Google’s John Mueller answered a question about why some websites use multiple XML sitemaps instead of a single file. His answer suggests that what looks like unnecessary complexity may come from reasons that are not immediately obvious.
The question came from an SEO trying to understand why managing multiple sitemap files would be preferred over keeping everything in one place.
Question About Using Multiple Sitemaps
The SEO framed the issue as a matter of efficiency, questioning why anyone would choose to increase the number of files they have to manage.
“Can I ask a silly question, what’s the advantage of multiple site maps? It seems like your going from 1 file to manage to X files to manage.
Why the extra work? Why not just have 1 file?”
It’s a good question, avoiding extra work is always a good idea in SEO, especially if someone has a relatively small website, it makes sense to have just one sitemap but as Google’s Mueller explains, there may be good reasons to split a sitemap into multiple files.
Mueller Explains Why Multiple Sitemaps Are Used
Mueller responded by listing several reasons why multiple sitemap files are used, including both practical and less intentional causes.
He responded:
“Some reasons I’ve seen:
want to track different kinds of urls in groups (“product detail page sitemap” vs “product category sitemap” — which you can kinda do with the page indexing report)
split by freshness (evergreen content in a separate sitemap file – theoretically a search engine might not need to check the “old” sitemap as often; I don’t know if this actually happens though)
proactively split (so that you don’t get to 50k and have to urgently figure out how to change your setup)
hreflang sitemaps (can take a ton of space, so the 50k URLs could make the files too large)
my computer did it, I don’t know why”
Mueller’s answer shows that sitemaps can be used in creative ways that serve a purpose. Something I’ve heard from enterprise level SEOs is that they find that keeping a sitemap to well under 50k lines ensures better indexing.
Takeaways
Mueller’s answer shows that sometimes keeping things “simple” isn’t always the best strategy. It might make sense apply organization to the sitemaps appears to be unnecessary complexity is often the result of practical constraints, evolving site structures, or automated systems rather than deliberate optimization.
Multiple sitemaps can be used to group different types of content
They help avoid hitting technical limits like the 50,000 URL cap
Some implementations are based on theory rather than confirmed behavior
Not all sitemap structures are intentional or strategically planned
Featured Image by Shutterstock/Rachchanont Hemmawong
AI-powered shopping is winning over American consumers when it saves time or makes buying decisions easy, but a slew of recent surveys show shoppers are not ready for autonomous purchase agents.
For example, in January 2026 email platform Omnisend commissioned a survey of 4,000 shoppers across the U.S., Canada, and Australia as to their use of AI for shopping in the previous six months.
Of the 1,072 U.S. shoppers surveyed, only 8.29% were “fully comfortable” with AI completing online purchases. Nearly three-quarters of respondents wanted some form of transactional restriction, and 20.28% were “not comfortable at all” with “handing over transactions to AI tools.”
Shopping Effort
Yet shoppers are using AI in the buying journey. Omnisend’s survey found that 47% of U.S. respondents use AI for product research and comparisons, 40.9% for finding deals or coupons, and 38.6% for summarizing reviews.
Separately, eMarketer, citing a Q3 2025 IBM survey of 18,000 global consumers, reported last month that shoppers most often use AI for general help, product research, and reviewing options. And McKinsey’s February 2026 survey of roughly 4,000 U.S. consumers found that 68% had used AI tools in the previous three months, mostly to support decision-making.
Collectively, the data indicate that shoppers value AI when it makes shopping easier.
In Omnisend’s data, 47.2% of U.S. respondents said AI saves time. Another 40.1% said it simplifies the process, and 38.6% said it helps discover products they might not have found otherwise.
Generally, saving time, simplifying, and identifying all reduce cognitive load or effort. Instead of sorting through dozens of product pages or reviews, shoppers can compress that work into a few prompts or queries.
This distinction is significant. Prompt-based shopping shifts how consumers decide what to buy. AI narrows the selection before a shopper reaches an ecommerce product page.
Purchase Control
Despite the ease of use, survey respondents are less comfortable giving up purchase control. Again, only 8.29% of Omnisend’s U.S. respondents said they are fully comfortable with AI completing a transaction.
Thus shoppers remain cautious. Some 56.4% of Omnisend respondents said they always or usually double-check AI-generated recommendations before buying.
Moreover, a February 2026 survey of 1,500 U.S. adults from Ipsos, the research firm, found that just 27% of Gen Z respondents (born 1997-2012) said they would allow an AI agent to choose and buy a product without approval, while just 4% of Gen X (1965-1980) and younger Boomers (1946-1964) would do the same.
Ipsos found consumers tend to prefer automation over agent autonomy. AI agents could make purchases based on prior behavior, such as selecting familiar brands or working from a predefined list, but not from new, autonomous selections.
Ecommerce Implications
Hence survey data suggests consumers use AI for product discovery, but not yet for agentic buying. That distinction should inform ecommerce businesses where to prioritize efforts.
For example, ensuring that product data is structured and feed-ready is key. AI chat tools use that structured information to summarize, compare, and surface product recommendations. The mundane task of cleaning data is more important than launching a co-shopping agent.
Similarly, content marketing is a priority over cutting-edge AI widgets. GenAI-optimized product comparisons, buying guides, instructions, and even reviews feed discovery.
While merchants should monitor how AI platforms evolve, the shopping journey is the near-term opportunity. AI is helping consumers decide what to buy, but not yet buying for them.
The conversation around llms.txt is real and worth continuing. I covered it in a previous article, and the core instinct behind the proposal is correct: AI systems need clean, structured, authoritative access to your brand’s information, and your current website architecture was not built with that in mind. Where I want to push further is on the architecture itself. llms.txt is, at its core, a table of contents pointing to Markdown files. That is a starting point, not a destination, and the evidence suggests the destination needs to be considerably more sophisticated.
Before we get into architecture, I want to be clear about something: I am not arguing that every brand should sprint to build everything described in this article by next quarter. The standards landscape is still forming. No major AI platform has formally committed to consuming llms.txt, and an audit of CDN logs across 1,000 Adobe Experience Manager domains found that LLM-specific bots were essentially absent from llms.txt requests, while Google’s own crawler accounted for the vast majority of file fetches. What I am arguing is that the question itself, specifically how AI systems gain structured, authoritative access to brand information, deserves serious architectural thinking right now, because the teams that think it through early will define the patterns that become standards. That is not a hype argument. That is just how this industry has worked every other time a new retrieval paradigm arrived.
Where Llms.txt Runs Out Of Road
The proposal’s honest value is legibility: it gives AI agents a clean, low-noise path into your most important content by flattening it into Markdown and organizing it in a single directory. For developer documentation, API references, and technical content where prose and code are already relatively structured, this has real utility. For enterprise brands with complex product sets, relationship-heavy content, and facts that change on a rolling basis, it is a different story.
The structural problem is that llms.txt has no relationship model. It tells an AI system “here is a list of things we publish,” but it cannot express that Product A belongs to Product Family B, that Feature X was deprecated in Version 3.2 and replaced by Feature Y, or that Person Z is the authoritative spokesperson for Topic Q. It is a flat list with no graph. When an AI agent is doing a comparison query, weighting multiple sources against each other, and trying to resolve contradictions, a flat list with no provenance metadata is exactly the kind of input that produces confident-sounding but inaccurate outputs. Your brand pays the reputational cost of that hallucination.
There is also a maintenance burden question that the proposal does not fully address. One of the strongest practical objections to llms.txt is the ongoing upkeep it demands: every strategic change, pricing update, new case study, or product refresh requires updating both the live site and the file. For a small developer tool, that is manageable. For an enterprise with hundreds of product pages and a distributed content team, it is an operational liability. The better approach is an architecture that draws from your authoritative data sources programmatically rather than creating a second content layer to maintain manually.
The Machine-Readable Content Stack
Think of what I am proposing not as an alternative to llms.txt, but as what comes after it, just as XML sitemaps and structured data came after robots.txt. There are four distinct layers, and you do not have to build all of them at once.
Layer two is entity relationship mapping. This is where you express the graph, not just the nodes. Your products relate to your categories, your categories map to your industry solutions, your solutions connect to the use cases you support, and all of it links back to the authoritative source. This can be implemented as a lightweight JSON-LD graph extension or as a dedicated endpoint in a headless CMS, but the point is that a consuming AI system should be able to traverse your content architecture the way a human analyst would review a well-organized product catalog, with relationship context preserved at every step.
Layer three is content API endpoints, programmatic and versioned access to your FAQs, documentation, case studies, and product specifications. This is where the architecture moves beyond passive markup and into active infrastructure. An endpoint at /api/brand/faqs?topic=pricing&format=json that returns structured, timestamped, attributed responses is a categorically different signal to an AI agent than a Markdown file that may or may not reflect current pricing. The Model Context Protocol, introduced by Anthropic in late 2024 and subsequently adopted by OpenAI, Google DeepMind, and the Linux Foundation, provides exactly this kind of standardized framework for integrating AI systems with external data sources. You do not need to implement MCP today, but the trajectory of where AI-to-brand data exchange is heading is clearly toward structured, authenticated, real-time interfaces, and your architecture should be building toward that direction. I have been saying this for years now – that we are moving toward plugged-in systems for the real-time exchange and understanding of a business’s data. This is what ends crawling, and the cost to platforms, associated with it.
