Anastasia Braitsik stands at the intersection of data-driven SEO and the burgeoning world of AI-integrated marketing. As a global authority in content strategy, she has navigated the shifts from traditional keywords to the complex, conversational landscapes of modern AI search. Today, she unpacks the “GEO Awareness Gap,” a phenomenon where brand enthusiasm for generative engines outpaces organizational readiness. Through this discussion, she provides a roadmap for navigating the shift in consumer discovery, highlighting why the current skepticism among marketers is both a hurdle and a rational response to a technology that is still finding its voice in 2026.
A recent study shows that while 92% of B2B organizations are experimenting with generative engine optimization, fewer than 15% have a dedicated owner for these initiatives. Why do you think such a massive gap exists between interest and actual organizational ownership?
This gap exists because most organizations are currently navigating a thick fog, caught between the excitement of a new frontier and the cold reality of shifting budget priorities. When you look at that 92% figure, it represents a sea of marketers who are testing prompts or attending webinars, perhaps while sipping their morning coffee and wondering if their current SEO strategy is about to become a relic of the past. However, the fact that fewer than 15% have a dedicated owner shows a lack of structural confidence; companies are hesitant to hire a specific expert when they aren’t even sure which department should foot the bill. It feels reminiscent of the early days of social media where everyone had a profile, but no one knew if it belonged to PR, Marketing, or Customer Service. We see a lot of “operationalizing” that is really just “dabbling,” because until the path to revenue is as clear as a traditional search report, leaders will keep GEO as a side project rather than a core pillar.
With so many marketers still in a discovery mode, what are the primary questions they are asking about how AI is fundamentally changing the way consumers find information?
The questions I hear on the ground are much more visceral and practical than the high-level methodology debates happening on professional forums. Marketers want to know if the “click” is dying—they are asking if consumers are actually changing their daily habits or if they are just playing with a shiny new interface. They see the data but they want to feel the impact, asking whether AI referrals are actually showing up in their analytics with the same intent as a traditional search. There is a palpable anxiety about whether discovery is moving away from a list of links to a single, authoritative voice that might not even provide a direct citation to their brand. They are trying to decide if they need to pivot their entire content engine right now or if they can afford to wait and watch the market settle as they move through the middle of the decade.
You’ve mentioned that search, social media, and mobile all had their own periods of slow adoption before becoming dominant. Where do you see Generative Engine Optimization sitting on that historical timeline right now?
We are currently in that “quiet before the storm” period, very similar to the transition to mobile-first indexing where the people closest to the technology were shouting about change while the rest of the world was still focused on desktop layouts. In 2026, we are seeing the foundational shifts take hold, where the friction of finding information is being smoothed out by generative responses, making traditional search feel clunky and outdated by comparison. It took years for social media to change brand discovery, and we are seeing that same slow-motion earthquake happening with GEO. The industry is currently arguing over methodology and measurement frameworks, which is a classic sign that we’ve moved past the “magic trick” phase and into the “infrastructure” phase. It is a period of transition where the old rules still apply, but the new rules are being written in real-time by the very engines we are trying to optimize for.
Why is the skepticism we see from marketing leaders today considered a rational response rather than just a resistance to new technology?
Skepticism is the only logical reaction when you are asked to invest heavily in a system where the “ruler” used to measure success keeps changing. Right now, attribution is a massive headache; it is incredibly difficult to track a user’s journey through a conversational AI that provides a synthesized answer rather than a direct link, leaving a bitter taste for leaders who are used to crisp data. Traffic volumes from these engines, while growing, are still small compared to traditional search, so a CMO looking at a spreadsheet sees a lot of risk for a relatively minor immediate reward. There is also a complete lack of industry consensus on what “visibility” even looks like in a generative engine, leaving marketers feeling like they are shooting at a moving target in the dark. Until we have standardized metrics that can prove a direct line from an AI citation to a converted customer, the hesitation to go “all in” is actually a sign of a disciplined, data-driven marketing culture.
What is your forecast for Generative Engine Optimization?
From 2026 to 2028, I expect we will see a dramatic consolidation of ownership where that 15% of dedicated GEO owners will triple as measurement tools finally catch up to the technology. We are going to move away from “prompt engineering” as a novelty and toward “information architecture” as the primary way we feed these engines the high-quality data they crave. Brands that spend this year building authoritative knowledge bases will find themselves being cited more frequently as the engines become more discerning about their sources. Eventually, GEO won’t be a separate category at all; it will simply be the way search marketing is done, integrated so deeply into our workflows that we’ll look back and wonder why we ever thought it was a separate discipline. The winners will be those who stop asking “if” it matters and start asking “how” they can provide the most value to the AI’s end user.
