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.
1 billion users. That is the usage rate of AI mode reported by Google at Google I/O. It seemed for a while like Google was trying to slowly adapt traditional search by enhancing the “Featured Snippet” SERP feature by replacing it with the “AI overview” SERP feature. All while creating their own versions of ChatGPT like chat bot, in Gemini. However it is clear after Google I/O, that Google is seeking to transform the entire search experience. Transform it into an almost personal assistant, a chatbot on steroids. They are doing this by making search more conversational, using your search history as context to create custom, tailored to the user, search results. Using Search Agents, they will continuously scrape the web for updates, or new information on topics and products you are interested in, and provide summarized reports of its findings.
What are the strategic shifts that need to take place for GEO?
These changes have real implications for SEOs, content marketers, and anyone who cares about optimizing for search engines or generative engines. So, what should search marketers care about and watch out for in the coming months? Lets review a few strategic shifts that need to take place:
The shift from bots to agents is the real game changer.
Everything covered above, pixel depth, sentiment, brand mentions, crawl efficiency is going to be table stakes compared to where search is headed. The next 2-3 months will start to reveal something bigger: the difference between optimizing for a bot that crawls your content and optimizing for an agent that acts on it.
Google's Search Agents aren't just passively indexing. They are completing tasks, comparing products, summarizing findings, and delivering recommendations directly to users, often without the user ever visiting your site. That changes the goals of Search Marketers in a meaningful way — here's what that means for your conversion path.
So what should you be watching over the next 90 days?
First, watch how your brand gets used, not just mentioned. As agentic search matures, the question won't only be "does the LLM cite us?" it instead will be "does the agent choose us when it's acting on behalf of a user?" That means your content needs to be decision-ready. Structured data, clear pricing, availability signals, and unambiguous value propositions aren't just nice-to-haves anymore. They're ranking factors, inputs an agent evaluates when it's doing the shopping, researching, or comparing for someone.
Second, keep a close eye on how conversational context affects your citations. Because Google is now using search history to personalize results, the same brand mention or citation may appear for one user and not another. This makes aggregate tracking less reliable and user-journey-level thinking more important. Start mapping which prompts and queries at each stage of your funnel you want to own and measure accordingly.
Third, don't sleep on structured data for agents. Just like robots.txt told crawlers what to do, the next wave will likely include providing signals to agents on what we want them to do with your content. Stay close to what Google and other AI platforms announce around agent permissions and content licensing, this space is going to move fast.
The brands that win in GEO won't just be the ones writing helpful content. They'll be the ones making it impossible for an agent not to recommend them.
If you lead marketing at a mid-market or enterprise company, you've had some version of this meeting recently:
Then Google I/O 2026 happens.
At I/O, Google announced that AI Mode has surpassed 1 billion monthly active users, with queries more than doubling every quarter since launch. AI Overviews crossed 2.5 billion monthly users.
Google is calling the new AI-native Search box the biggest upgrade in over 25 years. Antigravity launched as the new agent layer. Multimodal queries now take in images, video, files, and whatever's open in your active Chrome tab. Agentic shopping and booking arrive later this summer.
Organic search (and SEO) just got its biggest expansion in a quarter century. Understanding that will decide which marketing leaders look prescient at next year's board meeting and which ones don't.
“How do we win back the click the AI Overview took?” It’s the question most marketing teams have been asking and is almost guaranteed to produce wasted effort. The click went to Google's own answer surface. The audience went with it. According to Pew Research, 34% of U.S. adults have now used ChatGPT (doubled since 2023) and 65% see AI summaries in their search results for most queries. Sixty percent have used AI to search for information, a number that climbs to 74% among people under 30.
You can't out-rank Google's AI Overview by writing a better article on the same topic! Google will just use your article to write the next Overview.
The right question is, “whose attention is in this query, what do they actually want to know, and what will make them choose us when they're ready to decide?”
If you built your last five years of organic strategy on ranking-first thinking (meaning picking a keyword, writing to whatever's at position 1, optimizing on-page, and repeating monthly), the playbook just stopped working because the SERP that playbook depended on no longer exists.
If you built those five years on audience-first thinking then the playbook works exactly the same way in an AI Overview, a Gemini response, a Perplexity citation, or a blue link. The same persona work, the same journey map, the same writing discipline produce the same outcome. Only the surface changed.
The Gartner 2026 CMO Spend Survey found that 56% of CMOs say their marketing organization lacks the budget required to deliver their 2026 strategy. Marketing budgets sit flat at 7.8% of revenue. Seventy percent of CMOs consider becoming an AI leader a critical 2026 goal but only 30% report mature AI readiness.
The C-suite expects AI-led marketing transformation, but the budget and operational readiness to deliver it both lag.
The wrong thing to spend the dwindling marketing budget on is chasing every new AI surface as if it were a distinct discipline. The right thing is to invest in the part of the work that compounds across every surface: 1) deep audience understanding, 2) content that earns citation, and 3) measurement that holds up at the board level.
For the past two years, every other LinkedIn post in the marketing world has been a version of "organic traffic is dying." If you only watch the “Sessions” chart in GA4, the trend lines on a lot of sites look bad. Click-through rates from AI Overviews are still lower than from the old blue-link SERP, even though early 2026 data suggests AIO CTRs are recovering as Google refines the experience.
But it's the wrong metric and the wrong frame.
The audience didn't go anywhere. They're searching more than they ever have. They're just doing inside Google (and other AI search platforms) what they used to do across five different tabs. When a user gets a satisfying answer from an AI Overview that cites your brand, then comes back later and goes directly to your site, GA4 sees a direct or branded organic visit. The GEO/AEO work that earned that visit is invisible in the dashboard.
