Over the past few weeks we've made a case in pieces. A ten-minute exercise with no keywords surfaced a 40% gap in mature, keyword-researched content calendars. G2's buyer data showed that half of B2B software buyers now start their research in an AI chatbot — asking questions that generate no trackable search data at all. And before any of that, we argued that keyword research could no longer be step one of a content strategy.
Fair response: okay — then what is step one?
The answer is the same one it’s always been for us: the audience. What’s new is the discipline behind getting there — in July, we rebuilt the process our teams use to turn that belief into a repeatable strategy.
We’ve said “audience first” since before GEO was an industry term. What’s changing isn’t the belief — it’s the machinery underneath it, and the case for changing that machinery stacked up fast this year. Because of that, we built our new process around one irrefutable mission statement: it's our job to figure out what questions people are asking, and to make our clients become part of the answer.
Search has gone through three eras of what winning meant. Early on, brands had to own the solution — people searched for the thing they already knew they wanted, and you fought to show up for it. Then search got bigger, people researched more, and it became our job to own the category — this is where classic keyword research was born: low-hanging fruit, content gaps, topical authority, hub and spoke. Now the search universe has exploded again, across AI assistants and conversational queries, and the job is to own the conversation — every question a buyer asks on the way to a decision, most of which no keyword tool can see.
Here's the friction, and we'll be honest about it because every experienced SEO feels it: a lot of this demand can still be captured by traditional keyword research. So why change anything? Because we tried the obvious shortcut — starting with keywords and expanding them into prompts — and it doesn't work. The prompts that come out of keywords are limited to the pool the keywords define, and when you push AI tools to generate them, you get generic, low-quality "what is X" questions. Starting from the audience produces a far larger universe of real questions; the keyword data then attaches to it, rather than defining it.
And to be equally clear about what we're not doing: we're not abandoning traditional SEO. Every client still has 5–10 non-negotiable core keywords tied directly to their product and revenue, each classified by the strategy it needs — Watch (already won, defend it), Win (can realistically capture #1), or Invest (long-term authority play). One of our e-commerce clients has held the #1 position for their money keyword for over five years, and we still track it, still build to it, still defend it. That work doesn't stop; it's just no longer the whole strategy.
Before the steps, the allocation decision that makes this a different process rather than the old one with new vocabulary: content volume is weighted roughly 45% problem-aware, 45% solution-aware, and 10% decision-stage.
That looks inverted if you grew up on keyword-first strategy, because it puts 90% of the work where the trackable volume can’t be found. But the early and middle journey is where AI-era research thrives — the long, contextual questions that change buying decisions. It's also where most opportunity is undefended, because everyone in your category is working from the same keyword dataset and converging on the same expensive bottom-of-funnel terms. The decision stage still matters and still converts, but you need to support your core keywords.
The strategy is built by two roles working as a duet — a content marketer who owns the audience, and a search marketer who owns the data — creating a collaborative harmony that achieves our audience-first, data-backed output.
We start with personas built on audience research and client priorities — primary motivators and pain points guided by focus products/services and UVPs. We also look at how long their buying journey runs. That last detail shapes everything downstream: a quick-purchase product leans harder on traditional keywords and conversion content, while a long B2B sale needs a much bigger upper funnel.
At the same time, we classify the client’s 5–10 core keywords as Watch, Win, or Invest, and confirm with the client that we’re building on the right foundation before going further.
Now the exercise we ran company-wide becomes a formal step. For each persona, at each stage of their journey, instead of leaning on traditional keyword research, we write the real questions they’d ask — fears, objections, comparisons, buying concerns — sourced from customer interviews, sales conversations, and how the brand already shows up in AI search. Awareness-stage questions stay diagnostic (“why is my interest rate higher than expected”), not solution-focused (“which lender should I choose”), because that’s how real buyers actually think before they’re ready to decide.
Here's where keyword data re-enters — as evidence, not as the gate. Each question gets paired with the keyword that best represents it — chosen for how well it captures the question, not just its metrics — and scored on search volume, traffic potential, difficulty, and CPC.
Notice what the score does and doesn't decide. A prompt with weak keyword signals doesn't automatically die — it's flagged as a risk signal, and it might still be worth building for AI visibility. Search volume is evidence of demand, not the boundary of demand.
This is the most crucial stage: raw research becomes a prioritized strategy, and it happens through scoring rather than vibes.