Layer four is verification and provenance metadata, timestamps, authorship, update history, and source chains attached to every fact you expose. This is the layer that transforms your content from “something the AI read somewhere” into “something the AI can verify and cite with confidence.” When a RAG system is deciding which of several conflicting facts to surface in a response, provenance metadata is the tiebreaker. A fact with a clear update timestamp, an attributed author, and a traceable source chain will outperform an undated, unattributed claim every single time, because the retrieval system is trained to prefer it.
What This Looks Like In Practice
Take a mid-market SaaS company, a project management platform doing around $50 million ARR and selling to both SMBs and enterprise accounts. They have three product tiers, an integration marketplace with 150 connectors, and a sales cycle where competitive comparisons happen in AI-assisted research before a human sales rep ever enters the picture.
Right now, their website is excellent for human buyers but opaque to AI agents. Their pricing page is dynamically rendered JavaScript. Their feature comparison table lives in a PDF that the AI cannot parse reliably. Their case studies are long-form HTML with no structured attribution. When an AI agent evaluates them against a competitor for a procurement comparison, it is working from whatever it can infer from crawled text, which means it is probably wrong on pricing, probably wrong on enterprise feature availability, and almost certainly unable to surface the specific integration the prospect needs.
A machine-readable content architecture changes this. At the fact-sheet layer, they publish JSON-LD Organization and Product schemas that accurately describe each pricing tier, its feature set, and its target use case, updated programmatically from the same source of truth that drives their pricing page. At the entity relationship layer, they define how their integrations cluster into solution categories, so an AI agent can accurately answer a compound capability question without having to parse 150 separate integration pages. At the content API layer, they expose a structured, versioned comparison endpoint, something a sales engineer currently produces manually on request. At the provenance layer, every fact carries a timestamp, a data owner, and a version number.
When an AI agent now processes a product comparison query, the retrieval system finds structured, attributed, current facts rather than inferred text. The AI does not hallucinate their pricing. It correctly represents their enterprise features. It surfaces the right integrations because the entity graph connected them to the correct solution categories. The marketing VP who reads a competitive loss report six months later does not find “AI cited incorrect pricing” as the root cause.
This Is The Infrastructure Behind Verified Source Packs
In the previous article on Verified Source Packs, I described how brands can position themselves as preferred sources in AI-assisted research. The machine-readable content API is the technical architecture that makes VSPs viable at scale. A VSP without this infrastructure is a positioning statement. A VSP with it is a machine-validated fact layer that AI systems can cite with confidence. The VSP is the output visible to your audience; the content API is the plumbing that makes the output trustworthy. Clean structured data also directly improves your vector index hygiene, the discipline I introduced in an earlier article, because a RAG system building representations from well-structured, relationship-mapped, timestamped content produces sharper embeddings than one working from undifferentiated prose.
History, however, suggests the objection cuts the other way. The brands that implemented Schema.org structured data in 2012, when Google had just launched it, and nobody was sure how broadly it would be used, shaped how Google consumed structured data across the next decade. They did not wait for a guarantee; they built to the principle and let the standard form around their use case. The specific mechanism matters less than the underlying principle: content must be structured for machine understanding while remaining valuable for humans. That will be true regardless of which protocol wins.
The minimum viable implementation, one you can ship this quarter without betting the architecture on a standard that may shift, is three things. First, a JSON-LD audit and upgrade of your core commercial pages, Organization, Product, Service, and FAQPage schemas, properly interlinked using the @id graph pattern, so your fact layer is accurate and machine-readable today. Second, a single structured content endpoint for your most frequently compared information, which, for most brands, is pricing and core features, generated programmatically from your CMS so it stays current without manual maintenance. Third, provenance metadata on every public-facing fact you care about: a timestamp, an attributed author or team, and a version reference.
That is not an llms.txt. It is not a Markdown copy of your website. It is durable infrastructure that serves both current AI retrieval systems and whatever standard formalizes next, because it is built on the principle that machines need clean, attributed, relationship-mapped facts. The brands asking “should we build this?” are already behind the ones asking “how do we scale it.” Start with the minimum. Ship something this quarter that you can measure. The architecture will tell you where to go next.
Duane Forrester has nearly 30 years of digital marketing and SEO experience, including a decade at Microsoft running SEO for MSN, building Bing Webmaster Tools, and launching Schema.org. His new book about staying trusted and relevant in the AI era (The Machine Layer) is available now on Amazon.
Fair to say the majority of evergreen content will not drive the value it did five years ago. Hell, even one or two years ago. What we have done for the last decade will not be as profitable.
AIOs have eroded clicks. Answer engines have given people options. And to be fair, people are bored of the +2,000-word article answering “What time does X start?” Or recipes where the ingredient list is hidden below 1,500 words about why daddy didn’t like me.
So, you’ve got to be smart. This has to be framed as a commercial decision. Content needs to drive real business value. You’ve got to be confident in it delivering.
That doesn’t mean every article, video, or podcast has to drive a subscription or direct conversion. But it needs to play a clear part in the user’s journey. You need to be able to argue for its inclusion:
Is it a jumping-off point?
Will it drive a registration?
Or a free subscriber, save or follow on social
More commonly known as micro-conversions, these things really matter when it comes to cultivating and retaining an audience. People don’t want more bland, banal nonsense. They want something better.
The antithesis to AI slop will help your business be profitable.
Inherently, nothing. It’s a foundational part of the content pyramid.
In most cases, it’s been done to death, and AI is very effective at summarizing a lot of this bread-and-butter content.
The epitome of quantity over quality; it worked and made a fortune.
But I digress.
An authoritative enough site has been able to drive clicks and follow-up value with sub-par content for decades. That is, slowly diminishing. Rightly or wrongly.
And not because of the Helpful Content stuff. Google nerfed all the small sites long before the goliaths. Now they’ve gone after the big fish.
We have to make commercial decisions that help businesses make the right choice. Concepts like E-E-A-T have had an impact on the quality of content (a good thing). It’s also had an impact on the cost of creating quality content.
Working with experts.
Unique imagery.
Video.
Product and development costs.
Data.
This isn’t cheap. Once upon a time, we could generate value from authorless content full of stock images and no unique value. Unless you’re willing to bend the rules (which isn’t an option for most of us), you need an updated plan.
It depends.
You need to establish how much your content now costs to produce and the value it brings. Not everything is going to drive a significant conversion. That doesn’t mean you shouldn’t do it. It means you need to have a very clear reason for what you’re creating and why.
If particular topics are essential to your audience, service, and/or product, then they should at least be investigated.
One of the joys of creating evergreen content has always been that it adds value throughout the year(s). A couple of annual updates, even relatively light touch, could yield big results.
Commissioning something of quality in this space is likely more expensive. It needs to be worth it; it has to form part of your multi-channel experience to make it so.
Unique data and visuals that can be shared on socials.
Building campaigns around it (or it’s part of a campaign).
You can even build authors and your brand around it.
And if it resonates, you can rinse and repeat year after year.
Ahrefs created demand for their brand + an evergreen topic – AIOs (Image Credit: Harry Clarkson-Bennett)
And this type of content or campaign can increase demand for a topic. You can become a thought leader by shifting the tide of public opinion.
For publishers and content creators, that is foundational.
I don’t – on both counts. We should want to be targeted on driving real value for the business.
Something like:
Tier 1: Value – core, revenue, and value-driving conversions.
Tier 2: Registrations (and things that help you build your owned properties), links, shares, and comments.
Tier 3: Page views, returning visits, and engagement metrics.
Micro-conversions over clicks. We’re focusing on registrations, free or lower-value subscriptions. Whatever gets the user into the ecosystem and one step closer to a genuinely valuable conversion.
The messy middle has changed, and it is largely unattributable (Image Credit: Harry Clarkson-Bennett)
Now, could a click be a micro-conversion? If you know that someone who reads a secondary article (by clicking a follow-up link) is 10x more likely to register, that follow-up click could be a sensible micro-conversion.
This type of conversion may not directly drive your bottom line. But it forces you and your team to focus on behaviors that are more likely to lead to a valuable conversion.
That is the point of a micro-conversion. It changes behaviors.
You can tweak the above tiers to better suit your content offering. Not all content is going to drive direct tier one or even two value. You just need to have a very clear idea of its purpose in the customer journey.
If what you’re creating already exists, you’d better make sure you add something extra. You’ve got to force your way into the conversation, and unless you can offer something unique, you’re (almost certainly) wasting your time IMO.
I’ll break all of these down, but I think (in order of importance):
Writing content for people.
Information gain.
Getting it found.
Creating it at the right time.
Structuring it for bots.
Everyone is obsessed with getting cited or being visible in AI.
I think this is completely the wrong way of framing this new era. Getting cited there, or being visible, is a happy byproduct of building a quality brand with an efficient, joined-up approach to marketing.
The more you understand your audience, the more likely you will be to create high-quality, relevant content that gets cited.
If you know your audience really cares about a topic, that’s step one taken care of. If you know where they spend time and how they’re influenced, that’s step two. And if you know how to cut through the noise, that’s step three.
Really, this is an evolution in SEO and the internet at large.
