Agentic AI has remarkable potential to transform voice of the customer (VoC) programs.
Just consider the birth of behaviorally-focused large language models (LLMs). Powered by this technology, AI agents may analyze vast amounts of conversational data to identify tone, urgency, and even behavioral patterns.
From there, these agents may correlate that data with specific customer queries to drive deeper insight, which they can then democratize across the business.
As Liz Miller, VP and Principal Analyst at Constellation Research, told the Big CX News Update:
We can implement granular processes that continuously extract real-time insights from customer conversations, feeding directly into product development and operational decision-making.
That’s far more insightful than a quarterly PowerPoint from customer service saying: “Customers hate this feature.” Instead, CX leaders may gain real-time feedback as to why.
Already, generative AI is allowing businesses to ask questions of their customer feedback data, enabling various departments to unpack relevant issues quickly. Yet, agentic AI represents the next frontier.
Conversation Summaries Are Also Proving a Rich Source of VoC Data
One of the most popular use cases of generative AI in customer experience is auto-summarizing contact center conversations and funneling those summaries into the CRM.
By taking this away from human agents, contact centers not only accelerate customer interactions but also remove the risk of human bias creeping into these summaries.
Finbarr Begley, Senior Research Analyst at Cavell Group, noted this while appearing on the Big CX News Update. Yet, he also shared how these more accurate summaries are becoming a rich source of VoC data.
As this data improves, Begley noted that agentic AI may help correlate “a single customer’s sentiment with broader trends across all customer interactions, offering a more accurate and scalable approach to understanding customer needs.”
That then opens up several other possibilities. For instance, imagine AI agents funneling that customer understanding into CPQ (Configure, Price, Quote) system to bolster its outputs. Sharing this possibility, Miller noted:
We used to theorize about this possibility. Now, AI can do it, eliminating the need for robotic process automation (RPA) workarounds.
Voice Data Will Become an Increasingly Crucial Source of VoC Insight
With AI agents generating new insight into a customer’s tone, urgency, and behaviors, voice data will become an increasingly crucial source of VoC insights.
While some may worry that this data source may dry up with the expansion of AI and digital channels, Simon Harrison, Founder & CEO of Actionary, thinks that’s unlikely.
He noted how voice remains the first place people turn when self-service and digital engagement fail.
Moreover, Harrison suggests that agentic AI may even bolster the channel’s future.
“Historically, we’ve tried to solve customer experience challenges through chat and other digital channels because voice technology wasn’t advanced enough,” he said.
Now, agentic AI enables us to address those challenges directly within the voice channel.
Begley backed this point by offering an anecdote.
“While smart glasses didn’t catch on, everyone now wears earbuds,” he said. “Voice assistants, once clunky, are now smart enough to facilitate seamless interactions.
The ability to simply say, “Hey Siri, call my garage and schedule an appointment” is the most natural form of customer service.
In this sense, voice is not only still a good ‘fallback’; it’s becoming the primary interface.
As Zeus Kerravala, Principal Analyst at ZK Research, explained:
Imagine changing a flight: instead of navigating a website, you simply say (into your smartphone): “I’m thinking of changing my flight tomorrow—what’s available between 1 PM and 4 PM?” That level of conversational AI will make voice the preferred method of interaction.
What will be crucial here is that the system can process voice input and route it accordingly, and – again – that’s where agentic AI comes in.
Consumers should be able to speak to their phones naturally, and AI agents should handle the rest.
Of course, AI needs to retain the intelligence of traditional contact centers, such as detecting urgency.
After all, there’s a difference between someone calmly requesting a flight change and someone running through an airport shouting: “Get me a new flight now!’”
Yet, as AI can now capture precise sentiment from just a short snippet of speech, this future feels much more palpable.