Every topic gets three scores, one from each side of the duet, plus one that only exists because both sides agreed to be scored:
• Audience score: how many personas care, and how much the topic could sway a buying decision
• Client score: how directly it ties to the client’s revenue and stated priorities
• Demand score: search volume, traffic potential, keyword difficulty, and CPC, combined into one number
The three combine into a single ranked list. So when a team is staring at 150 possible topics wondering where to start, the answer is already sorted — by audience, business, and data together, with no single input allowed to take the lead. That’s the point. Plenty of teams claim to be audience-first right up until the sort-by-volume click. The scoring makes the claim structural.
Before building anything new, we check it against what the client already has. If an existing page is close to the mark, we optimize it instead of creating a duplicate — a small step that makes the most of existing resources (and as a bonus, saves a client from ever hearing “we recommend a new page” about something already sitting on their site).
From there, related topics get consolidated, and distinct audiences get split apart — for example, prompts would diverge for anxious parents and undergrads around the topic of student loan cosigning. These would be two content pieces, not one, because they're two audiences with two different intents.
Building the content calendar from there is easy — the strategic thinking already happened upstream.
Tracking starts the moment the calendar goes live, and it runs on layers rather than a single line. Traditional keywords and custom prompts are tracked side by side. Search Console gets watched for the long-tail, question-shaped impressions that new content should start earning. And around those, the downstream signals of AI visibility: brand mentions, citations, referral traffic from AI platforms, branded search, direct traffic, and — critically — conversion rates on high-traffic pages, because the endgame of zero-click research is a visitor who arrives already convinced and converts at a higher rate. Any one of those lines is easy to argue with. Layered together, the trend is hard to deny — which is exactly what reporting in a zero-click era has to be able to survive.
A process this concrete invites a misunderstanding: that GEO is only this. It isn't. Around the strategy build sit the factors that don't fit in a scoring column but move AI visibility anyway: technical optimization, original research and proprietary data (which win disproportionately in AI answers), unique points of view, content atomization and distribution, freshness — sometimes the right calendar entry is re-optimizing something you shipped eight months ago — and brand reputation, without which none of the rest gets very far. Those get their own treatment in how we brief GEO content versus SEO content and in why E-E-A-T matters more in AI search, not less.
None of this is keyword research's obituary. It's a reassignment. Keyword data is still genuinely excellent at four jobs: validating demand the persona work surfaced, forecasting the trackable portion of the opportunity, client education (numbers persuade stakeholders in ways personas don't), and the decision-stage plays it was always built for — including those 5–10 core keywords every client will always have.
What it lost is the job it was never qualified for: deciding what your audience cares about. Start with the human, weight the work toward where they actually are, and bring the data back in where it tells the truth. Trust us, it works — this approach grew one client's AI search results 261%.
That's the process now guiding how we build strategy for every client engagement. The next question is what it surfaces for yours.
Want to hear this philosophy debated out loud? This episode of The Campaign goes deep on audience-first marketing:
Want to see what this process would surface for your brand? Start with a free AI audit, or explore our GEO & AI Search services.
Platform properties are now available to everyone. Google announced this month that the feature has rolled out globally in Search Console, along with a new performance guide for social and video content. If you missed the original announcement, we covered it in full when the feature first launched — read that article here for a breakdown of what platform properties are and how to set them up. This article is about what to do next: how to actually use this data to your advantage.
First, a quick refresher on why this data is different. The data you get from Google Analytics is valuable primarily because it's your data. You own the domain, the tracking code fires on every user interaction, and you get to see how customers move through your content, which channels drive traffic, and where the journey breaks down. But your website is only one of many places your customers find your brand through search. Your YouTube channel appears in search results. So does your Pinterest profile, your TikTok account, even your Instagram posts. The difference: you don't own those platforms, so you've never had visibility into how people find your brand there.
Google Search Console has always been the exception, it shows data owned by Google, not dependent on a tag firing on your site, showing you exactly how your domain gets found organically. Platform properties extend that same visibility to the profiles you maintain on platforms you don't own. That's a genuinely new window into your brand's search footprint. Here's how we recommend using it to build effective content strategies.
Buyers are discovering, evaluating, and comparing brands through AI. 97th Floor CEO, Paxton Gray, is joining WordPress VIP CMO, Jodi Cerretani, for a three-part series on what that means for enterprise brands.
When you connect a platform property and open the Performance report, you're looking at the actual search queries that led people to your Instagram posts, TikTok videos, X threads, or YouTube content. These are a direct read on what your audience is typing into Google when they're in the market for the kind of content you create. And because this data lives outside your website, it often captures demand that never shows up in your standard GSC property or GA4 at all.
