This article was based on the interview with adMarketplace’s John Nitti on GEO and the future of paid search by Greg Kihlström, AI and MarTech keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
For the better part of two decades, the playbook for customer acquisition has been written in stone, or at least, in the crisp, clean language of a Google search results page. Careers have been built, and fortunes made, by mastering the intricate dance of Search Engine Optimization (SEO) and Search Engine Marketing (SEM). We learned the rules, we optimized for the algorithm, and we built predictable funnels based on a simple, powerful premise: when a customer has a need, they go to a search bar to find a solution. It was a reliable, if increasingly crowded, town square where brands could pay to meet consumers at their precise moment of intent.
This stability, however, is proving to be an illusion. The architectural foundation of digital marketing is shifting beneath our feet, and the culprit, as is often the case these days, is generative AI. This is not another incremental evolution where we simply add a new tactic to our spreadsheets. This is a fundamental re-architecture of how consumers discover products and services. The town square is decentralizing. Intent is no longer confined to a single box on a single page; it’s being expressed in conversations with chatbots, within fintech and payment apps, and across a growing landscape of new digital surfaces. For marketing leaders, the challenge isn’t just about learning a new tool; it’s about unlearning a generation of deeply ingrained assumptions and preparing for a world where “search” is an action, not a destination. This brings us to Answer Engine Optimization (AEO), often called Generative Engine Optimization (GEO).
The Great Acceleration of Distributed Intent
The first thing we must understand is that AI is not inventing a new consumer behavior out of thin air. Instead, it’s acting as a powerful accelerant for a trend that has been quietly gaining momentum for years: the distribution of consumer intent. Long before large language models became a topic of boardroom conversations, customers were already beginning to search for and discover products in places far beyond traditional search engines. The rise of social commerce, the evolution of payment apps into shopping experiences, and the embedding of discovery within content platforms were all early signs. Generative AI has simply taken this slow drift and turned it into a rapid-fire dispersion.
This shift requires us to recalibrate our entire strategic lens. The muscle memory that drives us to pour resources into a single, dominant channel is now a potential liability. As John Nitti explains, the change was already in motion; AI just stepped on the gas.
“I think a lot of things with AI have accelerated… things that were already sort of in motion, right? And so distributed intent… had already started to happen, right? It just now with AI that has accelerated the need to address it… If you think about things like buy now pay laters or you Fintech apps that had started as payments that became shopping experiences, right? That inherently have intent and search built into them. The consumer itself has already started to be distributed in that fashion across multiple platforms and I think AI has only accelerated that yet again further.”
For marketing leaders, this is a critical observation. It means our blind spot isn’t a failure to see a new technology, but a failure to recognize the consumer behavior it enables at scale. We are no longer operating in a hub-and-spoke model with Google at the center. We are entering an era of a distributed network, where intent can spark anywhere. The strategic imperative is to move from a mindset of channel optimization to one of surface-level presence. Where are the conversations happening? Where are decisions being made? And how can our brand be a useful, non-intrusive part of that new journey? Regulatory changes, particularly those aimed at curbing the dominance of tech giants, are further prying open this landscape, creating a strategic opening for brands to diversify and find customers where they are, not just where they’ve been.
The Last Mile Problem: Monetizing New Surfaces
Identifying these new surfaces is only half the battle. The next, and perhaps more immediate, challenge is that the user journey in these new environments is often incomplete. An AI can provide a brilliant, nuanced answer to a complex product query, but it often stops short of connecting the user to the point of purchase. This creates a new “last mile” problem. The intent is high, the answer is provided, but the path to conversion is a dead end of broken links or, worse, no links at all.
This is where the real commercial opportunity lies for savvy marketers. It’s not about interrupting the AI’s response or forcing a sponsored message where it doesn’t belong. It’s about intelligently enabling the next step in a way that feels additive, not extractive. Brands that can seamlessly bridge the gap between AI-driven discovery and commercial action will win. This means focusing on the user experience and removing friction from a journey that is still being defined.
“Let’s not ignore again what that user journey is, right? And how do you make sure that you take as much friction out of that process as possible?.. We’re not influencing, you know, the search result or the result from the the LLM, but if you hover over, we’ll give you a link to then go purchase… It’ll show you a picture of the product, give you the price point and let you know exactly where you’re going from a transparency and trust standpoint… that’s just meant to enable it and make the user experience better, but then help people monetize.”