The audience isn't gone. The measurement is just behind. The marketing teams that figure out how to help leadership understand AIO citation share, brand mentions across AI platforms, query coverage and sentiment, and downstream conversions from each of those surfaces are going to keep their budgets while everyone else fights to defend declining sessions.
The framework we run at 97th Floor is three words: Empathy, Innovation, Profitability. We call it P.I.E.
“Empathy” means obsessing over who our client's audience actually is, beyond their role as a buyer. What they're trying to do, what they already believe, what they're worried about, what they search the moment before they ever search.
“Innovation” means refusing to copy whatever is already ranking. The brand that produces the eleventh-best version of an existing SERP article is now going to get eaten by AI, fast. The brand that produces the actual answer to the question the audience is asking will get cited.
“Profitability” means tying every campaign back to revenue, not vanity metrics. AIO citations don't pay your bills. Brand mentions that lead to more branded search, more direct traffic, and more conversions do.
This framework was always a bet that algorithms and platforms would change but the principle wouldn't. From my POV, that bet just paid off.
Going into 2024, our client in B2C Finance was a SERP underdog in private student lending. The "student loans" category was dominated by federal sites and the giants of the private lending world:
- studentaid.gov: 11.8M monthly organic traffic, 3,118 pages
- consumerfinance.gov: 1.4M, 8,492 pages
- ed.gov: 1.2M, 93,208 pages
- salliemae.com: 380K, 406 pages
- earnest.com: 156K, 619 pages
- Our Client: 49.7K, 399 pages
That's the field our client was up against, right as AI Overviews started reshaping the SERP. From the outside, that looked like the wrong moment to invest in organic. They did it anyway, because the investment was in the audience, not in keyword positions.
The work was deliberate. We built a hub-and-spoke topical authority structure around "Student Loans." Pillar page in the middle. Cluster pages for FAFSA, Scholarships, Types of Student Loans, and College Planning. High-funnel content for the parents of student borrowers like “Credit Score Impact,” “Parent Involvement,” “Financial Literacy,” “How to Pay for College Without FAFSA,” etc... All with internal linking that made clear to Google which page was the authoritative answer for which query.
The writing approach mattered more than the structure. Instead of producing content based on whatever was currently sitting in the top 10 SERP results for each keyword, we researched every question a student or parent might have at every stage of the loan application process, then answered those questions in depth. Transparency on rates and loan terms was deliberate. So was incorporating proprietary survey data from the client themselves, which gave the content real experience, expertise, authority, and trust, the E-E-A-T signals Google's own quality guidelines (and the LLMs reading from them) reward.
We produced:
- 162 new pieces of content
- 62 re-optimized pages
- 455+ strategic mentions across the web
The results:
- 1,275% increase in brand mentions across AI surfaces
- 400% increase in owned AI Overview citations** since AI surfaces started becoming prevalent
- 47.5% YoY increase in search impressions
- 14.11% YoY increase in organic traffic
- Private Student Loans page +113.6% YoY organic
- Homepage +46.08% YoY organic
- 100% of new content published in Q3 2025 is currently cited in Google's AI Overview
All in a year when most sites in the category were watching organic decline. (Read the full case study here)
The client was directly up against some incumbents with much larger budgets. They didn't beat them on sheer volume. They beat them by answering the questions the audience was actually asking, at depth, with transparency, using the brand's own data.
If you're leading marketing at a mid-market or enterprise company right now, I’d take a look at five moves are worth committing to:
1. Stop measuring organic by sessions alone. Build a richer story for your executive team and help them understand AIO citation share, brand mentions across AI platforms, query coverage/sentiment, and lagging indicators like direct traffic and branded searches. With 56% of CMOs telling Gartner they don't have the budget to deliver their 2026 strategy, the orgs that can demonstrate where the work is actually paying off will defend their resources through this transition.
2. Pick the topic. Then own it. Topical authority is what wins in AI search. Our B2C Finance Client didn't win because they outranked Sallie Mae on the exact-match keyword "student loans." They won because they owned the conversation around student lending for the audiences that mattered. Pick the conversation your audience actually cares about. Be the obvious answer in it.
3. Refuse to produce the eleventh-best version of what's already on the SERP. AI is going to compress that work into one summary, and that summary will not cite you. The content that gets cited adds something like proprietary research, the brand's actual point of view, or depth on the questions the rest of the SERP isn't bothering to answer.
4. Optimize for the agent that's about to start buying things. Google's agentic shopping and booking features launch this summer. The brands with clean product information, transparent pricing, and well-structured data will be the ones AI agents include when a user says "find me three options for X." Treat the agent like a buyer. Make the path easy.
5. Treat audience research as a defensible asset, not a tactic. Persona work, journey maps, and voice-of-customer data survive every Google update, every platform shift, every UI change. They're the only competitive moat a marketing org can build that compounds over time. If you don't have a real one, build it before you spend another dollar on tactics.
The marketing leaders who will win the next five years are the ones who will refuse to panic when the platform changes and who will, instead, keep the audience at the center of every decision.
In the beginning, search marketers could work from a reasonably familiar playbook: publish useful content, optimize the page, build authority, and measure rankings until growth happened.
It was straightforward enough. And for a while, it was good.
But then there was AI. And with AI came AI search/GEO/AEO.
AI search took the playbook and started making edits in the margins. It took a position between the user and the web page, changing how search engines function—summarizing answers, selecting sources, determining which brands deserve mentions, and often turning a traditional search into a zero-click experience. And yes, search was still search. It still took user queries and provided them with answers and direction. It just wasn’t playing by the established rules. From a marketer’s standpoint, it was a lot less predictable, and that made it harder to systematize.