Start by filtering the Performance report by query rather than by post. Look for terms generating consistent impressions across pieces of content across multiple platforms— that's your clearest signal of which topics Google associates with your brand. This is actionable whether Google has it right or wrong. If the association is right, you've confirmed your positioning. If it's wrong, you've found a disconnect between the content you're producing and the brand you're trying to build.
Pay close attention to the gap between impressions and clicks. A post sitting at thousands of impressions with a low click-through rate usually means one of two things: either the content isn't compelling in how it appears in search results, or the query intent is better served by something other than a social post. Say one of your TikToks is pulling 12,000 impressions on a "how to" query but converting under 1% of them to clicks, don’t mistake that for a failing video, that's a searcher who wants a step-by-step guide, not a 30-second clip. Cross-reference these queries against your domain's GSC property, and you have a prioritized list of content your site is missing for topics Google already associates with your brand.
The introduction of AI Overviews (AIO), along with Google making every SEO's life harder by making positions 1–100 nearly impossible to track, has injected a lot of unknowns into content planning. Keyword research is less accurate than it used to be, and the keyword datasets from SEO tools are no longer as reliable or actionable. Content strategy has started to feel like guesswork.
Platform property data gives you a faster, more grounded way to pressure-test topics before you commit significant resources to them. Here's the logic: if your social or video content on a topic is already generating impressions and clicks through Google Search, that isn't projected demand from a keyword tool. That's actual people looking for that content right now. It's the kind of validation that moves a topic from "we think this might work" to "we have evidence this works" in one report.
The practice is simple. Before greenlighting a major content investment such as a pillar page, a video series, a guide, first filter your platform property queries by the topic and look at the impression trend over the last 28 days. Growing impressions across multiple posts? Green light. Flat or nonexistent? Either the demand isn't there or Google doesn't yet see you as relevant to it, both of which are worth knowing before you spend the budget. This works in reverse, too: topics your team is convinced are winners but that show zero organic pickup across any platform deserve a hard look before the next quarter's calendar gets built.
Topic clusters have been a staple of SEO strategy for years: a pillar page supported by related content, interlinked to signal depth on a subject. Platform properties let you extend your topic clusters beyond your domain.
Think about how Google now sees your brand. It isn't just crawling your website, it's also crawling and indexing your YouTube explainers, your Instagram carousels, your TikTok tutorials, and your X threads, and platform properties show you which of those are earning search visibility. When your blog post, your video, and your social content all rank for queries within the same topic neighborhood, you're demonstrating authority on that subject across the entire search results page, not just in the ten blue links.
Use your platform property data to find the clusters that are already forming. Pull the top queries from each connected platform and group them by theme. Where you see the same topic surfacing across two or more platforms plus your domain, you have an emerging cluster, ready to be reinforced. Fill the format gaps: if the topic has a strong video and a strong blog post but nothing on social, that's your next carousel. Where a topic performs on social platforms but has no corresponding pillar on your site, that's your next long-form piece, and your existing social content becomes the distribution engine for it the day it publishes.
It is also essential to convey the same messaging and be consistent on unique value propositions, products and offerings, and overall voice of your brand across all platforms. You are trying to build an entity on the web that clearly tells Google who your brand is, what you do, and who you serve. That messaging needs to be consistent across all platforms that talk about your brand.
The goal is to stop treating your website content and your social content as separate strategies with separate teams and separate calendars. Google is evaluating your brand's expertise holistically. Your planning should work the same way.
The teams that get the most out of platform properties won't be the ones who check the report once out of curiosity. They'll be the ones who build it into their operating rhythm.
Add a platform property review to your monthly or quarterly content planning sessions, right alongside your standard GSC and GA4 reporting. Three questions worth asking every time:
This is also a report worth putting in front of stakeholders who don't live in Search Console. Social teams have historically had to justify their work with platform-native metrics such as likes, follows, engagement rate. These never quite connect to the pipeline. Platform properties give them something new: proof that social content is capturing organic search demand, in the same report and the same language the SEO team already uses. For agencies and in-house teams alike, that's a bridge between two functions that have been measured separately for too long.
Platform properties won't replace your keyword research or your analytics stack, but they close a visibility gap that's existed as long as brands have had social profiles. The brands that win with this feature will be the ones who treat it as a strategy input, not a vanity report. Connect your platform properties this week, and if you want help turning what you find into a content strategy, we can support. Let’s talk.
If you can't track where your traffic comes from, how do you prove your marketing works? It's the question keeping marketers awake right now as zero-click search dismantles the link between a question and a website visit. But here's the thing: We’ve been here before.
