Anastasia Braitsik is a renowned authority in the digital advertising landscape, specializing in the intersection of data analytics and automated marketing strategies. With a career dedicated to refining how brands leverage search and social platforms, she has become a leading voice in navigating the shift from manual optimization to AI-guided campaign management. Today, she shares her insights on the latest advancements in advertising technology, focusing on how creative flexibility and algorithmic precision are reshaping the way businesses connect with their audiences.
The conversation explores the evolving capabilities of automated campaign types, the critical role of mobile-first creative assets, and the strategic implementation of brand safeguards within AI frameworks. We also delve into technical workflow efficiencies and the future of budget management in an increasingly automated ecosystem.
Performance Max now allows for up to 15 videos and 9:16 vertical images within a single asset group. How should advertisers prioritize these different formats to maximize AI testing, and what specific creative variations tend to drive the most engagement on mobile-first surfaces?
To truly capitalize on the 15-video limit, advertisers must move away from the “one-size-fits-all” mentality and embrace a strategy rooted in variety. You should prioritize a mix of high-production brand stories alongside raw, user-generated content styles that feel native to short-form environments. The inclusion of 9:16 vertical images is a game-changer for mobile-first surfaces, and I recommend testing at least five distinct vertical variations that focus on tight framing and clear calls to action. We often see the highest engagement when the first three seconds of a video utilize high-contrast visuals or direct-to-camera addresses, which pull the user in before they can swipe away. By saturating the asset group with these diverse formats, you provide the AI with enough “fuel” to determine exactly which visual hook resonates with specific audience segments.
Demand Gen campaigns have introduced new brand guideline controls alongside customer acquisition goals. What practical steps can a team take to ensure AI-generated assets stay on-brand, and how do these new constraints affect the stability of a campaign’s initial setup?
The most practical step a team can take is to meticulously define their brand parameters within the new text and asset guideline settings before the campaign even goes live. This means uploading specific color palettes, font styles, and exclusionary themes to ensure that the AI doesn’t produce “hallucinated” creatives that look off-brand. These constraints actually improve the stability of a campaign’s initial setup because they narrow the machine learning’s playground, preventing it from wasting budget on experimental combinations that would never pass a manual brand audit. When you combine these safeguards with clear customer acquisition goals, the algorithm focuses more on high-intent users rather than just broad reach, resulting in a more focused and predictable launch phase.
Total campaign budgets now allow for a fixed spend over a defined period, specifically for seasonal promotions. What are the potential trade-offs of letting an algorithm handle daily pacing, and how can advertisers monitor delivery to ensure the budget isn’t exhausted too early?
The primary trade-off of a total campaign budget is the loss of granular control over daily fluctuations; the algorithm might choose to spend 40% of your budget in the first two days if it detects a spike in high-value traffic. While this maximizes efficiency, it can be nerve-wracking for brands with strict day-to-day reporting needs. To prevent early exhaustion, you must use the updated campaign status filters and real-time delivery signals to monitor the “burn rate” relative to your promotion’s end date. If you see the system pacing too aggressively, you can intervene by tightening your target ROAS or adjusting your bid guidance to slow down the machine without stopping the campaign entirely.
Modern workflows are shifting toward account-level tracking and bulk link replacement to reduce manual labor. How do these technical efficiencies change the daily responsibilities of a media buyer, and what specific metrics should be monitored to ensure Final URL expansions are performing correctly?
Media buyers are moving away from the “button-clicking” era of manual link updates and are becoming more like strategic architects. With bulk link replacement and account-level tracking, the time saved on administrative tasks should be reinvested into analyzing cross-channel attribution and creative strategy. When using Final URL expansion, it is vital to monitor the “Landing Page Report” and “Search Terms Report” to ensure the AI isn’t directing traffic to low-converting or irrelevant pages, like a “Contact Us” or “Privacy Policy” page. You should also watch your bounce rates and conversion lag times closely; if a specific expanded URL shows a 20% higher bounce rate than your primary landing page, it’s a clear signal to add that page to your exclusion list.
With updates to non-skippable video formats and real-time bid guidance, advertisers are gaining more granular control. In what scenarios should a brand prioritize non-skippable assets over flexible AI placements, and how should they interpret real-time signals to adjust their bidding strategy?
Non-skippable formats should be your primary weapon when the objective is absolute message retention, such as during a 15-second product launch or a critical brand announcement where the narrative cannot be cut short. These are best used when you have a high-impact creative that can hold attention without causing user frustration. To manage this effectively, you have to look at the real-time bid guidance provided in the editor; if the guidance suggests your bids are too low to win premium non-skippable slots, you shouldn’t just raise the bid blindly. Instead, evaluate the “Impression Share Lost to Rank” and decide if the premium cost aligns with your reach goals, or if shifting back to flexible, skippable AI placements would offer a better cost-per-completed-view.
What is your forecast for AI-driven campaign management?
I foresee a future where the role of the advertiser shifts entirely from “manager” to “governor,” where our primary job is to set the ethical and aesthetic boundaries for a fully autonomous system. We will see even deeper integrations of hotel, retail, and local feeds that allow AI to build thousands of hyper-personalized ads in milliseconds based on a single brand prompt. However, as the technical barriers to entry drop, the only remaining competitive advantage will be the quality of the first-party data we provide and the unique emotional resonance of the human-led creative strategy. The brands that win will be those that learn to guide the AI with precision rather than those that simply let it run on autopilot.
