Visibility in LLM-generated responses can generate conversions for these sites.
However, for content delivery websites like news publishers, there’s significantly less value in optimizing for LLM visibility. Citations in LLM responses don’t lead to clicks in any meaningful way, so the traffic opportunity there is non-existent.
However, if you adopt the survival strategies I summarized above, ironically, you’ll also do better in LLMs. As it stands, 99.9% of LLM optimization aligns with proper SEO, and the last 0.1% falls under the remit of what we’d call “good marketing,” which is what becoming unforgettable is all about.
I know, those suits in board rooms want to be reassured that you got this AI Optimization thing in hand. When you do SEO well, you will have. Don’t let AI hype get in the way of good business decisions.
Not coincidentally, several sessions at NESS 2025 will be dedicated to AI and its impact on publishers:
When you use the code barry2025 at checkout, you get 20% off the ticket price. Grab yours while you can!
That’s it for another edition. As always, thanks for reading and subscribing, and I’ll see you at the next one.
What I’m about to say will feel uncomfortable to a lot of SEOs, and maybe even some CEOs. I’m not writing this to be sensational, and I know some of my peers will still look sideways at me for it. That’s fine. I’m sharing what the data suggests to me, and I want you to look at the same numbers and decide for yourself.
Too many people in our industry have slipped into the habit of quoting whatever guidance comes out of a search engine or AI vendor as if it were gospel. That’s like a soda company telling you, “Our drink is refreshing, you should drink more.” Maybe it really is refreshing. Maybe it just drives their margins. Either way, you’re letting the seller define what’s “best.”
SEO used to be a discipline that verified everything. We tested. We dug as deep as we could. We demanded evidence. Lately, I see less of that. This article is a call-back to that mindset. The changes coming in 2026 are not hype. It’s visible in the adoption curves, and those curves don’t care if we believe them or not. These curves aren’t about what I say, what you say, or what 40 other “SEO experts” say. These curves are about consumers, habits, and our combined future.
ChatGPT is reaching mass adoption in 4 years. Google took 9. Tech adoption is accelerating.
The Shocking Ramp: Google Vs. ChatGPT
Confession: I nearly called this section things like “Ramp-ocalypse 2026” or “The Adoption Curve That Will Melt Your Rank-Tracking Dashboard.” I had a whole list of ridiculous options that would have looked at home on a crypto shill blog. I finally dialed it back to the calmer “The Shocking Ramp: Google Vs. ChatGPT” because that, at least, sounds like something an adult would publish. But you get the idea: The curve really is that dramatic, but I just refuse to dress it up like a doomsday tabloid headline.
Image Credit: Duane Forrester
And before we really get into the details, let’s be clear that this is not comparing totals of daily active users today. This is a look at time-to-mass-adoption. Google achieved that a long time ago, whereas ChatGPT is going to do that, it seems, in 2026. This is about the vector. The ramp, and the speed. It’s about how consumer behavior is changing, and is about to be changed. That’s what the chart represents. Of course, when we reference ChatGPT-Class Assistants, we’re including Gemini here, so Google is front and center as these changes happen.
And Google’s pivot into this space isn’t accidental. If you believe Google was reacting to OpenAI’s appearance and sudden growth, guess again. Both companies have essentially been neck and neck in a thoroughbred horse race to be the leading next-gen information-parsing layer for humanity since day one. ChatGPT may have grabbed the headlines when they launched, but Google very quickly became their equal, and the gap at the top, that these companies are chasing, it’s vanishing quickly. Consumers soon won’t be able to say which is “the best” in any meaningful ways.
What’s most important here is that as consumers adopt, behavior changes. I cannot recommend enough that folks read Charles Duhigg’s “The Power of Habit” book (non-aff link). I first read it over a decade ago, and it still brings home the message – the impact that a single moment of habit-forming has on a product’s success and growth. And that is what the chart above is speaking to. New habits are about to be formed by consumers globally.
Let’s rewind to the search revolution most of us built our careers on.
Google launched in 1998.
By late 1999, it was handling about 3.5 million searches per day (Market.us, September 1999 data).
By 2001, Google crossed roughly 100 million searches a day (The Guardian, 2001).
It didn’t pass 50 % U.S. market share until 2007, about nine years after launch (Los Angeles Times, August 2007).
Now compare that to the modern AI assistant curve:
ChatGPT launched in November 2022.
It reached 100 million monthly active users in just two months (UBS analysis via Reuters, February 2023).
According to OpenAI’s usage study published Sept. 15, 2025, in the NBER working-paper series, by July 2025, ChatGPT had ~700 million users sending ~18 billion messages per week, or about 10 % of the world’s adults.
Barclays Research projects ChatGPT-class assistants will reach ~1 billion daily active users by 2026 (Barclays note, December 2024).
In other words: Google took ~9 years to reach its mass-adoption threshold. ChatGPT is on pace to do it in ~4.
That slope is a wake-up call.
Four converging forces explain why 2026 is the inflection year:
Consumer scale: Barclays’ projection of 1 billion daily active users by 2026 means assistants are no longer a novelty; they’re a mainstream habit (Barclay’s).
Enterprise distribution: Gartner forecasts that about 40 % of enterprise applications will ship with task-doing AI agents by 2026. Assistants will appear inside the software your customers already use at work (Gartner Hype Cycle report cited by CIO&Leader, August 2025).
Infrastructure rails: Citi projects ≈ $490 billion in AI-related capital spending in 2026, building the GPUs and data-center footprint that drop latency and per-interaction cost (Citi Research note summarized by Reuters, September 2025).
Capability step-change: Sam Altman has described 2026 as a “turning-point year” when models start “figuring out novel insights” and by 2027, become reliable task-doing agents (Sam Altman blog, June 2025). And yes, this is the soda salesman telling us what’s right here, but still, you get the point, I hope.
This isn’t a calendar-day switch-flip. It’s the slope of a curve that gets steep enough that, by late 2026, most consumers will encounter an assistant every day, often without realizing it.
What Mass Adoption Feels Like For Consumers
If the projections hold, the assistant experience by late 2026 will feel less like opening a separate chatbot app and more like ambient computing:
Everywhere-by-default: built into your phone’s OS, browser sidebars, TVs, cars, banking, and retail apps.
From Q&A to “do-for-me”: booking travel, filling forms, disputing charges, summarizing calls, even running small projects end-to-end.
Cheaper and faster: thanks to the $490 billion infrastructure build-out, response times drop and the habit loop tightens.
Consumers won’t think of themselves as “using an AI chatbot.” They’ll just be getting things done, and that subtle shift is where the search industry’s challenge begins. And when 1 billion daily users prefer assistants for [specific high-value queries your audience cares about], that’s not just a UX shift, it’s a revenue channel migration that will impact your work.
The SEO & Visibility Reckoning
Mass adoption of assistants doesn’t kill search; it moves it upstream.
