Milena Traikovich has built a career at the intersection of analytics and demand generation, helping businesses navigate the complexities of modern advertising to nurture high-quality leads. With extensive experience in performance optimization, she possesses a unique vantage point on the industry’s rapid evolution. Today, she joins us to dissect the seismic shifts underway, from the operational pivot required by agentic AI and the strategic rebalancing toward customer retention to the persistent challenges of cross-platform measurement and the new frontiers of creative strategy in an AI-driven world.
With two-thirds of advertisers now focusing on agentic AI for campaign execution, what specific operational changes must teams make? Could you walk through the new skills required when AI handles tactical tasks like budget pacing and audience optimization, prompting a need for human oversight and strategy?
It’s a fundamental rewiring of a marketing team’s DNA. The day-to-day rhythm is no longer about pulling levers and manually adjusting bids. It’s about moving from being the pilot to being the air traffic controller, managing a fleet of autonomous agents. Operationally, this means breaking down the silos between media, creative, and analytics. Teams must create a unified “strategy hub” where the primary task is to define the AI’s objectives with extreme clarity. The new required skills are less tactical and more philosophical. We need “AI trainers” who can set sophisticated guardrails and business rules, “data storytellers” who can interpret the AI’s outputs and translate them into strategic insights, and “creative directors” who can feed the system with diverse concepts for testing. The human element becomes about asking the right questions, challenging the AI’s assumptions, and ensuring the autonomous execution aligns perfectly with the brand’s soul, not just its ROI targets.
We’re seeing a significant shift from customer acquisition to retention, with driving repeat purchases nearly doubling as a priority. How are brands using their first-party data and AI to power this change? Please share a step-by-step example of a successful, AI-driven loyalty strategy you’ve seen.
This pivot is one of the most exciting developments because it’s a move toward sustainable, profitable growth. The days of endlessly pouring money into a leaky customer bucket are numbered. A successful AI-driven loyalty strategy I’ve seen implemented starts with data unification. First, a brand consolidates its first-party data from its CRM, e-commerce platform, and loyalty program within a clean room environment. Next, they deploy a predictive AI model to analyze purchase history, browsing behavior, and engagement, which then segments customers into tiers like “at-risk of churn,” “high-potential upsell,” or “brand loyalist.” From there, an agentic AI takes over the activation. It might see a loyalist who just bought running shoes and autonomously serve them a personalized CTV ad showcasing new performance apparel, complete with a unique “thank you” discount code. The final step is closed-loop measurement; the system connects that ad exposure directly to the subsequent purchase, proving tangible ROI and making the 25% of buyers prioritizing repeat purchases feel very smart. It transforms marketing from a cost center into a powerful engine for building lasting customer relationships.
Connected TV and commerce media are projected to see growth of 13.8% and 12.1% respectively, yet cross-platform measurement remains a top advertiser priority. What are the biggest hurdles to achieving reliable measurement across these channels, and what tangible steps are being taken to standardize accountability?
The biggest hurdle is the fragmentation of identity and metrics. A single user looks like a different person on their laptop, their smart TV, and their mobile phone. Each platform—from a social network to a commerce media network—has its own proprietary measurement system, its own “language” for success. This creates a dizzying ‘walled garden’ effect, making it incredibly difficult to understand the true customer journey and assign credit accurately. It’s why we’ve seen cross-platform measurement jump to a 72% priority for advertisers; they are pouring money into these growing channels but can’t confidently connect the dots. Tangible steps are finally being taken, though. We’re seeing a push for standardization, like the guidelines released by the IAB to create common definitions for incrementality versus attribution. The adoption of clean rooms is also a massive step forward, allowing advertisers to match their first-party data with platform data in a privacy-compliant way to get a more holistic view of performance. It’s about building bridges between these walled gardens so we can finally get a coherent, unified picture of what’s truly driving business outcomes.
As marketers prioritize optimizing content for AI-generated answers, how does this change the fundamentals of creative strategy and search visibility? Could you describe the key differences between creating content for a human-first search engine versus an AI-driven answer engine, perhaps with a specific example?