Invest in and create content that will resonate with your audience.
Create a cross-channel marketing strategy that will genuinely reach and influence them.
Share, share, share. Be impactful. Get out there.
Make sure it’s easy to read, share, and consume.
Your content still needs to reach and be remembered by the right people. Do that better than anybody else, and wider visibility will come.
In SEO, we have a different definition of information gain than more traditional information retrieval mechanics. I don’t know if that’s because we’re wrong (probably), or that we have a valid reason…
Maybe someone can enlighten me?
In more traditional machine learning, information gain measures how much uncertainty is reduced after observing new data. That uncertainty is captured by entropy, which is a way of quantifying how unpredictable a variable is based on its probability distribution.
Events with low probability are more surprising and therefore carry more information. High probability events are less surprising and novel. Therefore, entropy reflects the overall level of disorder and unpredictability across all possible outcomes.
Information gain, then, tells us how much that unpredictability drops when we split or segment the data. A higher information gain means the data has become more ordered and less uncertain – in other words, we’ve learned something useful.
To us in SEO, information gain means the addition of new, relevant information. Beyond what is already out there in the wider corpus.
A representative workflow of Google’s Contextual estimation of link information gain patent (Image Credit: Harry Clarkson-Bennett)
Google wants to reduce uncertainty. Reduce ambiguity. Content with a higher level of information gain isn’t only different, it elevates a user’s understanding. It raises the bar by answering the question(s) and topic more effectively than anyone else.
So, try something different, novel even, and watch Google test your content higher up in the SERPs to see if it satisfies a user.
This is such an important concept for evergreen content because so many of these queries have well-established answers. If you’re just parroting these answers because your competitors do it, you’re not forcing Google’s hand.
Particularly if you’re still just copying headers and FAQs from the top three results. Audiences are not arriving at publisher destinations through direct navigation at the same scale. They encounter journalism incidentally, through social feeds, not through habitual site visits.
Younger audiences spend less time on news sites and more time on social every year (Image Credit: Harry Clarkson-Bennett)
You’ve got to meet them there and force their hand.
According to this patent – contextual estimation of link information gain – Google scores documents based on the additional information they offer to a user, considering what the user has already seen.
“Based on the information gain scores of a set of documents, the documents can be provided to the user in a manner that reflects the likely information gain that can be attained by the user if the user were to view the documents.”
Bots, like people, need structure to properly “understand” content.
Elements like headings (h1 – h6), semantic HTML, and linking effectively between articles help search engines (and other forms of information retrieval) understand what content you deem important.
While the majority of semi-literates “understand” content, bots don’t. They fake it. They use engagement signals, NLP, and the vector model space to map your document against others.
They can only do this effectively if you understand how to structure a page.
Frontloading key information.
Effectively targeting highly relevant queries.
Using structured data formats like lists and tables, where appropriate (these are more cost-effective forms of tokenization).
The more clearly a page communicates its topic, subtopics, and relationships, the more likely it is to be consistently retrieved and reused across search and AI surfaces. This has a compounding effect.
Rank more effectively (great for RAG, obviously) – feature more heavily in versions of the internet – force your way into model training data.
If you need to get development work put through, frame it through the lens of assistive technology. Can people with specific needs fully access your pages?
Track and pay very close attention to spikes in demand (Google Trends API being a very obvious option here).
Make sure you’re adding something of value to the wider corpus.
If quality content is already out there and you have nothing extra to add, consider whether it’s worth spending money on (SEO is not free).
Create and update timely evergreen content (Image Credit: Harry Clarkson-Bennett)
While this is primarily for news, you can apply a similar logic to evergreen content if you zoom out and follow macro trends.
Evergreen content still spikes at different times throughout the year. Take Spain as an example. There’s much more limited interest in going to Spain in the Winter months from the UK. But January (holiday planning or weekend breaks) and summer (more immediate holiday-ing with the kids) provide better opportunities to generate traffic.
You’re capturing the spike in demand by updating content at the right time. Particularly if you understand the difference in user needs when this spike in demand happens.
In January, get your holiday planning content ready.
In the summer, get your family-friendly and last-minute holiday content up and running.
Image Credit: Harry Clarkson-Bennett
Demand for evergreen topics can be cyclical. In this example, you would want to capture the spike(s) with carefully planned updates, so you have up-to-date content when a user is really searching for that product, service, or information.
Well, what matters to your brand and your users? Have you asked them?
By the very nature of new and evolving topics and concepts, not everything “evergreen” has been done.
New topics rise. Old ones fall. Some are cyclical.
My rule(s) of thumb would be to establish:
Is the topic foundational to your product and service?
Does your current (and potential) audience demand it?
Do you have something new to add to the wider corpus of information?
If the answer to those three is a broad variation of yes, it’s almost certainly a good bet. Then, I would consider topic search volume, cross-platform demand, and whether the topic is trending up or down in popularity.
There are some things you should be doing “just for SEO.” Content isn’t one of them. You can yell topical authority until you’re blue in the face. If you’re creating stuff just for SEO – kill it.
IMO, these plays have been dead or dying for some time. The modern-day version of the internet (in particular search) demands disambiguation. It demands accuracy. Verification that you are an expert. Otherwise, you’re competing with those who have a level of legitimacy that you do not.
Social profiles, newsletters, real people sharing stories. You’re competing with people who aren’t polishing turds.
If all you’re thinking about is search volume or clicks, I don’t think it’s worth it.
And this brings me nicely onto rented land. Platforms you don’t own.
We’ve spent years creating assets (your websites) to deliver value in search. Owning all of your assets and prioritizing your site above all else. But that is changing. In many cases, people don’t reach your website until they’ve already made a purchasing decision.
I think Rand has managed this transition better than anybody (Image Credit: Harry Clarkson-Bennett)
So, you have to get your stuff out there. Create large, unique studies. Cut them into snippets and short-form videos. Use your individual platform to boost your profile and the content’s chances of soaring.
This is, IMO, particularly prescient for publishers. You’ve got to get out there. You’ve got to share and reuse your content. To make the most of what you’ve created.
Sweat your assets. Even if senior figures aren’t comfortable with this, you need to make it happen.
People have been espousing how important it is to feature as part of the answer. And that may be true. But you’re going to have to be good at selling your projects in if there’s no clear attribution or value.
It might not have the spikes of news, but evergreen interest still spikes at certain times in the year.
Get people – real people – to share it. To have their spin on it.
Outperform the expected early stage engagement and maximize your chance of appearing in platforms like Discover with wider platform engagement.
You have to work harder than before.
I shared an example of this around a year ago, but to revisit it, I now have 11 recommendations from other Substacks.
You can’t do this alone (Image Credit: Harry Clarkson-Bennett)
They have accounted for over 40% of my total subscribers. Admittedly, mainly from Barry, Shelby, and Jessie. But they are, if I may be so bold, superhumans.
And when our main driver of evergreen traffic to the site (Google) has really leaned into the evil that surrounds big tech, we’ve got to be cannier. We have to find ways to get people to share our content.
Even evergreen content.
If we’re being honest, a lot of SEO content has been rubbish. Churned out muck.
People are still churning out muck at an incredible rate. When what you’ve got is crap, more crap isn’t the answer. I think people are turned off. They’re tuning out of things at an alarming rate, especially young people.
It is all about getting the right people into the system. Evergreen content is still foundational here. You just have to make it work harder. Be more interesting. Be shareable.
Hopefully, this makes decisions over what we should and shouldn’t create easier.
Enterprise SEO doesn’t fail because teams don’t care, lack expertise, or miss tactics. It fails because ownership is fractured.
In most large organizations, everyone controls a piece of SEO, yet no single group owns the outcome. Visibility, traffic, and discoverability depend on dozens of upstream decisions made across engineering, content, product, UX, legal, and local markets. SEO is measured on the result, but it does not control the system that produces it.
In smaller organizations, this problem is manageable. SEO teams can directly influence content, technical decisions, and site structure. In the enterprise, that control dissolves. Incentives diverge. Workflows fragment. Coordination becomes optional.
SEO success requires alignment, but enterprise structures reward isolation. That mismatch creates what I call the accountability gap – the silent failure mode behind most large-scale SEO underperformance.
SEO Is Measured By The Team That Doesn’t Control It
SEO is the only business function I am aware of that, judged by performance, cannot be delivered independently. This is especially true in the enterprise, where SEO performance is evaluated using familiar metrics: visibility, traffic, engagement, and increasingly AI-driven exposure. The irony is that the SEO function rarely controls the systems that generate those outcomes.
Function
Controls
SEO Dependency
Development
Templates, rendering, performance
Crawlability, indexability, structured data
Content Teams
Messaging, depth, updates
Relevance, coverage, AI eligibility
Product Teams
Taxonomy, categorization, naming
Entity clarity, internal structure
UX & Design
Navigation, layout, hierarchy
Discoverability, user engagement
Legal & Compliance
Claims, restrictions
Content completeness & trust signals
Local Markets
Localization & regional content
Cross-market consistency & intent alignment
SEO depends on all of these departments to do their job in an SEO-friendly manner for it to have a remote chance of success. This makes SEO unusual among business functions. It is judged by performance, yet it cannot deliver that performance independently. And because SEO typically sits downstream in the organization, it must request changes rather than direct them.
