Milena Traikovich is a leading authority in demand generation and performance optimization, renowned for her ability to translate complex behavioral data into high-growth marketing strategies. With an extensive background in analytics, she currently helps global brands navigate the increasingly automated landscape of digital commerce, ensuring that lead generation initiatives remain effective as consumer habits shift toward artificial intelligence. As the “agentic economy” takes hold, Milena’s expertise has become essential for businesses trying to understand how autonomous agents—rather than human clicks—are making purchasing decisions. In this discussion, we explore the rise of one-stop AI shopping experiences, the critical five-pillar framework for measuring agentic influence, and the emerging identity standards that are finally allowing marketers to distinguish between human and bot-driven transactions.
With the introduction of Google’s Universal Commerce Protocol and OpenAI’s Agentic Commerce Protocol, we are seeing the traditional shopping funnel collapse into a single interface. How are these new protocols fundamentally changing the way brands must present their products to ensure they aren’t left out of these one-stop AI shopping journeys?
These protocols are essentially creating a new infrastructure where the friction of the traditional web disappears, allowing a consumer to go from discovery to a final purchase without ever leaving the AI’s chat bubble. For a brand, this means you are no longer just fighting for a spot on a search results page; you are fighting for “agentic shelf visibility” within a closed system that processes everything from product comparisons to the checkout. We are seeing brands move away from simple SEO toward what I call “content readiness,” where product data is structured in a specific format that these assistants can digest instantly. If your product information isn’t optimized for these protocols, you effectively become invisible to the AI, which can be devastating when you consider that these agents are now handling the entire lifecycle of a purchase. It’s a sensory shift for marketers who are used to visual storytelling, as they now have to prioritize how an algorithm “reads” the value and utility of their offerings.
As AI agents move beyond just finding products to actually executing the purchase, what are the primary hurdles in connecting this discovery phase with retail sales data, and how is the partnership between NIQ and Similarweb addressing this?
The biggest hurdle right now is that the industry is operating in a bit of a “black box” where we can see that an agent is active, but we can’t always see how that visibility translates into a real-world sale. Our clients at NIQ are constantly asking how quickly this influence is growing and, more importantly, what specific actions they should take to capture that momentum. By integrating Similarweb’s discovery insights with NIQ’s Commerce Intelligence, we are finally building a bridge that connects AI-driven product recommendations directly to retail sales figures. This allows a brand to see if being mentioned in a ChatGPT session actually resulted in a transaction, rather than just a “hallucination” of interest. It provides that much-needed transparency into whether the “shelf visibility” provided by an agent is moving the needle on the bottom line.
You have identified five key areas of measurement that are essential for success in this environment—intent, visibility, content readiness, traffic, and omnichannel behavior. Which of these provides the most actionable insight for a brand trying to pivot its strategy in the current year?
While all five are vital, consumer intent is where the real magic happens because it involves analyzing the specific questions and prompts that shoppers are feeding into AI assistants. When you understand the “why” behind a prompt, you can tailor your product content to be the definitive answer the AI provides. Following that, measuring AI-driven traffic—the actual percentage of visitors coming from these platforms—gives you a hard number to justify your investment in these new channels. It’s no longer enough to look at general traffic; you have to see if that traffic is originating from an agent that has already pre-qualified the product for the user. We are also looking closely at omnichannel behavior to see how an AI recommendation on a phone might lead to a purchase in a physical store or through a different digital channel, ensuring that no part of the journey is lost to the “dark funnel” of unmeasured data.
The concept of “Know Your Agent” frameworks from Mastercard and Visa introduces a layer of cryptographic verification to the shopping process. How will these identity standards help marketers distinguish between a human buyer and an autonomous agent, and why is this distinction so critical for long-term customer value?
This is a game-changer because it allows us to separate traditional API transactions or standard human clicks from verified agent-driven purchases. Using cryptographic verification, these systems can signal to a retailer that an AI is acting with the explicit authority of a human user, which brings a level of trust and security that was previously missing. From a measurement standpoint, this lets us link an agent-mediated purchase back to a known customer profile through signals like email and payment methods. When we can link these together, we can track repeat purchases and accurately calculate the long-term value of a customer who uses an agent as their primary shopper. It feels like we are finally moving away from the “infancy” of agentic commerce and into a professional era where data is clean, verified, and highly actionable.
What is your forecast for agentic commerce?
By the end of 2028, I expect that agentic commerce will no longer be an “emerging” trend but the dominant mode of transaction for high-intent, routine purchases. We will see the gap between AI influence and real-world sales close completely as measurement infrastructures become as standardized as the credit card processors we use today. Brands that invest now in “content readiness” and participate in these agentic identification standards will see a 20% to 30% increase in their discovery-to-conversion rates compared to those stuck in traditional SEO models. Ultimately, the winners will be those who treat AI agents as a new tier of “super-influencer” that requires its own dedicated data strategy and measurement ecosystem to thrive.
