Milena Traikovich is a powerhouse in the field of demand generation, recognized for her ability to weave complex analytics into high-performance campaigns that do more than just capture attention—they nurture high-quality leads into loyal customers. With an extensive background in performance optimization and a keen eye for technological shifts, she has spent years helping businesses navigate the transition from traditional digital marketing to a world where artificial intelligence is the primary engine of growth. In this conversation, we delve into the radical restructuring of modern agencies into cross-functional squads, the decline of the linear marketing funnel in favor of simultaneous touchpoints, and the emergence of “outcome commerce,” where service providers are held strictly accountable for their clients’ bottom-line results.
The traditional linear marketing funnel has been a staple of advertising for decades, yet there is a growing sentiment that this model is no longer effective in a world dominated by AI and fragmented consumer touchpoints. How should brands be thinking about the consumer journey now that the old rules have changed?
The marketing funnel as we once knew it is effectively dead because the path to a purchase is no longer a straight line from awareness to consideration to sale. In 2026, we are seeing a shift toward “simultaneity,” where multiple touchpoints influence a consumer at the exact same moment. You might see a large physical hoarding on your commute, receive a ChatGPT recommendation while researching a problem, and read an Amazon review all within the same hour, and each of these interactions carries equal weight in the final decision. We can no longer afford to treat these as sequential steps; instead, they have to work together in a synchronized ecosystem. This means brand awareness and the actual transaction are happening across several platforms at once, requiring a strategy that emphasizes being present and persuasive everywhere simultaneously rather than trying to shepherd a lead through a rigid, multi-week process.
With the rise of generative AI, there is a lot of talk about how agencies must evolve to avoid being disrupted. You have advocated for a “human-led, but AI-native” structure—could you explain how a squad-based model outperforms the traditional departmental hierarchy in this new landscape?
The traditional agency model, with its heavy hierarchies and siloed departments, is simply too slow to keep up with the current pace of technological change. We’ve found that rebuilding the internal structure around “pods” or “squads” is the only way to remain agile. Each squad is led by an “intelligence core” of critical thinkers who possess a deep understanding of business context and human psychology—the things AI still can’t replicate. Alongside them, we deploy function-specific AI agents that absorb the repetitive production work, such as basic visualization, image searches, and creative adaptations. This structure raises the premium on people who can question a brief and solve complex problems, while the AI handles the heavy lifting of execution. Independent agencies actually have a significant advantage here because they don’t have the dead weight of legacy systems to move; they can pivot their entire workforce into this AI-native model almost overnight.
You’ve introduced the concept of “outcome commerce” as a replacement for standard media buying. How does this shift the relationship between an agency and a client, and what does it look like in practice when you are building brand-trained language models?
Outcome commerce is about moving away from being a mere buyer of media and instead behaving like a consultant who is directly accountable for the client’s revenue and growth. In practice, this often involves building bespoke, brand-trained language models that understand a company’s specific “DNA.” For a major healthcare group like Burjeel, for example, we feed the model every piece of approved creative, ensuring it understands the exact fonts, imagery, and tonality required. When a marketer enters a single prompt, the model returns finished, brand-compliant creative instantly. This transforms the agency’s role from a service provider that bills for hours into a growth partner that provides the “Growth Operating System” for the entire business. It blurs the lines between the CMO, CIO, and CDO, forcing them to work together to build systems that drive the bottom line rather than just running isolated campaigns.
One of the most striking changes in the industry is how quickly a concept can go from a brief to a live campaign. Can you talk about how AI has “collapsed” the traditional marketing timeline and what that means for the quality of consumer research?
The time to market hasn’t just reduced; it has fundamentally collapsed. In the past, you would have a sequence: you’d get a brief, do your research, move to brainstorming, and then finally start production. Today, those processes run in parallel. While we are briefing the team, AI-led brainstorming and deep consumer research are happening at the same time. This parallel processing allows us to see insights and solve problems with a human approach while the machines handle the data-heavy discovery phases. It means a brand can react to a market shift in hours rather than weeks. However, we have to be careful; some organizations mistakenly cut too much staff in anticipation of these efficiencies and then had to rehire when they realized that you still need human insight to solve the “why” behind the data. We’re currently in a period where many companies face the paradox of having talent over-capacity and under-capacity simultaneously as they figure out the right balance.
You often use the analogy that data is a “rearview mirror” while vision is the front windshield. In an era where everyone is obsessed with big data, why is it dangerous for brands to rely too heavily on their historical performance metrics?
Data is a structural necessity, and we use platforms like D1 to create a single source of truth across Meta, Google, and CRM systems, but looking only at that data can lead to disastrous decisions. Data, by its very nature, comes from the past. If a retailer in the Middle East only looks at historical patterns, they might see a seasonal upswing and decide to overstock, completely ignoring a sudden geopolitical shift or a change in consumer priorities that the data hasn’t captured yet. You cannot drive a car safely if you are only looking in the rearview mirror; you need your own eyes and a clear vision of the road ahead. Decision models must be built from a brand’s history to understand efficiency and pricing, but the human “intelligence core” must provide the forward-looking context that tells the brand when the past is no longer a reliable guide for the future.
Search behavior is moving away from traditional engines and toward AI-led discovery. Based on your work with over 150 brands in this space, what kind of measurable impact does being cited by a Large Language Model have on consumer behavior?
The shift toward AI search is one of the most profitable frontiers we’ve explored recently. When a brand is cited by a model—what we call organic AI recommendation—the impact on engagement is staggering. Our data shows that the time spent on a brand’s site can jump by as much as 300 percent. More importantly, the likelihood of a transaction also increases by about 150 to 180 percent. This happens because being cited by an AI feels like an objective recommendation rather than a paid advertisement. However, there are no shortcuts here. You can’t just buy your way into an AI’s favor with a quick ad; it takes a sustained, year-round effort to ensure your brand’s information is surfaced correctly across multiple LLMs. Earning that organic trust is what separates the winners from the losers in this new discovery landscape.
There is a massive industry fixation on Gen Z, but you’ve expressed some skepticism about over-indexing on this cohort. Why do you think legacy brands are falling into a “trap” when it comes to targeting the youngest generation of consumers?
The obsession with Gen Z is often short-sighted because, frankly, many of them don’t even have enough disposable income yet to sustain legacy brands. When older, established brands try to borrow Gen Z’s language or “vibe,” it often feels performative and fails because Gen Z already has its own native brands that they’ve grown up with. It is a trap to ignore the channels and audiences that have served you for years just to chase a trend. We still see a massive subconscious pull from traditional media; a celebrity on a TV screen or a massive physical billboard still signals a level of trust and scale that a mobile ad can’t replicate. My advice to FMCG advertisers is to spread budgets more evenly and test continuously across OTT, connected TV, and mobile, rather than funneling everything into a single demographic that might not be ready to buy.
What is your forecast for the advertising technology industry over the next few years?
I believe we are entering a phase where the most successful companies will be software companies that behave like services companies. The old SaaS model struggled because clients often didn’t know how to fully adopt the tools they were buying. From 2026 to 2028, the value will accrue to those who combine a powerful platform with a services-led commitment to outcomes. We are moving toward vertical intelligence platforms—specialized AI systems for sectors like healthcare, retail, and education—that don’t just provide data, but actually deliver growth. My forecast is that the next trillion-dollar companies won’t just sell you a subscription to a tool; they will be the ones that say, “We have the technology and the human expertise to be responsible for your bottom line, your revenue, and your brand.” In this environment, services and outcomes will become synonymous.