That structural imbalance is not a process issue. It is an ownership problem.
The Accountability Gap Explained
The accountability gap appears whenever a business-critical outcome depends on multiple teams, but no single team is accountable for the result.
SEO is a textbook example as fundamental search success requires development to implement correctly, content to align with demand, product teams to structure information coherently, markets to maintain consistency, and legal to permit eligibility-supporting claims. Failure occurs when even one link breaks.
Inside the enterprise, each of those teams is measured on its own key performance indicators. Development is rewarded for shipping. Content is rewarded for brand alignment. Product is rewarded for features. Legal is rewarded for risk avoidance. Markets are rewarded for local revenue. SEO lives in the cracks between them.
No one is incentivized to fix a problem that primarily benefits another department’s metrics. So issues persist, not because they are invisible, but because resolving them offers no local reward.
KPI Structures Encourage Metric Shielding
This is where enterprise SEO collides head-on with organizational design.
In practice, resistance to SEO rarely looks like resistance. No one says, “We don’t care about search.” Instead, objections arrive wrapped in perfectly reasonable justifications, each grounded in a different team’s success metrics.
Engineering teams explain that template changes would disrupt sprint commitments. Localization teams point to budgets that were never allocated for rewriting content. Product teams note that naming decisions are locked for brand consistency. Legal teams flag risk exposure in expanded explanations. And once something has launched, the implicit assumption is that SEO can address any fallout afterward.
Each of these responses makes sense on its own. None are malicious. But together, they form a pattern where protecting local KPIs takes precedence over shared outcomes.
This is what I refer to as metric shielding: the quiet use of internal performance measures to avoid cross-functional work. It’s not a refusal to help; it’s a rational response to how teams are evaluated. Fixing an SEO issue rarely improves the metric a given department is rewarded for, even if it materially improves enterprise visibility.
Over time, this behavior compounds. Problems persist not because they are unsolvable, but because solving them benefits someone else’s scorecard. SEO becomes the connective tissue between teams, yet no one is incentivized to strengthen it.
This dynamic is part of a broader organizational failure mode I call the KPI trap, where teams optimize for local success while undermining shared results. In enterprise SEO, the consequences surface quickly and visibly. In other parts of the organization, the damage often stays hidden until performance breaks somewhere far downstream.
The Myth: “SEO Is Marketing’s Job”
To simplify ownership, enterprises often default to a convenient fiction: SEO belongs to marketing.
On the surface, that assumption feels logical. SEO is commonly associated with organic traffic, and organic traffic is typically tracked as a marketing KPI. When visibility is measured in visits, conversions, or demand generation, it’s easy to conclude that SEO is simply another marketing lever.
In practice, that logic collapses almost immediately. Marketing may influence messaging and campaigns, but it does not control the systems that determine discoverability. It does not own templates, rendering logic, taxonomy, structured data pipelines, localization standards, release timing, or engineering priorities. Those decisions live elsewhere, often far upstream from where SEO performance is measured.
As a result, marketing ends up owning SEO on the organizational chart, while other teams own SEO in reality. This creates a familiar enterprise paradox. One group is held accountable for outcomes, while other groups control the inputs that shape those outcomes. Accountability without authority is not ownership. It is a guaranteed failure pattern.
The Core Reality
At its core, enterprise SEO failures are rarely tactical. They are structural, driven by accountability without authority across systems SEO does not control.
Search performance is created upstream through platform decisions, information architecture, content governance, and release processes. Yet SEO is almost always measured downstream, after those decisions are already locked. That separation creates the accountability gap.
SEO becomes responsible for outcomes shaped by systems it doesn’t control, priorities it can’t override, and tradeoffs it isn’t empowered to resolve. When success requires multiple departments to change, and no one owns the outcome, performance stalls by design.
Why This Breaks Faster In AI Search
In traditional SEO, the accountability gap usually expressed itself as volatility. Rankings moved. Traffic dipped. Teams debated causes, made adjustments, and over time, many issues could be corrected. Search engines recalculated signals, pages were reindexed, and recovery, while frustrating, was often possible. AI-driven search behaves differently because the evaluation model has changed.
This is where the accountability gap becomes fatal. When even one department blocks or weakens those elements – by fragmenting entities, constraining content, breaking templates, or enforcing inconsistent standards – the system doesn’t partially reward the brand. It fails to form a stable representation. And when representation fails, exclusion follows. Visibility doesn’t gradually decline. It disappears.
AI systems default to sources that are structurally coherent and consistently reinforced. Competitors with cleaner governance and clearer ownership become the reference point, even if their content is not objectively better. Once those narratives are established, they persist. AI systems are far less forgiving than traditional rankings, and far slower to revise once an interpretation hardens.
This is why the accountability gap now manifests as a visibility gap. What used to be recoverable through iteration is now lost through omission. And the longer ownership remains fragmented, the harder that loss is to reverse.
A Note On GEO, AIO, And The Labeling Distraction
Much of the current conversation reframes these challenges under new labels GEO, AIO, AI SEO, generative optimization. The terminology isn’t wrong. It’s just incomplete.
These labels describe where visibility appears, not why it succeeds or fails. Whether the surface is a ranking, an AI Overview, or a synthesized answer, the underlying requirements remain unchanged: structural clarity, entity consistency, governed content, trustworthy signals, and cross-functional execution.
Renaming the outcome does not change the operating model required to achieve it.
Organizations don’t fail in AI search because they picked the wrong acronym. They fail because the same accountability gap persists, with faster and less forgiving consequences.
The Enterprise SEO Ownership Paradox
At its core, enterprise SEO operates under a paradox that most organizations never explicitly confront.
SEO is inherently cross-functional. Its performance depends on systems, processes, platforms, and decisions that span development, content, product, legal, localization, and governance. It behaves like infrastructure, not a channel. And yet, it is still managed as if it were a marketing function, a reporting line, or a service desk that reacts to requests.
That mismatch explains why even well-funded SEO teams struggle. They are held responsible for outcomes created by systems they do not control, processes they cannot enforce, and decisions they are rarely empowered to shape.
This paradox stays abstract until it’s reduced to a single, uncomfortable question:
Who is accountable when SEO success requires coordinated changes across three departments?
In most enterprises, the honest answer is simple. No one.
And when no one owns cross-functional success, initiatives stall by design. SEO becomes everyone’s dependency and no one’s priority. Work continues, meetings multiply, and reports are produced – but the underlying system never changes.
That is not a failure of execution. It is a failure of ownership.
They establish executive sponsorship for search visibility, shared accountability across development, content, and product, and mandatory requirements embedded into platforms and workflows. Governance replaces persuasion. Standards are enforced before launch, not debated afterward.
SEO shifts from requesting fixes to defining requirements teams must follow. Ownership becomes structural, not symbolic.
The Final Reality
This perspective isn’t theoretical. It’s grounded in my nearly 30 years of direct experience designing, repairing, and operating enterprise website search programs across large organizations, regulated industries, complex platforms, and multi-market deployments.
I’ve sat in escalation meetings where launches were declared successful internally, only for visibility to quietly erode once systems and signals reached the outside world. I’ve watched SEO teams inherit outcomes created months earlier by decisions they were never part of. And more recently, I’ve worked with leadership teams who didn’t realize they had a search problem until AI-driven systems stopped citing them altogether. These are not edge cases. They are repeatable organizational failure modes.
What ultimately separated failure from recovery was never better tactics, better tools, or better acronyms. It was ownership. Specifically, whether the organization recognized search as a shared system-level responsibility and structured itself accordingly.
Enterprise SEO doesn’t break because teams aren’t trying hard enough. It breaks when accountability is assigned without authority, and when no one owns the outcomes that require coordination across the organization.
That is the problem modern search exposes. And ownership is the only durable fix.
Coming Next
The Modern SEO Center Of Excellence: Governance, Not Guidelines
We’ll close the loop by showing how enterprises institutionalize ownership through a Center of Excellence that governs standards, enforcement, entity governance, and cross-market consistency, the missing layer that prevents the accountability gap from recurring.
Google’s John Mueller responded to a question about whether core updates roll out in stages or follow a fixed sequence. His answer offers some clarity about how core updates are rolled out and also about what some core updates actually are.
Question About Core Update Timing And Volatility
An SEO asked on Bluesky whether core updates behave like a single rollout that is then refined over time or if the different parts being updated are rolled out at different stages.
The question reflects a common observation that rankings tend to shift in waves during a rollout period, often lasting several weeks. This has led to speculation that updates may be deployed incrementally rather than all at once.
“Given the timing, I want to ask a core update related question. Usually, we see waves of volatility throughout the 2-3 weeks of a rollout. Broadly, are different parts of core updated at different times? Or is it all reset at the beginning then iterated depending on the results?”
Core Updates Can Require Step-By-Step Deployment
Mueller explained that Google does not formally define or announce stages for core updates. He noted that these updates involve broad changes across multiple systems, which can require a step-by-step rollout rather than a single deployment.
He responded:
“We generally don’t announce “stages” of core updates.. Since these are significant, broad changes to our search algorithms and systems, sometimes they have to work step-by-step, rather than all at one time. (It’s also why they can take a while to be fully live.)”
