Milena Traikovich is a seasoned expert in the world of demand generation, but her true superpower lies in untangling the complex, often chaotic, web of marketing technology that so many companies find themselves trapped in. She’s seen firsthand how bloated MarTech stacks can cripple productivity, drain budgets, and obscure the very data marketers need to succeed. Today, we’ll explore the hidden costs of this “technology debt,” discussing how to diagnose an overstuffed toolkit, the challenges of integrating AI without adding to the clutter, and the practical steps leaders can take to build a lean, effective, and sustainable marketing stack that actually makes work easier, not harder.
When new marketing hires take over two weeks just to learn the company’s toolset, what underlying issues does this signal about the stack’s complexity? Please share an example of how this “training tax” impacts a team’s strategic output and morale.
That two-week mark is a massive red flag. It’s a clear signal that your stack has become a beast to manage. When a new hire, who is typically eager and ready to contribute, spends that much time just learning logins and workflows, it tells you the system is unintuitive, likely has redundant tools, and lacks a clear purpose. This “training tax” is devastating. Instead of focusing on marketing strategy and execution, your team is constantly in training mode. I’ve seen teams where senior members spend more time showing people how to pull a report from three different places than they do analyzing the data in that report. Morale just plummets. New hires feel overwhelmed and ineffective, and veterans get frustrated because they’re bogged down with onboarding instead of driving results. The strategic output grinds to a halt because all the energy is being spent just wrestling with the technology.
Many teams find their data scattered across five or more platforms, leading to conflicting reports. What are the first practical steps a marketing leader should take to diagnose and begin consolidating these disparate data sources? Could you walk us through that process?
The very first step has to be a complete and honest inventory. You can’t fix a problem you can’t see. This means listing every single tool the team uses, including the subscriptions on various credit cards and the “shadow IT” that people have signed up for on their own. For each tool, you document its cost, its primary users, and, most importantly, when someone last logged in. This initial audit is often a shocking experience for leaders who realize just how much they’re paying for. Once you have that list, you can start mapping your data. You’ll quickly see that you have analytics in one platform, email metrics in another, and social data in a third. The consolidation process begins by identifying those integration failures. Where are you manually exporting spreadsheets to combine data? That manual work is not only a time sink but also the reason your reports never agree. The goal is to find platforms that can centralize this data or, in some cases, simply eliminate the redundant tools that are fragmenting your view of performance.
A common justification for a complex tool is, “we’ll grow into it,” but this often doesn’t happen. At what point does this become a clear sign of tech debt, and how can a manager definitively measure a specific tool’s ROI against actual business outcomes?
The “we’ll grow into it” excuse has a shelf life. If you bought a powerful, complex platform more than 12 months ago and you’re still not using most of its features, it’s no longer an investment—it’s tech debt. You’re paying a premium for capabilities that bring zero value. At that point, the dream of “growing into it” is just a justification for avoiding a tough decision. To measure ROI, you have to be ruthless. Ask yourself, “If I can’t directly connect this tool to a specific business outcome, like lead generation, conversion rates, or customer retention, what am I paying for?” If the answer is fuzzy, the ROI is likely negative. I’ve seen companies paying for massive enterprise platforms where they use less than 30% of the features. A much better approach is to choose a tool that meets 80% of your needs that your team will actually use, rather than a perfect tool that gathers digital dust.
The emergence of “shadow IT”—tools purchased by employees without approval—is often a symptom of a larger issue. How does this reactive tool adoption harm a company’s overall strategy, and what can leadership do to create a more intentional, centralized buying process?
Shadow IT is a direct response to a failing tech stack. When your team starts buying their own tools, it’s because the official, approved ones are too difficult to use or don’t solve their problems. This reactive adoption is incredibly harmful. It creates data silos, security risks, and redundant spending. Suddenly you have three different people paying for three different social media schedulers, and none of the data talks to each other. Leadership needs to see this not as a rebellion, but as valuable feedback. To fix it, you have to create a sustainable buying process. Before any new tool is purchased, the team must document the exact problem it solves and check if an existing tool can already handle it. Then, pilot it with a small group. Don’t roll it out company-wide until you’ve proven its value. This shifts the culture from reactive problem-solving to intentional, strategic tool adoption.
The concept of an “honesty test” for tools, particularly asking, “what would actually break if we canceled this tomorrow?” is very powerful. Could you provide an anecdote about a team that cut a tool they thought was essential and describe the actual, perhaps surprising, outcome?
I worked with a team that was paying a hefty annual subscription for a very sophisticated analytics platform. Everyone was convinced it was essential, even though pulling reports was a chore and few people understood its deeper features. When we posed the “what would break?” question, the initial response was panic. “Our entire reporting system!” they said. But when we dug deeper, we realized they were only using it to track a few top-level metrics, which they were also tracking in two other platforms. We decided to run an experiment and not use it for one month. The surprising outcome? Nothing broke. In fact, things got better. The team standardized their reporting around a simpler, more user-friendly tool they were already using. Reports were generated faster, and more people felt confident pulling and interpreting the data. They canceled the expensive platform, saved a significant amount of money, and actually improved their data accessibility in the process.
AI-powered tools are often added to a stack with the promise of efficiency, yet they can increase complexity. Beyond checking for AI features in existing platforms, what critical integration or adoption challenges should teams anticipate before committing to a new AI tool?
The biggest challenge with new AI tools is that they often create more work before they save any. Teams need to anticipate the “integration tax.” A shiny new AI content generator is useless if it doesn’t seamlessly connect with your CMS or social media scheduler. If you have to manually copy and paste its output, you’ve just traded one task for another. Another major challenge is adoption. An AI tool is only effective if the team trusts its output and knows how to use it strategically. You have to budget time for training and for developing new workflows. Before committing, I always advise teams to pilot a free version first. Don’t sign an annual contract until you’ve proven that the tool not only works but that your team will actually incorporate it into their daily routines. Otherwise, it just becomes another expensive, unused subscription.
Do you have any advice for our readers?
My main piece of advice is to embrace the discipline of saying no. In an era of endless new tools and AI-driven hype, the real competitive advantage comes from focus. More tools don’t make you a more effective marketer; the right tools, used well, do. Your marketing stack should feel like a well-oiled machine that makes your team’s work easier, not a labyrinth that adds friction and frustration. Start by auditing what you have, be ruthless about cutting what you don’t need, and build a process to ensure every new tool you add has a clear, strategic purpose. A lean stack empowers your team to be agile, data-driven, and focused on what truly matters: delivering results.
