Google announced an update to its voice search, which changes how voice search queries are processed and then ranked. The new AI model uses speech as input for the search and ranking process, completely bypassing the stage where voice is converted to text.

The old system was called Cascade ASR, where a voice query is converted into text and then put through the normal ranking process. The problem with that method is that it’s prone to mistakes. The audio-to-text conversion process can lose some of the contextual cues, which can then introduce an error.

The new system is called Speech-to-Retrieval (S2R). It’s a neural network-based machine-learning model trained on large datasets of paired audio queries and documents. This training enables it to process spoken search queries (without converting them into text) and match them directly to relevant documents.

Dual-Encoder Model: Two Neural Networks

The system uses two neural networks:

1. One of the neural networks, called the audio encoder, converts spoken queries into a vector-space representation of their meaning.
2. The second network, the document encoder, represents written information in the same kind of vector format.

The two encoders learn to map spoken queries and text documents into a shared semantic space so that related audio and text documents end up close together according to their semantic similarity.

Audio Encoder

Speech-to-Retrieval (S2R) takes the audio of someone’s voice query and transforms it into a vector (numbers) that represents the semantic meaning of what the person is asking for.

The announcement uses the example of the famous painting The Scream by Edvard Munch. In this example, the spoken phrase “the scream painting” becomes a point in the vector space near information about Edvard Munch’s The Scream (such as the museum it’s at, etc.).

Document Encoder

The document encoder does a similar thing with text documents like web pages, turning them into their own vectors that represent what those documents are about.

During model training, both encoders learn together so that vectors for matching audio queries and documents end up near each other, while unrelated ones are far apart in the vector space.

Rich Vector Representation

Google’s announcement says that the encoders transform the audio and text into “rich vector representations.” A rich vector representation is an embedding that encodes meaning and context from the audio and the text. It’s called “rich” because it contains the intent and context.

For S2R, this means the system doesn’t rely on keyword matching; it “understands” conceptually what the user is asking for. So even if someone says “show me Munch’s screaming face painting,” the vector representation of that query will still end up near documents about The Scream.

According to Google’s announcement:

“The key to this model is how it is trained. Using a large dataset of paired audio queries and relevant documents, the system learns to adjust the parameters of both encoders simultaneously.

The training objective ensures that the vector for an audio query is geometrically close to the vectors of its corresponding documents in the representation space. This architecture allows the model to learn something closer to the essential intent required for retrieval directly from the audio, bypassing the fragile intermediate step of transcribing every word, which is the principal weakness of the cascade design.”

Ranking Layer

S2R has a ranking process, just like regular text-based search. When someone speaks a query, the audio is first processed by the pre-trained audio encoder, which converts it into a numerical form (vector) that captures what the person means. That vector is then compared to Google’s index to find pages whose meanings are most similar to the spoken request.

For example, if someone says “the scream painting,” the model turns that phrase into a vector that represents its meaning. The system then looks through its document index and finds pages that have vectors with a close match, such as information about Edvard Munch’s The Scream.

Once those likely matches are identified, a separate ranking stage takes over. This part of the system combines the similarity scores from the first stage with hundreds of other ranking signals for relevance and quality in order to decide which pages should be ranked first.

Benchmarking

Google tested the new system against Cascade ASR and against a perfect-scoring version of Cascade ASR called Cascade Groundtruth. S2R beat Cascade ASR and very nearly matched Cascade Groundtruth. Google concluded that the performance is promising but that there is room for additional improvement.

Voice Search Is Live

Although the benchmarking revealed that there is some room for improvement, Google announced that the new system is live and in use in multiple languages, calling it a new era in search. The system is presumably used in English.

Google explains:

“Voice Search is now powered by our new Speech-to-Retrieval engine, which gets answers straight from your spoken query without having to convert it to text first, resulting in a faster, more reliable search for everyone.”

Read more:

​​Speech-to-Retrieval (S2R): A new approach to voice search

Featured Image by Shutterstock/ViDI Studio

https://www.searchenginejournal.com/google-announces-a-new-era-for-voice-search/558866/

GEO/AEO is criticized by SEOs who claim that it’s just SEO at best and unsupported lies at worst. Are SEOs right, or are they just defending their turf? Bing recently published a guide to AI search visibility that provides a perfect opportunity to test whether optimization for AI answers recommendations is distinct from traditional SEO practices.

Chunking Content

Some AEO/GEO optimizers are saying that it’s important to write content in chunks because that’s how AI and LLMs break up a pages of content, into chunks of content. Bing’s guide to answer engine optimization, written by Krishna Madhavan, Principal Product Manager at Bing, echoes the concept of chunking.

Bing’s Madhavan writes:

“AI assistants don’t read a page top to bottom like a person would. They break content into smaller, usable pieces — a process called parsing. These modular pieces are what get ranked and assembled into answers.”

The thing that some SEOs tend to forget is that chunking content is not new. It’s been around for at least five years. Google introduced their passage ranking algorithm back in 2020. The passages algorithm breaks up a web page into sections to understand how the page and a section of it is relevant to a search query.