When the first answer or action happens inside an assistant, our old SERP tactics start to lose leverage. Three shifts matter most:
1. Zero-Click Surfaces Intensify
Assistants answer in the chat window, the sidebar, the voice interface. Fewer users click through to the page that supplied the answer.
2. Chunk Retrievability Outranks Page Rank
Assistants lift the clearest, most verifiable chunks, not necessarily the highest-ranked page. OpenAI’s usage paper shows that three-quarters of consumer interactions already focus on practical guidance, information, and writing help (NBER working paper, September 2025). That means assistants favor well-structured task-led sections over generic blog posts. Instead of optimizing “Best Project Management Software 2026” as a 3,000-word listicle, for example, you need “How to set up automated task dependencies” as a 200-word chunk with a code sample and schema markup.
3. Machine-Validated Authority Wins
Systems prefer sources they can quote, timestamp, and verify: schema-rich pages, canonical PDFs/HTML with stable anchors, authorship credentials, inline citations.
The consumer adoption numbers grab headlines, but the enterprise shift may hit harder and faster.
When Gartner forecasts that 40% of workplace applications will ship with embedded agents by 2026, that’s not about adding a chatbot to your product; it’s about your buyer’s daily tools becoming information gatekeepers.
Picture this: A procurement manager asks their Salesforce agent, “What’s the best solution for automated compliance reporting?” The agent surfaces an answer by pulling from its training data, your competitor’s well-structured API documentation, and a case study PDF it can easily parse. Your marketing site with its video hero sections and gated whitepapers never enters the equation.
This isn’t hypothetical. Microsoft 365 Copilot, Salesforce Einstein, SAP Joule, these aren’t research tools. They’re decision environments. If your product docs, integration guides, and technical specifications aren’t structured for machine retrieval, you’re invisible at the moment of consideration.
The enterprise buying journey is moving upstream to the data layer before buyers ever land on your domain. Your visibility strategy needs to meet them there.
A 2026-Ready Approach For SEOs And Brands
Preparing for this shift isn’t about chasing a new algorithm update. It’s about becoming assistant-ready:
Restructure content into assistant-grade chunks: 150-300-word sections with a clear claim > supporting evidence > inline citation, plus stable anchors so the assistant can quote cleanly.
Tighten provenance and trust signals: rich schema (FAQ, HowTo, TechArticle, Product), canonical HTML + PDF versions, explicit authorship and last-updated stamps.
Mirror canonical chunks in your help center, product manuals, developer docs to meet the assistants where they crawl.
Expose APIs, sample data, and working examples so agents can act on your info, not just read it.
Track attribution inside assistants to watch for brand or domain citations across ChatGPT, Gemini, Perplexity, etc., then double-down on the content that’s already surfacing.
Get used to new tools that can help you surface new metrics and monitor in areas your original tools aren’t focused. (SERPRecon, Rankbee, Profound, Waikay, ZipTie.dev, etc.)
Back To Verification
The mass-adoption moment in 2026 won’t erase SEO, but it will change what it means to be discoverable.
We can keep taking guidance at face value from the platforms that profit when we follow it, or we can go back to questioning why advice is given, testing what the machines actually retrieve, and trust. We used to have to learn, and we seem to have slipped into easy-button mode over the last 20 years.
Search is moving upstream to the data layer. If you want to stay visible when assistants become the first touch-point, start adapting now, because this time the curve isn’t giving you nine years to catch up.
Out of many direct conversations I’ve had in the industry, there’s a mixed reaction to how much AI might impact SEO and search. It depends on your business model as to just how much of a catastrophic effect LLM platforms have taken away your clicks and, more importantly, your end business outcomes.
Google still remains the dominant search engine, and right now is still referring the majority of traffic. Although, traffic volumes are significantly reduced, especially for news publishers.
From my conversations, many SEOs believe that despite this Google is not going anywhere and it’s business as usual.
To dig into this topic, I spoke to Carolyn Shelby, who co-founded an ISP in 1994 and has worked in the search industry since for 30 years, working with major brands such as Disney, ESPN, and Tribune Publishing.
Over three decades, Carolyn has seen disruption in the industry many times over, so I asked for her IMHO: Is AI search overhyped?
Her opinion is that focusing on just 1% of a huge share is a good strategy, that we should be focused on technical accessibility and that no one should be ignoring AI search. She also thinks that Google is purposely throttling it’s own progression right now.
The Blogging Economy Is Imploding
Right now, AI and LLMs are dramatically changing search business models and how you can make money online. The biggest impact of this is within blogging for dollars and page views-for-AdSense business models.
As Carolyn said, “It’s not viable going forward as a sustainable business strategy to spin up garbage content sites and slap AdSense all over them and then make enough money to live. Hobby creators or people that are creating out of love will continue to create because they’re doing it for themselves, not for the money. And the amount of money they will make will be enough to maybe buy them coffee every month, but it is not going to be enough to pay their mortgage.
So, the people that are looking for the money to pay their mortgage or buy them a Lamborghini are going to go where there is money to be made, which is over to TikTok and over to YouTube and over to the video platforms.”
This isn’t a temporary disruption. Right now, we’re experiencing a fundamental restructuring of how value is created and captured on the internet.
The influence of TikTok has been building for a few years and is one platform that could be resistant and even flourish in the face of the changes happening in search.
Recently, in a podcast, Carolyn said that less than 1% of traffic comes from AI tools/platforms. On the surface, 1% might seem to be insignificant, but if you consider that 1% of a trillion is 10 billion, that’s a huge amount of traffic.
“If you told me today that if I focused on nothing but ChatGPT and I could guarantee I would monopolize the 1% of traffic, I would jump on that because that is so much traffic.” Carolyn said.
As marketers, we can easily get swept away by the big ‘trillion’ numbers, but if we remember that it can be far easier to gain traction in a smaller niche with less competition than to drown in a crowded space.
For example, SEOs have all been focused on Google because it has so much traffic potential. However, Bing is less competitive and could convert better, so it could be far more beneficial to invest in Bing.
Carolyn believes that the same logic applies to AI platforms. “It’s better to have the traffic from the people that convert, and it’s better to have people coming to your website that are going to convert in general. If you can increase that, increase that.”
Carolyn was clear that in her opinion AI is not overhyped. “I think if you ignore these other opportunities with the LLMs and with AI, then you’re doing yourself a disservice. I wouldn’t call this overhyped. I would call this a shifting mindset, a shift in a paradigm.”
Google Is Holding Back As A Strategic Play
I asked Carolyn if she thought that Google could claw back its dominance, and she has an interesting theory centered on how Google’s Department of Justice battles might be influencing its competitive behavior.
Carolyn explained that during the appeals process, Google needs to prove it’s not a monopoly, which creates an incentive structure.