Updates Depend On Systems And Teams Involved
Mueller next added that there is no single mechanism that governs how all core updates are released. Instead, updates reflect the work of different teams and systems, which can vary from one update to another.
He explained:
“I guess in short there’s not a single “core update machine” that’s clicked on (every update has the same flow), but rather we make the changes based on what the teams have been working on, and those systems & components can change from time to time.”
Core Updates May Roll Out Incrementally Rather Than All At Once
Mueller’s explanation suggests that the waves of volatility observed during core updates may correspond to incremental changes across different systems rather than a single reset followed by adjustments. Because updates are tied to multiple components, the rollout may progress in parts as those systems are updated and brought fully live.
This reflects a process where some changes are complex and require a more nuanced step-by-step rollout, rather than being released all at once, which may explain why ranking shifts can appear uneven during the rollout period.
Connection To Google’s Spam Update?
I don’t think that it was a coincidence that the March Core update followed closely after the recent March 2026 Spam Update. The reason I think that is because it’s logical for spam fighting to be a part of the bundle of changes made in a core algorithm update. That’s why Googlers sometimes say that a core update should surface more relevant content and less of the content that’s low quality.
So when Google announces a Spam Update, that stands out because either Google is making a major change to the infrastructure that Google’s core algorithm runs on or the spam update is meant to weed out specific forms of spam prior to rolling out a core algorithm update, to clear the table, so to speak. And that is what appears to have happened with the recent spam and core algorithm updates.
Comparison With Early Google Updates
Way back in the early days, around 25 years ago, Google used to have an update every month, offering a chance to see if new pages are indexed and ranked as well as seeing how existing pages are doing. The initial first days of the update saw widescale fluctuations which we (the members of WebmasterWorld forum) called the Google Dance.
Back then, it felt like updates were just Google adding more pages and re-ranking them. Then around the 2003 Florida update it became apparent that the actual ranking systems were being changed and the fluctuations could go on for months. That was probably the first time the SEO community noticed a different kind of update that was probably closer a core algorithm update.
In my opinion, one way to think of it is that Google’s indexing and ranking algorithms are like software. And then, there’s also hardware and software that are a part of the infrastructure that the indexing and ranking algorithms run on (like the operating system and hardware of your desktop or laptop).
That’s an oversimplification but it’s useful to me for visualizing what a core algorithm update might be. Most, if not all of it, is related to the indexing and ranking part. But I think sometimes there’s infrastructure-type changes going on that improve the indexing and ranking part.
Where Part 1 told you where on a page AI looks, and Part 2 told you which pages AI routinely considers, this one tells you what AI actually rewards inside the content it reads.
The data clarifies:
Most AI SEO writing advice doesn’t hold at scale. There is no universal “write like this to get cited” formula – the signals that lift one industry’s citation rates can actively hurt another.
The entity types that predict citation are not the ones being targeted. DATE and NUMBER are universal positives. PRICE suppresses citation in five of six verticals, and KG-verified entities are a negative signal.
The one writing signal that holds across all seven verticals: Declarative language in your intro, +14% aggregate lift.
Heading structure is binary. Commit to the right number for your vertical or use none. Three to four headings are worse than zero in every vertical.
Corporate content dominates. Reddit doesn’t. AI citation behavior does not mirror what happened to organic search in 2023-2024.
1. Specific Writing Signals Influence Citation, While Others Harm It
While “The Science Of How AI Pays Attention” covers parts of the page and types of writing that influence ChatGPT visibility, I wanted to understand which writing-level signals – word count, structure, language style – predict higher AI citation rates across verticals.
Approach
I compared high-cited pages (more than three unique prompt citations) vs. low-cited across seven writing metrics: word count, definitive language, hedging, list items, named entity density, and intro-specific signals.
I analyzed the first 1,000 words for list item count, named entity density, intro definitive language token density, and intro number count.
Results: Across all verticals, definitive phrasing and including relevant entities matter. But most signals are flat.
Image Credit: Kevin Indig
What The Industry Patterns Showed
When splitting the data up by vertical, we suddenly see preferences:
Total word count was strongest in CRM/SaaS (1.59x).
Finance was an anomaly with word count: Shorter pages win (0.86x word count).
Definitive phrases in the first 1,000 characters were positive for most verticals.
Education is a signal void. Writing style explains almost nothing about citation likelihood there.
Image Credit: Kevin Indig
Top Takeaways
1. There is no universal “write like this to get cited” formula. For example, the signals that lift CRM/SaaS citation rates actively hurt Finance. Instead, match content format to vertical norms.
2. The one universal rule: open with a direct declarative statement. Not a question, not context-setting, not preamble. The form is “[X] is [Y]” or “[X] does [Z].” This is the only writing instruction that holds regardless of vertical, content type, or length.
3. LLMs “penalize” hedging in your intro. “This may help teams understand” performs worse than “Teams that do X see Y.” Remove qualifiers from your opening paragraph before any other optimization.
2. The Entity Types That Predict Citation Are Not The Ones Being Targeted
Most AEO advice focuses on named entities as a category: Pack in more known brand names, tool names, numbers. The cross-vertical entity type analysis below tells a more specific (and more useful) story.
Approach
Ran Google’s Natural Language API on the first 1,000 characters (about 200-250 words) of each unique URL.
Computed lift per entity type: % of high-cited pages with that type / % of low-cited pages.
Analyzed 5,000 pages across seven verticals.
* A quick note on terminology: Google NLP classifies software products, apps, and SaaS tools as CONSUMER_GOOD, a legacy label from when the API was built for physical retail. Throughout this analysis, CONSUMER_GOOD means software/product entities.
Results: DATE and NUMBER are the most universal positive signals. Interestingly, PRICE is the strongest universal negative.
Image Credit: Kevin IndigImage Credit: Kevin Indig
What The Industry Patterns Showed
DATE is the most universal positive signal, with the exception of Finance (0.65x).
NUMBER is the second most universal. Specific counts, metrics, and statistics in the intro consistently predict higher citation rates. Finance (0.98x) and Product Analytics (1.10x) mark the floor and ceiling of that range.
PRICE is the strongest universal negative. Pages that open with pricing signal commercial intent. Finance is the sole exception at 1.16x, likely because price here means fee percentages and rate comparisons, which are the actual reference data financial queries are looking for.
CONSUMER_GOOD (software/product entities) is mixed. In Healthcare, product entities signal established brands and tools. In Crypto, naming specific protocols and products is core to answering technical queries.
PHONE_NUMBER is a positive signal in Healthcare (1.41x) and Education (1.40x). In both cases, it is almost certainly a proxy for established brands/institutions/providers with real physical presence, not a literal signal to add phone numbers to your pages.
The Knowledge Graph inversion deserves its own note here:
The data showed that high-cited pages average 1.42 KG-verified entities vs. 1.75 for low-cited pages (lift: 0.81x).
Pages built around well-known, KG-verified entities (major brands, institutions, famous people) tend toward generic coverage, which isn’t preferred by ChatGPT.
High-cited pages are dense with specific, niche entities: a particular methodology, a precise statistic, a named comparison. Many of those niche entities have no KG entries at all. That specificity is what AI reaches for.
Top Takeaways
1. Add the publish date to your pages and aim to use at least one specific number in your content. That combination is the closest thing to a universal AI citation signal this dataset produced. But Finance gets there through price data and location specificity instead.
2. Avoid opening with pricing in non-finance verticals. Price-dominant intros correlate with lower citation rates.
3. KG presence and brand authority do not translate to an AI citation advantage. Chasing Wikipedia entries, brand panels, or KG verification is the wrong lever. Specific, niche entities (even ones without KG entries) outperform famous ones.
3. Heading Structure: Commit To One Or Don’t Bother
We know headings matter for citations from the previous two analyses. Next, I wanted to understand whether heading count predicts citation rates and whether the optimal structure varies by vertical.
Approach
Counted total headings per page (H1+H2+H3) across all cited URLs.
Computed high-cited rate (% of URLs that are high-cited) per bucket per vertical.
Results: Including more headings in your content is not universally better. The sweet spot depends on vertical and content type. One finding holds everywhere: Strangely, 3-4 headings are worse than zero.
Image Credit: Kevin Indig
What The Industry Patterns Showed
CRM/SaaS is the only vertical where the 20+ heading lift is confirmed: 12.7% high-cited rate at 20-49 headings vs. a 5.9% baseline. The 50+ bucket reaches 18.2%. Long structured reference pages and comparison guides with one section per tool outperform everything else here.
Healthcare inverts most sharply. The high-cited rate drops from 15.1% at zero headings to 2.5% at 20-49 headings. A page with 30 H2s on telehealth topics signals optimization intent, not clinical authority.
Finance peaks at 10-19 headings (29.4% high-cited rate). Structured but not exhaustive: think rate tables, regulatory breakdowns, and advisor comparison pages with moderate heading depth.
Crypto peaks at five to nine headings (34.7% high-cited rate). Technical documentation in this vertical tends toward dense prose with moderate navigation structure. Over-structuring breaks up the technical depth.
Education is flat across all heading counts, which is consistent with the writing signals finding. Heading structure explains almost nothing about citation likelihood in education content.