Google says:

“Passage ranking is an AI system we use to identify individual sections or “passages” of a web page to better understand how relevant a page is to a search.”

Google’s 2020 announcement described passage ranking in these terms:

“Very specific searches can be the hardest to get right, since sometimes the single sentence that answers your question might be buried deep in a web page. We’ve recently made a breakthrough in ranking and are now able to better understand the relevancy of specific passages. By understanding passages in addition to the relevancy of the overall page, we can find that needle-in-a-haystack information you’re looking for. This technology will improve 7 percent of search queries across all languages as we roll it out globally.”

As far as chunking is concerned, any SEO who has optimized content for Google’s Featured Snippets can attest to the importance of creating passages that directly answer questions. It’s been a fundamental part of SEO since at least 2014, when Google introduced Featured Snippets.

Titles, Descriptions, and H1s

The Bing guide to ranking in AI also states that descriptions, headings, and titles are important signals to AI systems. It’s not necessary to belabor the point that descriptions, headings, and titles are fundamental elements of SEO. So again, there is nothing about optimizing these HTML elements that is unique to AEO/GEO.

Lists and Tables

Bing recommends bulleted lists and tables as a way to easily communicate complex information to users and search engines. This approach to organizing data is similar to an advanced SEO method called disambiguation. Disambiguation is about making the meaning and purpose of a web page as clear as possible, to make it less ambiguous.

Making a page less ambiguous can incorporate semantic HTML to clearly delineate which part of a web page is the main content (MC in the parlance of Google’s third-party quality rater guidelines) and which part of the web page is just advertisements, navigation, a sidebar, or the footer.

Another form of disambiguation is through the proper use of HTML elements like ordered lists (OL) and the use of tables to communicate tabular data such as product comparisons or a schedule of dates and times for an event.

The use of HTML elements (like H, OL, and UL) give structure to on-page information, which is why it’s called structured information. Structured information and structured data are two different things. Structured information is on the page and is seen in the browser and by crawlers. Structured data is meta data that only a bot will see.

There are studies that structured information helps AI Agents make sense of a web page, so I have to concede that structured information is something that is particularly helpful to AI Agents in a unique way.

Question And Answer Pairs

Bing recommends Q&A’s, which are question and answer pairs that an AI can use directly. Bing’s Madhavan writes:

“Direct questions with clear answers mirror the way people search. Assistants can often lift these pairs word for word into AI-generated responses.”

This is a mix of passage ranking and the SEO practice of writing for featured snippets, where you pose a question and give the answer. It’s a risky approach to create an entire page of questions and answers but if it feels useful and helpful then it may be worth doing.

Something to keep in mind is that Google’s systems consider content lacking in unique insight on the same level of spam. Google also considers content created specifically for search engines as low quality as well.

Anyone considering writing questions and answers on a web page for the purpose of AI SEO should first consider the whether it’s useful for people and think deeply about the quality of the question and answer pairs. Otherwise it’s just a page of rote made for search engine content.

Be Precise With Semantic Clarity

Bing also recommends semantic clarity. This is also important for SEO. Madhavan writes:

• “Write for intent, not just keywords. Use phrasing that directly answers the questions users ask.
• Avoid vague language. Terms like innovative or eco mean little without specifics. Instead, anchor claims in measurable facts.
• Add context. A product page should say “42 dB dishwasher designed for open-concept kitchens” instead of just “quiet dishwasher.”
• Use synonyms and related terms. This reinforces meaning and helps AI connect concepts (quiet, noise level, sound rating).”

They also advise to not use abstract words like “next-gen” or “cutting edge” because it doesn’t really say anything. This is a big, big issue with AI-generated content because it tends to use abstract words that can completely be removed and not change the meaning of the sentence or paragraph.

Lastly, they advise to not use decorative symbols, which is good a tip. Decorative symbols like the arrow → symbol don’t really communicate anything semantically.

All of this advice is good. It’s good for SEO, good for AI, and like all the other AI SEO practices, there is nothing about it that is specific to AI.

Bing Acknowledges Traditional SEO

The funny thing about Bing’s guide to ranking better for AI is that it explicitly acknowledges that traditional SEO is what matters.

Bing’s Madhavan writes:

“Whether you call it GEO, AIO, or SEO, one thing hasn’t changed: visibility is everything. In today’s world of AI search, it’s not just about being found, it’s about being selected. And that starts with content.

…traditional SEO fundamentals still matter.”

AI Search Optimization = SEO

Google and Bing have incorporated AI into traditional search for about a decade. AI Search ranking is not new. So it should not be surprising that SEO best practices align with ranking for AI answers. The same considerations also parallel with considerations about users and how they interact with content.

Many SEOs are still stuck in the decades-old keyword optimization paradigm and maybe for them these methods of disambiguation and precision are new to them. So perhaps it’s a good thing that the broader SEO industry catches up with many of these concepts for optimizing content and to recognize that there is no AEO/GEO, it’s still just SEO.