“They need to prove that they don’t hold absolute control over absolutely everything that happens. Which means they’re going to be inclined to allow other people to encroach on their position because that reinforces their point that they’re not a monopoly.”
Think of it like a driver spotting a speed trap; you slow down until you’re out of range, then floor it again. Google is playing the long game.
Carolyn also identified Chrome data as a critical factor, as it’s Google’s biggest competitive advantage. User signals and behavioral data from Chrome give them insights that drive innovation and performance and forcing the search engine to share this data would fundamentally alter the competitive landscape.
“You take the Chrome data away, that’s a different story. And I think that would be taking the gas out of their engine.” Carolyn commented.
AI Mode Is Here To Stay
We moved the conversation on to AI Mode, and I asked what she thought of the Google AI-generated search results.
Carolyn’s opinion is that Google is not going to roll it back, and it’s here to stay. “I think they’re going to take steps to make sure that we all get used to it and that we all start using it the way they want us to use it to get the best results.”
Carolyn acknowledged that AI Mode creates friction for users conditioned to traditional keyword searches.
“I feel weird asking Google questions like I would ask ChatGPT,” she admitted. “I’m conditioned to interface with ChatGPT in one way and I’m conditioned to interface with Google in a different way and my habits just haven’t changed yet.”
Her belief is that adaptation is inevitable. Google’s dominance means it can guide users toward new interaction patterns.
“They’ll just keep giving us bad answers and we’ll keep trying again because that’s what we do until we figure out how to get the answers that we want out of the machine … together we’ll all keep iterating.”
Google has maintained a position at the forefront of industry development for the last 25 years with constant iteration, and it has wanted to be a personal assistant for years. AI is enabling that to happen.
“It would be ridiculous for Google to say, ‘We’re going to not evolve and we’re going to stay the way we’ve been doing things for 20 years while everyone else is doing AI.’” Carolyn commented. “There’s too much investment in the infrastructure. It’s to everyone’s benefit to learn how to operate within this new environment.”
For me, the actual marketing strategy has been long overlooked in SEO, and Carolyn echoed this in her response to say there are a lot of marketing aspects that have been ignored.
Although in her opinion, the main focus should be on the technical aspects of SEO, not just for search engines but also for LLMs. She emphasized ensuring content accessibility at the machine level.
“I think focusing on the technical fundamentals.” Carolyn explained, “Can the machines [LLMs] traverse your site and retrieve the content and is the content retrievable in the way you need it to be retrievable?”
SEOs should be aware that different LLMs access content differently. Carolyn noted that some platforms, like Anthropic, only capture first-view content, missing anything in toggles or tabs.
“Your job is to figure out what is being found and making sure that the things that the message that you need to have conveyed is in that stuff that is being read. If it’s not, if it’s hidden in something, you have to unhide it.
“There are a lot of different things to do to get to that point, which is what constitutes SEO. Making sure that it’s accessible and it’s the message that you want seen, that if you boil it all down, that is your job.”
The Future Belongs To Those Who Adapt & Adopt
Rather than dismissing AI search as hype, Carolyn thinks we’re witnessing a fundamental transformation that requires strategic adaptation. Business models are changing, and success demands understanding how machines access and interpret content.
“If you ignore these opportunities with the LLMs and with AI, then you’re doing yourself a disservice.”
The future belongs to those who understand that 1% of a trillion is a huge market, who ensure their content is truly accessible to every machine that matters, and who can adopt real marketing.
The professionals who embrace AI will define the next era of SEO.
If you’ve read “From Line Item to Leverage” or “Who Owns Web Performance?,” you know I’ve argued that enterprise SEO failures are rarely due to incompetence or lack of effort. The playbook is known. The teams are capable. The opportunity is massive. Yet results often stall or underdeliver.
Why?
Because the real problem isn’t only technical, it’s organizational. The website might be modern, the content fresh, and the SEO team skilled. But underneath the surface, hidden forces are quietly undermining performance: political turf wars, outdated workflows, key performance indicator (KPI) misalignment, and siloed ownership.
These aren’t bugs in the system. They’re features of how many organizations operate. Until we confront them, no amount of tactical SEO or any of the current alphabet soup of AI optimization schemes will produce strategic outcomes.
Across hundreds of enterprise search performance audits, I have found these five forces are the biggest blockers of SEO progress, not crawl errors or content gaps.
Force 1: Structural Silos And The Fallacy Of Distributed Ownership
Many enterprises have convinced themselves that “distributed ownership” is modern and empowering. But when everyone owns the website, no one is accountable for outcomes. Product owns UX. Brand owns messaging. IT owns the CMS. SEO owns … what exactly?
The result is fragmented decision-making and reactive prioritization. Optimization becomes an endless round of ticket submission and compromise. Big problems fall through the cracks because no single person is tasked with connecting the dots.
In “Who Owns Web Performance?,” I broke down the dangers of this model – and the alternative: centralized digital accountability with clear authority to align stakeholders and drive performance.
Force 2: Incentive Misalignment And The KPI Trap
Most enterprise teams aren’t incentivized to care about organic search performance. Developers are measured on delivery speed. Content teams are judged on brand tone. Paid media is chasing return on ad spend (ROAS).
This is the classic KPI trap: When each team optimizes for its success metrics, no one is accountable for shared business outcomes. The result? Collaboration stalls, priorities diverge, and high-impact opportunities like SEO fall through the cracks, not because teams aren’t trying, but because the system pulls them in different directions.
This creates massive opportunity costs. Even when teams want to collaborate, their KPIs pull them in different directions. Without shared goals and visibility, SEO becomes a bottleneck rather than a multiplier.
Force 3: Political Gatekeeping And Departmental Turf Wars
Let’s say the SEO team identifies a technical issue that’s hurting crawlability. They submit a ticket. Nothing happens. Why?
Because the dev team has a different backlog and a different boss.
SEO often finds itself in the middle, lacking the priority, budget, or political capital to push changes through. Decisions are filtered through layers of management that prioritize their own fiefdoms over collective outcomes.
This isn’t personal. It’s structural. But it kills velocity.
We need executive air cover. Someone who sees digital performance as a cross-functional mandate that directly impacts the bottom line, and not a side hustle for marketing.
Force 4: Change Aversion Masquerading As Process
How often have you heard this: “That’s not how we do things?”
It sounds like a process, but it’s really fear. Fear of change, fear of accountability, fear of being wrong.
Enterprise inertia is real. Established brands often cling to workflows that were optimized for a different era – print, events, old-school PR. SEO’s iterative, fast-moving nature clashes with these cycles. That friction slows everything down.
If your content takes six weeks to publish and two months to update a template, you’re not playing the same game as Google.
Force 5: The Devaluation Of Web As A Strategic Channel
Too many executive teams still view the website as a marketing brochure. Something the CMO owns and the IT team maintains.
But as argued in “Closing the Digital Performance Gap,” the website is now a strategic revenue engine, support channel, and trust platform. It’s the digital front door and the only channel you fully control.
