I’m thrilled to sit down with Milena Traikovich, a seasoned Demand Gen expert who has dedicated her career to helping businesses craft impactful campaigns that nurture high-quality leads. With her deep expertise in analytics, performance optimization, and lead generation, Milena offers a unique perspective on the evolving role of AI in marketing. In our conversation, we explore the current landscape of AI adoption, the overwhelming demand for content, the challenges of personalization, the efficiency divide between AI users and non-users, and the frustrating delays in campaign launches. Let’s dive into her insights on how marketers can navigate these challenges and move beyond experimentation to achieve real transformation.
How would you describe the current state of AI use in marketing based on what you’ve seen in your work?
From my vantage point, AI in marketing is at a fascinating crossroads. Many teams are dipping their toes into the water, using AI for tasks like content generation or basic analytics. However, there’s a noticeable hesitation to fully commit. I often see companies stuck in what I call a testing loop—trying out tools but not integrating them into broader strategies. The potential is there, but the execution is still patchy for most.
What are the biggest hopes or expectations marketers seem to have for AI right now?
Marketers are really banking on AI to be a game-changer in terms of speed and scale. The hope is that AI can automate repetitive tasks, churn out content faster, and deliver hyper-personalized experiences without breaking a sweat. There’s this vision of AI as a magic wand that’ll solve resource constraints and let teams focus on big-picture strategy rather than getting bogged down in the weeds.
Have you noticed a gap between what marketers aspire to achieve with AI and what they’re actually pulling off? Can you share an example?
Absolutely, there’s a huge gap. Many marketers envision AI crafting tailored campaigns for every customer segment overnight, but in reality, they’re struggling just to get consistent output. For instance, I’ve worked with a team that invested in an AI tool for email personalization, expecting dynamic content for thousands of leads. But without proper data integration and training, the tool kept spitting out generic messages. The dream was there, but the groundwork wasn’t.
In your experience, are marketing teams feeling the pressure of rising content demand?
Oh, without a doubt. Almost every team I’ve collaborated with is grappling with this. The appetite for fresh, engaging content—whether it’s social posts, blogs, or videos—is insatiable. Marketers are being asked to produce more with the same, or even fewer, resources. It’s a constant race to keep up, and the stress is palpable.
How are teams trying to manage this growing need for content?
Many are leaning on quick fixes like repurposing old content or outsourcing to freelancers. Others are experimenting with AI tools to generate drafts or automate social media posts. But honestly, a lot of these efforts are patchwork. There’s often a lack of cohesive strategy—teams are just throwing stuff at the wall to see what sticks, rather than building sustainable processes.
What do you think is the biggest barrier holding teams back from meeting content demands?
It often comes down to a mix of limited bandwidth and poor alignment. Teams are stretched thin, and there’s not enough collaboration between departments to streamline workflows. Plus, many don’t have the right tech stack or data infrastructure to scale production. It’s like trying to run a marathon in flip-flops—you’re not set up for success no matter how hard you try.
Personalization seems to be a sticking point for many leaders. What challenges have you encountered in delivering truly personalized campaigns?
Personalization is tough because it requires a deep understanding of your audience, and that’s where most teams stumble. I’ve seen cases where data is either incomplete or siloed, so you can’t get a full picture of the customer. There’s also the challenge of balancing personalization with scale—crafting unique messages for thousands of people often feels like an impossible task without the right tools or time.
How do you see AI stepping in to tackle these personalization hurdles?
AI has huge potential here. It can analyze massive amounts of data to uncover patterns and preferences, then tailor content accordingly. For example, AI can segment audiences in real-time and suggest messaging tweaks based on behavior. If used right, it takes the guesswork out of personalization and makes it scalable. But again, it’s about having clean data and clear goals—AI isn’t a fix-all on its own.
Have you found any specific strategies or tools that help with personalization, whether AI-driven or not?
Definitely. One approach that’s worked well for me is starting with robust customer profiling—really digging into demographics, behaviors, and pain points manually before layering on tech. On the tool side, I’ve seen success with platforms that combine AI with human oversight, allowing for automated suggestions but with a final edit by the team. Even simple CRM integrations that pull in behavioral data can make a big difference if you’re strategic about it.
There’s evidence that AI users are far more efficient at keeping up with content demands. Have you seen this difference play out in the teams you’ve worked with?
Yes, it’s pretty stark. Teams using AI often breeze through tasks that bog down others. For instance, I’ve seen AI-driven content creation cut ideation time in half for some groups, while non-users are still brainstorming manually for weeks. The efficiency boost is real, especially for those who’ve moved past just experimenting with AI.
What kinds of tasks or processes seem to gain the most from AI in your observation?
Content drafting and data analysis are the big winners. AI can whip up blog outlines, email copy, or ad variations in minutes, freeing up creative energy for refinement. It’s also fantastic for sifting through data to spot trends or predict campaign performance. Those repetitive, time-intensive tasks are where AI shines brightest.
Why do you think teams not using AI are falling behind in terms of speed and output?
It often boils down to manual processes and resistance to change. Without AI, teams are stuck doing everything by hand, which just doesn’t scale in today’s fast-paced environment. There’s also a learning curve—some marketers are hesitant to adopt AI because they don’t fully understand it or fear it’ll replace their roles. That mindset keeps them tethered to outdated methods.
Campaign turnaround times are a pain point for many, often taking weeks longer than desired. Is this something you’ve run into in your work?
Oh, for sure. I’ve seen campaigns drag on for a month when the goal was a two-week launch. It’s frustrating because every delay means missed opportunities. Clients want results yesterday, and when you’re stuck in endless review cycles or waiting on assets, it feels like you’re always playing catch-up.
What do you think are the main factors slowing down these campaign launches?
A lot of it comes down to bottlenecks in approval processes and poor coordination. You’ve got too many cooks in the kitchen, with feedback loops that take forever. There’s also the issue of resource allocation—teams often don’t have dedicated staff for certain tasks, so things pile up. And honestly, sometimes it’s just a lack of clear priorities; without a tight plan, everything grinds to a halt.
What is your forecast for the future of AI adoption in marketing over the next few years?
I’m optimistic but realistic. I think we’ll see more marketers move past the experimental phase and start embedding AI into core operations, especially as tools become more user-friendly. The efficiency divide will likely widen before it narrows, though—those who adopt early and scale smartly will pull way ahead. My hope is that within five years, AI will be as standard as email marketing is today, but it’ll take cultural shifts and better training to get there.
