As a global leader in SEO, content marketing, and data analytics, Anastasia Braitsik lives on the cutting edge of digital strategy. Today, she shares her frontline experiences navigating the turbulent but promising world of AI in performance marketing. We’ll explore her team’s approach to leveraging AI for creative generation, the trade-offs in workflow automation, and the new frontier of measuring brand visibility in AI-powered search. This discussion moves beyond the hype to offer a practical look at how to test, adopt, and pivot in an ecosystem where today’s top tool can become tomorrow’s afterthought.
You chose AdCreative.ai for its strength in brainstorming creative variations. Could you walk us through a specific campaign where you used it to generate and test new angles? What did your human-in-the-loop process look like to ensure brand alignment and avoid “AI slop”?
Absolutely. We were running a campaign for a client where ad fatigue was setting in fast. The same visuals, the same headlines—our click-through rates were starting to dip. Instead of a lengthy and expensive creative brief with our design team, we turned to AdCreative.ai. We fed it our core brand assets, product images, and key value propositions. Within minutes, it generated over 50 distinct ad variants with different copy, layouts, and imagery. It was like having an entire junior creative team brainstorming at lightning speed. But the real work started there. Our human-in-the-loop process is non-negotiable. Every single output was reviewed against our brand guide, which includes a strict list of banned words and stylistic rules. A human marketer had to give final approval on every asset, ensuring the tone was right and the claims were accurate. We found some real gems in that batch, but we also discarded plenty of what you aptly call “AI slop” that felt generic or slightly off-brand. That human oversight is the critical guardrail that makes these tools effective rather than just noisy.
Your article highlights n8n for workflow automation, particularly for tasks like UTM cleanup. Considering its integration gaps with platforms like LinkedIn or TikTok Ads, what specific benefits of its agentic workflows made it worth the extra manual work? Please share a detailed example of one such automation.
That’s a great question because it gets to the heart of our decision-making. Yes, the lack of a built-in connector for TikTok Ads is a hurdle. But the power of n8n’s agentic workflows makes it worth the occasional need to build a direct API call. It’s not just a simple “if this, then that” tool like some others. Its workflows can handle complex logic and data transformation. Our UTM cleanup automation is the perfect example. When a lead fills out a form, HubSpot often dumps the source data into a single “first URL seen” field. Manually untangling that for hundreds of leads was a miserable, error-prone task. Now, an n8n workflow triggers automatically. It ingests that raw URL, intelligently parses every UTM parameter—source, medium, campaign, content—and normalizes the data to fix inconsistencies. Then, it pushes those perfectly clean, structured fields into our CRM. That one workflow saves us hours each week and ensures our attribution data is pristine. That level of intelligent, multi-step processing is why we commit to it; it’s a true digital assistant, not just a simple connector.
You recommend purpose-built tools like Profound to measure visibility in AI search. Can you provide a concrete example of a “persona-level” insight this tool revealed that a traditional SEO platform might have missed? How did you then use that data to refine your content strategy?
This is where things get really exciting and move beyond simple keyword rankings. We were working with a client in the cybersecurity space. Traditional SEO tools like Semrush showed them ranking well for their target keywords. But when we used Profound, we ran a query from the persona of a Chief Information Security Officer asking an AI model like Perplexity to compare top solutions. The insight was jarring. While our client’s brand was mentioned, the AI’s summary consistently described their product as a “cost-effective solution for small businesses.” This was a narrative disaster, as their primary target is large enterprises that prioritize robust features over price. A traditional SEO tool would never have caught that semantic nuance; it only sees keywords, not the story being told. Armed with this data, we immediately pivoted our content strategy. We launched a series of technical whitepapers and PR initiatives focused on enterprise-grade scalability and advanced threat detection, directly addressing the narrative gap Profound had uncovered. It completely changed how we thought about influencing our reputation, not just our rankings.
The article stresses caution with long-term AI tool contracts and the need for knowledge-sharing. How does your team’s structured knowledge-sharing function in practice? Can you describe the step-by-step process you use to evaluate and decide when it’s time to pivot from one tool to another?
Our process is built on the idea that everyone on the team is a scout in this new territory. It’s aggressive, but it has to be. When a team member discovers a new AI tool, they are responsible for creating a “scouting report” in our shared knowledge base. This isn’t just a link; it’s a structured brief on the tool’s core promise, its pricing, and a hypothesis for how it could improve a specific workflow. We then assign a small, two-person “sprint team” to test it for a defined period, usually two weeks, against a clear success metric. They document everything—the wins, the bugs, the time saved, the frustrations. At the end of the sprint, they present their findings to the entire team. The decision to pivot from an existing tool is a high bar. We ask, “Does this new tool provide a genuine step-change in efficiency or capability, or is it just incrementally better?” This structured, team-wide approach prevents us from getting locked into expensive annual contracts for a tool that might be eclipsed in three months. It keeps us agile and ensures the best ideas, not just the loudest opinions, win out.
What is your forecast for the role of the performance marketer as AI agents become more autonomous in managing campaigns?
I believe the role of the performance marketer is about to become more strategic and more human, not obsolete. The forecast isn’t about us being replaced, but about being elevated. All the tedious, granular work that consumes so much of our time now—manually adjusting bids across a thousand keywords, A/B testing minor copy variations, pulling numbers for weekly reports—will be delegated to autonomous AI agents. The marketer’s role will transform into that of an architect and a strategist. We will be the ones who define the campaign’s goals, set the ethical and brand guardrails, and feed the AI the creative and narrative soul of the brand. The most valuable skill will no longer be technical proficiency in a platform’s UI, but the ability to ask the right strategic questions and interpret the AI’s output to make bold, creative leaps. We’re moving from being pilots in the cockpit to being the mission commanders in the control room.