But even if the playbook has changed, it’s still essential. Google’s E-E-A-T framework gives marketers a way to rebuild that system around the thing AI search depends on most: credible, useful, human-validated content that deserves to be seen.
Ask any marketer five years ago about the most important metric in search visibility, and they’d tell you it’s rankings: The top spots get rich, lower ones get bupkis. But modern search doesn’t work quite the same way it used to. In fact, search is moving from a ranking environment to a selection environment.
That may sound like a small distinction, but it is not. A ranking environment gives users a list of options. A selection environment gives users an answer, then decides which sources deserve to support that answer. Now you can be sitting pretty in spot #1, and the majority of relevant searches will still fail to land.
In traditional SEO, weak credibility might mean a lower ranking (hidden, but still findable). In AI-driven results, weak credibility can mean you are not surfaced at all. No honorable mention. No trickle of traffic made up of those who want to see what else is available. Just the silence of your content getting swallowed by the algorithmic void.
It all comes down to the fact that AI systems are no longer trying to improve how users find and connect with pages that can answer their questions; they’re trying to answer those questions directly. And to do that, they need sources they can trust.
Content has to meet a certain credibility threshold before it can be summarized, cited, or recommended. Pages with thin authorship, generic claims, outdated information, flimsy sourcing, (etc.) are at a disadvantage.
And yes, that has always been the case. Bad content digs its own grave. It’s just that AI search gives weak pages fewer places to hide. Instead of slipping into the lower half of a results page and hoping for a wandering click, it may be filtered out before the user ever sees the options.
The Google E-E-A-T framework gives us a useful way to think about content credibility: AI Search makes experience, expertise, authoritativeness, and trustworthiness more visible and less optional. AI search optimization depends on signals that help machines understand whether a source is worth using. Does the author know the subject? Has the brand demonstrated authority over time? Is the content accurate? Is the page structured clearly enough to be understood? In essence, does the content show good quality? Not just in terms of grammar or relevant keywords; usefulness, accuracy, originality, and evidence of real experience are just as important.
E-E-A-T and AI should not be treated as a side quest. They are the plot, belonging inside the broader SEO strategy just as much as content planning, technical SEO, analytics, and conversion strategy.
That is why modern SEO services need to connect credibility signals across the complete digital ecosystem. Content has to be strong. Technical foundations have to be clean. Authority-building has to be intentional. Measurement has to account for search visibility that may not produce a traditional click. Everything has to work together, or it will all fall apart.
Sound complex? Well, sure. But the Google E-E-A-T framework is useful because it breaks credibility down into bites we can actually chew. Specific trust signals that can be improved, strengthened, and measured over time.
AI can summarize common knowledge quickly. It can explain definitions, reorganize existing information, and produce a perfectly acceptable paragraph that sounds like it was raised in a content farm and taught to roll over on command. What it cannot easily do is recreate real experience. First-hand insights, customer examples, field observations, testing notes, case studies, and lessons learned from actual work all help prove that content is grounded in reality. This gives both users and AI systems something specific to trust.
Experience is the part that says, “We have actually done this,” rather than “We read six similar articles and turned them into soup.”
AI can help teams move faster. It can support research, organize messy notes, generate draft structures, identify gaps, and speed up production. That is useful. But let’s be very clear here: Human expertise still has to steer the ship. I’m reminded of a piece I co-authored back in 2019. This was before modern AI, but its point about not letting data have the final say in strategy is still totally relevant.
A human-in-the-loop AI workflow keeps subject-matter experts involved where they matter most: planning, validation, accuracy, nuance, and final approval. The machine can help build the scaffolding, but a knowledgeable human needs to decide whether the thing is safe to stand on.
This is especially important for topics where the cost of being wrong is high. Medical, financial, legal, technical, and enterprise strategy content all need expert review. But even lower-risk content benefits from human judgment, because credibility is not created by sounding confident.
Domain authority (in AI SEO) is built when other people and systems recognize that your brand knows what it is talking about.
Backlinks are part of this larger authority pattern. Mentions from respected publications, expert contributions, third-party citations, industry partnerships, podcasts, webinars, and strong omnichannel campaigns all help reinforce that your brand belongs in the conversation. AI systems are more likely to trust sources that have already earned recognition across the web. Authority compounds through consistent signals, and those signals become harder for competitors to fake over time.
You can have experience. You can have expertise. You can even have the kind of authority that only comes from years of well-earned recognition. But if your content is inaccurate, outdated, insecure, or weirdly evasive about who is behind it, trust starts leaking out of the page.
Trustworthiness is built through clear authorship, visible credentials, accurate sourcing, updated information, transparent policies, HTTPS, usable site design, and consistency between what your brand says and what it actually does. In terms of E-E-A-T and AI, trust is the inclusion filter. Without it, those other pillars start to wobble.
The problem with AI content is not that AI is in the room. The problem is when everyone else leaves the room.
AI-assisted content can absolutely meet E-E-A-T standards. But it needs strategy, oversight, and a clear reason to exist beyond “we can publish 40 pieces of AI slop before lunch.” AI content quality is built on what humans bring back into the process.
The best model is not human vs. AI. That makes for great movies but it’s just not a good way to approach digital marketing. A better approach is human plus AI, with humans firmly in charge of determining what ‘quality’ means in context.
A human-in-the-loop AI process allows teams to scale production while preserving expertise. AI can help draft outlines, identify related questions, summarize research, suggest structure, and even take a hand in plotting course or suggesting next steps. Humans then refine the argument, add experience, verify claims, sharpen examples, finalize decisions, and make sure the published content sounds like it came from a brand that knows what a heartbeat feels like.
