I’m thrilled to sit down with Anastasia Braitsik, a global leader in SEO, content marketing, and data analytics, whose insights into the future of digital marketing are shaping how we think about performance strategies. With AI transforming the landscape at lightning speed, Anastasia offers a unique perspective on how agentic PPC (pay-per-click) could redefine marketing by 2030. In this conversation, we dive into the evolution of AI agents as personal marketing assistants, the power of collaboration between these agents, the economics of monetizing their expertise, and the challenges and opportunities that lie ahead. Let’s explore what the future holds for performance marketing through her expert lens.
How do you envision performance marketing changing over the next decade with the integration of AI agents?
I believe we’re on the cusp of a massive shift. Over the next ten years, performance marketing will move from rigid automation to highly adaptive, personalized AI agents that act as extensions of ourselves. Today’s tools follow strict rules and schedules, but by 2030, these agents will anticipate needs, optimize campaigns in real-time, and even earn revenue while we sleep. They’ll understand not just data, but our instincts and decision-making quirks, making them true digital partners. It’s not just about efficiency—it’s about amplifying human expertise at scale.
What do you see as the key differences between current PPC automation tools and the AI agents we might rely on in 2030?
Current PPC tools are like calculators—they execute predefined tasks without much context or intuition. They can’t grasp why you make certain choices, like pausing a campaign during a competitor’s big launch. By 2030, AI agents will be more like trusted colleagues. They’ll learn from your past actions, performance data, and even your gut feelings documented over time. These agents won’t just automate; they’ll strategize and adapt in ways that mirror how you think, creating a seamless blend of human insight and machine precision.
Can you describe what a personal marketing agent is and how it could function as a digital twin for a marketer?
A personal marketing agent is essentially a custom-built AI that becomes your digital counterpart. It’s trained on your campaign history, decision patterns, and even personal notes about what’s worked or failed. Over time, it starts to mimic your style—whether it’s how you structure ad groups or adjust budgets during specific times. As a digital twin, it doesn’t just follow instructions; it predicts what you’d do in a given scenario and acts on it, almost like having a second version of yourself running campaigns 24/7.
How crucial is it for marketers to train AI agents with personal data, such as past campaigns or decision-making habits?
It’s absolutely essential. Without personal data, an AI agent is just a generic tool with no unique edge. Feeding it your past campaigns, performance metrics, and even your thought process behind key decisions allows it to tailor its actions to your specific style. This isn’t about handing over raw data—it’s about teaching the agent your philosophy, like when to scale budgets or test creatives. The more it knows about you, the more it can replicate your success patterns and avoid your past mistakes.
What specific types of data or insights would you suggest marketers share with their AI agents to reflect their unique approach?
I’d recommend starting with the basics: historical campaign data, performance reports, and budget allocation rules. But go deeper—share how you prioritize certain metrics, like focusing on conversion rates over clicks, or your creative testing sequence. Document your instincts too, like why you push harder on weekends or hesitate during specific market conditions. Even competitor analysis habits can be useful. The goal is to give the agent a full picture of not just what you do, but why you do it, so it can emulate your mindset.
What are some potential risks of sharing extensive personal or business data with an AI agent for training purposes?
There are definitely risks to consider. Privacy is a big one—sharing detailed campaign data or decision logs could expose sensitive business strategies if the system is breached or data is mishandled. There’s also the issue of over-reliance; if the agent misinterprets your input, it might make costly errors. And in regions with strict regulations like the EU, compliance with data protection laws could be a hurdle. Marketers need to weigh these risks and ensure robust security measures and clear boundaries on what data is shared.
Can you explain the concept of Agent2Agent collaboration and how it could benefit marketers with diverse strengths?
Agent2Agent, or A2A, collaboration is the idea that AI agents can work together, much like human experts do. Imagine one agent excels at ecommerce campaigns but struggles with B2B lead gen. It can connect with another agent that’s a B2B specialist, share insights, and solve problems collectively. This benefits marketers by filling gaps in expertise without needing to personally master every niche. It’s like having a network of specialists at your fingertips, where agents learn from each other and improve overall campaign outcomes.
How do you think AI agents could monetize their expertise, and what role might something like the Agent Payments Protocol play in this?
AI agents could become revenue generators by selling their specialized knowledge or services to other agents. For instance, an agent trained on killer ecommerce strategies could charge for sharing its product feed optimization tricks. The Agent Payments Protocol, or AP2, facilitates this by creating a framework where agents can transact—think of it as a digital marketplace for expertise. A marketer could link their agent to a payment system, define services it offers, and earn passive income as other agents buy access to its insights, turning the agent into a profit center.
What challenges do you foresee in the widespread adoption of AI agents collaborating and sharing data across platforms?
One major challenge is trust—how do you verify an agent’s performance claims or ensure it’s worth collaborating with? There’s also the issue of data privacy; sharing strategies across platforms could risk exposing proprietary tactics. Compatibility is another hurdle—agents built on different tech stacks might struggle to communicate without a universal standard like A2A. And then there’s competition; if everyone’s agent is equally smart, sharing too much could erode your edge. These issues will need careful solutions as adoption grows.
Looking ahead, what is your forecast for the role of AI agents in performance marketing by 2030?
By 2030, I predict AI agents will be indispensable in performance marketing, handling the bulk of tactical work like campaign optimization and media buying while marketers focus on strategy and creativity. They’ll collaborate globally, forming a network that makes the entire advertising ecosystem smarter and more efficient. But I also see a counter-trend emerging— a push for human-driven marketing as AI becomes ubiquitous. The marketers who thrive will be those who balance AI’s efficiency with authentic human connection, knowing when to let agents take the wheel and when to steer with raw intuition.