In 2013, a report existed that no marketer could produce today. It showed a client's organic keywords next to their monthly search volume — standard stuff. But the column next to it was the one worth staring at: exact visits, by keyword, down to the misspellings. Not a rounded estimate. Not a range. A number like 19 visits from "employee rewards program," 6 more from its singular form, and a dollar figure attached to each one specific enough to say, out loud, to a client: this keyword drove $13,750.30 in revenue last month. That kind of precision was normal.
Then, later that year, Google rolled out a Secure Search update. Overnight, that column went dark — just row after row of “(not provided)” — which became the tongue-in-cheek name for the event. Every marketer running paid or organic search lost the ability to tie a keyword to a visit, to a dollar. They were also left explaining to their bosses why half of what they were used to seeing in a report could no longer be found.
It read like an extinction event. If you can't prove which keyword paid for itself, how do you justify the budget behind it? How do you do the job at all?
Here's the part that's easy to forget more than a decade later: The data didn't come back, and the industry didn't collapse. SEO and content marketing kept absorbing bigger budgets, kept getting more competitive, kept becoming a default line item instead of a nice-to-have. One company’s own trajectory makes the point bluntly — the same business doing roughly $120,000 a month in total revenue when "(not provided)" hit was doing something like 8x that by the summer of 2026. That's not a story about one agency's growth engine. It's a data point inside a much bigger one: an entire industry kept expanding after losing the single metric everyone assumed it couldn't survive without.
The reason is simpler than it feels in the moment. Nobody had the data. Not your agency, not the client's last agency, not the biggest competitor in the space. When an entire industry loses the same piece of information at the same time, the businesses that pull back and the businesses that keep going are separated by something other than access to better numbers — because there is no better number to access. They're separated by whether they kept showing up for an audience that, "(not provided)" or not, was still searching, still buying, still there.
It also helps to be honest about how solid that lost data actually was. Search volume was never a hard count — it was a bucket. A keyword estimated at "10" a month might really be 7 or 13; Google just rounds it into the same tier. At real scale, the buckets get enormous: A keyword estimated at 246,000 monthly searches might genuinely be anywhere from roughly 190,000 to 300,000, and it still gets reported as one tidy number. Even today, research from Ahrefs puts their own keyword-volume accuracy at around 60%, and Google's own Keyword Planner is even lower than that. Their CMO's response to the criticism was blunt: directionally accurate is accurate enough. The precision the industry mourned in 2013 was, in large part, always a little bit of a story marketers told themselves.
Buyers are discovering, evaluating, and comparing brands through AI. 97th Floor CEO, Paxton Gray, is joining WordPress VIP CMO, Jodi Cerretani, for a three-part series on what that means for enterprise brands.
Search is going through another one of these moments, and it has a name: zero-click search. AI Overviews, AI Mode, ChatGPT, and a growing list of assistants now answer the question directly, inside the search or chat interface, before a user ever lands on a website. The visit that used to generate a session, a pageview, a trackable event — the raw material attribution was built on — increasingly never happens at all. That's not a smaller version of the "(not provided)" problem. It's the same problem at a larger scale: the industry is once again losing the clean, attributable trail between a person's question and a business's website, only this time it's not one column in a report, it's the click itself.
And the audience isn't waiting for marketers to catch up. Google has said AI Mode now sees over a billion users a month, with query volume roughly doubling each quarter. ChatGPT is now among the five most-visited websites in the world. Research from G2 found that 51% of B2B software buyers now start their research inside an AI chat interface, that two-thirds say what the AI told them changed the direction of their decision, and that 80% say it made them buy faster than a traditional search journey would have. The audience is unmistakably there — it has simply moved to a place that's harder to measure.
The panic this produces is familiar, because it's the same panic. Brands are asking whether their traffic decline means their strategy failed, whether they picked the wrong platform, whether the whole channel is breaking down. Usually none of that is the actual problem. The problem is that the tidy, attributable version of the data that made everyone comfortable is gone, and comfort is not the same thing as evidence of demand.
Both moments — 2013 and now — reward the same instinct and punish the same mistake. The mistake is treating the loss of a metric as a signal to pull back, to wait for a new dashboard before doing anything differently. The instinct that wins is simpler and less comfortable: stop trying to measure the audience perfectly and start following it directly. In 2013, that meant investing in search and content even without a dollar figure attached to every keyword, because the audience was still searching whether or not anyone could prove it. Today it means showing up in AI Overviews, in chat answers, in zero-click results, because that's where the questions are being asked now — whether or not a session ever gets logged for it.
Search volume, keyword data, and attribution reports were always evidence of demand, never the boundary of it. Losing the cleanest version of that evidence hasn't shrunk this market once in its history. It has only ever separated the companies willing to adapt and go where their audience actually is from the ones waiting for the data to make that decision feel safe. The first group is the one that keeps growing.