That approach supports E-E-A-T and AI because it combines efficiency with accountability. You get the speed benefits of AI without letting generic content wander onto your website wearing a little name tag that says “thought leadership.”
Everybody likes structure, because everyone likes to see how pieces fit together. But you know who really loves structure? Cold, calculating machines.
Can you blame them? Structure gives AI systems something to follow. Clear headings, direct definitions, focused sections, and logical flow all help the content make sense when it gets parsed, summarized, or divided up. Without that structure, even good information can turn into a junk drawer — useful things are probably in there somewhere, but nobody (not even a machine) wants to go elbow-deep.
This AI search optimization is not a full replacement for traditional search engine optimization. But it is an extension of it. The same content still needs technical accessibility, internal linking, page speed, mobile usability, metadata, topic relevance, and all those elements blogs like this one wouldn’t shut up about just a few years ago.
AI is adept at seeing structure. It’s also pretty good at seeing when something stands out.
Original insights make your content more useful and more defensible. That could mean proprietary data, client learnings, expert interviews, market analysis, custom frameworks, internal benchmarks, or even just a strong point of view. If your content contains something competitors do not have, it becomes more valuable to users and harder for AI systems to treat as interchangeable.
Rome wasn’t built in a single blog post, and neither is authority. It’s built through repeated evidence. AI systems look for patterns. Does this brand cover the topic consistently? Do other trusted sources reference it? Are its authors credible? Does the site maintain accurate, useful content over time?
In other words, an E-E-A-T and AI strategy needs to focus on establishing long-term credibility.
Authority influences whether content is trusted enough to be surfaced, cited, or summarized. High-authority brands have an advantage going in because they have already earned recognition across search engines, publications, users, and industry communities.
That might not seem fair to newcomers, but don’t lose hope. Authority is not permanent. It has to be maintained through ongoing quality and relevance. A strong domain can still lose ground if its content becomes stale, generic, or disconnected from what users actually need. By that same rule, fledgling sites can start strong by building the kind of consistent quality that eventually turns into authority that can then begin to snowball.
Good content marketing is reputation-building that just happens to look like web pages.
When a brand consistently answers important questions, explains complex topics clearly, and brings original perspective to the market, it builds familiarity. Familiarity builds trust. Trust builds authority. And authority gives content a better chance of being selected in AI-driven environments.
It’s probably no surprise that, when it comes to AI, trust has a technical side.
Structured data helps search engines and AI systems understand authorship, organization details, article information, FAQs, products, and relationships between entities. Fast load times improve user experience. Secure browsing protects users. Accessibility makes content available to more people.
None of these elements can replace strong content. Even so, weak technical signals can undercut strong content. If you’ve got everything else in place but the technical signals aren’t up to snuff, it’s like your content is trying to compete with its shoelaces tied together.
By this point, the broad strokes should be clear: AI search rewards content that is credible, specific, structured, and backed by real authority.
Easy enough, right?
Hold up a sec; I have an emoji for this: 😬
No. Easy is obviously not the right word. If it were easy, every brand would already be doing it, and the internet would be a glorious garden of helpful, accurate information.
The challenge is figuring out where your content already demonstrates E-E-A-T and where it still looks a little undercooked. That means evaluating the pieces users can see, the signals AI systems can interpret, and the gaps competitors may already be using to their advantage.
The best place to start is with the content itself:
If the answer is no (or even a very quiet “kind of”), then that content could probably be improved.
The easiest test is this: Strip away your logo, your formatting, and your preferred brand color. Would that piece look just as at home on any competitor’s site? If so, it may be useful, but it is not differentiated.
Next, look at the credibility signals surrounding the content:
The E-E-A-T framework gives you a way to move beyond vague content-quality conversations and ask more practical questions: Who created this, and why should anyone trust it?
Once you’ve looked at content quality and authority, you get to evaluate whether your content is structured for AI readability:
Again, this does not mean you should prioritize writing for machines instead of humans. Please do not do that. Nobody needs more content that reads like a command line. It means creating useful content with enough structure that both humans and AI systems can understand why it deserves attention.
If you’re ready to evaluate your current content, authority signals, expert workflows, and AI search readiness, download the E-E-A-T for AI Search Checklist. Use it to identify where your strategy is strong, where credibility signals are missing, and where your content may need a makeover.
At 97th Floor, E-E-A-T and AI are not treated as separate checklists, and they definitely are not treated as a reason to churn out more generic content at industrial speed.
The goal is not volume for volume’s sake. The goal is visibility that holds up as search changes.
That means building strategies around credibility, authority, structure, and measurable business impact. AI can support that work, but it does not replace the thinking behind it. The brands that win in AI search will be the ones that know what they stand for and can prove it in a way that is accessible.
At 97th Floor, that looks like:
AI search will keep changing. That part is not really up for debate. But the brands that build around credible content, real expertise, technical trust, and long-term authority will be better prepared for whatever search decides to become next. 97th Floor is at the forefront of this shift, helping brands we believe in turn E-E-A-T and AI into a practical strategy for growth.
After all, the playbook may have changed, but trustworthy, high-quality, useful content will always win the game.
Some search marketers have been declaring SEO dead for over a decade. Yet every year, search keeps driving brand discovery and revenue.
What has changed is how visibility works. Google’s AI Overviews summarize answers before users click, and generative engines talk about the brand inside responses. Search behavior now also spreads across YouTube, LinkedIn, marketplaces, and AI platforms.
Now, we aren’t gaslighting you—we are also seeing the declining click-through rates and unstable traffic that were so different just five years ago. When people ask, “Is SEO dead?” they’re reacting to something very real, and it’s affecting industries across the board.
