Voice AI is becoming a more serious part of the enterprise customer service conversation. Legacy IVR systems are still frustrating and earlier versions of bots often created more irritation than relief. But the market is changing.
According to Dmitry Timofeev, Director of Product at Parloa, early enterprise deployments have shown that voice AI is now mature enough for practical use in customer service. As Timofeev told CX Today:
“The majority of enterprises have not really deployed proper voice AI solutions. But some early adopters already did about a year ago, some of the largest companies on the planet… and they’ve already demonstrated success and that the technology is mature enough to be deployed in practice.”
As customer expectations are shifting, that is affecting their perception of other brands. Once consumers experience an AI agent that connects instantly and resolves their issue, they become less tolerant of long queues elsewhere.
“Consumers now have this expectation that they don’t need to hang in the queue for 90 minutes waiting for a human agent, but instead can get instant service with an AI agent. They get connected instantly, and they start having that same expectation from other enterprises that they are customers of.”
It is no longer enough for voice automation to answer the call. It has to be useful once the call begins.
Speed Alone Is Not Enough
The biggest pressure on enterprises is speed of connection, but speed alone is not enough, Timofeev said. “The simplest AI agents that can only recite your FAQ from your website and cannot actually solve the problem are also not helping.”
Many customers have already experienced poor voice automation. They know what it feels like when a system is rigid, repetitive, or designed to keep them away from help rather than move them closer to a solution.
“IVR is a really outdated experience,” Timofeev said. “But then the rule-based bots are not much better, because they are very rigid, very robotic.”
These basic systems often fail to manage the shape of a real conversation.
“You have to repeat the same conversation again from scratch if you missed some piece of information because it can only go a certain path,” Timofeev said. “It cannot have a dynamic conversation.”
The same problem can appear even with more advanced AI agents if escalation is handled poorly. One of the most frustrating moments for customers is being transferred to a human agent and having to start again. Timofeev said:
“Even with more complex AI agents, they cannot always resolve a problem and need to escalate to a human, t. The worst– case scenario in that situation is that they don’t give any context in the handoff.”
Resolution Is the Real Measure of Value
For Parloa, this is why resolution has to become the central measure of voice AI value.
“The difference is dramatic,” Timofeev said. “If your AI agent can solve at least a portion of the issues that callers are calling about, then it’s not only deflection, but actually solving the problem for the customer fast.”
That perspective changes how enterprises should think about customer calls, treating them as a moments of customer need rather than operational burdens.
“Customers are calling because they have a problem,” Timofeev said. “And that is an opportunity to build a long-lasting relationship.”
To reach that point, voice AI needs more than a polished interface. It needs the right context to deliver personalized experiences to every customer.
“In practice, that means an AI agent needs access to a lot of enterprise systems and systems of record—the same systems your human agents are using,” Timofeev explained.
Agents also need to navigate complex knowledge bases and act reliably, which Timofeev noted remains one of the practical challenges with large language models (LLMs).
That reliability question should shape how enterprise buyers evaluate voice AI, looking beyond the promise of automation to understand how systems are tested, monitored, improved and governed over time.
“Buyers should demand more reliable AI agents, they should demand the ability to thoroughly test them and be able to observe them because they want to know what is happening on those thousands of calls that AI is handling,” