Uncovering Insights Across Every Agentic AI Experience

Speechless Episode 1: Your Customers Are Speaking. Why Aren't You Listening? With Lisa Fairbanks, Product Experience and Marketing Leader.

Contact centers have millions of customer conversations that reveal insights about messaging resonance, friction in processes, product feedback, and customer experience. When connected, these conversations create a complete story of how customers engage with your business.

This becomes increasingly important as organizations scale AI across customer experiences. Today, AI-powered bots are handling thousands of customer conversations, creating opportunities to understand customers' needs, identify friction, and improve experiences. However, scaling AI also introduces new risks. According to Sinch research from 2026, 62% of organizations have AI agents that live across customer communication channels. However, 74% of organizations have been forced to shut down or roll back a live AI agent due to a governance failure.

When embedding AI in CX, organizations must understand the risks that come with delivering these experiences, including inaccurate responses, hallucinations, inconsistent brand messaging, and interactions that negatively impact customer trust.

As AI becomes a larger part of the customer's journey, organizations need new ways to understand whether these interactions are truly successful. The challenge is that success can look different depending on the metrics being measured. While metrics like containment, sentiment, and accuracy provide valuable insights, they only reveal a portion of the story.

What we really want to uncover is: Are these metrics revealing the full customer experience?

How do you know if your AI is truly helping customers, when conversations begin to break down and when it recognizes when a customer needs human support?

As AI continues to evolve from simple automation toward agentic experiences, understanding the "why" behind every interaction becomes even more important. AI can understand context and continuously improve, but only when organizations have the right foundation to support it.

In this blog, we'll explore three questions:

• Are your bots creating the customer experiences you intended?
• Where do AI conversations break down and require human intervention?
• How can you identify which customer intents your AI handle successfully and where they need improvement?

Understanding the full AI experience

Traditional metrics provide valuable insights, but they don't always capture the full customer experience. For example, An AI bot can have a high containment rate while still creating a frustrating customer experience. Customers may repeat themselves, get stuck in loops, receive inaccurate information, or abandon the conversation entirely. From a dashboard perspective, the interaction can appear successful because the customer stayed within self-service. From the customer's perspective, it may feel like they never received the help they needed.

To gain complete visibility of what is happening behind these interactions, it's important to understand that every AI interaction is made up of individual moments called turns. Within the AI space, conversations can range from single-turn conversations, where an AI bot responds to one request, to multi-turn conversations, where AI engages in multiple exchanges and builds knowledge over the course of the conversation.

Each turn plays an important role in understanding customer intent, maintaining context, and guiding the next response. By analyzing these individual moments, organizations can identify where conversations succeed, where friction is introduced, and where AI may need additional support or improvement.

Tracking and optimizing these turns is essential to understanding why conversations break down and when human intervention is needed. Some common problematic turns include:

• Context loss: When AI fails to retain relevant information from previous parts of the conversation, forcing customers to repeat themselves.
• Self-contradiction: When AI provides conflicting information within the same interaction, creating confusion and reducing trust.
• Hallucinations: When AI generates inaccurate or unsupported responses, that may mislead customers.
• Premature resolution: When AI assumes a customer's issue has been resolved before confirming the customer’s needs have been met.
• Role or topic drift: When AI moves away from the customer's original intent or fails to stay aligned with the conversation.
• Escalation dismissal: When AI fails to recognize that a customer needs human support or does not provide an appropriate path to escalation.

These breakdowns are often hidden when organizations focus only on the final outcome of a conversation. The key idea is that every conversation turn is an opportunity to better understand the customer experience and start uncovering patterns: which customer intents AI handles well, where customers need more support, and where experiences can be redesigned.

The Insights Hidden Within Every Conversation Turn

Analyzing the individual turns within an AI conversation provides critical insight into how organizations can improve their customer experiences. To understand why each turn matters, consider this common customer interaction:

Turn 1: Customer explains the issue

A customer reaches out to a chatbot because they noticed an unexpected charge on their bill and requests help to understand why they're being charged.

Turn 2: AI provides a generic response

Instead of addressing the customer’s specific request, the bot directs them to a general FAQ page about billing and charges.

The customer has already reviewed this information, which is why they reached out for additional support. They are not looking for a general explanation of what the charge is; they need help understanding why it is being charged to their account.

Turn 3: Customer requests for human support

The customer asks to speak with a customer service agent.

Turn 4: AI provides the same response

Instead of recognizing that the customer may need additional support, the bot asks the customer to explain their request again. The customer repeats that they need help with the unexpected charge, but the bot provides the same FAQ recommendation.

Turn 5: the customer changes their behavior

At this point, the customer is already frustrated and ends the conversation. From a metrics perspective, the interaction may appear successful: the customer asked a question, the bot provided a response, and the conversation remained within self-service.

However, the customer experience tells a different story. The AI failed to recognize that the customer needed human support and created additional friction instead of moving the customer closer to resolution.

Turn 6: Customer contacts support and requests cancellation

Eventually, the customer decides to call customer service and says, "I want to cancel my service."

However, canceling was never their original intent. It was a reaction to an unresolved issue and a poor experience. When the customer finally reaches a human agent, they explain the original request again: they wanted help understanding an unexpected charge.

Insights hidden in this conversation:

• The AI failed to recognize the customer’s true intent and provide a response specific to their account issue.
• The bot did not identify repeated questions and frustration as signals that the conversation was not progressing.
• The AI did not recognize the request for human support as an opportunity to provide a better customer experience.
• The final customer action (cancellation) did not represent the original intent, creating the risk of inaccurate insights about why the customer wanted to leave.

Without visibility into the full conversation in different channels, businesses may optimize based on the wrong data. The outcome may show a cancellation request, but the conversation reveals the real story: a customer who needed help, not a reason to leave.

Turning AI insights into better customer experiences

Understanding where AI conversations break down is only the first step. The real value comes from turning those insights into meaningful insights across the customer experience.

By analyzing interactions at the turn level, organizations can identify patterns across thousands of conversations, not just individual failures. These insights help answer critical questions: Which customer intent is AI handling successfully? Where are customers experiencing friction? Which interactions are best suited for self-service, and where does human support create a better outcome?

However, identifying these patterns is only part of the journey. Organizations also need the expertise to interpret what these insights mean and determine the right improvements for their specific customer journeys, business processes, and goals.

Build AI experiences customers' trust

Working with a CX partner can help organizations uncover hidden friction points, evaluate AI performance, and develop personalized strategies based on their customers’ needs.

According to MIT NANDA research, AI projects reach deployment 67% of the time when organizations partner with external experts, compared to just 33% for internally built initiatives. This highlights the value of combining AI capabilities with the right expertise, governance, and customer experience strategy.

At USAN, an AWS Launch Partner, we combine AI capabilities and CX expertise within the AWS ecosystem to transform raw conversations into clear, actionable insights with AI Contact Analytics. This allows us to analyze 100% of your customer interactions and uncover true customer intent, track sentiment at scale, and build AI-based Agent Training. The results? Lower operational costs, improved customer and agent satisfaction, and a contact center that becomes a strategic driver of business growth, not just a support function.

Ready to understand what your customer conversations are telling you?

• Learn more about AI Contact Analytics

• Connect with the USAN team for a CX assessment