I’m thrilled to sit down with Milena Traikovich, a powerhouse in B2B marketing who has dedicated her career to helping businesses craft campaigns that nurture high-quality leads. With her deep expertise in demand generation, analytics, and performance optimization, Milena has a unique perspective on how artificial intelligence is transforming the B2B landscape. Today, we’ll dive into the evolving role of AI in marketing, exploring how it can unify data, streamline workflows, and drive measurable results, all while maintaining trust and brand safety.
How do you see the concept of becoming an “AI-Native” B2B marketer differing from simply using AI tools in day-to-day tasks?
Becoming “AI-Native” is about a fundamental shift in mindset, not just adopting tools. It means embedding AI into every aspect of your strategy and operations, from how you think about data to how you design customer journeys. Using AI tools—like a chatbot or content generator—is often a surface-level approach, where you’re still operating with traditional workflows and just plugging in tech for efficiency. An AI-Native marketer, on the other hand, reimagines the entire process. For example, instead of manually segmenting leads, they leverage AI to dynamically analyze behaviors and predict needs in real time. It’s about letting AI shape your decisions, not just support them.
Why is unifying fragmented marketing data—such as emails, CRM notes, and customer calls—so critical for leveraging AI effectively in B2B marketing?
Fragmented data is the silent killer of impactful marketing. When your information is scattered across systems, AI can’t see the full picture, and its insights or actions end up being incomplete or even misleading. Unifying data creates a single source of truth—think of it as giving AI a complete puzzle to solve rather than random pieces. This context allows AI to identify patterns, like a prospect’s hesitations from a call transcript or a trend in support tickets, and turn them into actionable strategies. Without this, you’re basically handicapping the technology before it even starts.
What challenges do marketers often face when trying to bring all this disparate data together, and how can they start overcoming them?
The biggest challenge is often technical silos—different teams using separate tools that don’t talk to each other. Sales might have their CRM, marketing might use a different platform for campaigns, and customer support has its own system. This creates data islands. On top of that, there’s often resistance to change; teams are comfortable with their existing processes. The first step to overcoming this is to audit your data sources and map out where information lives. Then, invest in integration tools or platforms that can centralize this data. Start small—maybe integrate just CRM and email data first—and build from there. Getting buy-in from leadership by showing quick wins, like improved lead scoring, also helps break down resistance.
For marketers who aren’t tech-savvy, the idea of building AI agents can feel overwhelming. Can you walk us through what a simple AI agent might look like in a marketing workflow?
Absolutely, it’s not as complicated as it sounds. A simple AI agent is essentially a set of automated rules or tasks powered by AI to handle repetitive work. For instance, imagine an agent that monitors incoming leads from your website forms. It can analyze the data, score the lead based on predefined criteria—like industry or company size—and automatically route it to the right sales rep with a personalized email draft. This can be set up using no-code platforms that have drag-and-drop interfaces. The beauty is, it saves hours of manual sorting and follow-up, letting marketers focus on strategy. It’s like having a virtual assistant that never sleeps.
What’s a practical tool or approach you’d recommend for someone just starting to experiment with AI agents in their marketing efforts?
For beginners, I’d suggest looking into no-code automation platforms that are user-friendly and widely used in marketing. These tools often come with templates for common tasks like lead nurturing or email follow-ups, so you don’t need to start from scratch. Many integrate seamlessly with popular CRMs and email systems, which lowers the learning curve. My advice is to pick one specific, time-consuming task—like tracking webinar sign-ups and follow-ups—and automate that first. Most of these platforms offer free trials, so you can test the waters without a big commitment. Just play around, see what works, and scale up as you get comfortable.
How can AI help B2B marketers detect customer or competitive signals that might otherwise slip through the cracks?
AI excels at spotting patterns and anomalies in massive datasets that humans might miss. For example, it can analyze social media chatter, website interactions, or even public data to flag when a competitor launches a new product or when a key account shows signs of churn—like reduced engagement or negative sentiment in support tickets. These signals are often subtle and spread across channels, so without AI, you’d need a team manually monitoring everything, which isn’t feasible. By catching these early, marketers can pivot strategies, whether it’s crafting a counter-campaign or reaching out to at-risk clients with a tailored offer. It’s like having a radar system for your market.
When it comes to using AI for personalization and speed, how do you ensure it doesn’t compromise brand safety or quality?
This is a critical balance. AI can churn out content or responses at lightning speed, but without guardrails, you risk off-brand messaging or factual errors. The key is to set clear boundaries upfront—define your brand voice, tone, and no-go areas in the AI’s training or rules. For instance, if you’re in a regulated industry, you might program AI to avoid making claims without legal approval. Additionally, always have a human-in-the-loop for sensitive outputs, like customer-facing emails or high-stakes campaigns. Validation layers, like automated checks for tone or accuracy before anything goes live, also help. It’s about using AI to draft and scale, but not to decide without oversight.
What’s one common mistake marketers make when they first start scaling AI across their teams, and how can they avoid it?
One big mistake is diving in without a clear plan or training. Teams often get excited about AI, roll it out broadly, and then face chaos because not everyone understands how to use it or what it’s for. This leads to inconsistent results or wasted resources. To avoid this, start with a pilot project—maybe just one team or campaign—and document every step, from setup to outcomes. Train your staff on the specific tools and goals, emphasizing how AI complements their skills, not replaces them. Once you’ve ironed out the kinks, create a playbook for scaling. It’s all about building confidence and alignment before going all-in.
Looking ahead, what’s your forecast for the role of AI in B2B marketing over the next few years?
I believe AI will become the backbone of B2B marketing, not just a nice-to-have. We’re moving toward a future where every interaction—whether it’s a lead nurture email or a sales pitch—is hyper-personalized and predictive, driven by AI that understands context at an unprecedented level. I expect we’ll see more seamless integration between marketing, sales, and operations, with AI acting as the glue that aligns these functions through shared insights. But the challenge will be maintaining the human touch; the brands that win will be those that use AI to enhance relationships, not automate them entirely. It’s an exciting time, and I think we’re just scratching the surface of what’s possible.