The three to four heading dead zone holds across every vertical without exception. Partial structure confuses AI navigation without providing the full benefit of a committed hierarchy.
Top Takeaways
1. The 20+ heading finding from Part 1 is a CRM/SaaS finding, not a universal one. Applying it to healthcare, education, or finance could actively suppress citation rates in those verticals.
2. The principle that holds everywhere: Commit to structure or don’t use it. The middle ground costs you in every vertical. A fully-structured page with the right heading depth outperforms a half-structured page in every vertical.
3. Use the optimal heading range for your vertical. Crypto: 5-9. Finance and Education: 10-19. CRM/SaaS: 20+ (with H3s). Healthcare: 0 or 5-9 at most. Long CRM reference pages with 50+ sections are the one case where maximum heading depth pays off.
4. UGC Doesn’t Dominate
The “Reddit effect” reshaped organic search between 2024 and 2025. I wanted to understand whether ChatGPT cites user-generated content (Reddit, forums, reviews) at meaningful rates or whether corporate/editorial content dominates.
The common industry assumption – that AI also preferentially cites community voices – is not what we found in the data.
Approach
Classified these cited URLs as (1) UGC: Reddit, Quora, Stack Overflow, forum subdomains, Medium, Substack, Product Hunt, Tumblr, or (2) community/forum prefixes or corporate/editorial by domain.
Computed citation share per category per vertical.
Dataset: 98,217 citations across 7 verticals.
Results: Corporate content accounts for 94.7% of all citations. UGC is nearly invisible.
Image Credit: Kevin Indig
What The Industry Patterns Showed
Finance is the most corporate-locked vertical at 0.5% UGC. YMYL (Your Money, Your Life) content appears to systematically suppress citations to community opinion.
Healthcare sits at 1.8% UGC for the same structural reason. Clinical, telehealth, and HIPAA content draws almost exclusively from institutional sources.
Crypto has the highest UGC penetration in the dataset at 9.2%. Community-generated content (Reddit technical threads, Medium tutorials, developer forum posts) answers a meaningful proportion of analyzed queries. In a fast-moving technical niche where official documentation consistently lags, community posts fill the gap.
Product Analytics and HR Tech sit at 6.9% and 5.8% UGC. Both are verticals where Reddit comparison threads and product review communities provide genuine signal alongside corporate content.
Top Takeaways
1. The “Reddit effect” in SEO has not translated proportionally to AI citations. In most verticals, reddit.com captures 2-5% of total citations. This finding is in line with other industry research, including this report from Profound.
2. For finance and healthcare: UGC has near-zero AI citation value. Invest in structured, authoritative corporate content with clear sourcing. Community engagement may matter for other reasons, but it does not contribute meaningfully to AI citation share in these verticals.
3. For crypto, product analytics, and HR tech: Community presence has measurable citation value. Detailed Reddit comparison threads, technical Medium posts, and structured developer forum answers can supplement corporate content reach.
What This Means For How You Strategize For LLM Visibility
Across all three parts of this study, the consistent finding is that AI citation is not primarily a writing quality problem.
Part 2 showed it is a content architecture problem: Thin single-intent pages are structurally locked out regardless of how well they’re written. This piece shows the same logic applies inside the content itself.
The aggregate writing signals table is the most important chart in this analysis. Not because it shows you what to do, but because it shows how much of what the AI SEO/GEO/AEO industry is telling you doesn’t survive cross-vertical scrutiny. Word count, list density, named entity counts … all flat or negative at the aggregate. The signals that work are vertical-specific and smaller than our industry’s consensus implies.
The meta-lesson from this analysis is that findings are vertical (and probably topic) specific, which is no different in SEO.
This part concludes the Science of AI – for now. Because the AI ecosystem is constantly changing.
Methodology
We analyzed ~98,000 ChatGPT citation rows pulled from approximately 1.2 million ChatGPT responses from Gauge.
Because AI behaves differently depending on the topic, we isolated the data across seven distinct, verified verticals to ensure the findings weren’t skewed by one specific industry.
Analyzed verticals:
B2B SaaS
Finance
Healthcare
Education
Crypto
HR Tech
Product Analytics
Featured Image: CoreDESIGN/Shutterstock; Paulo Bobita/Search Engine Journal
We’ve all seen it. Brands with healthy websites and excellent content have been watching their organic traffic from Google’s SERP erode for years. In a recent webinar hosted by Search Engine Journal, guest speaker Nikhil Lai, principal analyst of Performance Marketing for Forrester Research, estimated his clients are losing between 10 and 40% of organic and direct traffic year-over-year.
However, a stunning bright spot is this: Lai said referral traffic from answer engines is growing 40% month over month. Visitors arriving from those engines convert at two to four times the rate of traditional search visitors, spend three times as long on site, and arrive with queries averaging 23 words, compared to the three or four words that defined the last decade of search.
Lai asserted that the channel driving this shift deserves a seat at the CMO’s table. Answer engines influence brand perception before purchase intent forms, which makes answer engine optimization (AEO) a brand investment, and puts budget and measurement decisions at the CMO level.
Here is the strategic roadmap Lai laid out at SEJ Live. He highlighted the decisions, org structures, and measurement frameworks that will move AEO from a search team initiative to a C-suite priority.
Answer Engines Build Demand Before Buyers Know What They Want
Classic search captures intent that already exists. A user types “running shoes,” clicks a result, and evaluates options. Answer engines operate earlier and differently: users hold extended conversations with large datasets, rarely click through, and leave those sessions with specific brand associations formed across multiple follow-up questions.
A user who once searched “running shoes” now asks ChatGPT, “What’s the best shoe for overpronation with wide feet in cold weather on pavement?” They exit that conversation with a brand name in mind and search for it directly. Your brand appeared in an AI conversation before the user ever reached your site. Every day, demand generation is created from users’ research sessions.
The Forrester data Lai presented reinforces the quality of that exposure: Sessions on answer engines average 23 minutes, with users asking five to eight follow-up questions per session. Each turn is another brand impression. The click-through rate stays low; the conversion rate on the traffic that does arrive runs two to four times higher than search-sourced traffic, with stronger average order value and lifetime value.
Brand familiarity is built in answer engines before purchase intent crystallizes in the user’s mind.
SEO Is The Foundation Of AEO
The brands pulling back on SEO investment in response to AEO are making a costly mistake. Lai put it directly: 85 to 90% of current SEO best practices remain fully valid for answer engine visibility.
Google’s E-E-A-T framework (experience, expertise, authoritativeness, trustworthiness) still governs how quality is evaluated across every index. Site architecture, mobile load speed, structured data, and indexation hygiene all strengthen performance across every engine. Every alternative index (Bing’s, Brave’s) is benchmarked against Google’s for completeness. Every bot (GPTBot, Claudebot, Perplexitybot) is benchmarked against Googlebot for sophistication.
SEO is the infrastructure on which AEO runs. The shift is an expansion of scope and emphasis, but AEO is not a replacement of SEO fundamentals.
What changes is where additional effort goes: natural-language FAQ optimization, off-site authority building, pre-rendering for less sophisticated bots, and a measurement framework built around share of voice rather than click volume.
Bing Is Now Your Distribution Network For Every Non-Google Engine
Most answer engines outside Google draw primarily from Bing’s index.
Bing evaluates credibility by weighting what others say about your brand more heavily than what your own site claims. This explains why Reddit threads, Quora answers, Wikipedia entries, G2 reviews, YouTube videos, and Trustpilot pages dominate AI-generated answers. The off-site web has become the primary source of record for how AI describes your brand.
The immediate tactical implication: Push every sitemap update directly to Bing via the IndexNow protocol. This triggers Bingbot to crawl fresh content and feeds that content into Perplexity, ChatGPT, and the broader answer engine ecosystem faster than waiting for organic discovery.
Bing’s index remains the fastest route to non-Google answer engine visibility. Perplexity is building its own index (Sonar), and OpenAI has signaled plans to build or acquire one, but Bing is the distribution network that matters today.
AEO Requires Cross-Functional Ownership
AEO arguably spans more functions than SEO, with these three in common with SEO: content, web development, and paid search. AEO also more strongly interfaces with PR, brand marketing, and social media.
PR earns a seat because off-site authority outweighs on-site signals in AEO. Brand mentions in publications, influencer mentions, and third-party reviews all directly shape how answer engines describe your brand.
Social belongs in the room because Reddit threads and Facebook group discussions show up in AI-generated answers. Community management and reputation management, previously handled separately from SEO, are now integral to AEO. When your social listening data reaches content teams before they draft, the content responds to the questions buyers are actually asking. When it doesn’t, you’re optimizing for questions nobody asked.
Lai proposed two organizational models that work to capture the opportunities inherent in AEO:
Center of Excellence: A senior SEO specialist evolves into an AEO evangelist, runs a COE, and publishes cross-functional standards: clear rules like “every piece of content must answer these five questions” or “every page must include author schema.”
AI Orchestrator: A dedicated hire who builds agents to handle repeatable AEO tasks (schema implementation, JavaScript reduction, FAQ content creation) and governs the cross-functional workflow with published guidelines for all stakeholders.
The CMO’s decision is which model fits the organization’s scale, and whether to build it internally or partner with an agency that has already built the infrastructure.