This is a profound shift from optimizing for clicks to optimizing for comprehension. For years, SEO was about keywords, backlinks, and ranking on a list of blue links. Now, with 73% of marketers focused on AI answer engines, the goal is to become the definitive, cited source in a single, authoritative answer. The fundamental difference is a move from breadth to depth and from persuasion to factual accuracy. For example, if you were creating content about “best running shoes for marathons” for a traditional search engine, you’d write a listicle, sprinkle in keywords, and aim for a high click-through rate. For an AI answer engine, you need to create a comprehensive resource. You’d include structured data on shoe weight, heel drop, and cushioning type; you’d cite scientific studies on foam technology; and you’d embed customer testimonials and expert reviews. The content needs to be so thorough, trustworthy, and well-structured that the AI concludes it’s the most reliable source of truth on the topic. Your creative strategy becomes less about catchy headlines and more about building a fortress of verifiable information.
Linear TV’s decline is being temporarily cushioned by major events like the Olympics and World Cup. How should media buyers approach this complexity? Please explain the strategic trade-offs when allocating budgets between short-term, high-reach broadcast events and long-term, digitally-focused audience engagement.
Media buyers are walking a tightrope right now. The 1.7% decline in linear TV spending is deceptively small because of the immense gravitational pull of these major cyclical events. The trade-off is essentially between a massive, short-term “fireworks display” and a long-term, sustained “drip irrigation” campaign. The fireworks—buying a spot during the World Cup—offer unparalleled reach and a chance to be part of a massive cultural moment. It’s powerful for top-of-funnel brand awareness. However, the audience is broad and untargeted, and the cost is astronomical. The drip irrigation approach—allocating that same budget to a year-long CTV and social media campaign—offers precision targeting, personalization, and the ability to build a relationship with a specific audience over time. The strategic calculus for a media buyer involves balancing these. You might allocate a portion of the budget to the big event for a brand-building halo effect, but you must reserve the majority for digital channels where you can directly measure impact, engage with customers, and drive measurable, long-term growth. Relying solely on these temporary viewership spikes is like trying to survive on a diet of only birthday cake; it’s unsustainable.
The industry is trying to avoid fragmentation by standardizing agentic AI protocols. What are the primary risks if competing, incompatible frameworks become the norm? From a practical standpoint, how would a fragmented ecosystem impact a brand’s ability to run and measure a unified campaign?
A fragmented ecosystem would be a nightmare scenario, essentially creating a new set of walled gardens, but on a technical, operational level. The primary risk is a massive loss of efficiency and insight. Imagine trying to run a single campaign where your Google agent can’t communicate with your commerce media agent, and neither can properly report back to your central measurement platform. It would be like having a team where no one speaks the same language. From a practical standpoint, a brand’s ability to run a unified campaign would be shattered. You would lose the ability to manage frequency capping across platforms, leading to consumer ad fatigue. You couldn’t share audience learnings, so an insight gained on one platform couldn’t be used to optimize another. And measurement would become a patchwork of contradictory reports, making it impossible to de-duplicate conversions or understand the holistic customer journey. This is why initiatives like the IAB Tech Lab’s agentic roadmap are so critical. Without a common set of protocols, the promise of AI-driven efficiency will be completely undermined by a new layer of man-made complexity.
What is your forecast for the advertising industry’s talent and skills development over the next two years?
My forecast is for a dramatic “great upskilling” within the industry. The demand for purely tactical, executional roles—the people who manually set up campaigns and pull daily reports—will decline sharply. In their place, we’ll see a surge in demand for three key archetypes. First, the “AI Strategist,” a hybrid professional who understands both marketing fundamentals and the capabilities of machine learning, and who can translate business goals into sophisticated instructions for autonomous systems. Second, the “Data Ethicist,” a role that will become essential for ensuring brand safety, privacy compliance, and responsible governance as automated decisions are made at a massive scale. Finally, we’ll need more “Creative Technologists,” people who can blend human ingenuity with AI’s generative power to produce novel, high-performing creative concepts at a speed we’ve never seen before. The next two years won’t be about replacing humans with AI, but about augmenting human talent, forcing a shift from rote tasks to strategic oversight, ethical judgment, and creative innovation.
