Milena Traikovich stands at the forefront of a major shift in how businesses connect with their audiences. As an expert in demand generation and digital strategy, she has spent years decoding the complex relationship between data analytics and human behavior. In this era of 2026, where artificial intelligence has become the primary lens through which consumers view the world, her insights are more critical than ever. Milena specializes in helping organizations move beyond the rigid constraints of traditional search engines to embrace a more fluid, conversational approach to lead generation. Her work focuses on optimizing performance not just for algorithms, but for the intricate, often messy realities of human intent.
The following discussion explores the evolution of search behavior as it moves away from short, robotic keywords toward multi-layered, highly personal dialogues. We examine how the “memory” of modern AI assistants creates a new standard for one-to-one personalization, where context often matters more than the words explicitly typed into a search bar. The conversation also touches on the changing landscape of SEO metrics, the strategic necessity of creating content for specific human situations, and how search data is now being used to drive broader business decisions and product development.
For years, we taught ourselves to speak the “language of machines” by condensing our needs into short keyword phrases. How is the rise of AI assistants allowing consumers to “untrain” themselves and return to more natural, human ways of asking for what they want?
For decades, we forced our lives to fit into a tiny search box, stripping away the flavor and specifics of our needs just to get a functional result. We might have had a complex vision for a family trip, but we learned to type “best family hotel Tenerife” because that’s what the technology could handle. Today, we are seeing a massive reversal where people are finally expressing the complexity that was always there, asking for things like a hotel with a heated kids’ pool, a separate sleeping area for the children within the same room, and proximity to a car rental. It feels like we are returning to the days of sitting across from a travel agent and explaining the “why” behind our choices, rather than just the “what.” This shift is incredibly liberating for the user because they no longer have to be an amateur search engineer; they can just be a person with a specific problem to solve.
Beyond just providing better answers, how does AI search help marketers uncover the underlying motivations and anxieties that a simple keyword might never reveal?
Keywords have always been a decent indicator of intent, but they are often cold and lack the emotional depth required for true personalization. When a user engages in a fuller conversation with an AI, they start to reveal the drivers and barriers that sit beneath their search—things like their previous bad experiences, specific health anxieties, or very tight trade-offs they need to make. In the past, a brand might not have discovered these nuances until a customer was deep in the funnel, perhaps talking to a salesperson on the phone. Now, that rich understanding is available right at the start of the discovery process, allowing us to see if someone is looking for a product because they’re afraid of failing a goal or because they’ve had a specific injury. This level of insight allows us to move from broad category marketing to situational marketing, where we address the real-world circumstances that prompt a person to look for a solution in the first place.
You’ve mentioned that the “meaning” of a search can now exist entirely outside the prompt itself. How does the memory of AI assistants redefine what it means for a brand to be relevant to an individual?
This is perhaps the most significant change we’ve seen in the current 2026 landscape: the prompt is no longer the complete expression of intent. Imagine two different people asking for the same running shoe recommendation; if one person has spent the last few weeks discussing a chronic knee injury with their AI assistant, the recommendation they receive will be fundamentally different from the person who hasn’t. The AI knows their history, their preferences, and even their physical limitations without the user having to repeat those details every single time. It functions like a brilliant human assistant who already knows you care about comfort over speed and that you have a $150 budget limit. For brands, this means that “relevance” is no longer about matching a word; it’s about having the depth of content and authority to be the logical choice when the AI considers all that invisible context.
With AI providing different answers to different people for the same query, why is the traditional focus on “rankings” and “prompt tracking” becoming an incomplete way to measure SEO success?
We grew up in an industry where being “number one” on a spreadsheet was the ultimate goal, and it’s tempting to try and recreate that with AI by tracking what ChatGPT says for a specific prompt. However, if two people get different answers based on their unique history and situation, then a standardized prompt report only gives you one tiny, potentially misleading slice of the truth. We have to stop looking for a single “best” answer and start looking at how our brand is surfaced in specific, high-value situations. This requires us to look deeper into our own site data, asking which specific, long-tail content is attracting visits from AI platforms and what that tells us about the real-world problems we are solving. Prompt tracking isn’t dead, but its purpose has changed; it’s now more about understanding the landscape of possibilities rather than claiming a static “throne” at the top of a list.
How should a brand’s content strategy evolve to ensure it provides the depth and nuance required to answer these increasingly specific, multi-layered human prompts?
Brands can no longer afford to stay at the surface level of a topic; we have to go deep into the “situations” that sit underneath the broad categories. Instead of just writing about “running shoes,” we need to provide content that speaks to the first-time marathoner who is worried about knee pain and prefers a specific fit. This means focusing on niche needs and creating highly specific evidence—whether that’s through detailed guides, specialist publications, or community-driven content—that proves why we are the right choice for that exact scenario. We want to give the AI enough “reason and evidence” to trust us as the solution for a particular type of person. It’s about being “brilliant for someone” rather than just “okay for everyone,” and that requires a level of detail that might have felt like overkill just a few years ago.
You’ve suggested that AI search insights should be taken “upstream” into the business. How can what we learn from search behavior actually shape the products and services a company offers?
This is where the role of the search marketer becomes a business strategy role, because the questions people ask AI are effectively a massive, real-time focus group. By analyzing these complex prompts, we might discover an entirely new audience we’re unexpectedly perfect for, or we might realize that a feature our customers value is buried three clicks deep on our website and needs to be a core part of our proposition. We might even identify a gap in the market where a specific combination of needs is being ignored by everyone else in the category. These aren’t just SEO problems to be solved with better tags; they are business opportunities that can lead to new product lines or a complete shift in how a brand positions itself. It allows us to move from simply optimizing what we already have to creating what the market is actually crying out for.
In an era dominated by advanced AI, why do you believe that human insights from salespeople and customer service teams are more valuable than ever for digital marketing?
Even the most sophisticated AI can benefit from the raw, unedited human feedback that comes from the front lines of a business. Salespeople and customer service teams hear the oddly specific, “human” questions every day that might not even be showing up in our digital tools yet. They know the emotional hesitations a person has before they buy and the specific words people use when they’re frustrated or delighted. By bringing that knowledge back into the marketing and search strategy, we can ensure our content sounds like a real person talking to another real person. As AI becomes better at understanding individuals, brands have to get better at understanding them too, and there is no better source for that than the people who already interact with our customers in the physical or digital “real world.”
What is your forecast for behavior-led search?
I believe we are entering a phase where the “search box” as we knew it will effectively disappear, replaced by an ongoing, ambient dialogue between people and their technology. In this future, brands will no longer “buy” their way to the top through keywords, but will instead earn their place through “situational authority”—the proven ability to solve a very specific set of human problems. We will see a shift where the most successful companies are those that prioritize “behavior-led” data over “keyword-led” data, focusing on the psychological motivations of their users. Ultimately, the winners won’t just be the ones with the best AI optimization, but the ones who use that technology to be more human, more empathetic, and more precisely relevant to the individuals they serve.