The Content Strategy That Wins In AI Responses
Long-form skyscraper content is an ancient relic. Answer engines reward precise, specific answers to real questions, delivered succinctly and across multiple formats. Lai framed this as Forrester’s question-to-content framework: Every piece of content maps directly to a FAQ being asked on answer engines, including the follow-up questions that emerge within a single session.
Five content moves that produce results:
Build surround-sound FAQ coverage. Create glossaries, FAQ pages, videos, and blog posts that address the same topic cluster from different angles. When Claudebot crawls 38,000 pages for every referred page visit (per Cloudflare data), each page it indexes is an opportunity to signal topical authority. Volume and variety matter.
Publish direct competitor comparisons. Users ask answer engines to compare brands. Brands that create honest, data-backed comparison guides are gaining prominent visibility, because they directly answer the queries being asked that pit a brand against its competitors. This was once a taboo content format; it has become a competitive requirement.
Treat off-site syndication as the new backlinking.Hosting AMAs on Reddit, answering questions on Quora, and contributing to industry publications that rank in AI responses all earn the off-site authority that answer engines weigh most heavily. Give third-party voices data and perspective they couldn’t generate themselves, and they will produce mentions that shape how AI describes your brand.
Pre-render pages for bot access. The bots crawling your site lack the compute budget to render JavaScript-heavy pages. Claudebot’s 38,000:1 crawl-to-referral ratio compared to Googlebot’s 5:1 ratio reflects this sophistication gap. Pre-rendering a JavaScript-free version for bots while serving the full experience to human visitors ensures your content gets indexed across every engine. Over time, limit the amount of JavaScript on site. Have content directly in HTML so bots can understand your content, and index it more often. The more you’re crawled and indexed, the more visible you become.
Create unique content. Lai said, “Being distinctive, differentiated, and unique will help your brand stand out in a sea of sameness. Implicit in all this is that you need a lot more content, greater content velocity and diversity, which means you can use AI to create content. Google won’t automatically penalize AI-created content unless it lacks the watermarks of human authorship. The syntax and diction have to be natural. Use AI to create content, but don’t make it seem AI-generated. Get down into the details. It’s not enough to say your product is great. Explain why in different temperatures, conditions, the thickness, and so on, to satisfy long-tail intent.”
Replace Legacy KPIs With Metrics That Predict Market Share
The internal conversation, Lai said, he hears most from Forrester clients: “The hardest part of this transition from SEO to AEO has been trying to convince management to not focus as much on CTR and traffic. Those were indicators of organic authority. They are no longer reliable indicators.
“The new KPIs to focus on are visibility and share of voice. Share of voice can be measured in many ways. The most common are citation share: how often is my brand cited, how often is my content linked, of the opportunities I have to be cited; and mention share: how often is my brand mentioned of the opportunities I have to be mentioned. I’m also seeing more clients look into citation attempts: how often is ChatGPT trying to cite my content, and are there things I can do on the back end of my site to make that citation attempt score go up? Those are the new indicators of authority,” said Lai.
These metrics connect directly to branded search volume, which Lai called “the single strongest leading indicator of market share growth.” The chain of logic to present to the board: higher citation and mention share drives more branded searches, which converts at higher rates, which compounds into measurable market share gains against competitors.
Lai said he expects Google to add citation metrics to Search Console once AI Max adoption reaches critical mass, and an OpenAI Analytics product before year-end.
For now, Lai suggested, the best course of action is to establish a baseline with your current SEO platform and track the directional trend. Lai contended that, to address concerns of accuracy within today’s popular SEO tools of answer engine mentions, even imperfect measurement reveals which content clusters are earning citations and which need rebuilding.
The Agentic Phase Starts The Clock On B2B Urgency
Answer engines are moving from conversation to action. The current phase, characterized by extended back-and-forth with large datasets, is the warm-up. The agentic phase is defined by engines’ booking, filing, researching, and purchasing on users’ behalf. This will mean fewer clicks, longer sessions, and richer intent signals available to advertisers.
For B2B CMOs, the urgency is immediate. Forrester research shows GenAI has already become the number one source of information for business buyers evaluating purchases of $1 million or more, coming in ahead of customer references, vendor websites, and social media. Your largest deals are being influenced by AI conversations before your sales team enters the picture.
AEO visibility in B2B is a current-pipeline variable that requires immediate attention.
The brands building complete search strategies now, covering answer engines, on-site conversational search, and structured data across every indexed channel, will own discovery and have greater control over brand perception in the next phase of buying behavior.
The window to gain an early-mover competitive advantage is shrinking, before AEO visibility becomes just another standard expectation everyone has to meet.
Key Takeaways For CMOs
Reframe the traffic story. Lower overall traffic volume paired with two-to-four-times higher conversion rates is a net performance gain. Build that case proactively before your CEO draws the wrong conclusion from a falling traffic chart.
Fund AEO as an upper-funnel brand channel. That means applying the same budget logic, measurement frameworks, and executive ownership you would bring to any major brand awareness investment, where success is measured in visibility, perception, and long-term share of voice rather than clicks and conversions.
Move to share-of-voice KPIs. Citation share and mention share drive branded search volume, which drives market share. Make that causal chain visible to your leadership team.
Assign cross-functional ownership with clear governance. Choose between a center of excellence or an AI orchestrator model and make that structural decision this quarter.
Prioritize off-site authority as a content strategy responsibility. Reddit, Quora, third-party publications, and YouTube shape AI’s perception of your brand. PR and social teams own the channels that matter most for AEO.
Push every sitemap update to Bing via IndexNow. Bing’s index feeds most non-Google answer engines. This is a 15-minute technical change with compounding distribution benefits.
Use AI to help with content, but always apply human editing for authority. Content that reads as machine-generated loses trust across every engine, including Google.
What Does A Smart CMO Do Next?
Start with a 90-day experiment using some or all of these strategies.
Audit your current citation and mention share in one category using your existing SEO platform. Identify three high-intent FAQ clusters where your brand should be visible and build surround-sound content for each: a dedicated FAQ page, a comparison guide, and one off-site piece in a publication that appears in AI responses. Push fresh sitemaps to Bing. Track citation share and branded search volume at 30, 60, and 90 days.
The data may make the investment case for broader rollout. If not, tweak your approach. The brands moving first will capture the highest-quality traffic at the lowest incremental cost, and set the citation baseline that becomes progressively harder for competitors to close.
Traditional search engine optimization is fundamental to visibility on generative AI platforms.
Large language models query Google to research topics and find answers. Thus low or unranked pages are largely invisible to ChatGPT, Perplexity, Gemini, and others.
Here are the top SEO tactics to elevate genAI mentions and citations.
Keyword research
To date, genAI platforms provide no prompt data. We have no definitive info on how consumers discover brands or products on those platforms.
Keyword research remains the primary source for how online consumers decide to buy. Third-party tools can organize keywords by intent, offering clues for targeting prospects at every step of their research.
Keyword gaps identify what’s missing on a site to attract would-be customers.
Prompts are longer than traditional search queries and, anecdotally, wildly unpredictable. Yet higher-level keyword optimization informs content and landing pages that cater to shoppers’ needs.
Optimized content
The best ecommerce content explains how a merchant’s products help consumers address needs and solve problems.
It may generate less traffic than a few years ago, but it remains essential for product discovery. Focusing on “bottom-of-the-funnel” queries (a common recommendation from “GEO experts”) leads to fewer new customers.
Yes, LLMs may summarize your content and include it in an answer without referring to your company. But the content will be part of that answer as a trusted LLM solution provider, foretelling potential future recommendations.
Optimizing for buying journeys, then, includes keywords to understand shoppers’ desires and relevant content for search and LLM bots to find solutions.
Site architecture
Horizontal site architecture (pages aren’t buried) and internal links ensure bot crawlability and long-tail ranking opportunities.
Clear architecture helps LLMs understand a business and correctly place its products in the training data.
Optimized site navigation is:
Structured for humans and LLM agents to find what they need quickly.
Usable without JavaScript and accessible with all web browsers.
Focused on a site’s most important sections and key benefits.
Link building
LLMs’ use of authority signals, such as backlinks, remains unclear. Nearly a year ago I speculated on Reddit that Gemini and AI Mode rely on PageRank at least indirectly. Whether that includes backlinks, however, is a mystery.
Yet backlinks, brand mentions, and co-citations are important for LLM visibility:
Google published a blog post on a new breakthrough in vector search technology called TurboQuant. The potential implications of this technology for Search are staggering!
TurboQuant is a suite of advanced algorithms that drastically reduce AI processing size and memory requirements. Their blog post says, “This has potentially profound implications … especially in the domains of Search and AI.”
Let’s talk about how TurboQuant works, and then I’ll share thoughts on how this will open the door for more AI Overviews, more personalized AI, instantaneous indexing, greatly increased ability to present searchers with content that meets their needs, and massive progress in AI use in both agents and the physical world.
How TurboQuant Works
TurboQuant is a technique that dramatically speeds up the process of building vector databases. The abstract of the TurboQuant paper tells us that not only does this method outperform existing methods for vector search, but it also reduces the time needed to build an index for vector search to “virtually zero.”