When leadership doesn’t treat it that way, performance suffers. Investments are piecemeal. Priorities are reactive. And talent leaves because they’re stuck defending the basics.
Case In Point: When All 5 Forces Collide
At Hreflang Builder, I worked with a large CPG company that had identified a $25 million monthly cross-market cannibalization problem across more than a dozen brands. The culprit? Poor implementation of hreflang elements. Due to different content management systems and web structures, hreflang XML sitemaps were the only option for them.
They had tried to solve the cannibalization problem, but the organization’s decentralized structure made it nearly impossible. Regional development teams, a patchwork of digital agencies, and siloed market ownership meant no one had end-to-end control.
The internal process was a nightmare: 60+ days to make a simple XML sitemap change, with hreflang page alternates maintained manually in Excel files. One-third of the URLs were invalid. Markets weren’t notified of new pages. Updates require submitting support tickets to an already backlogged IT queue.
Let’s connect the dots:
Silos (Force 1): Each region wanted its own solution, even though this was a global requirement. No one entity owned the problem.
KPI Misalignment (Force 2): Despite measurable cannibalization, SEO fixes weren’t prioritized because they didn’t map to short-term KPIs.
Political Turf Wars (Force 3): IT didn’t want to license an external solution nor take responsibility for building an internal solution. The global SEO team wanted a commercial solution. Local teams demanded local control or their agency to manage it.
Change Aversion (Force 4): Those managing the manual spreadsheet process resisted change. “It works well enough,” they argued, despite overwhelming evidence that it didn’t.
Web Devaluation (Force 5): Even with $25 million in monthly loss, there was no executive mandate or budget to solve it. Management views this as a Google issue, not a business problem.
Everyone acknowledged the cannibalization. Everyone intuitively knew the external solution was cheaper than the losses. But no one wanted to cede control to a centralized fix. This is what happens when no one owns the whole picture.
Why This Matters: These Forces Compound
Each of these forces is dangerous on its own. But together, they form a silent killer of enterprise SEO:
The SEO team lacks authority.
Other teams lack incentive.
Decisions are slow and political.
Execution is trapped in a legacy process.
And the web isn’t treated as strategic.
In the era of AI-powered search, these organizational flaws are no longer just speed bumps; they’re structural liabilities. AI Overviews and generative engines reward sites that are fast to update, intensely structured, and unified in message. When SEO is hindered by bureaucratic lag, misaligned priorities, or outdated processes, you not only lose rankings but also become invisible in the results entirely.
Web effectiveness now demands real-time coordination across content, data, tech, and performance. That’s not possible when decisions are stuck in silos and SEO is treated as a reactive service ticket.
And here’s the shift no one’s talking about: SEO’s value isn’t just in rankings, it’s in data structure, discoverability, and serving the buyer’s journey. Generative search surfaces answers. If your content isn’t connected, structured, and licensed, or can’t answer fundamental questions, it will be skipped.
Even internal site search, untouched by AI results, is often neglected. We’ve helped clients unlock millions in value by optimizing internal search data, which is frequently the clearest signal of what users want but can’t find.
In this new world, treating SEO as a patchwork of technical fixes is organizational malpractice. It’s time to treat it like the infrastructure for digital visibility it truly is.
A Better Path Forward
Fixing this doesn’t require heroics. It requires leadership.
Executives must:
Designate accountable ownership of web performance.
Align KPIs across content, dev, and marketing teams.
Fund SEO as infrastructure, not just a channel.
Remove structural bottlenecks and reframe SEO as a strategy.
Govern with outcomes, not outputs.
This is a mindset shift as well as an organizational shift. Organizations need to move from just optimizing pages to redesigning the organizational systems that enable performance.
Because the real search problem isn’t the algorithm, it’s the org chart.
What if your SEO strategy could predict what customers want before they even search?
The shift from keyword-centric to behavior-driven SEO is important. When you understand why people search, not just what they search for, your content naturally becomes more relevant and your performance more sustainable.
Google processes over 5 trillion searches annually, and many of those queries are completely new. This means traditional keyword research tools miss a massive chunk of actual search behavior. Your customers use language that feels natural to them, not how marketers think they should search.
Here’s how to tap into real customer behavior to build an SEO strategy that actually converts.
Why Customer Behavior Trumps Keyword Volume
Your customers aren’t randomly clicking through Google results; they’re following predictable patterns based on intent, device, and context. Understanding these behaviors is the difference between traffic that bounces and traffic that converts.
Consider this scenario: Two people search for [project management software]. Person A searches at 9 A.M. on desktop, spends 8 minutes reading comparison articles, then bookmarks three vendor pages. Person B searches at 6 P.M. on mobile, skims for 30 seconds, then closes the tab.
Same keyword, completely different intent and behavior. Person A is researching for their team; Person B probably got distracted during a meeting and needs a quick answer.
When you analyze “project management software” in the SERPs today, Google reveals three distinct user intents:
Screenshot by author, August 2025
Comparison seekers want comprehensive feature-by-feature analysis of multiple tools.
Budget-conscious users specifically need free options and pricing information.
Tool researchers are investigating specific platforms like Trello or Microsoft Project.
This split intent validates creating separate content pieces rather than trying to serve everyone with one page. You might develop:
“15 Best Project Management Software Tools Compared (2025)”
“Free Project Management Software: 8 Tools That Don’t Cost a Dime”
Individual tool reviews like “Trello Review: Features, Pricing & Best Use Cases”
Each piece targets the same root keyword but serves a specific behavioral intent that Google is already rewarding with page one rankings.
The Psychology Behind Search Patterns
Search behavior follows cognitive patterns that smart marketers can leverage. Anchoring bias means the first piece of information users see heavily influences their decisions. If your search snippet promises “complete guide,” but your page starts with a sales pitch, you’ve broken their mental model.
Social proof bias drives local search behavior especially hard. When someone searches [best pizza near me], they’re not just looking for pizza; they’re probably also looking for validation that others think it’s good, too. Your content should acknowledge this psychological need.
Screenshot from search for [best pizza near me], Google, August 2025
Understanding these patterns helps you create content that feels intuitive rather than forced.
How To Collect Customer Behavior Data That Actually Matters
The best behavior insights come from combining quantitative data with qualitative feedback. Here’s a systematic approach:
Start With Your Existing Analytics
Google Analytics 4 Path Exploration shows how users navigate your site. Look for patterns like:
Which blog posts lead to product page visits.
Where users drop off in your conversion funnel.
What content keeps visitors engaged the longest.
Screenshot from support.google.com, August 2025
Google Search Console can reveal the gap between what you optimize for and what people actually search. Export your query data monthly and look for:
Pro tip: Sort queries by impressions, not clicks. High-impression, low-click queries (aside from highlighting a dominance of SERP features, or AI Overview summaries) often reveal content gaps where you’re visible but not compelling.
