A global leader in SEO, content marketing, and data analytics, Anastasia Braitsik is a guiding voice for brands navigating the shift from traditional search to conversational AI. As Google’s “AI Mode” begins to reshape how users interact with information, Anastasia provides a roadmap for maintaining visibility in an era where machine learning dictates the rules of engagement. This interview explores the transition from keyword-centric bidding to audience-driven discovery and the financial implications of a consolidated AI ecosystem.
Established tech giants are leveraging decades of ad optimization while newer competitors are just beginning to test auction models with third-party partners. How do these distinct starting points affect the speed of innovation, and what specific technical hurdles must a newcomer clear to achieve global scale?
The gap between a 25-year head start and a fresh pilot program is monumental in the world of digital advertising. Google enters this phase with mature systems and deep advertiser adoption, whereas a newcomer like OpenAI is still navigating an inefficient auction model confined to a small group of large advertisers. To reach a global scale, a newcomer must build a robust reporting infrastructure and a user-friendly interface that can compete with the $3.6 trillion valuation-backed ecosystems we see today. They face the steep learning curve of balancing user experience with monetization, a challenge that recently led some to pull back on “checkout in chat” features after seeing limited consumer adoption. Ultimately, innovation for a newcomer isn’t just about the LLM’s intelligence; it’s about creating a scalable ads engine that doesn’t make the 130% stock rallies of established giants look like an unreachable peak.
Major technology providers are increasingly forming strategic alliances to power native AI features across mobile devices. What does this consolidation mean for the broader advertising ecosystem, and how should brands adjust their financial projections when a single provider dominates both search and integrated mobile AI?
When Apple chooses Google to power its AI, it signals a massive validation of existing infrastructure over speculative “better mousetraps.” For the ecosystem, this means that the shift in user behavior—how search works and how it makes money—will likely reinforce existing business models rather than weaken them. Brands should adjust their financial projections by looking at visibility through the lens of long-term planning rather than immediate SERP positions. Even if a brand is comfortable with 30% less business coming from traditional search, they must account for the fact that the “AI Mode” layer will likely be controlled by the same dominant players. It is critical to maintain a large allocation of the annual digital budget to these consolidated providers, as they will define the standards for privacy, safety, and enforcement across all mobile touchpoints.
Conversational “AI Mode” offers a different user experience than standard search overviews found at the top of results pages. How do user intent and browsing behavior shift when moving from a quick summary to a deep dialogue, and what practical steps should marketers take?
The shift from a quick summary in an AI Overview to a deep dialogue in AI Mode represents a transition from finding a fact to exploring a concept. In AI Mode, Google downplays the “reasoning” process and the specific model used to create a seamless, fluid experience that feels less like a search and more like a workflow. Marketers need to stop focusing on “ad campaign FOMO” and instead double down on content and reputation fundamentals, as organic visibility in these results will carry immense commercial value. Practically, this means implementing AI-eligible campaigns—like Performance Max or Standard Shopping—to ensure your products appear when the conversation turns toward a purchase. You should watch and learn at your own pace, recognizing that even if ads only appear in 0.5% of these sessions initially, the data gathered will be vital for understanding this new conversational intent.
Some platforms are currently outsourcing their ad inventory to programmatic partners while pulling back on direct “checkout in chat” features. What are the trade-offs of relying on external ad tech, and how can businesses bridge the gap between AI-driven discovery and actual sales conversion?
Outsourcing inventory to partners like Criteo or The Trade Desk is a pragmatic move for platforms in their infancy, but it underscores a lack of native, scalable ad tools. The primary trade-off is a loss of granular control over the user journey, which is why we see a retreat from integrated “checkout in chat” as merchants and consumers struggle with the friction of buying within a dialogue. To bridge the gap, businesses must rely on data-driven attribution to connect the dots between a high-level AI conversation and a eventual sale on their own site. Since the direct “buy button” in chat isn’t quite ready for primetime, the strategy should focus on using feed-based ads to keep products visible during the discovery phase. This ensures that when the user is ready to move from research to transaction, your brand is the most familiar and accessible option.
Automated campaign types are increasingly the primary gateway into AI-driven placements, often reducing manual control over specific keyword matching. Since these systems rely heavily on machine learning, what metrics should advertisers prioritize to evaluate performance, and how can they maintain brand safety?
As we move away from micromanagement, the primary metrics to prioritize are those tied to the real-world behavioral ecosystem, such as conversion value and audience engagement levels. You have to accept that the massive machine learning controlling the matching is held by the platform, meaning you must shift your focus toward quality signals like feed accuracy and audience definitions. Brand safety is maintained by strictly adhering to the platform’s privacy and safety policies, while also monitoring where your ads serve across the expanding “Shopping Expansion” betas. Don’t be fooled into thinking a single “AI Max” campaign is the only gateway; run a mix of keyword and feed-based campaigns to keep a foot in both traditional and AI-driven environments. Evaluating performance now requires looking at “higher-order” signals, such as how often your brand is associated with specific thematic research sessions.
Granular reporting for conversational queries is still in its infancy compared to traditional search metrics. What specific data points do you consider essential for judging the ROI of these sessions, and how should agencies explain the value of these emerging placements to skeptical clients?
We are all impatiently awaiting more transparent guidance, but for now, the essential data points include specific reporting breakouts for AI-conversation sessions, similar to what Microsoft has started to offer. Agencies should explain to skeptical clients that while the ROAS might look lower initially, these placements capture users in “higher-order thinking” moments that traditional search often misses. Use the available data from “Shopping Expansion” tables to show how ads are appearing in AI Overviews, and emphasize that being an early mover provides a feedback loop that competitors won’t have. It’s about explaining that the funnel is moving, and the value lies in being present when a user is forming a brand preference during a complex research phase. You can’t wait for “perfect” reporting to prove ROI when the consumer has already migrated to a new way of searching.
AI interactions often capture users during complex research phases, which can lead to lower immediate ROAS but higher thematic relevance. How can brands justify investing in these top-of-funnel conversations, and what strategies help capture the long-term value of these rich user personas?
Investing in these conversations is justified because it allows a brand to align itself with audience personas that are far richer than the fragmented behavioral signals we used in the past. These interactions capture users when they are most engaged, providing a unique opportunity to define your company’s “thematic relevance” before they even reach the bottom of the funnel. To capture long-term value, brands should focus on novel audience buckets—such as a user’s language ability level or specific life stages—that emerge from how they interact with an LLM. This shift up-funnel means that if you ignore these sessions because of “ugly early numbers,” you risk losing the consumer at the very moment their intent is being shaped. The strategy is to treat these sessions as a way to build a consumer persona that is contextually deep, ensuring your brand is the natural choice when the research phase ends.
What is your forecast for the future of AI-driven monetization?
I expect the transition to AI-driven monetization to be much smoother and more measured than the initial “code red” panic suggested, with platforms prioritizing a popularity contest over immediate ad density. We will likely see a period where ads are tested sparingly—perhaps in as few as 0.5% of sessions—to avoid user recoil while the systems gather massive amounts of feedback. Google, in particular, will play the long game, using its 25-year advantage to gradually ease users into AI Mode while keeping a tight grip on advertiser control through automated campaign types. Eventually, the most successful brands will be those that stopped chasing “ad campaign FOMO” and instead mastered the art of appearing natively within a conversational workflow. The future isn’t just about showing an ad; it’s about being the most relevant part of a deep, AI-driven dialogue.