But SEO is not dead or even dying. Like most things being affected by technology and digital initiatives, SEO is simply changing. Technical excellence, authoritative content, and visibility across systems is still essential. Now, you just need to optimize for AI systems and search platforms, too.
The biggest shift is the rise of AI-generated answers directly in search results. Google’s AI Overviews and generative engines can summarize information before a user ever clicks a page. In many cases, the search experience ends right there on the results page. When teams see traffic dip even though rankings remain strong, it naturally sparks concern about the long-term value of SEO.
At the same time, search itself is no longer confined to Google. People discover products on Amazon, research ideas on YouTube, ask questions inside AI tools, and follow recommendations from LinkedIn or Reddit threads. That fragmentation means visibility is happening across a wider ecosystem than traditional search analytics tools were built to track. For a lot of businesses, it can feel like you have no control over so many channels.
Those two forces together have created real volatility in organic traffic. If you have historically measured SEO success only through clicks and sessions, these changes can feel like the ground moving underneath your entire strategy.
For brands willing to adapt, the opportunity is still massive. Strong search visibility now depends on building authority, technical clarity, and content that AI systems trust as a source. That kind of SEO strategy sits at the center of modern search growth.
The phrase “is SEO dead” is what marketers are saying when they see declining organic clicks and evolving search interfaces that don’t seem as compatible with classic SEO. AI-generated summaries, knowledge panels, and expanded SERP features often deliver answers before users reach a website, so why should businesses bother with SEO?
But this evolution of search optimization has not necessarily lost its relevance. In fact, all it really means is that the role of SEO has expanded. Instead of focusing exclusively on ranking individual pages, your strategy should heavily focus on building authority and structured visibility across search and AI ecosystems.
A few patterns tend to fuel the idea that SEO is disappearing:
Remember that, ultimately, organic search remains one of the strongest discovery channels on the internet. High-intent queries flood search engines every day that drive your revenue. People still rely on search to solve problems and evaluate options, and your brand needs to show up in those results.
Enterprise organizations still invest heavily in search because it contributes directly to their pipeline growth. As you become an authority in your space (rather than focusing so heavily on ranking), and have technical, structured content performance, your visibility will increase.
For years, SEO success looked fairly straightforward, but there are a couple of other players on the field.
Traditional SEO says that success looks like top rankings and organic traffic. If your page appeared near the top of search results, the assumption was that clicks and engagement would follow.
Meanwhile, AI Overviews and generative systems increasingly pull answers from multiple sources. When that happens, business influence shows up through citations, summaries, and brand mentions inside those responses.
In other words, when AI search systems generate answers, they rely on sources they trust. If your content becomes one of those sources, your brand shows up in the answer itself—even when the user doesn’t click.
“SEO” is also one slice of a much larger pie, where AEO and GEO are a part of a well-rounded strategy.
Answer Engine Optimization, or AEO, focuses on structuring content so search systems can extract clear answers. Generative Engine Optimization, commonly referred to as GEO, looks at how AI platforms summarize and reference sources. Both ideas reflect the same larger trend: search engines are becoming answer engines.
Modern SEO strategies bring these concepts together. Instead of separating them, organizations combine traditional ranking strategies with content structures designed for AI summarization and entity clarity. This approach is how you can be at the top of your game with AI search and how to optimize for the future of search engines.
Another major change is where discovery happens. Search behavior no longer lives inside a single engine.
Someone researching a product might start with a Google query, watch comparison videos on YouTube, scan reviews on marketplaces, and read thought leadership on LinkedIn. Users also ask questions inside AI assistants before visiting a website.
Brands that want consistent visibility build authority across multiple ecosystems where search intent appears. So yes, you need to optimize for Google—that’s not going anywhere. But you also need to show up where people compare products or services and ask questions. That might mean:
That broader presence strengthens the signals search engines and AI systems rely on when deciding which sources to surface. Over time, those signals reinforce brand authority in ways that pure keyword targeting never could.
Featured snippets started this trend years ago: search engines want to answer the question in the search bar without ever even visiting a website. Now, AI Overviews are taking it a step further.
Because more queries are answered directly in SERPs, AI Overviews have reduced the reliance on blue links for consumers—your audience.
So why are you pouring money into producing so much content for people to not even enter your website?
Because traffic declining does not necessarily mean your influence declines, too.
When your brand appears inside an AI Overview, a featured snippet, or a cited source within a generated answer, users still see your expertise. They may not click in that moment, but the exposure shapes awareness and credibility. Later, when they search again with a stronger intent, your brand is already familiar.
Instead of focusing exclusively on traffic, many organizations now look at a broader set of indicators:
The zero-click environment also forces some strategic decisions.
Chasing raw traffic can lead teams to prioritize high-volume informational queries that rarely convert. Meanwhile, focusing on authority and expertise often produces fewer visits but better downstream impact.
Enterprise organizations increasingly balance both sides of that equation. They invest in content that builds authority within a category while also strengthening owned channels like email, communities, and product education hubs.
Building authority earlier in the research process also helps teams connect search visibility to revenue attribution models, which track how organic discovery contributes to pipeline and closed deals.
We know that the technical side of SEO especially matters, but more than ever before, so does the human element of your content. Generic or recycled material just doesn’t quite cut it anymore. It’s your expertise and credibility that the AI models are going to trust.
Google describes these signals through E-E-A-T: experience, expertise, authority, and trust. This is exactly what it sounds like: search systems try to surface information that comes from knowledgeable sources.
AI-generated answers rely on the same signals. When models summarize content, they still look for sources that demonstrate real-world expertise and established authority within a topic area.