Add Heat Mapping And Session Recording
Tools like Hotjar or Microsoft Clarity (free) show you where users actually click, scroll, and abandon pages.
I once worked with an ecommerce client whose heatmaps revealed users repeatedly clicking on product images that weren’t linked to detail pages. We added those links and saw a 23% increase in product page visits within two weeks.
Mine Your Customer Service Data
Your support team handles the questions your website doesn’t answer. Export tickets from the past quarter and categorize them by topic. Common support questions often represent high-value, low-competition search opportunities.
If you’re getting 20 tickets per month about “how to integrate with Slack,” that’s content your competitors probably aren’t creating yet.
Listen To Social Conversations
Monitor industry hashtags, Reddit threads, and LinkedIn discussions in your space. Social media language is usually more casual and authentic than what people type into search; it’s where people complain about real problems using the exact words they’ll later search for solutions.
Reddit is particularly valuable because users share unfiltered frustrations and solution requests. Tools like GummySearch help you cut through Reddit’s noise by surfacing curated content themes like “Pain & Anger” and “Solution Requests” within your target audience communities.
Instead of manually scrolling through thousands of posts, you get direct access to the exact language your customers use when they’re frustrated.
Screenshot from GummySearch by author, August 2025
These authentic conversations reveal content opportunities that traditional keyword research misses.
When someone posts “I can’t believe there’s still no simple way to sync data between these platforms,” that frustration will likely become search queries like “easy data sync tools” or “simple platform integration” within weeks.
Translating Insights Into SEO Opportunities
Raw data means nothing until you turn it into actionable content strategies. Here’s how to connect behavior patterns to search opportunities:
Map Content To Customer Journey Stages
Your behavior data reveals different intent patterns that map to specific journey stages:
Awareness Stage
Consideration Stage
Decision Stage
Broad, educational searches
Comparison and evaluation searches
Specific product/vendor searches
“Why do small businesses need CRM software?”
“HubSpot vs. Salesforce for small teams”
“HubSpot pricing plans 2025”
Focus on educational content with minimal promotional elements
Create detailed comparisons with pros/cons
Optimize for conversion with clear CTAs
Internal links should guide toward mid-funnel content
Include pricing, features, and use case scenarios
Address common objections directly
Identify Content Gaps Through Competitor Analysis
Use Ahrefs or Semrush to analyze competitor content, then cross-reference with your customer behavior data. Look for topics where:
Competitors rank well, but their content doesn’t match user intent.
You have unique customer insights they’re missing.
Your support data reveals questions they don’t address.
For example, if competitor articles about “email marketing automation” focus on features but your customer interviews reveal people struggle with setup, create implementation-focused content instead.
Optimize For Behavior-Based Keywords
Traditional keyword research starts with seed terms and expands outward. Behavior-driven research starts with customer language and searches for gaps.
Instead of: “Best email marketing software”
Try: “Easy email marketing setup for non-technical founders”
The second phrase has lower search volume but higher intent alignment. Someone searching for [easy setup] has different needs than someone searching for [best software].
Create Dynamic Content Formats
Your analytics reveal format preferences by device, time, and topic:
Mobile users during commute hours: Scannable lists and quick tips.
Desktop users during work hours: Detailed guides and tutorials.
Weekend browsers: Visual content and case studies.
Don’t create one piece of content and hope it works everywhere. Adapt format to behavior patterns.
Measuring What Actually Moves The Needle
Behavior-driven SEO requires different success metrics than traditional approaches. Rankings matter less than engagement and conversion alignment.
Track Engagement Quality, Not Just Quantity
Traditional SEO celebrates traffic volume, but behavior-driven strategies focus on how well that traffic matches customer intent.
Average session duration becomes a strong indicator of content relevance. When someone spends 8 minutes reading your guide instead of bouncing in 30 seconds, you’ve aligned content with search intent. The key is tracking improvements over time rather than hitting arbitrary benchmarks.
Bounce rate tells a different story when you segment by traffic source. A high bounce rate might be terrible for targeted organic traffic, but completely normal for broad brand searches.
Compare your targeted organic bounce rate against your own baseline rather than industry averages. If you’re seeing consistent improvement month over month, your content is becoming more aligned with user expectations.
Pages per session reveals engagement depth and site navigation effectiveness. Users who visit multiple pages during a session are actively exploring your content ecosystem, suggesting strong topical authority and effective internal linking strategy.
Goal completion rates vary dramatically by industry and funnel complexity, so focus on your own conversion trends rather than external benchmarks. A B2B software company’s “good” conversion rate looks completely different from an ecommerce site’s performance.
Monitor Search Query Evolution
Your target keywords evolve as customer language changes, industry trends shift, and new problems emerge. Set up monthly Search Console exports to track these patterns systematically. New long-tail variations often appear before keyword tools catch them.
Seasonal language shifts reveal opportunities that competitors miss. B2B software searches change dramatically between the Q4 budget planning season and the Q1 implementation periods. Ecommerce terms shift from “best products” in research phases to “deals” and “discounts” during purchase windows.
Pay attention to emerging competitor terms appearing in your query data. When people start searching for “[competitor name] alternative” or “[your product] vs. [new competitor],” you’re seeing market shifts in real-time.
A/B Test Based On Behavior Insights
Your behavior data generates testing hypotheses that go far beyond traditional “red vs. blue button” experiments. Test different content depths for mobile and desktop users; mobile visitors often prefer scannable summaries, while desktop users engage with comprehensive guides. Experiment with heading structures based on user scanning patterns revealed in your heatmap data.
I recently helped a SaaS client test two versions of their pricing page. Version A used traditional feature comparisons organized by product tier. Version B addressed specific use cases revealed through customer interviews, such as scenarios like “growing startup needs better lead tracking” and “enterprise team wants advanced reporting.”
Version B increased conversions by 34% because it matched how customers actually think about solutions rather than how the product team organized features.
Set Up Feedback Loops
Customer behavior evolves constantly, so your measurement strategy needs systematic review cycles.
Create a monthly rhythm where Week 1 focuses on analyzing Search Console and Analytics data for new patterns. Week 2 involves reviewing customer service tickets and social media mentions for emerging language trends. Week 3 is for testing new content approaches based on fresh insights, while Week 4 handles planning next month’s content calendar around discovered opportunities.
This cycle keeps you responsive to behavior changes rather than reactive to ranking drops. Economic shifts, social trends, and industry developments all impact search patterns faster than traditional SEO tools can track them.
The Bottom Line
Behavior-driven SEO isn’t about abandoning keywords; it’s about understanding the humans behind every search query. When you align your content strategy with actual customer actions and intentions, engagement improves naturally and conversions follow.
Start by really listening to your customers through data, support interactions, and direct feedback. Your most successful content will come from solving real problems using language your audience actually uses.
Your customers are already telling you what they want; you just need to pay attention.
