Your customer conversations are trying to tell you something.
Every day, contact centers generate thousands of voice and digital interactions packed with insights about customer sentiment, agent performance, and operational gaps. But most of that value is locked away because traditional analytics tools are manual, siloed, and reactive—until now. New developments (and innovation) are finally enabling businesses to realize the full value of the contact center conversation with artificial intelligence.
In our recent webinar, “Ask Your Data Anything: Get Instant Insights with Natural Language to Reduce Costs and Elevate CX,” we were joined by Beth Schultz, Vice President of Research and Principal Analyst for Metrigy, an enterprise research and advisory firm. Together, we explored how enterprises are interacting with—and capitalizing on—their contact center conversations with artificial intelligence.
LLMs: Turning Conversation Data Into Action with artificial intelligence
Contact centers generate one of the most valuable and underutilized assets in the enterprise: unstructured conversation data. For decades, contact centers have scoured that data for answers to how customers think, feel, and act. Recently, organizations have begun analyzing their contact center conversations with artificial intelligence. However, most have only ever scratched the surface. That’s because turning all that data into usable insights has largely been a slow, manual, and resource-intensive process—even with basic AI capabilities.
The problem: legacy systems rely on keywords, static reports, and predefined queries. And while they can help contact centers reorganize the data to a certain extent, agents often spend more time reconfiguring the data to rigid business rules than they do finding the answers they need. What business users really need is a way to ask the data:
- “Why are refund calls trending this week?”
- “Which agent behaviors are most correlated with low CSAT?”
- “Are agents greeting customers per our new script?”
With large language models (LLMs), they can. These models interpret natural language queries and return contextual insights using generative AI—no SQL, tagging logic, or delay required. “If you had a [generative AI-powered] interaction tool, you could drill down into this information,” said Schultz. “You could figure out [things like] why customers are abandoning the digital channel as well as assess how well agents are handling those calls—especially if they’re coming from disgruntled, dissatisfied customers—and then coach them to get them over to the satisfaction side versus the dissatisfaction side.”
Today, LLMs are reshaping the contact center conversation with artificial intelligence, with many organizations moving from evaluating to implementing LLM-powered generative AI solutions. In fact, Metrigy research shows nearly 70% of companies already use generative AI, with strong improvements across CSAT, employee efficiency, and revenue.
That’s no accident, says Schultz. “With LLMs, you can really go into exploring vast volumes of unstructured data, including call and text transcripts and call recordings, and generate meaningful insights out of those,”
The next evolution? Actionable conversations with artificial intelligence
Until now, the primary goal of analyzing contact center conversations with artificial intelligence has been generating insights. That changed with the advent of agentic AI. Now, contact centers can not only get the insights they need to impact change; they can execute that change independently. That’s because:
Agentic AI = intelligence + action
Unlike basic generative AI, agentic AI systems can take action on insights autonomously. That’s a significant improvement over systems that, while more advanced than anything before, still required considerable human handholding. With agentic AI, contact centers can go beyond simply surfacing actionable insights; it can execute them efficiently, accurately, and—most importantly—independently.
And it couldn’t come at a more critical time. “In the last six to eight months, [the conversation] has really been about taking generative AI further by [having it take] those actions, performing autonomously, and helping humans in the moment or behind the scenes,” Schultz explained.
Much of that conversation has been happening at the executive level, as CEOs and CIOs consider how best to invest in the new technology. Schultz strongly encourages enterprise decision-makers to consider a voice analytics agent. While other digital channels have grown in recent years, Metrigy research shows that the voice channel still remains the overwhelming favorite among consumers, with 77% of all interactions either starting in or escalating to voice. “If you’re thinking about, ‘where do I get started with interaction analytics?’, voice calls is a pretty good bet, That’s where a lot of conversations are going to be taking place.”
Delivering on the promise of agentic AI
With its ability to analyze and act autonomously, agentic AI is unlike any other AI. To show just how powerful this revolutionary tool is within the contact center, Erik Johnson, Vice President of Product Management at Uniphore, led a live demonstration of our industry-best Conversation Insights Agent.
In the demo, Uniphore’s Agentic Approach to Conversation Insights Agent, Johnson walked viewers through the different steps taken by a hypothetical insurance claims agent operating in the wake of a busy wildfire season. Using the AI agent interface, he easily:
- Spotted a spike in wildfire-related insurance claims
- Flagged a performance dip tied to that topic
- Recommended a new agent script
- Deployed the updated script instantly
- Scheduled a follow-up to measure agent score improvement
That’s more than analytics—it’s orchestration. The system learns, adapts, and improves outcomes with every cycle. That’s a whole new way to get insights with artificial intelligence. “This is really where the promise of agentic AI comes in,” explained Johnson. “Not only are we asking the AI assistant to do analysis; we can also ask them to take action on our behalf.”
Enterprise example: insight in four weeks
While hypothetical situations are nice, nothing compares to seeing how the agentic approach impacts real-world contact center conversations with artificial intelligence. To illustrate the real-world power of Uniphore’s Conversation Insights Agent, Johnson shared how a Fortune 500 insurer tasked the solution with:
- Ingesting 50K+ daily interactions
- Scoring 100% of calls automatically
- Deploy agent scoring, interactive dashboards
- Provide executive-level insights on surfacing hidden trends and driving consistency across service operations
The result: faster decisions, higher performance visibility, and better outcomes across the board. Not only did the solution execute each task optimally—it did it all in just 30 days.
“This isn’t hypothetical—this is really where organizations are trying to go,” said Johnson. “[It’s about] partnering in a way to reduce costs, get faster insights, and increase customer satisfaction and customer loyalty.”
What to look for in an agentic conversation insights solution
For enterprises evaluating agentic conversation intelligence solutions, the considerations may seem overwhelming at first. After all, the underlying technology is more capable than anything that’s come before it. However, Johnson urges business leaders to focus on what matters most—outcomes—and avoid overcomplicating matters:
“The best solutions are really geared toward helping you and your business achieve outcomes—with actions and [the ability to do them] at scale. Because, ultimately, insights aren’t really effective if they don’t drive action.”
To drive real impact, enterprises should look for a conversation intelligence platform with the following. The ideal platform should feature the following core capabilities:
- Able to ask targeted natural language questions which generates visualization and evidence on reasoning
- Reporting insights to embed them into AI workflows that drive measurable business impact
- Ability for different teams/ personas to build and share prompts or dashboards
- Actively redefining how conversation quality is measured for accuracy / continuously learn to improve
Powered by generative AI, Uniphore’s Conversation Insights Agent empowers contact centers to not only extract targeted, actionable insights from natural language queries, but also integrate them into AI workflows that drive outcomes. From uncovering emerging trends and hidden customer needs to assessing agent performance and quality management, Uniphore’s agentic AI solution is reshaping how contact centers leverage their most valuable asset: customer conversations.
See it in action
Watch the full webinar on demand to see how agentic AI is turning unstructured conversation data into strategic outcomes.