Featured Image by Shutterstock/Roman Samborskyi

https://www.searchenginejournal.com/review-of-aeo-geo-tactics-leads-to-a-surprising-seo-insight/558796/

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:

1. AI Overviews
2. Multimodal search
3. 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.

See also: The Three Pillars Of SEO: Authority, Relevance, And Experience

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.

Related: Google’s AI Mode: What We Know & What Experts Think

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.

Download: The Future Of Search

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

[embedded content]

Featured image/Screenshot of Lenny’s Podcast video

https://www.searchenginejournal.com/google-explains-next-generation-of-ai-search/558206/

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:

<IfModule mod_rewrite.c>
RewriteEngine On
RewriteCond %{HTTP_USER_AGENT} Google-NotebookLM [NC]
RewriteRule .* - [F,L]
</IfModule>

https://www.searchenginejournal.com/google-quietly-signals-notebooklm-ignores-robots-txt/558067/

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:

1. 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).
2. 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).
3. 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).
4. 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:

1. 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.
2. Tighten provenance and trust signals: rich schema (FAQ, HowTo, TechArticle, Product), canonical HTML + PDF versions, explicit authorship and last-updated stamps.
3. Mirror canonical chunks in your help center, product manuals, developer docs to meet the assistants where they crawl.
4. Expose APIs, sample data, and working examples so agents can act on your info, not just read it.
5. 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.
6. 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.

More Resources:

This post was originally published on Duane Forrester Decodes.

Featured Image: Roman Samborskyi/Shutterstock

https://www.searchenginejournal.com/2026-when-ai-assistants-become-the-first-layer/557542/

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.

SEO experts I have spoken to cited TikTok as a space where a startup could break into a niche.

1% Of A Trillion Is Traffic Worth Taking

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.”

What SEOs Should Focus On Right Now

My final question to Carolyn was to ask what she thought SEOs should focus on right now.

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.

Watch the full video interview with Carolyn Shelby here:

[embedded content]

Thank you to Carolyn Shelby for offering her insights and being my guest on IMHO.

More Resources: 

Featured Image: Shelley Walsh

https://www.searchenginejournal.com/30-year-seo-expert-why-ai-search-isnt-overhyped-what-to-focus-on-right-now/557802/

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.

And that’s fixable.

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

https://www.searchenginejournal.com/hidden-organizational-forces-that-undermine-enterprise-seo/552893/

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:

• Long-tail variations of your target keywords.
• Questions you haven’t answered yet.
• Seasonal shifts in search language.

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:

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.

More Resources:

https://www.searchenginejournal.com/how-to-build-seo-strategies-around-real-customer-behavior/554283/

Search Atlas held an event last week to showcase new capabilities and improvements to their SEO platform, which make it easier for digital marketers 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, track LLM visibility and impact, and scale Google Business Profile management. That’s just a sample of all the new functionalities coming to the platform.

OTTO PPC Retargeting

Search Atlas introduced a new retargeting feature in OTTO PPC. This feature is designed for agencies and advertisers that manage paid media. It simplifies campaign setup with a quick-start wizard that enables retargeting site visitors, which the company claims 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 manage Google Business Profiles for clients. The GBP Galactic feature now includes 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 identifies visibility trends beyond Google Search.

Related: LLM Visibility Tools: Do SEOs Agree On How To Use Them?

Expanded Press Release Network

Bhan announced that Signal Genesys, a press release company they acquired last year, has expanded its distribution to financial news and 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.

I asked Search Atlas for more information and this is their explanation of this feature:

“In OTTO, Domain Knowledge Networks (DKN) are AI-powered maps of your brand’s niche. Basically they show all the key topics, entities, and their relationships which gives you a clear blueprint for building content clusters, internal linking, and boosting your authority in search. The idea is to help users create a unified content plan backed by your strategy and brand’s expertise.”

Related: The Complete Guide to On-Page SEO

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 integration 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, and image optimization, GBP posts, and content generation.

After spending the day listening to their presentations, it became evident that OTTO Agent typified Search Atlas’s approach toward developing a useful SEO platform. Having come from an SEO agency background, they understand what agencies need and aren’t waiting for competitors to act first; they’re moving forward with features they feel agencies will find useful.

OTTO Agent is an example of that forward-looking approach because it is built on the idea that managing SEO will become agentic, conversational, and autonomous.

I didn’t know much about Search Atlas before attending the event, but now I have a better understanding of why so many agencies embrace it.

Featured Image by Shutterstock/Digitala World

https://www.searchenginejournal.com/search-atlas-announces-new-features-for-agencies/557725/

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.

Source: BrightEdge report, September 2025

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.

Learn More: Google Speculates If SEO ‘Is On A Dying Path’

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.

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

https://www.searchenginejournal.com/the-cmo-seo-staying-ahead-of-the-multi-ai-search-platform-shift-part-2/556130/