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Google’s Robby Stein, VP of Product at Google, explained that Google Search is converging with AI in a new manner that builds on three pillars of AI. The implications for online publishers, SEOs, and eCommerce stores are profound.
Three Pillars Of AI Search
Google’s Stein said that there are three essential components to the “next generation” of Google Search:
AI Overviews
Multimodal search
AI Mode
AI Overviews is natural language search. Multimodal are new ways of searching with images, enabled by Google Lens. AI Mode is the harnessing of web content and structured knowledge to provide a conversational turn-based way of discovering information and learning. Stein indicates that all three of these components will converge as the next step in the evolution of search. This is coming.
Stein explained:
“I can tell you there’s kind of three big components to how we can think about AI search and kind of the next generation of search experiences. One is obviously AI overviews, which are the quick and fast AI you get at the top of the page many people have seen. And that’s obviously been something growing very, very quickly. This is when you ask a natural question, you put it into Google, you get this AI now. It’s really helpful for people.
The second is around multimodal. This is visual search and lens. That’s the other big piece. You go to the camera in the Google app, and that’s seeing a bunch of growth.
And then with AI mode, it brings it all together. It creates an end-to-end frontier search experience on state-of-the-art models to really truly let you ask anything of Google search.”
AI Mode Triggered By Complex Queries
The above screenshot shows a complex two sentence search query entered into Google’s search box. The complex query automatically triggers an AI Mode preview with a “Show more” link that leads to an immersive AI Mode conversational search experience. Publishers who wish to be cited need to think about how their content will fit into this kind of context.
Next Generation Of Google: AI Mode Is Like A Brain
Stein described the next frontier of search as something that is radically different from what we know as Google Search. Many SEOs still think of search as this ranking paradigm with ten blue links. That’s something that’s not quite existed since Google debuted Featured Snippets back in 2014. That’s eleven years that the concept of ten blue links has been out of step with the reality in Google’s search results.
What Stein goes on to describe completely does away with the concept of ten blue links, replacing it with the concept of a brain that users can ask questions and interact with. SEOs, merchants and other publishers really need to begin doing away with the mental concept of ten blue links and focus on surfacing content within an interactive natural language environment that’s completely outside of search.
Stein explained this new concept of a brain in the context of AI Mode:
“You can go back and forth. You can have a conversation. And it taps into and is specially designed for search. So what does that mean? One of the cool things that I think it does is it’s able to understand all of this incredibly rich information that’s within Google.
So there’s 50 billion products in the Google Shopping Graph, for instance. They’re updated 2 billion times an hour by merchants with live prices.
You have 250 million places and maps.
You have all of the finance information.
And not to mention, you have the entire context of the web and how to connect to it so that you can get context, but then go deeper.
And you put all of that into this brain that is effectively this way to talk to Google and get at this knowledge.
That’s really what you can do now. So you can ask anything on your mind and it’ll use all of this information to hopefully give you super high quality and informed information as best as we can.”
Stein’s description shows that Google’s long-term direction is to move beyond retrieval toward an interactive turn-based mode of information discovery. The “brain” metaphor signals that search will increasingly be less about locating web pages but about generating informed responses built from Google’s own structured data, knowledge graphs, and web content. This represents a fundamental change and as you’ll see in the following paragraphs, this change is happening right now.
AI Mode Integrates Everything
Stein describes how Google is increasingly triggering AI Mode as the next evolution of how users find answers to questions and discover information about the world immediately around them. This goes beyond asking “what’s the best kayak” and becomes more of a natural language conversation, an information journey that can encompass images, videos, and text, just like in real life. It’s an integrated experience that goes way beyond a simple search box and ten links.
Stein provided more information of what this will look like:
“And you can use it directly at this google.com/ai, but it’s also been integrated into our core experiences, too. So we announced you can get to it really easily. You can ask follow-up questions of AI overviews right into AI mode now.
Same for the lens stuff, take a picture, takes it to AI mode. So you can ask follow-up questions and go there, too. So it’s increasingly an integrated experience into the core part of the product.”
How AI Will Converge Into One Interface
At this point the host of the podcast asked for a clearer explanation of how all of these things will be integrated.
He asked:
“I imagine much of this is… wait and see how people use it. But what’s the vision of how all these things connect?
Is the idea to continue having this AI mode on the side, AI overviews at the top, and then this multimodal experience? Or is there a vision of somehow pushing these together even more over time?”
Stein answered that all of these modes of information discovery will converge together. Google will be able to detect by the query whether to trigger AI Mode or just a simple search. There won’t be different interfaces, just the one.
Stein explained:
“I think there’s an opportunity for these to come closer together. I think that’s what AI Mode represents, at least for the core AI experiences. But I think of them as very complementary to the core search product.
