In today’s fast-evolving landscape of marketing technology, few understand the transformative power of AI and customer context as deeply as Milena Traikovich. As a Demand Gen expert, Milena has dedicated her career to helping businesses craft impactful campaigns that nurture high-quality leads through sharp analytics and performance optimization. In this insightful conversation, we dive into the seismic shift from isolated data silos to a shared, customer-centric context, exploring how AI is redefining collaboration, enhancing customer experiences, and reshaping organizational structures. From the practical applications of AI in breaking down barriers to its role in uncovering deeper customer insights, Milena offers a compelling perspective on navigating this new era.
How would you describe the move from system-centric to context-centric data in a way that’s easy to grasp?
Think of system-centric data as looking at customers through the lens of a company’s tools and processes—everything is organized around internal systems like CRMs or marketing platforms. It’s fragmented and often misses the bigger picture. Context-centric data, on the other hand, flips that perspective. It’s about seeing the customer’s journey as they experience it, connecting every interaction, emotion, and intent across touchpoints. AI helps make this possible by pulling together scattered data into a meaningful story, so businesses can focus on relationships rather than just transactions.
What’s the biggest difference this shift makes in how companies understand their customers compared to the past?
In the past, companies often saw customers as a set of data points—clicks, purchases, or support tickets—tied to specific departments. There was no unified view. With this shift to context-centric data, companies start to see the customer as a whole person with a continuous story. AI stitches together these interactions, so instead of just knowing a customer abandoned a cart, you understand why—maybe they had a bad experience earlier. It’s a deeper, more empathetic way of engaging that builds trust and relevance.
How does AI serve as a bridge between departments like marketing, sales, and customer service?
AI acts like a translator between teams by interpreting signals that cross departmental lines. For instance, marketing might track campaign engagement, sales might focus on deal progression, and service might handle complaints. AI connects these dots by analyzing data from all these areas to create a single view of the customer. It helps everyone speak the same language—customer context—so a negative support ticket can inform marketing to pause outreach, or sales can tailor their pitch based on recent interactions. It’s about creating continuity where there used to be silos.
What are the risks of training AI on data that’s still stuck in isolated silos?
When AI is trained on siloed data, it’s like giving it half the puzzle. It can spit out results, but they’re often shallow or misleading because they lack the full context. For example, an AI might suggest upselling to a customer based on purchase history from sales data, but miss that they just logged a complaint in the service system. The result? Irrelevant or even frustrating interactions. Without connected data, AI can’t deliver meaningful insights or actions—it just amplifies the fragmentation.
How does focusing on customer context through AI enhance the experience for the end user?
When you prioritize customer context, you’re meeting people where they are, not where you assume they should be. AI helps by personalizing interactions based on a full picture of their journey. Imagine a customer who’s been browsing a product, then hits a snag with support. With context, AI can ensure the next email or chat isn’t a generic promo but a helpful follow-up addressing their issue. It makes interactions feel seamless and human, which builds loyalty because customers feel understood, not just targeted.
In what ways can AI help businesses uncover the ‘why’ behind customer behaviors, beyond just surface-level actions?
AI goes beyond tracking what customers do—like clicking a link or buying a product—and starts to piece together why they did it by analyzing patterns, language, and timing. For instance, it might detect frustration in the tone of a support chat or notice a sequence of abandoned carts after a price increase. By connecting these signals, AI reveals motivations and emotions, turning raw data into a narrative. This deeper insight lets businesses address root causes, not just symptoms, of customer behavior.
Why do you see this shift to context-centric data as more of an organizational change rather than just a tech upgrade?
Technology is just the enabler; the real shift happens in how teams think and work together. Moving to context-centric data means breaking down walls between departments, rethinking KPIs, and aligning around the customer instead of internal goals. AI exposes where silos create friction, forcing companies to reorganize around shared understanding. It’s not about installing a new tool—it’s about changing mindsets and workflows to prioritize collaboration over control, which is often the harder part.
How does AI encourage teams to collaborate in ways they might not have before this shift?
AI essentially demands collaboration by showing how interconnected customer interactions are. When data is shared through a customer lens, teams see how their actions impact others. For example, marketing might adjust campaigns based on real-time service feedback, or sales might loop in product teams when AI flags recurring customer pain points. Tools like AI copilots also create shared workspaces where everyone accesses the same insights, making alignment less about endless meetings and more about a common focus on the customer.
Can you share a practical example of a company or team that successfully moved from isolated data systems to a shared customer context?
I’ve worked with a mid-sized e-commerce company that struggled with disjointed data—marketing ran ads without knowing support issues, and sales pushed products ignoring recent returns. They adopted an AI-driven platform that integrated data across these functions, focusing on customer journey touchpoints. Within months, they could spot patterns like support tickets spiking after certain campaigns and adjust in real time. The result was a 20% uptick in customer satisfaction because responses became coordinated, not reactive. It showed how shared context can transform outcomes.
Looking ahead, what’s your forecast for how AI will continue to shape customer context and collaboration in the coming years?
I believe AI will only deepen its role as the backbone of customer context, moving beyond just connecting data to predicting and even shaping customer needs before they arise. We’ll see more intuitive tools that not only break down silos but also proactively guide teams on next steps, like suggesting cross-departmental strategies based on emerging trends. Collaboration will become second nature as AI embeds shared context into every decision. The challenge will be keeping the human element at the core—ensuring tech amplifies empathy, not just efficiency. I think the next few years will be about striking that balance.
