Welcome to an insightful conversation with Milena Traikovich, a seasoned Demand Gen expert who has transformed the way businesses nurture high-quality leads through innovative go-to-market (GTM) strategies. With her deep expertise in analytics, performance optimization, and lead generation, Milena has been at the forefront of adapting to the AI-driven buyer landscape. In this interview, we dive into the seismic shifts in buyer behavior, the evolution of GTM approaches, and actionable plays to stay ahead in a world where AI shapes decision-making. From aligning teams on a unified buyer narrative to building trust with dynamic social proof, Milena shares her proven tactics for turning theory into momentum.
How have you noticed buyer behavior evolving with the rise of AI tools and platforms in recent years?
Over the past few years, I’ve seen a dramatic shift in how buyers approach their research and decision-making. With AI tools and platforms like review sites becoming central to the process, buyers are no longer just browsing websites or waiting for outreach. They’re consulting AI agents, diving into peer discussions on private channels, and pulling insights from quick-search snippets—often before a company even knows they’re in the market. This means they’re forming opinions and shortlists way earlier, relying on anonymous research rather than traditional touchpoints like form fills or demo requests.
What stands out as the most significant change in how buyers research solutions compared to just a few years ago?
The biggest change is the speed and independence of the research process. A few years back, buyers depended on marketing content or sales reps to guide them through the funnel—think gated whitepapers or scheduled demos. Now, they have instant access to aggregated insights through AI. They’re cross-referencing data from multiple sources in seconds, often bypassing a company’s owned channels entirely. This self-directed journey means we’re often playing catch-up, trying to influence a decision that’s already half-made.
Can you share your perspective on why traditional methods like form fills are losing their effectiveness?
Traditional methods like form fills are fading because they clash with the modern buyer’s need for speed and autonomy. Buyers don’t want to trade their info for a piece of content when they can get similar insights elsewhere without the hassle. AI tools also give them the ability to find answers without engaging directly, so those old gatekeeping tactics feel like friction rather than value. If your strategy still hinges on capturing data this way, you’re essentially optimizing for a window no one’s looking through anymore.
How have GTM strategies transformed since 2020, particularly with AI playing a bigger role?
Since 2020, GTM strategies have undergone a complete overhaul. Back then, marketing, sales, and customer support often operated in silos, each chasing separate goals with little alignment. AI has forced a reckoning—buyers now expect a seamless experience, and fragmented teams can’t deliver that. Today, we’re seeing a push toward integrated, cross-functional approaches where data and AI insights are shared across teams to track intent and personalize outreach. AI’s influence has also shifted the focus from volume to precision, helping us identify and engage buyers at the right moment rather than just casting a wide net.
What does the concept of a ‘cross-functional revenue engine’ mean to you, and why is it so vital for the future of GTM?
To me, a cross-functional revenue engine is about creating a unified system where marketing, sales, and customer support work as one to drive revenue, powered by shared data and AI-driven insights. It’s vital because the AI-driven buyer doesn’t see departmental boundaries—they expect consistency from first touch to post-sale. This engine ensures everyone’s aligned on the same goals and buyer story, which builds momentum and helps us meet buyers where they are. Without it, you risk disjointed experiences that push prospects to competitors who can deliver a clearer, more cohesive journey.
What challenges did you see with older GTM tactics when trying to connect with AI-driven buyers?
Older GTM tactics often crumbled under the weight of AI-driven buyer behavior because they were built for a slower, more linear journey. Tactics like relying on gated forms or static personas couldn’t keep up with buyers who get instant answers from AI tools. These methods assumed we controlled the narrative, but AI has flipped that—buyers now have the upper hand with access to real-time data. Plus, the lack of integration between teams meant we often missed critical signals, leaving us out of the conversation before we even knew it started.
Can you tell us about the ‘dynamic buyer blueprint’ and why it’s such a game-changer for team alignment?
The dynamic buyer blueprint is a framework I use to get every revenue team on the same page about who the buyer is and what they need. It’s about crafting a single, clear buyer narrative that’s testable and readable by both humans and AI tools. This is a game-changer because when each team—marketing, sales, support—defines the buyer differently, you end up with noisy signals internally and in the market. Aligning everyone on one story cuts through that noise, ensuring consistent messaging and better positioning in AI-driven search results. It’s the foundation for executing as a unified front.
What kind of impact have you seen from implementing this blueprint in real-world scenarios?
The impact can be striking. I worked with a small SaaS start-up that used this blueprint to streamline their ideal customer profile from multiple vague descriptions to one sharp sentence. They built a quick ROI calculator based on that narrative, and within six weeks, their visibility in relevant AI search results jumped from 18% to 43%—without spending more on ads. It’s proof that alignment doesn’t just clarify internal focus; it directly translates to market traction and faster buyer trust.
How would you describe the ‘social proof-stack ladder,’ and how does it differ from traditional approaches to showcasing credibility?
The social proof-stack ladder is a strategy for building layered credibility that resonates with today’s buyers. Unlike the old approach of just sticking a logo on your site and calling it a day, this method stacks different types of proof—data for volume, stories for relevance, outcomes for ROI, and thought leadership for perspective. It’s designed to be dynamic, with regular updates to keep it fresh, because buyers and AI tools alike are looking for recent, relevant evidence. This layered approach addresses multiple facets of doubt, making it far more convincing than a static badge that might feel outdated.
Looking ahead, what’s your forecast for the role of AI in shaping GTM strategies over the next few years?
I believe AI will become even more integral to GTM strategies in the coming years, acting as both a tool and a driver of buyer expectations. We’ll see deeper integration of AI across the entire revenue stack, from predictive analytics for identifying intent to agentic marketing systems that automate personalized engagement at scale. The focus will shift toward real-time adaptability—strategies will need to pivot instantly based on AI-detected signals. Companies that can’t build this agility into their GTM motions will struggle to keep up with buyers who expect hyper-relevant, frictionless experiences at every turn.