That’s why enterprise brands with recognizable subject matter experts, credible research, and original, real-world insights tend to perform well over time. They give search engines and AI systems a clear signal that their content is worth referencing.
Keywords do still matter, but even more important is writing for readers. Answer the search intent before you optimize for the algorithm to give yourself the best chance in AI search and future search strategies. This looks like having clearer explanations on the topic and practical solutions that actually help consumers make their decisions. Remember to:
Human-first content thrives when it’s supported by broader SEO principles. Successful organizations treat search visibility as a combination of these 6 disciplines of SEO working together.
If a site is difficult to crawl, poorly structured, or confusing to interpret, even great content struggles to appear consistently in search results. Think of it like building a library. You could fill it with incredible books, but if the shelves are disorganized and the catalog is missing, people will have a hard time finding anything.
Before a page can rank or appear inside an AI-generated answer, search engines have to find it and understand how it fits with the rest of your site.
That usually comes down to a few practical things:
When those fundamentals are in place, search engines have a clearer picture of what a site covers and which pages provide valuable answers.
Search engines are good at reading pages, but they still appreciate a little help.
Structured data acts like labels on a library shelf. It tells search systems exactly what they’re looking at. Product schema can identify price and availability. FAQ schema highlights clear question-and-answer sections. Review schema points to customer feedback.
Those labels help search engines surface the right information in rich results and AI-generated answers.
Entity relationships add another layer. When your brand consistently appears alongside certain topics across trusted sites, search engines begin to connect the dots. Over time, your brand becomes associated with that subject area, which makes it more likely to appear when people search for related information.
For enterprise organizations, technical SEO becomes even more interesting. Large websites often contain thousands or even millions of pages across different products, regions, and content hubs.
At that scale, small issues multiply quickly. Duplicate pages compete with each other. Important sections become buried several clicks deep. Old pages stick around long after they stop providing value.
That’s why enterprise SEO often requires governance systems and technical enterprise SEO playbooks that keep large sites organized. Without that structure, even strong content can struggle to gain traction in search.
You see a lot of the trending “SEO solutions” on your LinkedIn feed, but what is really going to move the needle? Let’s talk about it.
One of the biggest changes in modern SEO is the move away from pure volume. Today, that approach rarely produces lasting results. Search systems have become much better at identifying which sources actually demonstrate expertise within a topic.
That’s why many organizations now focus on building strong topic clusters around high-intent themes. Instead of publishing dozens of loosely related pages, they develop deeper resources that connect logically and answer related questions across the research journey.
The goal of these evolving SEO strategies is simple: become one of the sources search engines consistently associate with a category. That kind of authority tends to hold up far better than isolated rankings.
AI-generated answers have added another layer to modern AI SEO strategy.
Content now needs to be clear enough for AI systems to extract and summarize. Pages that explain ideas directly, use structured formatting, and answer questions clearly are more likely to appear in generated responses.
This often means writing in a more conversational, question-driven format. When a page mirrors the way people naturally ask questions, it becomes easier for AI systems to recognize and reference the information.
Ecommerce brands face a slightly different set of priorities.
Product pages need structured data that clearly communicates details like price, availability, reviews, and product attributes. Category pages often carry the responsibility of establishing topical authority for entire product groups.
At the same time, ecommerce SEO must compete within crowded SERPs filled with product listings, reviews, and comparison content. Brands that succeed often combine strong technical optimization with helpful buying guides, comparison pages, and educational resources that support the purchasing journey.
There are a lot of moving pieces to SEO now, and many organizations reach a point when their internal teams need help. This often happens when:
Working with a specialized team focused on AI-driven search can help organizations move faster while maintaining a clear strategic direction, which is why many brands explore working with an AI SEO agency.
By this point, one thing should be clear: modern SEO isn’t a checklist, but an entire system of connected strategies that all influence one another. When those elements operate in isolation, results tend to plateau. When they work together, search becomes a much more durable growth channel. 97th Floor is here to make sure every move you make is contributing to a healthy and modern SEO strategy.
97th Floor approaches SEO as a growth system rather than a content production engine. The strategy connects traditional search optimization with authority building, digital PR, and AI search visibility.
We can help you rank for keywords, but we also help your brand become a leading resource in your industry. Instead of chasing short-term ranking spikes, the focus moves toward durable visibility that supports sustained growth.
97th Floor focuses on building content systems and authority frameworks that continue performing even as search interfaces change. Search will keep evolving. How will your team keep up? Every algorithm update can work to your benefit as we help you master long-term authority and move beyond obsessive keyword ranking.
Let’s assess where your organization currently stands and see where you can start making changes for today’s SEO environment.
Start by looking at how your organization defines SEO success. The way performance is measured often shapes the entire strategy.
Next, take a close look at the technical foundation of your site:
Finally, consider how your brand appears compared to others in your category. Visibility gaps often become obvious when you look at where competitors show up in search and AI answers:
If these questions highlight opportunities for improvement, it may be time to revisit your SEO strategy. The search landscape is evolving quickly, and adapting early can make a significant difference in long-term visibility. Learn more about how our team approaches search strategy through our SEO services.
A few years ago, ranking on page one felt like the finish line. If your page showed up near the top, traffic followed.
Now, being at the top of SERPs is valuable, but it doesn’t pack the same punch. When you ask a complicated question, the search engine often answers it immediately. AI Overviews summarize sources, or generative engines simply write explanations. In many cases, the user never clicks a link at all.
As a brand trying to gain visibility with your consumers, this change in search results affects how you approach. Pages still matter, but the real opportunity now is becoming one of the sources AI systems rely on when they generate answers.
