If you pull a keyword report for the term “network segmentation” it will show you 2.9k searches a month. The old SEO model started by building a page optimized for the term, getting to the top spot, and letting the traffic role in. Mission accomplished. That was the prevalent model held for fifteen years, and many marketing teams still run on it.
Even though cumulative search volume is on the rise, that model just isn't working anymore for effective SEO all because the original “2.9k” wasn’t what it seemed. It was always 2.9k people who had all been trained to flatten what they actually wanted into the two or three words in order to get the results they were looking for. Now AI search lets people ask a question in their own words and now that single 2.9k number splits into 20 or 30 distinct intents. It may be a CISO asking how segmentation reduces ransomware blast radius, a mid-market IT lead asking whether they can segment without ripping out their existing firewalls, or a compliance manager asking which framework requires it all. And previously they all might have simply searched “network segmentation.”
Keyword volume was always a proxy for demand, and it was never a great one at that. We tolerated the imperfect data because the search box forced everyone to round their question to the same handful of phrasings, so the proxy stayed roughly stable. AI search removed the rounding and added personalization to boot.
The search volume still tells you demand exists but it no longer tells you how anyone is actually asking.
If your target keyword is really 40 different intents, then “rank #1 for network segmentation” should stop being the primary objective (with the obvious caveat that a huge factor for showing up in AI search is showing up in SEO).
The goal should be to show up across a representative sample of the intents that volume is hiding. To cover the spread well enough that whichever way a real buyer asks, your brand is in the answer. That coverage can be achieved through larger, more comprehensive articles or guides, but we’re seeing more success with smaller and more direct content mapped to a hub and spoke model.
The big issue is that an AI answer is not stable like KW rankings typically are. Answers will vary with every search, every platform, and even between models on the same platform. If you track a single prompt and watch your “spot” in the response, you’re just measuring noise.
The current play seems to be to group related prompts into clusters (intent, funnel stage, product line) and read the aggregate over time across the most commonly used AI search platforms for your audience specifically. Then you should watch for changes over time, not focus too much on a single prompt.
If volume no longer tells you what people want, it becomes important to find other sources that do. Some areas we’re exploring and have found success include filtering search console queries, exploring forums and community threads like Reddit and Quora, customer interview, commonly asked questions or pain points from sales calls, expanding on concepts on the most visited and engaged with pages, and other internal data (support tickets, sales-call transcripts, site search, successful ad copy).
To be clear, I’m not advocating the industry stop using volume data and KW research entirely. We’ll continue to use it at 97th Floor. But we won’t treat KW research as a content calendar. It’s a market-validation signal and the work of deciding what to build comes from the real audience signals mentioned above.
Keywords aren't the only data worth rethinking, attribution is too.
If you've logged into Google Search Console over the last couple of weeks and spotted a new "Generative AI" option, you're not imagining things. Google has started quietly rolling out a dedicated report that shows how your site shows up in its AI-powered search experiences (think AI Overviews and AI Mode). We caught wind of it internally and had our team dig through client accounts to see who has access, where it lives, and what it actually shows. Here's the rundown.
The new report lives inside the existing Performance section of Search Console and gives you visibility into impressions your site is generating specifically from Google's generative AI search surfaces — separate from traditional Search results and Discover.
For an industry that's spent the last year and a half asking "how do we even measure AI visibility?", this is a meaningful first step. It's the clearest signal yet that Google intends to treat generative surfaces as their own reportable channel, not just a footnote inside classic search results.
Here's where it gets a little messy: the report isn't showing up in the same place for every property. Our team found it living in two different spots depending on the account:
Some accounts have it as its own top-level report directly under Performance, sitting alongside Search results and Discover:
Other accounts have it nested a level deeper, tucked under Performance > Search results as a sub-report:
If you go looking and don't see it right away, check both locations before assuming your property hasn't gotten it yet.
True to form for a Google beta rollout, access is inconsistent right now. Across our client portfolio, some team members are seeing it on roughly half their accounts, others on fewer than half, and a few of us have it on almost none. There's no obvious pattern yet by industry, site size, or traffic volume. It genuinely looks like a staggered rollout rather than an eligibility-based one. If you or your clients don't have it, don't worry just yet. It's likely just a matter of time.
Before you get too excited about a new dashboard to obsess over, temper expectations on what it actually reports. Right now, the metric available is impressions only. This lines up with Google's own announcement of the report on the Search Central blog, and our team confirmed directly with Google that impressions are the only metric available for now — so it's not a bug or a data delay on our end.