Uncertainty is uncomfortable. But an industry that only invests when it's certain isn't managing risk — it's guaranteeing it'll be standing still when the data finally catches up to where the audience already went.
At our July company meeting, we did something that would have felt like malpractice a few years ago: we asked every team at 97th Floor to plan content for a real client with keyword research completely off the table.
No search volume, no difficulty scores, no SERP analysis — just ten minutes, one client, and a blank doc.
By the end, one team had run their list against the client's actual content calendar and found that roughly 40% of the ideas they generated weren't covered anywhere on it.
Here's the exercise, why we ran it, and what came out of it.
Every team picked one of their clients and got the same four instructions:
That's it. Ten minutes on the clock.
Some context, because this exercise didn't come out of nowhere. It was a stress test of a belief we've been building toward for a while.
The way people search has fundamentally changed. Over a billion people a month are now using Google's AI Mode, and queries there are doubling every quarter. ChatGPT became the fifth most-visited website on the internet. And per G2's research on B2B software buying, 51% of buyers now start their research in an AI chatbot — and for two-thirds of them, what the AI said changed the outcome.
Here's the problem that creates for content strategy: keyword data only shows you the searches that happened often enough, in similar enough phrasing, to register as trackable volume. A keyword like "ai in education" shows 6,900 monthly searches. What it doesn't show you is everything orbiting it in AI search:
Three completely different intents, three completely different pieces of content — and none of them have trackable search volume. If your research process never surfaces these questions, your content won't answer them. And if your content doesn't answer them, someone else's will.
Internally, we've boiled this down to one line: search volume is evidence of demand, not the boundary of demand. (We've written before about why keyword research can no longer be step one of a content strategy — this exercise was us testing that thesis on ourselves.)
So the question became: if our strategists stopped looking at the data everyone else is looking at, and just thought like the actual human on the other end — what would they come up with that the keyword-first process misses?
To keep "think like the customer" from being hopelessly vague, every team worked from our customer journey map — the same framework we now use to build GEO prompt strategies for clients.
The short version:
Awareness (problem aware). The user is diagnosing a problem, not shopping for a product. They're asking "why is...", "what causes...", "how do I...", "is it normal that...". The content objective is to build understanding — connect symptoms to root causes.
Consideration (solution aware). They understand the problem and are evaluating ways to solve it — approaches, not vendors. "Best way to...", "pros and cons...", "build vs. buy", "in-house vs. agency", "worth it...".
Decision (vendor/product aware). Pricing, demos, reviews, integrations, competitor comparisons. This is where traditional keywords still shine, because these searches are trackable and high-converting.
Note the weighting: roughly 90% of the content opportunity, by sheer quantity, lives in those first two stages — and those are exactly the stages keyword research is worst at seeing. Teams were told to focus there.
Across six teams, idea counts ranged from 13 to 76. But the counts mattered less than what was on the lists.
The answering service. One team works with a client that provides live answering agents for businesses, including medical practices. Their keyword-informed strategy centered on the product category. Thinking persona-first, they landed on questions like:
The eyewear brand. A team working with an affordable eyeglasses e-commerce brand started with one team member's real-life story about his glasses and worked outward into use cases: an affordable backup pair for traveling, buying glasses for my aging parents, and true beginning-of-journey questions like do I have to buy glasses from the same place I get my prescription?
The art gallery platform. A team supporting an art gallery CRM and marketing platform got past features entirely: guidelines for hosting a gallery event, international laws and regulations for marketing artwork across borders (their client's customers sell globally), and even a brand-play idea — a Michelin-star-style rating program for galleries, leveraging the client's authority in the art world.
The cruise line. A team working with a premium cruise line looked at everything through the lens of their primary persona — older retiree couples who want to see the world in a relaxed, premium way. Their existing strategy is heavily destination-focused. The persona-first list went broader: safest ways to see the U.S. or the world as an older couple, bucket-list destinations with no kids, travel questions that sit two or three touchpoints before anyone types a destination name.
That last team did something we didn't ask for, and it became the headline of the meeting.
They generated 51 unique ideas, then pasted the client's entire existing content calendar — every term and query on it — into Claude and asked how many of the new ideas were already covered.
The answer: about 60%.
Which means roughly 40% of the ideas a team produced in ten minutes, for a client with a mature, keyword-researched content calendar, weren't on that calendar at all. Not deprioritized. Not scheduled for later. Just never surfaced, because the process that built the calendar couldn't see them.