Answer engine optimization is a large piece of that puzzle, which focuses on how content gets extracted and referenced inside AI responses. In this guide, we’ll show you how answer engine optimization fits into your overall AI search strategy and how to show up in relevant online spaces.
When AI systems generate a response, they choose a handful of sources to build that answer. If your brand is one of those sources, your expertise shows up immediately. If it isn’t, competitors shape the narrative instead.
Decision-makers are asking longer, more contextual questions than they used to. It’s less short phrases like “CRM tools,” and more questions about how a CRM integrates with existing systems or which platforms work best for a specific business model. These often appear during real evaluation cycles, which means the answers influence purchasing decisions.
Because of that shift, the goal of search strategy is expanding. Ranking still matters, but influence now depends on whether AI systems trust your content enough to extract it as a direct answer.
Answer engine optimization is one of the ways you can make your content more visible under these new search conditions. AEO focuses on structuring expertise so AI systems can interpret it clearly and reference it when generating responses. Many teams now integrate AEO alongside traditional optimization, authority development, and technical SEO as part of a largerAI search strategy.
Over time, brands that consistently appear in AI answers gain an advantage that rankings alone cannot provide. Their expertise shapes the information buyers see at the very beginning of research.
That advantage starts earlier than most brands realize — at the moment a buyer types their very first query. SEO expert Eli Schwartz reveals what today's AI-aware searchers are actually typing into Google, and why those queries look nothing like what most content teams are optimizing for. This short video breaks down the search behavior shift that determines whether your brand shows up at the start of the research cycle — or gets skipped entirely.
When someone asks an AI system a question, it doesn’t search the web the same way a person does. It analyzes sources, pulls relevant information, and generates a response.
Answer engine optimization focuses on influencing which sources that response comes from.
Answer engine optimization is the practice of structuring and validating content so AI systems recognize it as a reliable answer to a specific question.
Instead of optimizing only for rankings, AEO focuses on how information is interpreted by AI systems. That includes how clearly a concept is defined, how expertise is demonstrated, and how easily an answer can be extracted.
The objective is representation. When AI systems summarize a topic, the brands cited in that answer help shape how buyers understand the category.
Traditional SEO and answer engine optimization address different layers of search visibility.
| SEO | AEO |
| Focuses on ranking pages in search results | Focuses on being extracted, summarized, or cited in AI responses |
| Optimizes for keywords and backlinks | Optimizes for questions, structured answers, authority signals, and machine-readable clarity |
| Performance is measured in clicks | Performance includes visibility within AI answers, brand mentions, and authoritative citations |
For most organizations, AEO complements traditional SEO since you still need SEO to rank—now, you are more deeply considering how your brand appears in AI-generated explanations.
Content optimized for answer engines typically follows a simple structure.
Start with a question that reflects how people actually search. Place a concise explanation directly beneath it, usually 40 to 60 words. Then expand with supporting context, examples, or strategic insights, especially when you can back up your ideas and claims with real experience. You also need to cut back on ambiguity wherever possible.
That format makes it easier for AI systems to identify the core explanation quickly while still giving readers the deeper context they need.
AEO works best when it’s built into how content is planned and structured from the beginning. Teams that try to retrofit answer visibility after publishing usually find the results inconsistent. Meanwhile, when you have a solid architecture from the beginning, you can design pages around the kinds of questions buyers actually ask and make it work for the digital world.
AEO content planning usually begins with mapping the questions buyers actually ask during research. These are usually the “what is,” “how does,” and “why does” questions.
For example, a software company might map queries like:
Each of those questions becomes a distinct section with a clear answer followed by deeper explanation. You can make sure you are covering topics with enough depth by using semantic clusters, which are groups of closely related questions and subtopics that help search systems understand the full scope of a topic.
This structure does two important things. First, it mirrors how buyers research a topic. Second, it gives AI systems clearly defined answers they can extract without needing to interpret a long block of text.
Answer engines rely heavily on structured information to interpret content. Structured data provides that clarity by labeling important elements on a page so machines can understand them more easily.
Schema markup helps identify things like the organization publishing the content, the author responsible for the expertise, frequently asked questions within the page, and relationships between related topics. This added context helps search systems interpret who is providing the information and what the page is about.
For example, a consulting firm publishing a guide about marketing attribution could use schema to define the organization, the author’s professional role, and the FAQ sections within the article.
When those elements are clearly labeled, AI systems have a much easier time interpreting the page and connecting the expertise behind it to the topic being discussed.
Answer engines prioritize sources that demonstrate credible expertise. Google refers to these credibility indicators as E-E-A-T: experience, expertise, authority, and trust.
In practice, this means content should reflect real knowledge of the subject. Generic definitions only get you so far — strong AEO content includes insights drawn from actual work, industry experience, or original analysis.
For example, a cybersecurity firm writing about threat detection might reference internal research or share examples from real client engagements.
These types of details signal that the organization understands the topic in practice. Over time, consistent publication of this kind of expertise helps AI systems associate the brand with authority in that subject area.
Answer engines interpret questions the way people naturally ask them. That means content often performs better when it reflects natural language instead of rigid keyword phrasing.
For example, someone researching marketing attribution might ask:
Structuring sections around questions like these helps AI systems match your content with real user queries.
Strong AEO content also anticipates follow-up questions. A page explaining marketing attribution might include sections about data accuracy, implementation complexity, or how attribution influences budget decisions.
Connecting those related ideas helps search systems understand the topic more completely and reduces fragmentation across multiple pages.
Clear hierarchy also matters. Question-based headings followed by concise explanations make it easier for AI systems to summarize or extract specific sections when generating answers.