And so you should be able to not have to think about where you’re asking a question. Ultimately, you just go to Google.
And today, if you put in whatever you want, we’re actually starting to use much of the power behind AI mode, right in AI Overviews. So you can just ask really hard, you could put a five-sentence question right into Google search.
You can try it. And then it should trigger AI at the top, it’s a preview. And then you can go deeper into AI mode and have this back and forth. So that’s how these things connect.
Same for your camera. So if you take a picture of something, like, what’s this plant? Or how do I buy these shoes? It should take you to an AI little preview. And then if you go deeper, again, it’s powered by AI mode. You can have that back and forth.
So you shouldn’t have to think about that. It should feel like a consistent, simple product experience, ultimately. But obviously, this is a new thing for us. And so we wanted to start it in a way that people could use and give us feedback with something like a direct entry point, like google.com/AI.”
Stein’s answer shows that Google is moving from separate AI features toward one unified search system that interprets intent and context automatically.
For users, that means typing, speaking, or taking a picture will all connect to the same underlying process that decides how to respond.
For publishers and SEOs, it means visibility will depend less on optimizing for keywords and more on aligning content with how Google understands and responds to different kinds of questions.
How Content Can Fit Into AI Triggered Search Experiences
Google is transitioning users out of the traditional ten blue links paradigm into a blended AI experience. Users can already enter questions consisting of multiple sentences and Google will automatically transition into an AI Mode deep question and answer. The answer is a preview with an option to trigger a deeper back and forth conversation.
Robbie Stein indicated that the AI Search experience will converge even more, depending on user feedback and how people interact with it.
These are profound changes that demand publishers ask deep questions about how content:
Should you consider how curating unique images, useful video content, and step-by-step tutorials may fit into your content strategies?
Information discovery is increasingly conversational, does your content fit into that context?
Information discovery may increasingly include camera snapshots, will your content fit into that kind of search?
These are examples of the kinds of questions publishers, SEOs and store owners should be thinking about.
Watch the podcast interview with Robby Stein
Inside Google’s AI turnaround: AI Mode, AI Overviews, and vision for AI-powered search | Robby Stein
Featured image/Screenshot of Lenny’s Podcast video
Google is quickly changing organic search results as it integrates AI. Thus far, the new features have caused traffic losses to most external sites, necessitating new search engine tactics and priorities.
Here’s how AI is impacting traditional organic search visibility to date.
AI Overviews
AI Overviews are answers to search queries. They summarize and cite top-ranking pages, typically, correlating traditional SEO with visibility in Overviews.
AI Overviews:
Eliminates searchers’ need to click. If your target query triggers an Overview, the result is likely fewer clicks, even if the Overview cites your page.
Cites pages that then often appear in average position 1 in Google Search Console with abnormally low click-throughs. Thus the average position in the Performance tab will increase, but click-throughs will decrease.
‘People also ask’
Traditional SEO typically recommends “People also ask” questions in content, to generate clicks. However, Google now serves occasional AI-generated answers to “People also ask” queries, which decreases clicks in that section and in organic listings.
Google often serves AI-generated answers to “People also ask” queries, such as this example for “oil stains.”
Suggested topics
Google now provides a search-result section that I call “suggested topics.” It functions similarly to a fan-out result, wherein Google suggests related topics to queries having multiple intents. For example, a search for “roof repair” could trigger suggestions exploring the symptoms and causes of roof damage.
Clicking on any of these suggestions produces an AI-generated answer, which is unlikely to generate traffic to an external source.
A search for “roof repair” could trigger suggestions exploring the symptoms and causes of roof damage.
AI-generated search snippets
Google is apparently testing AI-generated search snippets, foregoing the practice of using publishers’ meta descriptions or body text.
Google reportedly enhances a snippet sometimes with additional info, which can increase clicks.
Google’s testing of AI-generated search snippets replaces or enhances publishers’ meta descriptions or body text.
Local search
Google is integrating AI in blended results, especially local packs. Reportedly, Google’s AI now invites users to learn more about a local business and will even suggest related fan-out-style questions.
Google’s AI suggests fan-out-style questions, such as “Do they offer roof cleaning?”
The feature mimics what Google’s URL bar does now: encourage users to learn more about any page.
Hence local businesses should focus on providing on-site details of products or services, encouraging customer reviews, answering questions, and more.
Google is also integrating AI actions into local packs, following the practice in AI Mode. For example, for a “car tires near me” search, Google might suggest having AI check prices.
A “car tires near me” search might include a suggestion, such as “Have AI check prices.”
I once feared generative AI platforms would replace organic search. Instead, search engines are adopting AI themselves, making organic results less predictable, less trackable, and less traffic-generating.
We know what is happening. The key is adjusting traffic, tactics, and expectations accordingly.
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.
