In the rush to innovate, many organizations turn to automation as a silver bullet, only to find it magnifies their existing problems. To unpack this critical issue, we sat down with Milena Traikovich, a Demand Generation expert who specializes in building effective campaigns and optimizing performance. She brings a wealth of experience in analytics and lead generation, offering a clear-eyed perspective on the promise and peril of marketing technology. Our conversation explored why automation often exposes, rather than fixes, organizational weaknesses; how to distinguish between vanity metrics and true business impact; and the foundational work on processes and data governance that must precede any technology investment.
A common belief is that automation can fix flawed business processes. Based on your experience, why does automation often accelerate dysfunction instead, and how can a rigid, fast system be more dangerous than a slow, flexible one? Please elaborate on this.
It’s a persistent and expensive myth that you can just inject technology into a broken process and expect it to heal. The reality is that automation only accelerates what’s already there. If you have a clear, lean, well-governed process, automation will make it faster and more reliable. But if your processes are bloated or tangled in internal politics, automation will simply help you fail sooner and with greater force. I saw this firsthand with an organization that automated its invoice approvals. On paper, the dashboards looked great—cycle time dropped from weeks to days. But in practice, the underlying approval logic was never challenged. It still required three functional approvals and regional sign-offs even for tiny items. What used to be a slow but flexible process, where finance teams could apply judgment, became a fast but rigid one. The friction that once signaled a problem was gone, and exceptions piled up in queues nobody owned until the whole system seized. The speed amplified the dysfunction to the breaking point.
Dashboards for automated systems can create a false sense of control, showing high activity but not necessarily effectiveness. How can leaders distinguish between these vanity metrics and those that measure true business impact, and what are some examples of better KPIs to focus on?
This is the illusion of control, and it’s incredibly dangerous. A dashboard might proudly show that manual work on campaign delivery is down by 70% or that your time-to-market has been halved. Leaders see these green lights and feel reassured, but they’re measuring activity, not impact. Behind those impressive numbers, you could be generating AI slop at a massive scale, producing off-brand visuals or ads with false claims. You might be hitting your production targets while simultaneously annoying potential customers and making your sales team’s workday a living hell. The true measure of success isn’t how fast you can push a campaign out the door; it’s the effect on the sales pipeline. Instead of focusing on speed and volume, leaders need to measure lead quality, conversion rates, and the actual revenue influenced by these “efficient” campaigns. It requires having manual checkpoints to ensure the automated output is actually working as intended before it does more harm than good.
Consider an automated process that fails silently for months because of an upstream data change. What essential control points and data governance practices can prevent this, and whose responsibility is it to ensure the data feeding these systems remains accurate? Please outline a few key steps.
When you talk about process flaws being dangerous, data governance issues are a ticking time bomb. I worked with an insurance company that experienced this exact nightmare. Their automated forecasting process produced confident but completely wrong outputs for months, dragging the lead pipeline down for three quarters. The cause? A simple change in an upstream data structure that no one caught because the process was failing silently. Preventing this starts with accepting that automation assumes a level of data stability and clarity that rarely exists. Organizations often have multiple definitions for the same KPI and siloed knowledge. The key is to build control points into the automation itself—mechanisms to monitor and fact-check the assumptions the system is making. You don’t need to be a data governance expert, but you do need to insist on clear data ownership and enforced quality rules. It’s a shared responsibility, but the team implementing the automation must ensure these checks and balances are in place from day one.
Many teams see buying a new AI-powered platform as a shortcut to innovation. Why does this approach often just postpone failure, and what difficult conversations about process, accountability, and legacy assumptions must an organization have before investing in new automation technology?
This is a classic failure mode: confusing a software purchase with genuine organizational change. Buying a shiny new AI-powered platform looks like progress. It makes you feel innovative and gives you something to show C-level stakeholders. But if you haven’t done the hard work first, all you’ve really done is add a new layer of complexity to a system that was already underperforming. You’re just postponing the inevitable failure. Before you even look at vendors, you have to have the difficult conversations. You need to map out the process as it exists today, identify the real gaps, and get ruthless about optimization. Most importantly, you must determine who is accountable for what. These conversations force you to question legacy assumptions and challenge the “that’s how we’ve always done it” mentality. The technology should be the final step to accelerate a well-designed process, not the first step to avoid fixing it.
You describe automation as a “mirror” that reflects an organization’s clarity and discipline. What does this reflection typically expose in most companies, and how can they use this insight to redesign their foundational processes before attempting to automate them? Please share your thoughts.
The mirror analogy is the most honest way to think about it. Automation isn’t a solution; it’s a reflection of your organization’s internal state. When you hold that mirror up, it exposes how clearly you think, how well you govern your data, how much unnecessary complexity you tolerate, and whether you’re truly willing to question old habits. For most companies, the reflection isn’t pretty. It shows tangled workflows, ambiguous ownership, and a reliance on manual workarounds that have become institutionalized. But seeing that reflection is an opportunity. Instead of ignoring it and buying a tool to cover it up, you can use that insight to fundamentally redesign your processes. Organizations that treat automation as a shortcut end up locking in yesterday’s problems with tomorrow’s technology. The ones who use it as a catalyst to clarify and simplify their foundations are the only ones who will see the true, transformative benefits.
Do you have any advice for our readers?
My advice is simple: think about the process first, the technology second. Before you get dazzled by any AI-powered platform, take a hard look at the foundational process you want to improve. Is it clear? Is it lean? Does everyone agree on the goals and their roles? Find the gaps and optimization opportunities first. If a process is failing slowly and silently today, automating it will only make it fail spectacularly tomorrow. The true difference between success and failure in automation isn’t found in the tool you choose. It’s found in the discipline to fix the basics first. When you do that, the tool becomes a powerful multiplier for the good work you’ve already done.