Answer engine optimization focuses on preparing content for the process of assembling responses from credible sources and summarizing it for the user. When information is structured clearly and supported by credible expertise, AI systems have an easier time referencing it while generating answers.
Content that appears inside AI-generated responses usually follows a predictable structure. It explains a concept clearly, avoids filler, and provides enough supporting context for the system to validate the information.
If you want to understand how to optimize content for generative AI, begin sections with a concise explanation of the topic, followed by examples, data, or deeper analysis that reinforces the credibility of the answer.
For example, a page explaining marketing attribution might begin with a definition, then expand into implementation considerations, measurement challenges, and how attribution influences budget decisions. Structuring content this way makes it easier for AI systems to extract the core explanation while still giving readers useful context.
Let us say it again: answer engine optimization works best when it supports a broader search strategy. It’s a core pillar, but it isn’t the whole coliseum of AI search SEO.
AEO focuses on how answers are structured and interpreted. Traditional SEO still influences how pages are discovered and how authority develops around a topic. When both approaches work together, brands are more likely to appear during the research stages where buyers gather information.
A company building authority around marketing analytics might publish in-depth resources on attribution models and data integration strategies. Over time, that connected coverage strengthens the brand’s association with marketing measurement.
Generative search does not exist on a single platform, either. AI Overviews, Perplexity, and other answer engines each generate responses differently.
Because of that variation, it helps to monitor how your brand appears across these environments. Some platforms may reference your research frequently, while others rely on different sources when generating answers.
A company might discover that its insights appear regularly in one AI platform but rarely in another—maybe they need to improve visibility on Perplexity. Observations like that can reveal gaps in how expertise is structured or referenced across the web, which becomes clearer when examining how brands appear in systems like Perplexity’s search engine and browser.
So, how do you actually evaluate whether your content is positioned to appear in AI answers? This is where the right tools can make all the difference.
AEO tools typically analyze how well content aligns with the structures AI systems rely on when generating answers.
One common area is entity clarity. Tools look at how consistently a brand, topic, or product appears across pages and whether the relationships between those entities are clearly defined. If your company publishes content about multiple services, for example, these tools help determine whether those services are clearly connected to your brand and expertise.
Another area is semantic coverage. Platforms often evaluate whether a topic includes the related questions and supporting explanations that give AI systems enough context to understand the subject. A page explaining marketing attribution might also need sections about attribution models, implementation challenges, and reporting accuracy for the topic to feel complete.
Many tools also examine question-to-answer structure. This includes identifying whether pages contain clearly defined explanations that AI systems can extract without needing to interpret long paragraphs.
Finally, platforms often review authority indicators such as citations, references, and how often your content appears across relevant sources on the web.
Not every platform labeled as an AEO tool is built for enterprise teams. Many focus on content analysis alone, which can leave large organizations without visibility into the broader search ecosystem.
When evaluating answer engine optimization platforms, look out for these capabilities especially.
The best platforms provide actual, actionable information on how AI systems interpret your expertise rather than simply pointing out missing keywords.
Tools can show you important gaps, but they rarely solve the strategic challenge on their own.
Answer engine optimization requires coordination across several departments. Content teams shape the explanations AI systems extract. Technical teams manage structured data and site architecture. Digital PR and communications teams strengthen authority across the web.
Without that coordination, even the best tooling will only surface problems rather than help solve them.
Over time, the organizations that succeed with AEO treat tools as diagnostic support while focusing most of their effort on building authority and expertise.
By the time most organizations start exploring answer engine optimization, they’ve already noticed something unusual in their search data since AI systems are taking the lead.
At 97th Floor, answer engine optimization isn’t treated as a standalone tactic. It’s integrated into a broader shift toward AI-driven search, where content structure, authority, and technical clarity all influence how a brand shows up online.
Enterprise organizations rarely struggle with producing content. The challenge is aligning that content so it reinforces expertise across a category.
That alignment requires several moving parts working together. Content needs to answer the right questions. Technical teams need to support structured data and site architecture. Digital PR helps strengthen authority signals across the web. We make sure all of your best people and AEO efforts actually work together and make progress.
AEO should never be measured by visibility alone. What matters is whether that visibility influences the conversations buyers are having when they research a category.
At 97th Floor, answer visibility is connected to the areas that actually drive revenue. Content is structured so AI-generated answers reference the topics that matter most to the organization’s services and solutions.
Over time, this approach shifts the goal of AEO from general awareness to category influence. When buyers encounter explanations that consistently reference your expertise, your brand becomes part of how they understand the problem itself.
Search will continue evolving as AI platforms mature. New answer engines will emerge, and existing platforms will refine how they interpret and summarize information. That’s why strong AEO strategies focus on building durable authority rather than chasing short-term optimization tactics.
Are you ready to shift into a new gear with answer engine optimization? Here are some questions you can ask yourself to know if it’s time.
Answer engine optimization often requires teams to rethink how search visibility is measured and managed.
Start by looking at how your organization currently approaches search.
These conversations usually surface quickly whether AEO can be implemented smoothly or whether internal alignment still needs work.
The next step is examining whether your existing content can actually support answer visibility. Key questions to review include:
Finally, it helps to look outward.
In many industries, answer engines already reference certain organizations repeatedly when explaining a topic. Those brands effectively shape how buyers learn about the category. Ask yourself:
These observations often reveal whether your brand is currently influencing the conversation or watching it happen from the sidelines.
If these questions surface opportunities, it may be time to develop a structured AEO strategy.
At 97th Floor, answer engine optimization is approached as part of a broader AI search transformation that connects technical SEO, authority development, and content strategy. Organizations exploring how to improve their answer visibility often begin by examining how their content aligns with modern search strategies.Discover how we can help you in the new age of AI search!
