A VP of Marketing pulls up her new GEO dashboard. Citation rates across ChatGPT, Perplexity, and AI Overviews. Sentiment analysis. Competitive share of voice. The data confirms what she suspected: her company is invisible on comparison queries in their core category.
Competitors are getting mentioned. They're not.
She clicks over to the recommendations tab.
"Add statistics to your content"
"Create FAQ pages"
"Write listicles"
She closes the laptop, pinches the bridge of her nose, and sighs.
The boom
The GEO tools market has exploded over the past twelve months. Goodie, Profound, Peec, Ahrefs Brand Radar, Conductor: the list keeps growing. And the money is following. According to Conductor's January 2026 report, enterprises now allocate 12% of their digital marketing budgets to GEO. 94% of CMOs plan to increase that investment this year.
These are self-reported survey numbers, and Conductor has skin in the game, but the direction is consistent across sources: budgets are moving. 97% of CMOs in that same survey report GEO has had a positive impact on their funnel.
The dashboards work. Brands can finally see where they're invisible in AI-generated answers, a problem most didn't even know they had just a year ago.
The tools have made visibility diagnosable. What they haven't solved is what comes next.
The stakes
The stakes here are not abstract.
According to 6sense's 2025 Buyer Experience Report, which tracked nearly 4,000 B2B buyers on deals averaging $300,000 to $400,000, 94% of buyers now use LLMs during their purchase journey. G2's 2025 Buyer Behavior Report found that 79% of software buyers say AI search has changed how they conduct research, with 29% starting in LLMs more often than Google. Forrester reports that B2B buyers are adopting AI-powered search at three times the rate of consumers.
And the engagement quality is different. Neil Cohen, former CMO of cybersecurity firm Kasada, reports that site visitors from AI platforms spend up to three times more time on-page than those from traditional search. Their queries are more complex (averaging 15 to 23 words) and they arrive more informed.
By the time someone clicks through from an AI-generated answer, they've already done the comparison shopping inside the chat window.
Citation matters enormously. Seer Interactive's September 2025 study, tracking 3,119 queries across 42 organizations, found that brands cited in AI Overviews earn 35% more organic clicks and 91% more paid clicks compared to brands that aren't cited.
Meanwhile, organic CTR dropped 61% for queries where AI Overviews appear. Even queries without AI Overviews saw a 41% decline, suggesting users are simply clicking less everywhere.
Here's what makes this existential for B2B: 6sense found that 95% of the time, the winning vendor is already on the buyer's Day One shortlist. Four out of five deals are won by the "pre-contact favorite."
If shortlists are increasingly forming inside AI conversations, and buyers aren't clicking through to discover you later, then visibility at the moment of research isn't a nice-to-have. It's the whole game.
The gap
So the data is clear. The investment is there. The dashboards are live. Why isn't it working?
Emarketer framed it precisely in January 2026: "AI visibility is the No. 1 goal for leaders, but creating AI-optimized content at scale is their top challenge."
Conductor's own survey confirms it. When C-suite leaders were asked about their biggest GEO obstacle, the top answer wasn't budget or buy-in. It was scaling AI-optimized content.
The tools tell you where you're losing. They don't tell you what to build.
Look at the out-of-the-box recommendations many platforms provide. They're category-level advice dressed up as strategy:
Add statistics. Improve E-E-A-T signals. Create comparison content. Structure your pages for extraction.
None of this is wrong, but all of it is obvious. You don't need a dashboard to know that FAQ pages and statistics help with AI visibility.
The actual hard work is knowing which comparison to write, for which persona, addressing which specific objections, structured for which platform's citation patterns, in what order of priority. That's strategy. "Add statistics" is a checklist.
Why the gap exists
The gap exists because bridging visibility to execution is genuinely difficult.
To move from "you're invisible on comparison queries" to "here's your content roadmap for Q1," you need to map specific queries to specific personas and journey stages. You need to understand why competitors are winning particular queries, not just that they're winning. You need briefs specific enough to hand to a writer Monday morning, not vague directives that require another round of interpretation.
Most GEO tools were built by SEO people working from SEO mental models. Track rankings, surface gaps, provide category-level recommendations. The playbook worked when the optimization target was a relatively stable set of ranking factors. But AI citation is a different game entirely.
The environment is volatile. Superlines' January 2026 data shows that roughly 70% of AI Overview content changes for the same query over time. About half of citations get replaced when the answer updates, and only 30% of brands maintain visibility from one snapshot to the next.
Generic advice can't keep pace with that volatility. By the time you've executed on "create more FAQ content," the citation landscape has already changed.
What closes it
What would actually close this gap?
Not better dashboards, more granular visibility metrics, or AI-generated content recommendations that read like they were written by a model trained on SEO blogs from five years ago.
The problem isn't knowing what to do at the category level. Every marketing team understands that structured content, authoritative sources, and clear answers help with AI visibility. That knowledge is table stakes.
The problem is knowing what to build next, for whom, addressing what.
Closing the gap requires moving from category-level recommendations to query-level specificity. Not "write a comparison page" but which comparison, between which competitors, for which persona, addressing which objections. The difference between a recommendation and a research-backed, data-informed brief.
It requires understanding why competitors win specific queries. What are models actually citing? What structure, what claims, what authority signals? Without that analysis, you're guessing at best practices rather than reverse-engineering what's working.
It requires mapping content to buyer journey stages. Discovery queries need different content than validation queries. A procurement manager asking "what are the best tools for X" is in a different mental state than one asking "does [your company] integrate with Okta." The content strategy for each is different, and the platform behavior for each is different.
It requires accounting for platform divergence. In one large prompt analysis, ChatGPT vs Perplexity domain overlap was a mere ~11%. Platform-agnostic advice is increasingly useless.
And it requires feedback loops. You ship content, you measure whether citation patterns shift, you adjust. Static playbooks don't work in an environment where 70% of AI Overview content changes over time and half of citations get replaced when answers update.
Taking the next step
The GEO tools have done their job: they've made the invisible visible. The next phase of the market will be defined by whoever figures out how to make that visibility actionable. Not at the level of "add statistics," but at the level of "here's exactly what to build next week."
That's the gap. The teams that close it will win the queries that matter, while the ones still staring at dashboards will keep wondering why the data isn't translating into results.