Today we’re sitting down with Milena Traikovich, a Demand Generation expert with extensive experience in analytics and performance optimization. She helps businesses navigate the complex landscape of marketing technology to drive effective campaigns and nurture high-quality leads. With the recent explosion of AI tools, we’re here to discuss how new, role-based AI agents are not just another shiny object, but a fundamental shift in how revenue teams operate. We will explore how these agents are helping marketing teams move from being overwhelmed operational grinders to strategic, creative thinkers. We will also discuss how they break down internal data silos, pivot marketing focus from unknown prospects to known customers, and ultimately, free up professionals to do what they do best.
For marketing teams feeling overwhelmed by new AI tools, you’ve advised a “think big, start small, act fast” approach. What is a concrete first step a team could take with an AI agent, and what specific metrics should they use to decide when to “act fast”?
That’s the perfect place to start because the feeling of being overwhelmed is very real. The key is to avoid trying to boil the ocean. A concrete first step is to pick one highly repetitive, time-consuming task. For instance, a team could start with the Copywriting Agent. Instead of trying to use it for a massive, flagship campaign, they could deploy it to generate initial drafts for a series of targeted email nurture streams. The “start small” part is crucial here. They would measure the time saved in drafting, the number of variations produced, and the initial engagement rates of the AI-generated copy versus their previous benchmarks. You’d “act fast” the moment you see a clear signal—for example, if the agent cuts down the copy creation timeline by 50% without a dip in click-through rates. That’s your green light to scale its use across more campaigns.
Many organizations struggle with data silos across finance, operations, and service. How do role-based AI agents expose this siloed information for marketers, and can you share an example of an insight a Program Planning Agent might gain from internal enterprise data to shape a campaign?
This is one of the most powerful aspects of enterprise-level AI. These agents don’t necessarily break down the walls of the silos, but they act like skilled intelligence operatives who can see over them and report back. They expose the information trapped inside. For example, a Program Planning Agent, tasked with creating an up-sell campaign, wouldn’t just look at past marketing engagement. It would dive into the service organization’s data and might discover that a specific customer segment has a high rate of maintenance requests for an older product model. That is a golden insight sitting right there in the service records! The agent would then flag this as a prime opportunity for a targeted upgrade campaign, using data on their procurement cycles from the finance system to time the outreach perfectly. The marketer never had to manually request or merge that data; the agent simply surfaced the pattern.
The role of a marketer is increasingly technical, with some now looking more like IT professionals. How do prebuilt agents help reverse this trend by handling operational work, and what creative or strategic tasks does this free up for a marketing professional to focus on?
It’s a trend many of us have seen and worried about. Marketers have spent years cobbling together tech stacks, managing integrations, and troubleshooting data flows, making them look more like IT staff. Prebuilt, natively integrated agents completely flip that script. They handle the “grind-it-out” work. Think about the Program Orchestration Agent streamlining the integration of narratives into materials, or an Image Picker Agent ensuring brand alignment. This isn’t just about saving time; it’s about reclaiming a marketer’s core function. When you’re not bogged down in the mechanics, you’re free to do the high-value work: thinking big thoughts about market positioning, collaborating with sales on strategy, identifying subtle patterns in customer behavior, and focusing on the genuinely creative aspects of building a compelling brand story.
The assertion is that the battleground for marketers has shifted from acquiring unknown prospects to growing business from known customers. How does an agent like the Customer Insights Agent practically help a marketer achieve this, and what kind of internal data does it leverage?
Absolutely, the days of casting a wide digital net and hoping for the best are over. The most valuable opportunities are with the customers you already have. The problem is, the information about these customers is scattered across the enterprise. This is where an agent like the Customer Insights Agent becomes a marketer’s best friend. It practically serves up a unified customer dossier. It pulls together their complete service record, details on their equipment maintenance schedules from operations, their order management history, and even their customer support interaction logs. By synthesizing all this internal data, it gives a marketer a deep, holistic understanding of a customer’s health, challenges, and potential needs, allowing them to create incredibly relevant and timely cross-sell and up-sell campaigns instead of just another generic promotion.
Let’s discuss a specific sales example. How does the Quote Generation Agent go beyond simple automation? Could you walk through the steps it takes to assemble a complex quote, and how it uses data from other departments to inform the final proposal for a seller?
This is a fantastic example because it shows how this technology is about intelligence, not just automation. A simple automation tool might just populate a template. The Quote Generation Agent is more like a strategic partner. When a seller initiates a quote, the agent first accesses the Contact Insights Agent to understand the relationship history. Then, it queries the operations department’s systems for real-time product availability and deployment timelines. It simultaneously checks the service department’s records for that specific customer to see if there are any outstanding issues or if they are eligible for a loyalty discount. Finally, it can even reference finance data on past procurement cycles to structure the payment terms favorably. It assembles all of these disparate data points into a single, accurate, and highly contextualized quote for the seller, dramatically reducing manual effort and ensuring the proposal is both competitive and deliverable.
What is your forecast for agentic AI in the enterprise over the next three years?
My forecast is that over the next three years, we will see a profound shift from individual, task-based agents to collaborative agent teams that manage entire business processes. We’re already seeing the foundation with tools like the AI Agent Studio. It won’t be just about a marketing agent planning a program; it will be about a team of marketing, sales, and service agents working in concert to manage the entire customer lifecycle. This will make businesses far more agile and predictive, allowing them to identify and act on opportunities buried deep within their enterprise data. The true transformation will be when these agent teams become so seamlessly integrated into daily operations that we stop thinking of them as tools and start seeing them as essential, intelligent members of our digital workforce.