That's a frustrating limitation. Impressions alone tell you that you're being surfaced, but not whether that visibility is translating into anything a client can act on or attribute value to. We'd expect (or at least hope) that Google expands this over time, but for now, treat it as a directional signal rather than a full performance metric.
We'll keep monitoring the rollout and report back as Google adds more to this feature. In the meantime, go check your Search Consoles, you might have new data waiting for you.
Remember when brand visibility mostly meant ranking on page one? This was back when readers had to click on pages to get the info they were after and AI was relegated to science fiction. It was a simpler time.
Not necessarily better… but certainly more straightforward.
Now your brand can show up in an AI-generated answer, get cited from a page you forgot existed, lose ground to a competitor in a recommendation list, or influence a buying decision without the user ever touching a traditional blue link. Search has become a kind of interpreter or paraphraser, applying artificial intelligence to pull information from pages and present it to the user in a (hopefully) clear and accurate way. The result is that more than half of online searches are zero-click. And when Google cuts out the middlebot, it changes what marketers need to be watching.
What I'm trying to say is that if you want to know how to track brand mentions in AI search results, you need to widen your gaze. SEO no longer begins and ends with ranking. It now extends to questions like "Does AI mention us?" "Does it cite us, and does AI talk about the brand in a positive or negative sentiment?" "Which pages does it pull from?" "How often do we appear compared to competitors?" And "Does any of this turn into actual traffic, leads, or revenue?"
Tracking brand mentions in AI search means monitoring when and how AI-driven platforms reference your brand in generated answers, recommendation lists, summaries, and cited sources.
But here’s the thing: AI search does not behave like classic search. Google’s AI features (for example) can generate overviews that summarize a topic and link users to a range of sources, while Bing now offers AI performance reporting tied to how sites are cited across Copilot and related experiences. Google also makes clear that AI Overviews and AI Mode still rely on essentially the same fundamental search requirements as the traditional approach.
So yes, rankings are still important. However with AI search, simply ranking for a keyword with one page is not the end goal, there’s more to it:
For example, a brand can show up in an AI answer even when it is not the top traditional ranking. Or a page can get cited because it answers a narrow question clearly. A competitor might get mentioned because information across multiple pages for things like reviews, product info, or comparisons are easier for AI systems to synthesize.
The point is that the future of search will remain search. It has just become more conversational, more layered, and a little more expansive.
This distinction is one of the biggest places marketers get tangled up. So let’s be direct:
Which one do you want? Trick question, obviously; you want them both.
A mention can be flattering and still impossible to measure well. An owned citation can be less glamorous, but far more useful because it gives you something concrete to inspect. It's your page, and your site's analytics that can be measured and analyzed. Which page got referenced? How often? Did it receive traffic? Did users do anything useful after landing there? This is first-party data on the impact of AI search — which is more valuable than any AI search tracking tool, all of which are synthetic databases, or good guesses as to where and how you are showing up.
Or, think of it this way:
Google's documentation around AI features focuses heavily on how content becomes eligible for inclusion and how traffic from AI experiences is counted inside Search Console reporting. That suggests that source-level analysis should be part of the process.
OK. Let’s move beyond the academic: AI mentions, AI citations, cited URLs… does it all matter?
Yes. Unequivocally yes. Here’s why:
People are asking longer questions, more specific questions, and plenty of follow-up questions. Google has explicitly said AI search experiences are pushing usage in that direction, with users exploring more complex queries and broader source sets.
That means discovery is no longer confined to obvious high-volume keywords. Someone may find your brand while asking for the best agencies for AI SEO, the top platforms for generative engine optimization, tools similar to your product but better for mid-market teams with limited technical support and a weirdly aggressive CFO, etc., etc., etc...
The path from question to brand discovery is less clean and a lot less predictable. Measurement has to adjust to account for it.
AI assistants were built to assist, and that goes beyond just summarizing informational content. They can compare vendors, recommend providers, shortlist software, explain product categories, and shape buyer impressions before a click ever happens. As such, when a platform includes your brand in a recommendation set, you’ve already entered the buyer’s consideration stage — whether or not they ever visited your site.
And that’s great! It can also be unsettling.
If you’re going to let an opaque machine send potential customers to your virtual door, you’d better be paying close attention to how often it’s doing so, and on what terms. Otherwise, you’re letting the robot make your brand positioning decisions for you.
When your brand appears in AI-generated answers, it can function as a form of borrowed trust. Users are beginning to treat AI responses as synthesized expertise. But those answers are only as good as the sources underneath them.
You should not confuse that with permanent authority. AI can be fickle, inconsistent, and occasionally wrong (and when it gets something wrong, it does so with supreme self confidence). Still, repeated inclusion shapes perception, and perception has a funny way of becoming influence.