Image Credit: Marie Haynes
To understand how this works, we first need to understand vector embeddings, vector search, and then vector quantization.
Vector Embeddings
If you are new to understanding vectors and vector search, I would highly recommend this video by Linus Lee. He explains how text embeddings work.
Essentially, vector embedding is a way to take text (or images or video) and turn it into a series of numbers. The numbers encode the semantic meaning and relationship of words or concepts. It really is so amazing. If you have time, I would highly encourage you to read Google’s Word2Vec paper from 2013 or, better yet, paste the URL into the Gemini app, choose “guided learning” from the tool menu, and ask Gemini to walk you through it. It blew my mind to learn about how math can be done on vector embeddings. Because words are mapped in the vector space based on their context, you can actually do math with them.
In the paper, Google says that if you take the vector for King and subtract the vector for Man, then add the vector for Woman, you end up almost exactly at the vector for Queen.
Image Credit: Marie Haynes
Wow.
Vector Search
Now that we know that words and concepts can be mapped as mathematical coordinates, vector search is simply the process of finding which points are the closest to each other. Let’s say I am searching in a vector space for the query, “how to grow super spicy peppers in a backyard.” A traditional search engine hunts for text containing those exact words. With vector search, that query would be embedded in a vector space. Content in that space that is semantically similar to the query and the concepts embedded within will appear nearby in the vector space.
I’ve demonstrated this below in a two-dimensional space, but in reality, this space would have far more dimensions than our brains can comprehend.
Image Credit: Marie Haynes
Vector Quantization
Vector search is incredibly powerful, but there is a catch. Vector search in a space with multiple dimensions consumes vast amounts of memory. Memory is the bottleneck for nearest neighbor searches, which are used by the parts of Google Search that use vector search. This is where vector quantization comes in. Essentially, vector quantization is a mathematical technique used to reduce the size of these massive data points. It compresses the vectors, kind of like an ultra-efficient zip file.
The problem with vector quantization, though, is that when you compress the data, it degrades the quality of the results. Also, vector quantization adds an extra bit or two to every block of data, which adds to the load of memory required to do the calculations – defeating the point of compressing the data!
How TurboQuant Solves The Memory Problem
TurboQuant takes a large data vector and compresses it by rotating the vector in a way that simplifies its geometry. This step makes it easier to map the values into smaller, discrete sets of symbols or numbers to each part of the vector individually. It’s similar to JPEG compression and allows the system to capture the main concepts of the original vector but uses much less memory.
The problem with this type of compression, though, is that it can introduce hidden errors. The TurboQuant system uses something called QJL to mathematically error-check the tiny mistakes left behind, using just one bit of memory. The result is that the new vector is a fraction of its original size, but maintains the same accuracy, allowing AI to process information much faster.
I put the paper and Google’s announcement on TurboQuant into NotebookLM and asked it to simplify the explanation for me:
“To understand how Google’s TurboQuant fixes this memory bottleneck, imagine trying to pack thousands of awkwardly shaped items – like spiky lamps and rigid chairs – into a moving truck. Traditional compression simply crushes the items to make them fit, which damages them and, in the case of data, leads to bad search results.
TurboQuant does something entirely different. Instead of crushing the data, it mathematically spins and reshapes these massive, awkward vectors into identical, perfectly smooth cubes so they can be easily packed. To fix any minor scratches caused by this reshaping, it applies a metaphorical piece of “magic tape” – a single bit of data – that restores the item to its perfect, original condition.”
That’s still a little confusing. If you want to go deeper here, I had NotebookLM make a video to explain it further:
You don’t need to understand the exact processes used for TurboQuant, but rather, know that it makes it possible to assemble a vector embedded space and do vector search really quickly and with large amounts of data.
What Does TurboQuant Mean For Search?
What we’ve learned so far is that vector search across large amounts of data is slow and inaccurate, but TurboQuant makes it faster and accurate. The TurboQuant paper says that the technique reduces the time to index data into a vector space to “virtually zero”.
When I read this, I thought of Google engineer Pandu Nayak’s testimony on RankBrain in the recent DOJ vs Google trial.
(Fun fact: When RankBrain was introduced, Danny Sullivan, writing for Search Engine Land, said that Google told him it was connected to Word2Vec – the system for embedding words as vectors. Here is the 2013 Google blog post on learning the meaning behind words with Word2Vec.)
In the trial, Nayak said that traditional search systems are used to initially rank results, and then RankBrain was used to rerank the top 20 to 30 results. They only ran it across the top 20-30 results because it was an expensive process to run.
Image Credit: Marie Haynes
I think that TurboQuant changes this! If TurboQuant reduces indexing time to virtually zero, and drastically cuts the memory required to store massive vector databases, then the historical cost of running vector search across more than 20 or 30 documents completely vanishes.
TurboQuant makes it possible for Google to run massive-scale semantic search.
We may see all or some of the following happen:
Truly Helpful And Interesting Content That Meets The User’s Specific Needs And Intent May Be More Easily Surfaced
Google uses AI to understand what a searcher is really trying to accomplish and then again uses AI to predict what they are going to find helpful. TurboQuant should make that second step much faster and allow for more choices to be included in the vector space that AI draws from for its recommendations.
I know what you’re thinking. If AI Overviews answer the question, why would I create content for it? This is really the subject of a separate article, but to sum up my thoughts, I believe that some types of content are no longer beneficial to make, especially if that content’s main strength is to organize the world’s information. If you can create content that people truly want to engage with over an AI answer, then you have gold on your hands. It can be done! I mean, you’re reading this article right now, right?
We May See More AI Overviews
I know this will not be a popular thing for many. From the user’s perspective, however, AI Overviews are becoming more helpful. TurboQuant should allow Google to gather the information that could be helpful in answering a user’s question, even a complicated one, and then instantly produce an AI-generated answer.
Personalized Search Will Become Even More Powerful
TurboQuant should make it even easier for Google to become a highly personalized, real-time AI assistant as it can create searchable vector spaces loaded with your personal history. (I am reminded of DeepMind CEO Demis Hassabis’ post in which he laid out Google’s plans to build a universal AI assistant.)
The Capabilities Of Agentic Systems Will Drastically Improve
Agents are heavily limited by their context windows and how slowly they retrieve information. With TurboQuant, an AI agent will have boundless, perfectly recallable long-term memory. It will be able to instantly search every interaction, document, email, and preference you have shared with it in milliseconds. And, it will be able to communicate massive amounts of information with other agents. The implications are too many to grasp!
Vision-Powered Search (Soon On Glasses) Will Be Even More Helpful
The vast amount of visual data you see via AI glasses or Gemini Live will be able to be converted into a vector space. Also, this week, Search Live expanded globally.
Your glasses will be a powerful visual memory layer for you. Hey Gemini … where did I leave my keys?
Other tech that relies on gathering data from the real world (like Waymo and other self-driving cars, for example) will become smarter and faster.
Robots Will Become Much More Capable
Right now, if you put a robot in my living room and asked it to tidy, it would be overwhelmed by an overwhelming number of objects and trying to understand their semantic context and what to do with each of them. I expect TurboQuant to make it so that robots will be much smarter and capable. (Did you know that Google DeepMind recently partnered with Boston Dynamics?) I think robotics progress will speed up dramatically because of TurboQuant.
What Do We Do With This Information As SEOs?
We were discussing TurboQuant in my community, The Search Bar, and one of the members asked how this changes our jobs as SEOs. I think it does not change much for those of us who are focused on thoroughly understanding and meeting user intent over tricks or technical improvements.
For some businesses, there will be more incentive to create in-depth, truly helpful content. For others, though, especially those whose business model involves curating the world’s information, TurboQuant will likely make it so that you lose more traffic as AI Overviews will satisfy searchers who used to land on their site.
You may find this Gemini Gem helpful. I have put several documents, including the one that you are reading now, into the knowledge base. It will brainstorm with you and help you determine if your current business model is likely to be impacted as AI changes our world. It will also help you dream of what you can do to thrive.
My prediction is that we will see another core update soon. Well, Google launched the March 2026 core update before I could get this article out!
It would not surprise me if TurboQuant is introduced into the ranking systems.
Last year, I speculated that Google’s vector search breakthrough MUVERA was behind the changes we saw in the June 2025 core update. Some folks said, “But Marie, you can’t publish a breakthrough and then implement it into core ranking algorithms within a week.” What they missed was that Google’s announcement of MUVERA came a full year after they published the original research paper. It turns out that the same is true of TurboQuant. They published the blog post announcement in March of 2026, but the original paper was published in April of 2025. They have had loads of time to improve upon their AI-driven ranking systems.
If TurboQuant is a part of the March 2026 core update, then we will see Google have more ability to do semantic search across hundreds of possible results, providing searchers almost instantly with accurate and helpful information. If true, then there will be even less reliance on traditional SEO factors like links and SEO focused copy.
Demis Hassabis has predicted AGI (Artificial General Intelligence that can do anything cognitive that a human can) will be reached within the next 5 to 10 years. When asked this question, he almost always says that a few more breakthroughs in AI will be needed for us to get there. I believe that TurboQuant is one of those!
TurboQuant makes it much easier, cheaper, and faster for Google to do the intense computation required for AI. Amazingly, this was predicted by Larry Page many years ago.
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