Not every list was a revelation. The eyewear team was upfront that, as an e-commerce brand, many of their ideas probably would have eventually shown up in keyword research — the exercise mostly forced them to think about the same demand differently. That's a fair result, and worth stating plainly: persona-first ideation isn't magic, and it doesn't replace everything.
We're also not abandoning the trackable stuff. Bottom-of-funnel keywords still convert, and we still want our clients showing up for them. The point is allocation and honesty: some bottom-of-funnel terms are so competitive that over-investing there while ignoring the untrackable early journey means leaving the majority of the opportunity untouched. If the persona research is strong, "no search volume" is no longer a reason to say no.
The point wasn't that every question belongs in your content strategy.
Some of these ideas are two steps away from a purchase. Others are twenty.
That's where strategy takes over.
Funnel depth matters. A B2B cybersecurity company with a six-month buying cycle has far more room to educate around adjacent problems than an ecommerce brand selling reading glasses. A luxury cruise line can justify investing in content that helps travelers decide whether a cruise is the right vacation in the first place, while continuing to own destination- and itinerary-specific searches later in the journey. A local service business may need to stay much closer to buying intent.
The exercise isn't meant to replace prioritization. It's meant to widen the aperture before you prioritize.
Once you've generated the questions, the real work begins:
Every idea should then be pressure-tested against business value, audience relevance, and the role it plays in moving someone closer to becoming a customer.
Ten minutes is enough to uncover opportunities. It isn't enough to decide which ones deserve investment.
That's strategy.
For our content strategies, keyword research is no longer step one. Topic research now starts with the persona and the product: what is this person asking at each stage of the funnel — their fears, frustrations, objections, misconceptions? Keyword data comes back in later, where it's genuinely useful: validating demand, forecasting, client education, and the decision-stage plays it was always good at.
If a ten-minute exercise with no data surfaced a 40% gap, the obvious question is what a rigorous, repeatable version of that process looks like — how you prioritize persona-driven prompts, validate demand without search volume, forecast, measure, and build a content calendar around it. We've now trained our entire company on exactly that, and it's what we'll be sharing next.
For now, steal the exercise. One client (or your own company), one doc, ten minutes, no keywords. Then check your list against your content calendar and see what percentage is missing.
We'd bet it's more than you think.
So you know your brand can surface in AI-generated answers, recommendation lists, and citation panels — and you know which signals matter, from mention frequency to share of voice to the conversions that make it all worthwhile. Great. Now comes the practical question: how do you actually track any of it?
The good news is that tracking brand mentions and citations in AI search is entirely doable. It takes a mix of the right tools, a disciplined manual testing process, and a willingness to study the sources AI platforms keep pulling from. None of it requires a data science degree. It does require consistency — because AI answers can (and will) shift from one day to the next, and a single snapshot won't tell you much of anything.
This guide walks through the full process: the tool categories worth considering, a step-by-step manual testing workflow, how to monitor citations and source websites, and the strategies that turn all that tracking into more visibility.
Does this feel like a lot to keep an eye on? Third-party tools can help, especially when you need repeatability and competitor monitoring across multiple platforms.
A growing crop of tools now tracks prompt-level visibility, citations, and competitive presence across AI platforms. Some focus on recommendation prompts, others emphasize cited sources, and still others prioritize reporting workflows.
These tools are useful for:
One thing worth knowing before you start comparing options: at this point, practically every monitoring tool offers the same core set of tracking features. The list above will show up, in some form, on nearly every product page you visit. That means the feature list is rarely the deciding factor. Instead, choose based on the things that actually differ from tool to tool — price, how easily your team can collaborate inside the platform, and the strength of its reporting and exporting capabilities. Those are the factors that determine whether a tool fits your workflow six months from now.
If you want to understand why AI keeps describing your brand a certain way, it helps to look beyond the AI platform itself. A broader view of your presence across reviews, mentions, directory pages, and publisher sites can reveal the raw material those systems are pulling from.
Popular platforms in this category include Brandwatch, Meltwater, Brand24, Mention, and Sprout Social — and Ahrefs recently entered the space with Firehose. Each monitors how your brand shows up across news sites, social platforms, review sites, forums, and blogs — the very ecosystem AI systems draw from when they characterize your brand. And if you're budget-conscious, Google Alerts is free and works on the same basic concept: it won't match the depth, coverage, or analytics of a paid platform — social mentions largely slip through — but it will flag new pages across the web that mention your brand.