If you’ve been in marketing for more than a few weeks, you’re probably already familiar with a tidy set of traditional metrics. You could follow rankings, traffic, click-through rates, and conversions, then build your strategy from there.
AI search adds some new layers to that picture by introducing answer inclusion, source citations, prompt visibility, and recommendation presence — all of which are signals worth tracking. That’s part of what makes AI search engine optimization a meaningful extension of the modern search strategy.
Brand visibility can show up in several kinds of AI-driven experiences. So, if you want to know where and how your brand is surfacing, you need to understand the environments in which those mentions appear:
Before you run off to buy seventeen subscriptions, start with the native data from the platforms (Google Search Console, GA4, Bing Webmaster Tools, etc.) themselves. That’s usually the best place to get a baseline view of how your site is appearing and performing.
Again, Google’s official guidance states that AI feature traffic, including AI Overviews and AI Mode, is included in Search Console’s Performance reporting for web search. It is not a perfect dedicated AI visibility dashboard, but it is still one of the best sources for understanding how your pages perform across Google search experiences.
Look at:
GA4 helps you connect visibility to behavior. Once users arrive on cited or AI-visible pages, what do they do? Do they engage? Bounce? Convert? Wander around aimlessly?
Even better, Google has added "AI Search" as a primary channel in GA4's default channel grouping. That means sessions arriving from AI search experiences can now be broken out and analyzed alongside your other acquisition channels — no custom regex gymnastics required.
Without that layer, you are measuring attention without taking outcome into account.
Bing’s AI Performance reporting adds a very useful angle. Microsoft says the report shows how your site’s content is used in AI-generated answers across Copilot and partner experiences, including cited pages and changes over time. This makes it one of the clearest native examples of AI citation tracking from a platform owner. Yeah, from Bing.
If you only track how often your brand name appears, you will end up with a very incomplete picture. AI visibility is bigger than that. Your measurement approach needs to be bigger as well.
So, in addition to the tried-and-true standards, what should you also be tracking?
Visibility without outcome is like getting dressed up to sit on the couch — you might look good, but you’re still not going anywhere. Tie cited or visible pages back to business performance by tracking sessions, engagement, leads, or conversions wherever possible. That kind of connection is what keeps AI visibility from turning into a vanity metric, and is increasingly central to evolving SEO strategies.
A practical tracking list might look like this:
The list of things worth tracking in AI search is longer than it used to be, but it's not unmanageable. Once you understand the difference between mentions and citations, know which environments your brand can surface in, and have a clear set of metrics tied to real business outcomes, you've built the foundation for a modern visibility strategy. Rankings still matter — they've just been joined by answer inclusion, citation sources, share of voice, and prompt visibility.
Of course, knowing what to track is only half the equation. The next step is actually doing it: building prompt libraries, running tests across platforms, monitoring citations, and optimizing the pages AI already trusts. (We cover all of that in our companion guide on how to track brand mentions and citations in AI search.)
And if you'd rather have a partner in your corner, 97th Floor can help you measure and improve your brand's presence in AI search. Contact us to see how we can help you strengthen your search visibility today… and as AI continues to revolutionize the landscape for years to come.
Every week it feels like something new is being pushed on LinkedIn or Reddit as the next must-have for AI Search Optimization. Schema, FAQs, key takeaways, Markdown, markup, EntityMap, and now llms.txt, and it's a lot. The pressure from leadership to produce results, combined with the fear of missing the one thing that unlocks perfect AEO/GEO, is keeping digital marketers up at night (myself included).
One I've heard a lot about recently is llms.txt. There's real misinformation circulating around it, and it's spread well beyond the SEO world to people who don't fully understand what it means or does. I've personally heard it pitched by a non-SEO consultant as the magic tool that would help their client dominate the competition in LLMs.
So let me be clear: llms.txt is not a magic SEO or GEO bullet. It is not a guaranteed citation or mention in AI.
It is, however, a useful tool and one worth understanding now, even if you're not ready to implement it yet.
If you've seen llms.txt compared to robots.txt, that's not wrong and in fact it was directly inspired by it. Both are simple, standardized files that live at the root of your domain and give automated systems structured guidance about your site. But they work in opposite directions: robots.txt tells crawlers what they can't access, while llms.txt tells AI agents what they should go to. Same spirit, different job. There is one important caveat they both share, though: neither file physically forces compliance. Just as robots.txt is really just an instruction manual that bots can choose to ignore, llms.txt relies on AI agents actually choosing to use the map you've provided.
