We asked AI about Agentic AI in CX for CTOs

Your CTO’s inbox is drowning in vendor pitches about “agentic AI.” Every platform claims to have it. Every demo looks impressive. And the analyst reports keep ratcheting up the urgency, and Gartner predicts agentic AI will autonomously resolve 80% of common customer service issues by 2029.

What came back was refreshingly direct. No vendor pitch. No buzzword bingo. Just the technical reality of what works, what doesn’t, and why the decisions you make in the next six months will shape your customer relationships for years.

Below is our conversation, lightly edited for clarity.

Q: Everyone’s talking about agentic AI. Can you cut through the noise? What actually is it, and why now?

Sure. Let me give you the version your vendors won’t. A chatbot answers questions. Conversational AI conducts multi-turn dialogues. Agentic AI takes action. It processes a return, modifies an order, escalates to the right specialist with full context, and proactively reaches out when it detects a problem. End-to-end, no human in the loop unless one is needed.

The “why now” is actually straightforward: the underlying technology finally works at scale. Large language models understand context and intent. APIs are mature enough to connect AI to back-end systems. And labor costs plus customer expectations have created perfect conditions for adoption.

But here’s the part that gets glossed over in vendor decks: agentic AI is not a feature you bolt onto your existing customer service infrastructure. It requires a fundamentally different data architecture. If your CX platform is built around tickets—and most are—you’re going to hit limits fast.

Q: You said “fundamentally different data architecture.” What’s the specific technical requirement most systems can’t meet?

Unified customer context. Full stop.

Agentic AI needs to see the complete customer relationship—purchase history, conversation history across every channel, preferences, sentiment, product usage—in a single view. It needs this to make intelligent decisions about what action to take.

Here’s where it breaks in practice. A customer calls about a billing issue. Your AI pulls up the ticket, sees the billing question, and provides an answer. Seems fine. But what the AI doesn’t see—because it’s siloed in a different system—is that this customer has called three times this month, their sentiment is increasingly negative, they just canceled a subscription, and they’re a high-value customer at risk of churn.

A truly agentic system needs customer-centric architecture: one unified profile, one continuous conversation thread across every channel, complete history accessible in real-time. AI needs to know who it’s talking to and why it matters.

The technical litmus test is simple: Can your system query by customer ID as the primary key across all conversations? Or do you query by ticket ID and try to stitch customer data together afterward? That’s the architectural fork in the road.

Q: What actually happens when ticket-based systems try to implement agentic AI anyway?

The failure mode shows up fastest at the AI-to-human handoff. This is where you see the cracks.

When an AI built on a ticket-based system hits something it can’t resolve and escalates to a human, here’s the sequence: The AI creates a ticket. The team member opens it. They see the transcript of the AI conversation—maybe. They don’t see the three previous conversations from last month about related issues. They don’t see the customer’s order history or preferences. They don’t see that the customer started on the website, moved to SMS, and then called. The team member basically starts from zero.

What does the customer experience? “I already explained this to your chatbot. Why do I have to repeat everything?”

That moment of friction—that repetition—is where trust dies. And we’re seeing it play out in 2026 as what some are calling the “anti-AI slop” movement. Customers are increasingly rejecting impersonal, generic automation that makes them feel like they’re being avoided rather than served.

Voice makes this even more unforgiving. According to NVIDIA’s State of AI in Retail survey, nine in 10 retailers are increasing their AI budgets this year, and voice AI is a top investment priority. If your voice AI says “let me transfer you” and the team member says “can you tell me what you’re calling about?”—you’ve burned the relationship in real time.

Here’s the diagnostic question: Can your human team members access the exact same customer context your AI uses? If not, you don’t have the right architecture for agentic AI.

Q: Everyone’s saying voice AI is the top priority for 2026. Why, and what makes it so much harder?

Voice is where customer expectations and technical difficulty intersect—and it exposes architecture problems faster than any other channel.

Customer behavior data tells a consistent story: phone remains the preferred channel for complex, emotional, or high-stakes issues. When something really matters, people call. So if your AI can’t perform on voice, you’re failing at the moments that most shape loyalty and lifetime value.

Here’s the contrast. A voice AI built on unified customer context can say: “I see you’re calling about your order from last week that’s running late. I’ve already checked with our logistics partner—it’s arriving tomorrow. Would you like tracking updates via text?” That’s agentic AI.

A voice AI built on tickets says: “How can I help you today?” Then the customer explains the issue, the AI searches for order numbers, can’t access the full history, and eventually hands off to a human without context. That’s the experience customers are walking away from.

Q: What’s the biggest mistake you see companies making with agentic AI right now?

Treating it as a cost-cutting exercise instead of a customer relationship strategy.

The companies that approach agentic AI asking “how do we resolve more conversations without humans?” end up with sophisticated deflection machines. The AI gets fast at closing tickets but terrible at building the kind of loyalty that drives lifetime value.

The companies that ask “how do we use AI to know our customers better and serve them in ways that earn their long-term loyalty?” end up with something categorically different—a system that delivers the cost savings you need AND the customer devotion that compounds over time.

It’s not efficiency or customer value. It’s both. Every AI conversation either builds relationship equity or spends it. The question for every CTO and CIO is which one is your architecture designed to do?