Anastasia Braitsik has spent years at the forefront of the digital revolution, helping global brands navigate the complex intersections of data analytics and creative strategy. As search engines evolve into comprehensive discovery engines, her insights into how we bridge the gap between human intuition and machine learning have never been more critical. This conversation explores the shifting landscape of pay-per-click advertising, focusing on the transition from keyword-centric models to immersive, visual-first experiences across new surfaces like gaming and interactive showrooms. We delve into the necessity of maintaining a robust, distinct creative library to fuel AI systems and discuss the strategic shift from granular keyword tracking to broad, thematic audience analysis powered by high-quality first-party data.
AI-first formats like showroom ads and gaming placements are changing how users interact with brands. How do you balance brand security with immersive interaction in these environments, and what specific steps should advertisers take to enter the gaming space without alienating players?
Navigating these new surfaces requires a delicate touch because, as we have seen in many digital communities, gamers often have a visceral and justifiable distaste for intrusive ads that disrupt their flow. The key is to lean into AI-first formats like showroom ads, where the technology provides a vital layer of brand security by ensuring the content remains relevant and the environment stays controlled. By using tools like Copilot, we can create environments where the advertiser provides the core content, but the AI manages the interaction to keep it helpful rather than annoying. To avoid alienating audiences, advertisers must move away from the traditional keyword-to-click model and instead focus on conversational AI that respects the user’s immersion. It is about creating a dynamic journey where the ad feels like a natural extension of the digital space rather than a loud, unwanted interruption that pulls a player out of their experience.
Many marketers still reserve high-quality visuals for brand awareness or remarketing. Why is it becoming necessary to integrate brand-forward imagery at every stage of the funnel, and how does a robust visual library help AI systems improve conversion paths?
We are rapidly moving past the era where high-quality visuals were reserved solely for top-of-funnel brand awareness or basic remarketing efforts. Since most people are inherently visual learners, it is essential to integrate brand-forward imagery at every single stage of the conversion path to drive both discovery and action. When you provide a robust visual library, you are essentially giving the AI system the raw materials it needs to assemble the perfect message for a specific user at a specific moment. This approach allows the system to test multiple combinations across various channels, such as Demand Gen, where the sheer variety of assets often determines the ultimate success of the campaign. Without a deep well of creative options, you limit the AI’s ability to tell your brand story effectively, making your path to conversion feel stagnant and uninspired compared to competitors who embrace visual-first discovery.
While AI can generate vast amounts of content, there is a risk of losing brand distinctiveness. How do you ensure that single assets like headlines or images can stand alone effectively, and what is your process for using AI to amplify rather than replace human creative input?
There is a significant danger in delegating your entire creative process to AI, as it can quickly lead to a loss of the unique soul and “human touch” that makes a brand stand out in a crowded marketplace. I always emphasize that AI should be viewed as an enabler and an amplifier, not a total replacement for the human spark that drives true differentiation. Every single asset we produce—whether it is a lone headline or a solitary image—must be strong enough to stand on its own and communicate the brand’s essence instantly to a distracted user. Our process involves using AI to scale and test variations, but the core creative direction and positioning must come from a place of deep human understanding. If a single asset cannot clearly communicate who you are and what you offer without the help of ten other supporting elements, it simply isn’t ready for a high-speed, AI-driven environment.
Managing a diverse asset library is now essential to avoid “AI chaos” caused by overlapping creative elements. What specific indicators do you use to identify which niche assets deserve more budget, and how frequently should underperforming creatives be swapped to keep campaigns optimized?
Managing an asset library today is largely about preventing “AI chaos,” which happens when your creative elements overlap too closely and the system struggles to distinguish what is actually driving performance. To avoid this, we look for distinctiveness in both text and visuals, ensuring each piece of content offers a unique signal to the machine. We identify niche assets that deserve more budget by looking at how they perform with specific audience segments; if a niche image is driving high engagement, it is telling us something profound about what that specific audience values. Underperforming assets should be swapped out regularly to keep the campaign optimized and prevent the “decay” of user interest that happens when creative becomes stale. It is no longer enough to set and forget your campaigns; you have to treat your creative library as a living, breathing entity that requires constant pruning and strategic expansion.
Privacy-protective models are shifting measurement from granular query data toward broader thematic analysis and incrementality. How can advertisers better utilize first-party data to feed these AI systems, and what strategic shifts are required to evaluate performance based on audience themes rather than keywords?
The shift away from granular query data toward broader thematic analysis is a massive change, but it is one that actually rewards advertisers who have a strong handle on their own first-party data. By feeding AI systems high-quality inputs like customer lists, website content, specific business targets, and budgets, we allow the model to optimize for actual business outcomes rather than just vanity clicks. We have to let go of the old control patterns that kept us obsessed with individual keywords and instead focus on the underlying themes that truly matter to our audience. This requires a strategic shift to evaluating incrementality, which helps us understand the true added value of our ads rather than just counting every single touchpoint. When the AI understands the broader themes of your business through your creative copy and first-party signals, it can find your ideal customers more efficiently, even in a privacy-first world.
What is your forecast for PPC?
My forecast for PPC is a total move away from the “search box” as we know it, transitioning into a multi-surface ecosystem where intent is captured through sight, sound, and immersive conversation. We will see the role of the PPC marketer shift from a technical manager of bids and keywords to a high-level creative strategist and data steward. Success will be defined by those who can provide the best “fuel”—meaning high-quality first-party data and diverse creative assets—for the AI to process and optimize. As platforms become more automated, the only way to maintain a competitive edge will be through the unique brand stories and human-centric strategies that machines simply cannot replicate on their own.
