This article was based on the interview with Scrunch’s Head of Marketing Kevin White on being cited, not just found by Greg Kihlström, AI and MarTech keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
For decades, the fundamental goal of digital marketing has been elegantly simple: be found. We mastered the art and science of search engine optimization, bidding on keywords, and crafting content designed to climb the ranks of a search results page. The game was about visibility, clicks, and traffic. A click was a victory, a visit was an opportunity, and the website was the sovereign domain where we controlled the brand narrative. This established order, however, is undergoing a seismic shift. The most important visitor to your website may no longer be a prospective customer, but an AI agent acting on their behalf. This changes everything.
This new reality forces a profound re-evaluation of our digital strategies. When a large language model (LLM) becomes the primary intermediary between your brand and your audience, the objective is no longer merely to be found, but to be understood, trusted, and ultimately, *cited* as a source of truth. The user’s journey now often begins and ends within an AI-powered chat interface, where your brand is summarized, compared, and recommended without the user ever visiting your carefully crafted website. As enterprise marketing leaders, we must grapple with this new paradigm. It requires a new set of metrics, a new organizational mindset, and a new way of thinking about our digital properties. The work is no longer just about attracting eyeballs; it’s about earning the trust of a machine.
The Iceberg of AI Visibility: Why Referral Traffic is a Dangerous Metric
One of the first and most critical adjustments marketing leaders must make is in how we measure success. The familiar dashboards tracking referral traffic from search engines are becoming dangerously incomplete. In the world of generative AI, the value exchange is different. An AI agent visits your site, consumes information, and delivers a synthesized answer back to the user in its own interface. A successful interaction might result in zero referral traffic, yet your brand could have been instrumental in the user’s decision-making process. Relying on old metrics is like trying to navigate an ocean by only looking at the tip of the iceberg.
Kevin White of Scrunch emphasizes that we need to look beneath the surface to understand what’s truly happening.
“If you’re just looking at referral traffic from ChatGPT or Claude, you’re really just looking at the tip of the iceberg. And so exposing that whole rest of the, the iceberg, or what’s be- beneath the surface of is my brand being mentioned, is my brand being cited, you know, are AI agents visiting my site, and, like, are they getting blocked by… Do we, do we need to fix something about our JavaScript, our robots.txt? Like, those are the things that are… you need to be paying attention to.”
This shift requires a new vocabulary and a new set of KPIs. We must move from tracking visits to tracking visibility within AI-generated answers. Key questions now include: What is our share of voice for critical prompts? How often is our brand cited as a source? Are our key product specifications and brand messages being represented accurately? This isn’t just a reporting change; it’s a strategic one. It forces us to focus on the quality and authority of our information, not just its ability to rank. For an enterprise leader, this means equipping your teams with the tools and frameworks to monitor this new landscape and holding them accountable for metrics that reflect influence, not just traffic.
The Expanding Stakeholder Map: AI Search Is Not an SEO Problem
The natural inclination in many organizations is to delegate this new challenge to the team that has always handled search: the SEO department. While their expertise with crawlers and ranking is invaluable, framing AI optimization as merely “the next evolution of SEO” is a critical misstep. The implications of AI search extend far beyond technical optimization and into the core of brand management, communications, and demand generation. When an AI is the front door to your brand, the accuracy of its statements becomes a brand reputation issue.
The responsibility for what an AI says about you is inherently cross-functional. As White explains, the comms and brand teams have a significant stake in this new world.
“…because AI search is kinda like the front door to your website, or your brand. And nowadays when people are asking AI bots… they’re not going to your website anymore. And so the information that the AI bots need to go and retrieve needs to be accurate and needs to represent your brand in the right way, and that’s not always something that an SEO person is looking for.”
This insight should prompt leaders to re-examine their organizational structures. Who owns brand accuracy in AI-generated answers? Is it the PR team, who traditionally manages public perception? The brand team, who owns the messaging? Or the content team, who creates the source material? The answer is likely a collaborative effort, a task force that brings together technical SEO, brand marketing, public relations, and even legal. The growth and demand generation teams also see AI platforms as a nascent channel for acquiring highly qualified leads, with a different set of objectives than a traditional SEO team focused on broad organic traffic. Effectively tackling AI search requires a holistic approach, championed from the top down, that recognizes it as a shared, strategic imperative.