Last week OpenAI launched “Instant Checkout” for ChatGPT, a feature allowing consumers to buy products without leaving the platform.
The feature, which utilizes Stripe’s Agentic Commerce Protocol to facilitate AI transactions, is available for Etsy merchants and soon for Shopify. An open-source version allows any merchant or developer to build custom integrations.
OpenAI’s application form is for merchants not on Etsy or Shopify who want to “1) integrate their products into ChatGPT Search results and 2) enable Instant Checkout in ChatGPT via the Agentic Commerce Protocol.”
AI ‘Rankings’
The shift to AI shopping is ominous. Ecommerce merchants who rely on traditional organic search traffic will almost certainly lose traffic. Merchants with clean, comprehensive product data that’s easily digested by AI agents could slow the decline, if not benefit.
Will ChatGPT prioritize products from merchants that have enabled Instant Checkout? OpenAI’s announcement seems to hint that it might:
When ranking multiple merchants that sell the same product, ChatGPT considers factors like availability, price, quality, whether a merchant is the primary seller, and whether Instant Checkout is enabled, to optimize the user experience.
Thus early ChatGPT merchants may have a competitive advantage.
How to optimize for generative engines? Product data alone may not elevate visibility. Remember that ChatGPT doesn’t rely solely on keywords. The context of conversations is key.
A prompt may not initially request product recommendations. For instance, a user may start by seeking solutions for ankle pain from running. The ensuing dialogue may include buying running shoes with better ankle support.
Other details may come up. Does the user live in a rainy state and thus require waterproof shoes? Does the user run on trails or flat surfaces?
Addressing every possible scenario via product data is seemingly impossible, yet merchants should address as many use cases as practical while encouraging off-site discussions in Reddit and elsewhere for context.
Product Feeds
ChatGPT’s product feed specifications allow 150 characters for the product’s title and 5,000 for its description.
Populate all product feed fields and available characters. The more info it has, the better ChatGPT can surface your product for various prompts. For example, a product’s “weight” field can elevate visibility when consumers seek lightweight goods.
ChatGPT’s feed specs include unique fields to keep in mind:
“related_product_ID” for “basket-building recommendations and cross-sell opportunities.” Instant Checkout allows only single-product purchases, but OpenAI says multiple-product buying is coming. The related products field could eventually help ChatGPT recommend more of your products and associate similar items.
“q_and_a.” This field has no character limit — seemingly perfect for additional information. In my testing, AI agents can easily fetch data from question-and-answer formats.
“popularity_score” can convey your most sought-after goods. ChatGPT does not explain the field’s impact. But it’s the Wild West for generative engine optimization, and who knows? An item’s popularity may help it stand out.
Search Atlas held an event last week to showcase new capabilities and improvements to their SEO platform which make it easier for digital marketer to scale SEO and take on more clients.
The new features enable marketers to more easily handle on-page and off-page SEO, paid search, impact and track LLM visibility, and scale Google Business Profile management, and that’s just a sample of all the new functionalities coming to the platform.
Auto PPC Retargeting
Search Atlas introduced a new new retargeting feature in Otto PPC. This new feature is designed for agencies and advertisers that are managing paid media. It simplifies campaign setup with a quick-start wizard that enables retargeting site visitors, which they claim can be launched in under 60 seconds.
Manick Bhan, founder of Search Atlas explained:
“The hardest thing about taking paid media business from a client is doing it justice, doing a good job, right? Because every time they get a click, they’re paying for it. The best way that you can show a client ROI on paid media is through retargeting. Run a retargeting campaign, retargeting the traffic that they already have on their website.
We wanted to be able to make this easy for you, so all you have to do is enable it inside Otto PPC, and you’re able to run retargeting campaigns now. So we have a wizard set up for you — just a couple clicks and you can launch a retargeting campaign in less than 60 seconds. It’s that easy.”
GBP Galactic
Search Atlas announced a feature for digital marketers who handle Google Business Profiles for clients. The GBP Galactic feature now has Service Area Business (SAB) support. GBP Galactic offers integration with social media auto-posting to Facebook and Instagram, with plans to add more social networks soon.
Bhan explained the social network autoposting:
“We’ve learned the LLMs they want to see your information not just on your website and GBP profile, they want to see your data in the social media platforms.. So what we can do now is, one time, build our GBP posts, and publish to all social networks, which will increase your visibility in the LLMs. And instead of having to use third-party tools to do this, it will be completely integrated.”
Bhan also shared about their citation network:
“We also added support for service area businesses in our citations product, so now you can even build aggregator network citations and put yourself into the aggregator networks for your service businesses… Because normally these aggregator networks, they want an address. We figured out how to do it so we can get you in without one. Pretty cool.
…ChatGPT, Claude, all the LLMs pay for the data from all the aggregator networks. So if you want to put your local business into the aggregators, as well as into all the websites, the aggregator networks are a shortcut to being able to do that and upload directly to ChatGPT.”
LLM Visibility
Another useful feature is LLM Visibility tracking and sentiment analysis. LLM visibility is now measurable directly in Search Atlas. It also tracks brand presence across ChatGPT, Claude, and other LLMs and is able to identify visibility trends beyond Google Search.
Expanded Press Release Network
Bhan announced that Signal Genesys, a press release company they acquired last year, has expanded their distribution to financial news and with a local news media network.
Bhan commented:
“The financial news network costs a whopping $10. And then the news media network costs about $20. So these are really cost-effective, especially for agencies. If you are working with clients and you need to keep prices low for yourselves, there’s a lot of margin in there for you.
And these networks in particular we found were indexed very well in ChatGPT.”
On-Page SEO
Interesting feature launched in their Otto product is a module called Domain Knowledge Network which assists users in building topical relevance with a semantic interface, just speak instructions to it and it will analyze the brand and suggest a content topic structure.
Revamped WordPress Plugin
Their WordPress plugin has been overhauled to make it more user-friendly. It now includes one-click installation to connect WordPress directly to Search Atlas, two-way synchronization that keeps Otto data and WordPress in sync in real time, and auto-publishing that enables SEO fixes generated in Otto to be deployed directly into WordPress.
Universal CMS Integration
Search Atlas is aiming to become CMS-agnostic, able to integrate with any website regardless of the CMS for publishing blog posts and landing pages in one click through their Content Genius feature. Right now Search Atlas can work with Drupal, HubSpot, Magento, Wix, and WordPress. They are also testing to integrate with Joomla, Shopify, and Webflow. Soon they’ll be able to integrate with ClickFunnels, Contentful, Duda, Ghost, and Salesforce.
Near Future: Otto Agent
Otto Agent represents the future of Search Atlas’s agentic revolution, replacing traditional UI-driven workflows with natural-language commands. It’s currently available as a beta program. Users can speak to the platform (via text or voice) to perform SEO actions directly. Otto Agent can execute end-to-end actions: site audits, fixes, title/meta/image optimization, GBP posts, and content generation.