Think about it this way. When you were in school and needed to look up when a historic event occurred or the formula for a chemical reaction, would you flip through your textbook page by page? Absolutely not. That would've been a waste of time.
What you did ( hopefully) is use the table of contents or index to get right where you needed to go.
That's essentially what an llms.txt is. Per llmstxt.org, the standard's purpose is to provide a clean, structured entry point for LLMs and AI agents . It’s a simple markdown file that points to the most relevant, high-signal resources: documentation, APIs, structured data endpoints, key pages. Think of it as a directory that helps autonomous agents (like coding assistants or research bots) find what they need without burning tokens crawling an entire site.
And this is where a lot of the confusion creeps in: llms.txt is not a place to stuff marketing copy or brand messaging hoping LLMs will give it extra weight. That's not its purpose, and it's not how it works. Your website and the quality of the content on it still does that job.
The llmstxt.org spec is intentionally minimal. The only hard requirement is a title. From there, the file is meant to include brief descriptions of what your site or product does, and links — specifically links to documentation, APIs, or other machine-readable resources that an AI agent would actually need to take action.
The operative word there is agent. This standard was built with agentic browsing in mind: AI systems that don't just answer questions but actually do things — pull API data, complete tasks, navigate workflows. Google's own Chrome team has started acknowledging this explicitly. Their Lighthouse documentation for agentic browsing specifically calls out llms.txt as part of a forward-looking set of signals for helping AI agents interact with your site more efficiently.
Google Search's AI optimization guide echoes a similar principle: structured, accessible, high-signal content helps AI systems understand and surface your information. llms.txt is one expression of that — though notably, Google has not said it will treat llms.txt as a ranking signal. The value is functional, not algorithmic.
Here's the honest answer: for most sites right now, llms.txt is not urgent. Google is being vague about it. LLMs don't require it to crawl or cite your content. It won't single-handedly move your brand visibility in AI answers.
But we are moving fast toward a world where agentic AI is the norm, a world where users aren't just asking chatbots questions but deploying AI to complete tasks on their behalf. If your site has documentation, APIs, or structured resources, an llms.txt today is low-effort infrastructure for a future that's approaching quickly. If you're already building for agentic features, it's a meaningful signal of readiness.
The way I think about it: implementing llms.txt is less like installing a new engine and more like labeling your filing cabinet. It doesn't change what's inside. But when an agent shows up needing to find something fast, you'll be glad it's there.
Mike Witham and Rachel Bascom cut through the noise on what LLMs actually reward — and AI-generated filler isn't it. They break down what trustworthy content looks like to a model that's seen everything.
llms.txt is an emerging, evolving standard. It's not a shortcut to AI visibility. It's not a replacement for good content, clear site architecture, or authoritative expertise, and knowing your audience. But it is a reasonable, and fairly easy, low-cost step toward being ready for what's coming.
Keep an eye on it. Understand what it actually does. And when it makes sense for your site, implement it the right way.
A potential customer asks an AI tool for a recommendation. Your brand has the expertise, the service, the proof, and the answer they need. It seems like it’s a match made in search-marketing heaven. But then the AI response cites three competitors and leaves you out entirely.
That’s the kind of problem being faced by today’s marketers. Search visibility is no longer limited to rankings and clicks; it also depends on whether AI systems decide to highlight your contributions. And understanding how to encourage those systems to give you your shot means thinking beyond traditional search engine optimization (SEO).
Generative engine optimization (GEO) is a new frontier in search… one that requires not only a revised approach, but an updated marketing mindset.
OK. If you’re reading this post you’re probably familiar with the idea of AI search, so I’ll just do a quick recap: Instead of sorting through SERPs, users can now ask ChatGPT, Perplexity, Gemini, Copilot, Claude, or Google’s AI-powered results for direct responses to their search queries. The answer may include citations, brand mentions, summaries, comparisons, or recommendations — but in more and more searches, one thing it doesn’t include is a click.
This has led to a sometimes heated (for marketers, anyway) debate that centers on GEO vs. SEO and whether traditional search strategy is still enough on its own. The answer, inconveniently, is no. SEO still does the foundational work of helping content get discovered, indexed, ranked, and clicked. But GEO determines whether that same content is clear, credible, and structured enough to be used.
In the new AI-centric search environment, your brand needs to establish its presence in more than one place. Ideally, that means being mentioned in the AI Overview, cited as a source, present in organic results, visible through search ads (where appropriate), and supported by any SERP features that help the user make a decision.
