Once limited to support tasks, AI now plays a defining role in how brands communicate with customers. What used to be a bonus: personalized experiences, is now the bare minimum. And with smarter AI systems everywhere, companies have to work even harder to make those interactions feel human and authentic.
We’re seeing this shift happen everywhere, no matter the industry. Telecommunications, financial services, media, retail. Chatbots have moved beyond simple rule-based interactions. They’re now intelligent systems powered by large language models, capable of understanding context and predicting what customers want. But this technological leap creates important questions about privacy, ethics, and the line between helpful personalization and intrusive surveillance.
Customers share unprecedented amounts of personal data while worrying about how it’s used. Meanwhile, younger generations, especially Gen Z, feel comfortable with AI-driven interactions. This opens new ways to engage and gather insights that weren’t possible just years ago.
These issues were explored in depth at the CDO Vision New York panel discussion, moderated by Gabi Steele, Co-Founder / CEO at Preql, alongside panelists Randy Yanoshak – Vice President of Enterprise Applications at PeoplesBank, Vignesh CV – Senior Vice President, Head of Data Analytics LOB at SG Analytics, Arvind Rajagopalan – Global Head of Data/AI Enablement & Product Engineering at Verizon and Giorgio Suighi – Global Managing Director & Lead for AI, Tech & Platforms consulting at WPP Media.
How Personalization Is Evolving
Personalization has become the foundation of modern customer experience. It’s transformed how organizations interact with their audiences across every touchpoint. The goal remains the same: deliver the right message to the right person at the right time. What’s changed is how companies achieve this.
AI now handles the heavy technical work. It identifies customer needs at the point of interaction, predicts intent before it’s stated, and matches appropriate services or products to individual circumstances. This automation works across channels, whether customers engage through digital platforms, physical stores, or phone calls. It adapts to how different demographics prefer to communicate.
The evolution follows a clear pattern. AI excels at the first two phases of personalization: understanding what customers need and matching them with relevant solutions. This frees human team members to focus on the third phase: meaningful conversations that drive satisfaction and business value. Rather than searching knowledge bases or gathering basic information, people can focus on empathy, complex problem-solving, and relationship-building.
The result is a hybrid model. Technology provides speed and precision. Humans deliver judgment and genuine connection. This division of labor isn’t about replacing people. It’s about positioning them where they add the most value, handling the nuanced interactions that require emotional intelligence and creative thinking that AI can’t yet replicate.
Chatbots Then and Now
Chatbots have changed. Early versions operated on rigid rules and templates, forcing users down predefined paths that often led to dead ends and frustration. Large language models changed this. They enabled more natural conversations and expanded what chatbots can handle, from answering service questions to making product recommendations and processing sales.
But significant gaps remain. Many chatbots still function as glorified search tools. They struggle when conversations go beyond their training. Context awareness is a persistent problem. Bots lose track of what was said earlier, fail to connect information across interactions, and force customers to repeat themselves when transferred between systems.
Empathy poses an even harder problem. In sensitive sectors like healthcare and finance, where emotional intelligence matters most, chatbots often misread frustration or offer tone-deaf responses at critical moments.
User comfort varies dramatically by generation. Gen Z and Gen Alpha engage with chatbots naturally, even socially. Platforms like Character.ai report millions of daily conversations, with 60% of users between 18 and 25. This demographic shift is pushing companies to prioritize chat channels over traditional phone support.
The technology has advanced considerably. But the human element remains irreplaceable. Knowing when to step in, reading emotions accurately, and making nuanced judgments.
Empathy, Ethics, and Data Use
AI can simulate empathy. It adjusts tone, chooses softer language, responds to emotional cues. But it can’t genuinely understand human feelings. This trained empathy differs fundamentally from authentic emotional intelligence, especially in sensitive sectors like healthcare and finance. Misreading frustration or distress causes real harm. The human ability to recognize when someone needs reassurance, patience, or a different approach can’t be replaced.
Organizations face mounting pressure around data ethics. Customers freely share enormous amounts of personal information through social logins, email accounts, and biometric systems, often without considering the implications. The challenge isn’t collecting data. It’s filtering and using it responsibly. Hyper-personalization, when poorly executed, creates a “creep factor” where customers feel surveilled rather than served.
Regulated industries move more cautiously for good reason. Financial services companies must navigate strict compliance requirements. Every AI interaction must meet governance standards before deployment. This slower approach prioritizes customer protection over speed.
The solution requires human oversight at critical points. People must decide which data to use, when personalization crosses boundaries, and where emotional nuance demands human intervention. Ethical filtering, transparent data practices, and supervised AI systems help maintain trust. Companies must use just enough information to be helpful, not so much that customers feel uncomfortable.
Technical Challenges
Behind every smooth AI interaction lies complex technical infrastructure. Effective personalization requires unified customer data. Information from purchases, service history, billing records, and prior conversations must be stitched together into a coherent view. Without this foundation, customers face the frustrating experience of repeating themselves as they move between systems, bots, and human agents.
Context sharing presents a persistent challenge. When a chatbot hands off to another bot or human agent, that transition must carry forward everything discussed previously. Breaking this continuity destroys trust and wastes time.
Data quality matters as much as data quantity. Organizations possess enormous volumes of customer information. But the real work involves filtering and surfacing the right signals at the right moments. Sensitive industries face additional constraints, requiring tighter governance around how data flows through their systems.
Testing AI systems introduces unique difficulties. Traditional software testing assumes predictable outputs. Run the same test, get the same result. AI systems produce fluid, variable responses that shift with each interaction. This makes it nearly impossible to catch problematic behavior using conventional methods.
Companies are responding with new solutions. LLM-based testers that stress-test AI systems by simulating thousands of conversations. Digital twins that mirror production environments where humans monitor and correct AI behavior in real time, gradually improving reliability.
Moving Forward
There’s a clear consensus. AI is transforming customer experience at remarkable speed. But technology alone can’t deliver truly exceptional interactions. Personalization has become essential. Chatbots have evolved dramatically. Organizations now possess unprecedented data capabilities. But significant challenges remain.
Context awareness, emotional intelligence, and ethical data use all require careful human oversight. Younger generations embrace AI interactions naturally, creating new opportunities. But companies must avoid the “creep factor” that comes with excessive personalization. Testing AI systems demands new approaches. Technical infrastructure must support seamless handoffs and unified data views.
The path forward isn’t about choosing between AI and human touch. It’s about combining both thoughtfully. AI handles speed, scale, and pattern recognition. Humans provide judgment, empathy, and ethical guardrails. Organizations that master this balance, grounding technological advancement in responsible practices and genuine customer care, will define the next era of customer experience.