Winning the Trust of Machines with the “Long Long Tail”
So, how do you become a trusted, citable source for an AI? The answer lies in a strategic departure from the volume-based game of traditional SEO toward a precision-based approach. While broad, high-volume keywords still have their place, the nature of AI-powered queries is fundamentally different. Users are more conversational, more specific, and more demanding in their prompts. They aren’t just searching for “running shoes”; they’re asking, “What are the best waterproof trail running shoes for a person with wide feet and a tendency to overpronate?” To win in this environment, you need to provide the definitive answer to that highly specific question.
This is where White introduces a powerful concept for marketers to embrace: creating content for what he calls “the long long tail.”
“You can almost build or create that long tail content. I like to call it, like, the long long tail. And if you build it, the AI bots are very good at going and finding that specific type of content… focus on where you can absolutely win, versus, like, a more broader, like, general SEO type of, of strategy, which is more of, like, partially a volume game… I would say, like, the AI side of things is very much more, like, long tail intent. And yeah, you might get less traffic, but you’ll get a lot higher conversions.”
This strategy involves deeply understanding your ideal customer’s most niche problems and creating detailed, authoritative content that directly addresses them. It’s about leveraging your unique expertise—the kind of knowledge that comes from customer calls, support tickets, and forum discussions—and codifying it in a way that an AI can easily digest and cite. This isn’t about gaming an algorithm; it’s about being genuinely helpful at a granular level. When you are the only source that comprehensively explains a complex product specification or a nuanced use case, you become the default, trusted answer. The machine, in its quest to provide the best possible response to the human, will reward your authority with citations.
Serving Two Masters: The Human and the Agent
A common fear among marketers is that optimizing for machines will lead to sterile, soulless websites filled with bullet points and schema markup, sacrificing the rich, engaging brand experience we’ve worked so hard to build for our human visitors. This is a valid concern, but it presents a false choice. It is possible, and increasingly necessary, to serve both audiences effectively. The human visitor, arriving later in their journey, still needs an immersive brand experience. The AI agent, however, needs clean, structured, and easily parsable information. The solution is not to compromise one for the other, but to cater to each on its own terms.
The future of the web may involve delivering different experiences from the same URL, depending on who is visiting. White envisions a world where this dual-service model is seamless.
“You can have the best of both worlds, where, like, you have that human experience and then you also, like, deliver content in the way that the AI agents wanna consume it, at the edge… the AI gets what it wants and the human gets what they want, and, you know, the world is a happy, magical place.”
This technical approach allows marketing teams to continue creating compelling narratives, videos, and interactive elements for human users while providing a clean, rich HTML version for AI crawlers. This ensures the machine gets exactly what it needs to understand your content—unobstructed by heavy JavaScript or complex layouts—without diminishing the human experience. As leaders, our role is to champion the technology investments and strategic thinking required to build this dual-pathway digital presence. It’s about recognizing that our website now has two distinct, equally important audiences and designing for both.
The transition from a world of search to a world of answers is not a distant future; it is happening now. The brands that will thrive are those that recognize this shift is not merely tactical but foundational. It requires us to move beyond the comfortable metrics of traffic and rankings and embrace the more nuanced goals of accuracy, authority, and citation. It demands a more collaborative and integrated approach across our marketing organizations, breaking down the silos between SEO, brand, and communications.
This new landscape presents an incredible opportunity. By focusing on the “long long tail,” we can connect with customers on a more specific and meaningful level than ever before, establishing our brands as the definitive experts in our fields. By learning to serve both human and machine audiences, we can build a digital presence that is both engaging and authoritative. The work ahead is complex, but for those of us genuinely excited about using technology to forge better connections with customers, it’s also one of the most interesting challenges we’ve ever faced. The goal is no longer just to be found; it’s to be the answer.
