Are Zombie Martech Automations Haunting Your Customers?

Are Zombie Martech Automations Haunting Your Customers?

As we dive into the evolving world of marketing technology, I’m thrilled to sit down with Milena Traikovich, a seasoned Demand Gen expert who has helped countless businesses craft impactful campaigns to nurture high-quality leads. With her deep expertise in analytics, performance optimization, and lead generation, Milena has a unique perspective on the intersection of AI, automation, and customer engagement. Today, we’ll explore her personal encounters with martech tools, unpack the pitfalls of unattended automations, and discuss how companies can strike a balance between efficiency and a meaningful customer experience.

How have your recent interactions with martech automations and AI agents shaped your perspective as a customer or prospect?

I’ve had a couple of eye-opening experiences lately that really highlighted both the potential and the pitfalls of these tools. With one company, I was a customer receiving onboarding emails that were actually pretty helpful—great cadence and useful content. But when I had a billing question, I hit a wall. I emailed the support address, and despite multiple attempts, I got no response—not even an automated acknowledgment. Yet, the same address kept sending me cheerful onboarding messages, completely ignoring my issue. It was frustrating to feel like I was shouting into a void. Then, with another company, I downloaded a report as a pseudo-prospect and received a hyper-personalized email within minutes. It was impressive, clearly AI-driven, and tailored to my work. But when I replied, explaining my interest and disqualifying myself as a lead, the follow-up emails were totally disconnected—generic templates with wrong data, like someone else’s company name. It felt like the system had no memory of our earlier exchange. These moments made me realize how automation can dazzle at first but fall apart without proper oversight.

What do you think went wrong with the automations in these two scenarios?

In the first case, I suspect it was a routing issue. It seemed like the email inbox was shared by multiple teams with automated rules to direct messages, but something broke down. My billing question likely got sent to an unmanned queue or misinterpreted by a system that couldn’t handle it. There were no safeguards to catch that failure. In the second case, I think the AI agent that crafted the initial personalized email wasn’t integrated with the follow-up sequence. My reply probably got intercepted by the system, which didn’t know what to do with it, so it just dumped me into a generic sales funnel. On top of that, there were likely data errors—wrong company names inserted from a flawed enrichment process. It’s a classic example of compounding mistakes when systems aren’t aligned or checked regularly.

How do you see the balance between efficiency for companies and delivering a solid customer experience when using AI and automation?

It’s a tricky tightrope. From the companies’ perspective, these automations probably seemed efficient. In the first case, onboarding emails were sent out like clockwork, saving time for their team. In the second, the AI-generated email and follow-up sequence likely reduced manual work for their reps. But as a customer, the experience was far from efficient. I wasted time chasing answers to a billing issue with no resolution, and in the second instance, I felt like my interaction was meaningless since the system ignored my reply. Efficiency for the company shouldn’t come at the cost of frustrating the customer. I think a simple fix could be ensuring there’s always an easy way to reach a human when automation fails. That small touchpoint can turn a negative experience into a positive one while still leveraging the benefits of AI.

Why do you believe having a human in the loop is so critical when deploying these technologies?

Automation and AI are powerful, but they’re not foolproof. In my billing issue, a human could have stepped in after my first unanswered email, resolved the problem quickly, and saved me days of frustration. Even a basic escalation process to a real person would have made a huge difference. In the second case, if a human had reviewed the AI’s messages or my reply, they could have caught the disconnects—like the wrong company name or irrelevant follow-ups—and adjusted the conversation. Humans bring judgment and empathy that systems often lack. Companies need to design processes where reaching a real person is seamless, maybe through a visible support option or a quick review step for reps. It’s about catching the edge cases that automation can’t handle, which are often the moments that matter most to customers.

You’ve mentioned the idea of running secret shopper exercises. Can you explain how this could help companies avoid the kinds of issues you encountered?

Absolutely. Secret shopper exercises are a fantastic way to test the customer journey from an outside perspective. By having someone—ideally an external party with fresh eyes—go through the process as a prospect or new customer, companies can see where their automations or AI agents are failing. In my experiences, a secret shopper would have flagged the unresponsive billing support in the first case and the disjointed email sequence in the second. Recording every step of the interaction helps identify blind spots that internal teams might miss due to bias or familiarity. It’s harder in B2B because of data enrichment tools that can skew results, but even starting with internal staff can uncover issues. It’s about stress-testing the system before a real customer gets frustrated.

What’s your forecast for the future of AI and automation in martech, especially in terms of improving customer engagement?

I’m optimistic, but I think we’re in for a period of growing pains. AI and automation will continue to get smarter and more accessible, making personalization and efficiency even more achievable for companies of all sizes. We’ll see better integration between systems, so those disjointed experiences—like an AI forgetting a prior interaction—should become less common. However, the key to improving customer engagement lies in operational discipline and governance. Companies will need to prioritize regular audits of their AI agents and automations, ensuring rules and data stay relevant. I also expect martech platforms to step up with better monitoring tools to help manage these technologies. Ultimately, the future will hinge on a hybrid approach—leveraging AI for scale but keeping humans in the loop for empathy and oversight. If we get that balance right, customer engagement could become more meaningful than ever.

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