Spending the day listening to their presentations, it became evident that Otto Agent typified Search Atlas’s approach toward developing an SEO platform that is useful. Having come from an SEO agency background, they understand what agencies need and aren’t waiting for competitors to do things first, they’re just moving forward with features that they feel agencies will find useful.
Otto Agent is an example of that forward-looking approach because it’s built on the idea that managing SEO will become agentic, conversational, and autonomous.
I didn’t know that much about Search Atlas before attending the event but now I have a better understanding of why so many agencies embrace Search Atlas.
Where is search going to develop? Is ChatGPT a threat or an opportunity? Is optimizing for large language models (LLMs) the same as optimizing for search engines? These are some of the critical questions that are top of mind for both SEOs and CMOs as we head into a multi-search world.
In Part 2 of this two-part interview series, I try to answer these questions based on data from our internal research to provide some clear direction and focus to help navigate considerable change. If you haven’t already, go back and read Part 1.
What you will learn in this Part 2:
Traditional Search Engine Results Page (SERP) Evolution: Why traditional search isn’t dying but fundamentally transforming, where it still excels, and how it is part of Google’s integrated approach to AI evolution.
Google AI Mode Strategy: How AI Mode and AI Overview operate as the same strategy at different thresholds, with AI Mode being 2.1x more likely to include brands while AI Overview remains highly selective.
Agentic AI Revolution: Why 33% of organic searches now come from AI agents browsing on behalf of users, creating real-time interactions that demand immediate content accessibility.
Search Funnel Transformation: How the customer journey has evolved from linear progression to unpredictable funnel-stage jumping, with AI handling research while conversion still happens through traditional organic channels.
The Three Pillars Framework: Why CMOs need reporting for early AI shift detection, automation for seamless AI-readiness, and strategic recommendations to influence how AI tells their brand’s story.
Do You Think There Is Any Future For Traditional SERP Search, Or Do You Think It Will Become Obsolete?
I think we’re witnessing more of an evolution than an extinction. Traditional SERP search has a future, but it’s going to look completely different.
According to our internal data, 92% of all searches happen here. And when it comes to meaningful actions, such as downloads, sign-ups, or purchases, 95% start on Google. Search volume hasn’t gone down – it’s actually grown 10% year-over-year. With AI Mode, Google is layering AI directly into the experience.
The takeaway is clear: AI hasn’t replaced traditional SERPs; it’s utilizing and aligning with them.
Image from author, September 2025
Where Traditional Search Still Excels
Traditional search still absolutely shines in certain areas. When you’re dealing with complex queries or personal searches, those traditional SERPs still provide something AI cannot: depth, discernment, and diverse perspectives. Ecommerce is a perfect example – when shopping, I still want to see those traditional listings to compare sources, read different reviews, and check various offers.
Traditional SERP’s And Google’s Integrated Approach
Google is handling this integration cleverly. They’re not replacing classic SERPs; they’re augmenting them. Google’s Gemini model powers AI Overviews that appear above traditional listings, creating comprehensive summaries from multiple sources. Classic SERPs provide the foundational data, and AI distills and presents it in new, user-centric ways.
For brands and CMOs, this creates a new optimization challenge. You’re not just thinking about traditional SEO anymore; you need to optimize for AI inclusion, too. If you get cited in an AI summary, your visibility increases dramatically. It’s an interesting paradox where fewer traditional listings appear, but cited sources gain more prominence.
We’re seeing conversational capabilities, multimodal search with images and video, and direct answers that go way beyond static blue links. Users can now ask follow-up questions, search with photos, or engage in natural language conversations – capabilities that would have been impossible with traditional link-based results.
When AI Search Meets Traditional SEO
The overlap between AI citations and traditional search results has grown 22.3% since 2024. However, this varies significantly by industry, making your vertical a key factor in strategy development.
The variation is substantial. Ecommerce saw minimal change at 0.6 percentage points, while Education increased by 53.2 percentage points. Your industry determines the approach you should take.
In YMYL sectors like Healthcare, Insurance, and Education, overlap reaches 68-75%. When trust is critical, Google tends to favor content that already performs well in traditional search rankings.
Ecommerce operates differently. Overlap remained flat, and AI Overview coverage actually decreased by 7.6 percentage points. Google appears to maintain separation between shopping queries and AI answers, likely to preserve the transactional flow that drives commerce.
Image from BrightEdge, September 2025
The Interconnected Search And AI Engine Ecosystem
What’s happening is that AI Overviews are acting as content curators, selecting which sources to reference and cite. This means your content needs to be clear, authoritative, and structured in ways that both humans and AI can easily understand and extract value from. The fundamentals of relevant content – quality, clarity, technical optimization – they’re more critical than ever.
The likes of ChatGPT and Perplexity tap into traditional search engines for factual grounding, so this interconnected ecosystem is becoming the norm. It’s not just about ranking on SERPs anymore; it’s about being discoverable across multiple channels: social search, AI interfaces, traditional SERPs, and whatever comes next.
The New Traditional CMO, SEO, And AI Reality
But those traditional foundations remain crucial – they just serve both humans and AI now. For straightforward, fact-based queries, AI can generate instant answers, removing the need to browse multiple results. But for anything complex, local, or transactional, those classic blue links still appear, sometimes as fallback options, or often as primary results depending on the query type.
However, it’s worth noting that AI Overview shares the screen with classic SERPs and ads. Still, your visibility may significantly increase when you get cited in an AI-generated summary, a paradox in which traditional results may decline, but referenced sources tend to become more prominent.
Keeping Pace With Change
The pace of change is also something CMOs need to prepare for. Google’s AI Mode is evolving incredibly quickly – features, user interface (UI) presentation, and citation logic change frequently. You need to invest in technology and teams that provide real-time insights into SERP and AI Mode visibility. Keep new AI entrants on your radar, and their experimentation and pilot projects, which are crucial for understanding what drives referenced visibility and conversions through AI sources.
The role of traditional SERPs is not dying. AI and traditional search work hand in hand; it’s now Google’s default approach, and both systems co-exist beautifully, serving diverse needs within the same search journey.
What Do You Think CMOs Should Consider About How Google AI Mode Might Change An Enterprise Approach?
This is one of the most significant strategic shifts CMOs are facing right now, and it’s happening fast. Google’s AI Mode is fundamentally changing how enterprise visibility, engagement, and measurement work across search and discovery channels.
Understanding Google’s AI Strategy: AI Overviews And AI Mode
Our recent analysis reveals that AI Mode and AI Overview are not distinct strategies. They’re the same strategy but operating at different thresholds.
Think of it this way: AI Mode acts as the broad discovery engine. It’s 2.1x more likely to include brands (compared to AI Overviews), surfaces more unique brands overall, and maintains pretty stable week-over-week patterns. When it shows sources, you’ll see fewer but more prominent source cards. It’s casting a wide net with lower barriers to entry.