Traditional search engines provide options. Answer engines provide synthesis. They collect information, interpret the query, and return something that feels more like a destination than a map. The AIs aren’t standing between you and the solution; they’re sorting through the available info and presenting the (hopefully) best parts to you in a way that is much more accessible.
That does not mean traditional SERPs are dead. People still search, compare, click, skim, abandon pages for no clear reason, and return three days later from a different device like nothing happened. But AI platforms are increasingly becoming central to that journey. Especially when users want fast answers or support. Search is becoming less of a single path and more of an intricate spiderweb of touchpoints, with SEO and GEO helping brands appear in the various places where people now go to get answers.
The kicker is that ranking well does not guarantee inclusion in AI-generated responses. AI systems tend to favor content that is unambiguous, current, authoritative, and easy to interpret. If a page has strong rankings but buries the answer under meandering language and an early-2000s obsession with keywords, it still may not be useful enough to cite.
And just so we’re clear, a lot of those elements I just mentioned that AI systems gravitate toward are the same things that have always helped content rank well. It’s just that AI search has less patience for content that makes the answer difficult to extract. Traditional SEO may reward a strong page even when the good stuff is buried; AI systems are more likely to move on and cite the source that says the useful thing clearly. As a writer, I hate this (I think language should be a journey). But as a marketer I can see the value in getting right to the point.
AI Search requires information to be complete, unique and delivered efficiently to bots and agents. And that means being visible to potential customers now comes with the prerequisite of being visible to AI.
Like I said, the two approaches overlap. That’s good news for marketers! It means you can focus on strategy without having to pick one over the other. GEO and SEO should be working together to support the same customer journey. Even so, there are a few major distinctions you need to be aware of.
SEO focuses on rankings, impressions, organic sessions, click-through rates, and conversions. By comparison, GEO prioritizes inclusion in AI answers, citations, mentions, and accuracy of representation. That means that, in addition to standard keyword coverage, optimizing content for generative AI requires direct answers, consistent terminology, credible support, and information that can hold its shape outside the original page.
The goal of SEO is usually to get a user to click through to a website. GEO often operates in zero-click environments, where the user may get enough information directly inside the AI interface and thus never needs to visit the website at all.
But wait, if there’s no click to be had, why are we bothering?
The answer is that the value is still there; it just shows up differently. A buyer may see your brand in an AI-generated comparison, search for you later, revisit through branded search, and finally convert after talking with your sales team. And when your brand appears across multiple search surfaces — AI Overview mentions, citations, organic listings, paid ads, and SERP features — you create more chances to reinforce trust before the user ever reaches your site. But if your reporting only cares about the first click, that potential influence remains untapped.
For SEO, teams should continue tracking:
For GEO, the measurement model should expand to include:
Taking this big-picture approach will help you answer the most important performance question in modern digital marketing. Namely, were you part of the answer that shaped the buyer’s next step?
The marketers who reach the C-suite aren't the ones who mastered a channel — they're the ones who could tie it to an outcome. This short video breaks down the specific gap that stalls talented marketers before they get there.
Content needs to be easy for AI systems to parse, summarize, and trust. But before you go draining your content of any semblance of personality, take a step back. Remember: The goal is to make the useful parts easier to find. There’s no reason you can’t do that while still creating something engaging, entertaining, and inspiring on a personal level.
So, if you’re asking how to optimize content for AI search, start here:
Weak SEO makes AI visibility harder. If your content is difficult to crawl, poorly organized, thin, slow, or disconnected from the rest of your site, you are asking AI systems to look someplace else for a source that knows how to cross its Ts. SEO helps build the technical foundation and broader web presence, while GEO helps that presence become clear enough to cite, mention, and reuse.
Just to reiterate this point as explicitly clear as possible: SEO shouldn’t be fighting against GEO. A modern hybrid approach to search engine optimization is about optimizing for all parts of the journey — one that can easily start with an AI overview before transitioning onto more traditional search paths that ultimately lead into a conversion.
If your current SEO program is doing well, then good. That gives you a stronger foundation. But prominent spots on the SERP do not automatically mean your content is ready for AI search. That sucks, but here we are.
So let’s get introspective. Use these questions to assess where you stand:
97th Floor approaches AI search as part of a larger search ecosystem for a truly hybrid digital-marketing strategy. The focus is on using SEO as the foundation of GEO and connecting the pieces that determine visibility:
We recognize that fragmented tactics create fragmented results. Strong rankings without AI readiness can mean your zero-click audience never sees what you have to offer. 97th Floor has the experience and innovative drive to connect the technical foundation of SEO with the answer-first demands of GEO, preserving your brand’s place in the conversation.
Contact 97th Floor today, and optimize your content for both search engines and AI.
