Milena Traikovich has spent her career at the intersection of analytics and performance, helping businesses transform complex data into high-quality leads. As an expert in demand generation, she has a front-row seat to one of the biggest shifts in the industry: the rise of agentic AI. We sat down with her to discuss how this technology is moving beyond a simple optimization tool to become the fundamental infrastructure of modern digital marketing. Our conversation explored the compression of the operational layer that has burdened marketing teams, the practical evolution of a campaign manager’s role from hands-on execution to strategic supervision, and why establishing clear governance isn’t just a best practice but an absolute prerequisite for success.
The article states that complexity is the main cost driver and agentic systems compress the “operational layer.” Can you give a real-world example of this compression in action? What specific manual tasks are eliminated, and what metrics show a clear reduction in this operational burden?
Absolutely. Think about a large-scale programmatic campaign running across multiple channels and formats. The “operational layer” is the immense human effort spent just keeping the machine running. It’s the team of analysts who spend their mornings manually pulling reports, trying to spot performance dips, and then hours troubleshooting why a specific supply path is suddenly underperforming. It’s the constant, tedious work of monitoring, diagnosing, and reacting. Agentic systems absorb that entire cycle. I’ve seen teams where issue resolution time—from detecting a problem to implementing a fix—was cut from half a day to mere minutes. The specific tasks that vanish are things like manual bid adjustments, pacing checks, and troubleshooting data discrepancies between platforms. The clearest metric is a drastic reduction in what I call “human latency”—the time it takes for a person to notice and act on data. This frees up your most valuable people from being reactive problem-solvers to proactive strategists.
You describe the marketing team’s role shifting “upstream” to defining success metrics and priorities. Could you walk us through how a campaign manager’s daily routine practically changes with this shift? What new skills might they need to develop to effectively supervise these autonomous systems?
It’s a profound change. A campaign manager’s day used to be a frantic whirlwind of checking dashboards, pulling levers, and putting out fires. It was very tactical. Now, with an agentic system handling the execution, their day starts differently. Instead of diving into raw performance data, they’re reviewing the autonomous agent’s summary of actions and outcomes. Their focus moves from “What should I do right now?” to “Did we define our strategic intent correctly?” They spend their time designing experiments, refining the acceptable risk parameters for the system, and collaborating with the creative team to feed the system better inputs. The key new skills are less about technical platform knowledge and more about systems thinking and strategic communication. You have to become an expert at translating high-level business goals into precise, machine-readable instructions and guardrails. It’s a shift from being a pilot in the cockpit to being an architect designing the entire flight plan.
Governance is presented as a prerequisite, requiring the “explicit definition of guardrails.” Beyond brand safety, what are the most critical guardrails marketing leaders overlook when formalizing their intent? Please share an anecdote where a poorly defined escalation condition created an unexpected problem for a campaign.
This is where many early adopters stumble. Everyone remembers basic brand safety, but they often forget to define the performance hierarchies and trade-offs. For instance, is achieving the lowest possible cost-per-acquisition more important than maximizing the total number of conversions? The system needs to know which metric to prioritize when it can’t achieve both perfectly. Another critical, and often overlooked, guardrail is the escalation condition. I recall a situation where a team set a rule to alert them and pause the campaign if performance dropped by more than 15%. Well, a brief, hour-long dip due to a network fluctuation triggered the alarm, halting a massive, otherwise successful campaign right in the middle of a peak buying period. The team spent hours reactivating everything, creating a much bigger problem. A better-defined condition would have been “if performance drops more than 15% and sustains for over three hours.” It’s that nuance—teaching the machine not just what to look for, but the context and duration that truly matters—that separates a smooth operation from a chaotic one.
The piece advises evaluating agentic AI on metrics like “reduction in decision latency.” How can a marketing leader tangibly measure this? Could you provide a step-by-step method or specific KPIs for tracking these operational gains and proving their bottom-line impact to stakeholders?
You can absolutely measure this, and you should. First, you benchmark your current process before implementation. Start by tracking your “Mean Time to Resolution.” How many hours or days does it currently take from the moment a report flags an issue—like budget pacing being off or a channel underperforming—to the moment a human analyst implements a fix? Let’s say that average is eight hours. After you deploy an agentic system, you track that same metric. You’ll likely see it drop to minutes. That’s your “decision latency reduction,” and it’s a powerful KPI. Another is “Execution Consistency,” which should approach 100%, as the autonomous system eliminates the variance of different people making different judgment calls. To prove the bottom-line impact, you correlate these operational gains with financial results. You can build a business case that says, “By reducing our decision latency by 90%, we captured an additional 12% in revenue by capitalizing on real-time opportunities our team previously would have missed.” It connects the dots from operational efficiency straight to the balance sheet.
Do you have any advice for our readers?
My strongest advice is to start incrementally and focus on governance first. Don’t try to automate your entire marketing organization on day one. Pick one high-volume, rules-driven campaign where the outcomes are clear and measurable. This is your sandbox. Before you let the system run, get your senior team in a room and meticulously formalize your intent. What are the non-negotiable brand rules? What are the precise escalation conditions? What trade-offs between cost and volume are you willing to make? Documenting this is the most critical work you will do. Treat this first project as an exercise in building the discipline of translating human strategy into machine instructions. By starting small and proving value—not just in performance lift, but in time saved and consistency—you build the confidence and the skills needed to scale this transformation across your entire organization.