AI Overview, on the other hand, is the dynamic curator. It’s much more selective – only including brands in 43% of responses – but shows significantly higher volatility, which tells us the algorithm is actively evolving.
AI Mode provides stable, broad discovery, whereas AI Overviews are where Google tests new ranking approaches with much higher selectivity. It’s clever – they’re serving different user needs while continuously refining their AI capabilities.
The Multi-Query Reality Of Google AI Search
An AI query is never just one search anymore. AI Mode runs dozens of queries on behalf of the user before showing an answer.
That one question – “What’s a good treadmill for beginners?” – becomes dozens of searches instantly. Google breaks it down into features, price comparisons, reviews, safety tips, compact options, and warranty information. The AI runs these searches in parallel, pulls results, and stitches them together into a single conversational answer.
It’s no longer about matching one keyword. You’re competing to be included across the entire web of related questions that the AI asks on the user’s behalf.
AI Mode And Living In The Browser
Think about how much time you spend in your browser every day. Now imagine if it could actually think alongside you. That’s exactly what’s happening with Google Chrome’s latest AI features, and honestly, it’s pretty mind-blowing.
Here’s what’s new: AI Mode lets you ask complex questions right in the address bar – no more opening countless tabs just to find answers. Planning a trip? Chrome’s multi-tab intelligence can now pull information from all your open tabs and create one coherent plan. And soon, agentic browsing will let Gemini handle the boring stuff like booking appointments while you focus on what actually matters.
The cool thing is, AI Mode isn’t replacing Google – it’s just giving us a smarter way to use it. Think conversational search, but built right into where you already spend most of your time.
For CMOs and marketing teams, this means rethinking how people will find and interact with your content. We’re not just optimizing for search anymore; we’re optimizing for conversation.
The CMO Content Strategy And Keeping Pace With Change
Your content strategy needs a complete rethink. AI Mode pulls directly from content to generate overviews and summaries, which means you can’t just optimize for traditional SEO anymore. Your content needs to serve both AI and human audiences simultaneously. The goal is not just to rank anymore; it’s also to be selected for AI-generated overviews.
CMOs need to prepare for the pace of change. Google’s AI Mode is advancing at a rapid pace, with frequent shifts in features, UI presentation, and citation logic. You need to invest in tools and teams that provide real-time insights into SERP and AI Mode visibility.
How Are Agentic AI Agents (Crawlers And Bots) Changing The Search Funnel? How Might These Changes Impact Roles On The CMO And The SEO Team?
We’re seeing a major shift in how content gets discovered and delivered, as new types of AI agents engage with websites and surface information in real-time conversations. AI agents are now browsing on behalf of users. Unlike classic crawlers, it’s not about indexing pages to be served up later; it’s real-time interactions. If you have a dead page, or it can’t interpret what your content is saying, you lose that moment.
The Rise Of AI Agent Website Interaction
They’re acting like digital assistants – researching, comparing, recommending. If your page is slow, or your content isn’t clear, they move on instantly. They are your future customers – potential new clients – arriving through AI. In the last month, we’ve seen visits from ChatGPT’s new Agent crawler double in visits to customer websites. 33% of all organic searches are from these agents. The growth is massive.
The AI Agent Preprocessing Layer
This creates a preprocessing layer that influences every subsequent customer interaction. Unlike traditional crawlers that simply index content, these systems navigate websites, submit forms, compare options, and make recommendations on behalf of the user in real-time. Each visit represents AI doing a search on your customer’s behalf, looking for content to help explain, recommend, and help your customers in a conversation.
How This Impacts The Evolution Of The Customer Journey
The awareness phase has evolved from user-driven discovery to “pre-aware” algorithmic surfacing where AI agents proactively recommend options based on context, preferences, and behavioral patterns – often before users consciously realize they need information. Modern buyer behavior no longer follows a straight-line progression. Instead, customers jump between funnel stages unpredictably, sometimes moving directly from initial awareness to making purchases, or cycling back to discovery phases for related products.
AI Search Users: Enter the funnel at the research and exploration stage, asking questions and gathering information to inform their decisions. They’re seeking understanding, not yet ready to transact.
Organic Search Users: Demonstrate clearer purchase intent, often searching for specific products, services, or solutions. They know what they want and are closer to conversion.
The Journey Dynamic: Many users begin with AI-powered research but ultimately convert through organic search or direct channels – making AI search valuable for top-of-funnel discovery despite its lack of direct conversions.
The Research Vs. Conversion Channel Reality
As AI search functions as a research channel, not a conversion channel, this confirms that AI systems are handling awareness and consideration stages, while conversion still requires traditional touchpoints. We found that 34% of AI citations come from PR-influenced sources and 10% from social platforms, demonstrating that traditional SEO concepts like E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) remain critical but must now work at machine scale across multiple platforms.
Immediate CMO Transformation Requirements
Foundation Strengthening: Companies must rapidly enhance SEO fundamentals – structured data, content authority, and technical excellence – that determine whether AI agents can find, understand, and cite their content. Brands not only need to keep the door open to agents, but they also need to embrace them, so they are not invisible to the AI agent processing layer I mentioned earlier.
New Measurement Frameworks: Marketing teams must develop new measurement frameworks that capture AI citation frequency, cross-platform visibility, and influence within AI responses, even when traffic attribution is impossible. Key metrics include brand visibility monitoring, AI presence testing, reference share analysis, and indirect conversion tracking.
CMO And Marketing Team Structure
The team structure evolution reflects a fundamental shift from departmentalized hierarchies to fluid, cross-functional pods. Technical teams become increasingly AI-augmented for scale, content teams shift from creation to curation and refinement, and new integration teams bridge SEO with data science and machine learning departments.
Concluding Thoughts: The CMO, SEO, And AI Reality Check
Here’s the critical takeaway: While you’re optimizing your funnel for AI discovery, remember that organic search is still where conversions happen. AI search serves as the research phase, helping users discover options and gather information.
But when they’re ready to take action – making a purchase, signing up, or downloading – they’re still turning to traditional organic search results. They recognize that AI discovery feeds into the organic funnel. Your SEO foundation becomes the conversion engine that AI discovery feeds into.
The smartest CMOs and marketers aren’t choosing between AI and organic search. They’re using proven SEO strategies as their foundation while adapting for AI discovery.
Pat Reinhart, VP of Services & Thought Leadership at Conductor, and Luiza Shahbazyan, Sr. R&D Product Manager at Conductor, will show you exactly how to win in the age of AI search. You’ll learn:
How to maximize your brand’s visibility across AI answer engines.
Key signalsthat influence AI citations, including content authority and digital PR.
Practical strategies to earn mentions and strengthen trust signals.
How to adapt your SEO workflows for Answer Engine Optimization (AEO).
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