Before a page becomes readily visible in AI-generated answers, it usually has to be structurally sound and substantively useful. We'll table the discussion of whether you should be using AI to write content, but whether it’s human- or machine-generated, you just need to know if it works. That is where crawl data, content performance, backlinks, internal links, and topical depth become valuable, because they help show whether your content is actually built to compete.
If you want a direct look at how AI platforms are mentioning, citing, or excluding your brand, manual testing is still one of the most useful methods available. It is not the fastest process in the world (and it can feel repetitive), but it gives you a level of firsthand visibility that tools alone cannot always match.
Here’s how to make it happen:
Start by creating a reusable list of prompts that reflects the different ways real users might discover your brand. This sample of reusable prompts will act as your own personal AI Search visibility database, that measures the success of your optimization efforts. The goal here is to build a consistent testing set you can run again later and compare against itself without wondering whether the change came from the platform or from your wording.
Your prompt library should include a mix of:
Your custom prompt library should be built in a way that gives you an accurate view of how the LLMs understand your brand, your UVPs, position in the market, as well as the sentiment of the brand (does the LLM speak favorably or negatively about your brand).
One necessary step before you run any of these: instruct the LLM to forgo all prior knowledge about you and to ignore any learned or remembered information when responding. Most platforms now personalize answers based on memory, chat history, and account context, which means an unprompted test reflects your relationship with the tool, not what a fresh prospect would actually see. Adding a standing instruction like "Disregard everything you know about me and any saved memories or past conversations when answering the following" to the start of each testing session keeps your results clean and comparable.
Keep the list somewhere centralized and stable — including that neutralizing instruction, worded the same way every time. If you change the wording every time you test, you will make your own tracking less trustworthy.
Once your prompt library is built, run the same set of prompts across the platforms you want to monitor (Google AI Overviews, ChatGPT, Perplexity, Gemini, Copilot, or any other AI-driven experience relevant to your audience). The important thing here is consistency. Use the same prompts in the same format and, if possible, test them within a similar time frame. That will make your comparisons much more useful.
Prompts might include things like:
Want a clearer picture of how your brand appears in real-world discovery scenarios? You can also create variants that reflect actual buyer concerns, such as industry, budget, company size, or business model.
Once you have responses from multiple platforms, it’s time to look more closely at how the answers are constructed. Pay attention to questions like these:
This is where you should start to see some patterns. One platform may consistently cite third-party review pages. Another may pull more often from brand websites. A third may mention your competitors in recommendation prompts while leaving you out entirely. Those differences are useful to be aware of; they can point to gaps in your content, reputation, or discoverability.
As you’re running prompts, record the results in a way you can revisit later. AI answers can (and will) shift from one day to the next. If you fail to document what appeared, where it appeared, and which sources were cited, it becomes much harder to spot meaningful changes over time. This data will also be valuable for your outreach efforts and give you a more targeted strategy as you work to increase your visibility across the web.
For each prompt, it helps to log the platform, the exact prompt used, the date, whether your brand was mentioned, whether your site was cited, which URLs appeared as sources, and which competitors showed up alongside you. A spreadsheet usually works fine for this (no need to get too technical, unless you’re into it).
Consider this the record-keeping half of a two-part job. Once you have a running log of citations, you'll want to dig into what those cited pages have in common and why AI systems keep returning to them — we cover exactly that in the citation monitoring section below.
A broad prompt gives you a starting point, but it does not always reflect how real users make decisions. People tend to refine their searches once they get an initial answer, and AI platforms are designed to respond to that refinement. By testing follow-up questions, you can see how your brand’s visibility changes as the conversation becomes more specific and more commercially relevant.
A single manual test will give you a snapshot, but what you need to do is turn it into a flipbook. So, you need to run your prompt set again. And again. And again.
And again.
A regular cadence, whether that is weekly, monthly, or quarterly, depending on how competitive and fast-moving your space is, gives you enough repetition to see the kind of movement that denotes trends. Keep the process as consistent as possible so you can see whether your visibility is improving, declining, or staying flat.
Citations show you which pages AI systems seem to trust, which sources keep shaping the conversation, and where your competitors may be gaining ground. In other words, if brand mentions tell you that you are visible, citations help explain why. This is where the citation log you built during manual testing starts paying off — you're no longer just recording what appeared, you're figuring out what to do about it.
Most GEO strategies fail because marketers optimize without knowing what's actually being surfaced. This short cuts to the real problem — and why tracking your citations is the first step to fixing it.
Start by looking for recurring URLs across the prompts you are testing. Pay especially close attention to pages that appear again and again in answers about your category, your services, or your competitors, because repetition usually signals that AI systems see those pages as useful reference points. And once you start seeing the same URLs repeatedly, you will have a better sense of which kinds of content are influencing AI-generated answers in your space.