This approach is fundamentally different from traditional SEM. The goal isn’t to win the top ad spot; it’s to be the most helpful utility at the moment the user is ready to act on the information they’ve just received. For leaders, this means tasking teams with exploring partnerships and technologies that can create these conversion pathways. It requires a deep focus on UX and a commitment to transparency. Consumers are inherently skeptical of AI-generated content; any commercial layer must be built on a foundation of trust. We are not just selling a product; we are facilitating a smoother, more efficient journey from intent to acquisition.
From Keywords to Conversations
For performance marketing teams who have spent years perfecting their keyword strategies, the shift to conversational, AI-driven search demands a new mental model. The atomic unit of search is no longer the keyword; it’s the conversation. A user might string together multiple queries, refine their needs in real-time, and express intent with a level of nuance and context that a simple keyword string could never capture. This changes the game for advertisers.
Bidding on “best running shoes” is a known quantity. But how do you bid on a conversation that starts with “I’m training for my first marathon, I have flat feet, and I tend to run on trails in wet conditions”? The tactics must evolve from precise keyword matching to a broader, more semantic understanding of categories, user needs, and conversational context. It requires a more expansive view of the customer’s mindset.
“You need to open it up a little bit more and look at things a bit differently, right? And and and understand what conversations you want to be a part of… You want to be able to take that as much as intent and semantics as possible and have that be what you’re serving up in that those instances. And so our technology is doing that and making it more relevant… you do need to expand and look at a different, you know, keyword and and category strategy to make sure that you’re capturing the full intent.”
This doesn’t mean keyword lists are obsolete, but they are now the starting point, not the destination. Marketing teams need to develop the skills and adopt the technologies that can parse these richer conversational data streams. The focus shifts from capturing a specific query to understanding a holistic intent. This also has profound implications for measurement. With more touchpoints across more surfaces, last-click attribution becomes an even more incomplete picture of reality. A greater emphasis on incrementality testing will be essential to truly understand the value each interaction contributes to the final conversion, whether that happens online or in-store.
The Convergence of Disciplines
This re-architecture of the customer journey will inevitably trigger a re-architecture of our marketing organizations. The traditional silos that have separated disciplines like SEO, SEM, social, and programmatic are becoming increasingly counterproductive. In a world of distributed intent, these channels are not separate swim lanes; they are interconnected nodes in a complex customer experience. The skills required to succeed are blending, and the teams must follow suit.
An effective strategy for conversational AI search will require insights from SEO (content and indexing), SEM (bidding and conversion), and even social and content marketing (understanding audience conversations). Keeping these functions in separate departments with competing budgets and KPIs is a recipe for inefficiency and missed opportunities. The future belongs to more integrated, performance-oriented teams.
“I think there’s gonna be… an evolution, right? Of an emerging of of disciplines… things that had been siloed and disciplined departments of the past, right? Of how you looked at SEO, SEM, social, programmatic… will start to merge more together… instead of having different performance channels competing against each other, I think you’re gonna see that merge together.”
For leaders, this is perhaps the most significant long-term challenge. It involves breaking down established structures, fostering new forms of collaboration, and championing a culture of continuous learning. It means upskilling teams to think more holistically about the entire performance marketing ecosystem. We must ask ourselves if our current organizational chart is designed for the world we’re in, or the world that’s quickly fading in the rearview mirror. The marketers who can seamlessly blend data analysis, user experience design, and conversational context will be the most valuable players in this new paradigm.
The Mandate to Test and Learn
The ground is undeniably shifting, and the temptation is to either dismiss the change as hype or become paralyzed by its complexity. Neither response is a viable strategy. The most pragmatic and effective path forward is to embrace a posture of disciplined experimentation. We must be testing, learning, and getting our organizations prepared for a future that is arriving faster than many anticipate. This isn’t about betting the farm on an unproven technology; it’s about making small, calculated investments to understand what works, where the friction points are, and how our customers are adapting their behaviors.
Ultimately, this new era of search will be defined by the same principles that have always underpinned great marketing: trust, transparency, and utility. As an industry, we have made mistakes in the past, chasing scale at the expense of user experience with things like content farms and opaque ad networks. We have an opportunity now to build this new ecosystem on a better foundation. The brands that succeed will be those that use AI not to trick or mislead, but to genuinely help customers get where they want to go more efficiently. If we can provide real value and make their journey easier, everyone wins. The playbook is being rewritten, and the leaders who pick up the pen and start experimenting will be the ones who define the next chapter.
