Today, we’re thrilled to sit down with Anastasia Braitsik, a global leader in SEO, content marketing, and data analytics. With her extensive expertise in digital strategies and data-driven insights, Anastasia offers a unique perspective on the evolving landscape of customer experience (CX) technology. In this interview, we dive into the critical challenges enterprises face with AI-powered CX, the importance of data integration, and the strategic approaches needed to turn data into a competitive advantage. We’ll explore how AI exposes inefficiencies, why unified data is becoming a game-changer, and what businesses should prioritize when evaluating CX solutions.
Can you walk us through how AI in customer experience is revealing inefficiencies within enterprises?
Absolutely. AI in CX often acts like a spotlight on underlying issues that many enterprises have ignored for years. It’s showing us where processes are fragmented, where data silos exist, and how inconsistent systems create gaps in understanding the customer. For example, when AI pulls from multiple disconnected sources—like CRM, marketing platforms, or data lakes—it can highlight discrepancies in customer profiles or service delivery. But instead of fixing these issues, AI can sometimes amplify them by producing conflicting insights or recommendations, making it clear that the real problem isn’t the tech but the fractured foundation it’s built on.
What does it mean when we say CX has become a systems integration problem, and why is this shift significant?
CX is no longer just about a single app or tool; it’s about connecting a web of systems that touch the customer journey. Enterprises today deal with dozens of platforms—think HR systems, marketing tools, customer support software—all holding pieces of the customer puzzle. When these systems don’t talk to each other, you end up with incomplete or outdated information, which directly harms the customer experience. This integration challenge is significant because it’s the root of delivering a seamless, personalized interaction. Without solving it, no amount of AI or fancy features can truly transform CX.
There’s a common belief that AI can magically sort out messy data. Why do you think this misconception persists, and what’s the reality?
I think this belief comes from the hype around AI as a cure-all for business problems. People see AI as this super-smart tool that can just dive into a pile of unorganized data—say, a bunch of PDFs or scattered databases—and pull out perfect insights. The reality is far messier. AI needs clean, structured data to work effectively. Without proper data hygiene, like organizing information into clear categories or building a solid taxonomy, AI just gets confused. It might churn out results, but they’re often unreliable or irrelevant, which can lead to more frustration than solutions.
How does the absence of unified data impact the effectiveness of AI in customer experience initiatives?
When data isn’t unified, AI struggles to deliver consistent or trustworthy outcomes. Imagine an AI system trying to recommend a product but pulling conflicting info from different sources—one says the customer bought recently, another says they haven’t engaged in months. The result is a recommendation that feels random or off-base. This inconsistency doesn’t just frustrate customers; it also erodes trust within leadership teams who expect AI to deliver quick wins. They end up questioning the investment when the real issue is that the data foundation wasn’t ready to support those AI efforts.
You’ve described data integrity as a competitive weapon. Can you elaborate on how unifying data gives businesses an edge?
Certainly. When businesses unify their CX and communications data, they create a single, accurate view of the customer, which is incredibly powerful. This means faster, more personalized service, better-targeted marketing, and the ability to anticipate needs before the customer even asks. Forecasts
Can you share some insights on the projected benefits of unified data for businesses by 2027?
Research suggests that by 2027, companies that unify their data could see significant gains—like reducing service costs by up to 25% through streamlined operations and improving customer retention by 10 to 15% by delivering more relevant experiences. These numbers show how data integrity isn’t just a technical fix; it’s a strategic move that can directly impact the bottom line and customer loyalty.
What kinds of pressures are pushing companies to prioritize data unification now rather than later?
There are several forces at play. First, regulatory pressures, like the EU AI Act, are demanding transparency in how data is used and managed, which means companies need clear data lineage and consent tracking. Then there’s the economic angle—with flat IT budgets, businesses are looking to consolidate vendors and avoid costly middleware. On top of that, there’s a growing frustration with patchwork solutions. People are tired of juggling tools that don’t integrate well, and they’re pushing for systems that work together seamlessly. Delaying unification risks falling behind competitors, facing compliance issues, and piling up technical debt.
You’ve suggested evaluating CX and AI vendors through specific lenses. Can you explain what a unified customer profile means in this context?
A unified customer profile is about creating a single, comprehensive view of each customer by pulling together data from every touchpoint—whether it’s their purchase history, support interactions, or marketing engagement. In terms of data architecture, this means designing systems that can centralize and standardize this information, eliminating silos. It’s crucial because without this unified view, you’re always working with incomplete puzzle pieces, which leads to inconsistent experiences and missed opportunities to build stronger customer relationships.
Why is it so important for AI to be explainable, and what does that look like in practice for CX systems?
Explainability in AI is about understanding how and why a decision was made—it’s like showing your work in a math problem. In CX, this matters because trust is everything. If an AI recommends a certain action, like upselling a product, businesses need to know the reasoning behind it, whether it’s based on past purchases or behavioral trends. In practice, this looks like AI systems providing clear breakdowns of their logic, maybe through dashboards or reports that highlight key data points. This transparency not only builds confidence but also helps teams refine strategies and ensure compliance with regulations.
Looking ahead, what is your forecast for the role of data integration in the future of customer experience?
I believe data integration will become the backbone of CX in the coming years. As AI and automation continue to evolve, the companies that thrive will be those who’ve mastered their data foundations—turning fragmented information into actionable insights. We’re already seeing a shift where integration is prioritized over flashy new features, and I expect this trend to accelerate as regulatory demands tighten and customer expectations for seamless experiences grow. By 2027 and beyond, unified data won’t just be a nice-to-have; it’ll be the defining factor separating leaders from laggards in delivering exceptional customer experiences.