The cited pages may come from a range of places, such as:
Once you know which pages are being cited, now you get to figure out what makes them citation-worthy. Take the time to study how the information is organized, how directly it answers questions, and how much authority it appears to carry. This usually comes from:
And, wouldn’t you know it, if these elements are working for competitors they can work for you too. Take what you learn here and use it to optimize your content for AI.
Some of the most important citation sources in your space may not belong to you or your competitors at all. Review sites, industry publications, directories, listicles, and third-party comparisons can all shape how AI platforms talk about the companies in a given category.
That is why it helps to look not only at whether a competitor is showing up, but also where the supporting information is coming from. If your competitors are being cited through trusted third-party pages while your brand is missing from those same ecosystems, that gap is worth paying attention to. It can reveal issues that go beyond on-site content and into the broader digital footprint surrounding your brand.
This is where generative engine optimization strategies become especially relevant. If certain pages on your site are already attracting citations, then those are the ones you want to invest in improving. Strengthen their clarity. Expand their usefulness. Tighten their structure. These pages have already caught the eye of AI. Now it’s just a matter of making them better at what they are already doing.
Why is tracking your brand presence in AI Search important? The only way this information and the work of tracking your brand within your custom prompt library is valuable, is if you use your findings to make meaningful optimizations. Understanding what those optimizations should be is more simple than you think.
Let me tell you a secret that’s really not a secret at all: In most cases, the same qualities that make content useful for humans also make it easier for AI systems to understand, trust, and cite. Your goal, therefore, is to give the machine better material to work with.
Here’s a quick overview of AI search engine optimization strategies to help get you there:
All of this might begin to look like a lot to handle on your own. If you’re feeling overwhelmed or if you’d rather have your people focusing more of their time on other areas, AI SEO agency services can make up the difference.
Modern visibility is about more than where you rank on the SERPs, but the core challenge really hasn’t changed that much: You want to be found, understood, and trusted. The difference now is that discovery can and does happen inside AI-generated answers, recommendation lists, and citation panels before a visitor ever reaches your site. Tracking brand mentions in AI search calls for a broader view that includes citations, cited pages, prompt visibility, competitor presence, and the business outcomes tied to each.
But don’t let the newness of it all discourage you. All of this is trackable, improvable, and well worth the effort. With the right mix of native analytics, manual testing, and focused optimization, you can get a thoroughly informed view of how your brand is showing up in AI search and what to do about it next.
And if you’d like someone to handle it for you, 97th Floor can optimize and track your brand mentions 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.
Google just gave content creators and publishers something they've been asking for: visibility into how content on other platforms performs on Search. On July 7, Google announced platform properties, a new Search Console property type built to show how your Instagram, TikTok, X, and YouTube content shows up in Google Search and Discover.
For an industry that's spent years piecing together search visibility from website data alone, this closes a real gap. You no longer need a website to know whether Google is sending people your way — a genuinely new option for creators and publishers who live primarily on social and video platforms.
Platform properties are a new type of property you can add directly in Search Console, separate from your usual site properties. Once connected, you can track which search terms lead people to your Instagram, TikTok, X, or YouTube content — and see how your audience is actually interacting with those posts once they land on them.
This follows an earlier, more limited experiment Google had been running, so the underlying idea isn't brand new. What's new is that it's now a real, general feature site owners and creators can set up themselves.
Once a platform property is connected, three reports become available:
Notably, this isn't an impressions-only report like the recent Generative AI reporting rollout — clicks are included from day one.
This is where it gets genuinely useful beyond just reporting. If you're running a topic cluster strategy, platform properties give you a new way to validate demand for a topic that goes beyond your own website's performance. If search terms are consistently driving traffic to a piece of social or video content on a topic, that's a real signal the topic has search demand — even if it hasn't shown up in your website's own Search Console data yet.
In other words: this isn't just a new report to glance at. It's a new data source for informing what to create next, and where.
Setup is straightforward, but it does require a verification step:
Keep in mind the rollout is gradual — Google has said platform properties will become available over the coming weeks, so don't be surprised if it's not live for every account right away. Full setup details are in Google's help center documentation.
This is worth bringing up in client or team conversations over the next couple of weeks — it's timely, it's genuinely new data, and it opens a real conversation about content strategy across platforms, not just the website. Where you have the access to do so, get the property set up directly. Where account permissions are the holdup, that's a good opening to walk the client or your team through what's needed on their end to get it connected.
We'll keep watching how this rolls out and report back on what the data actually looks like once more accounts have access.
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.
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.
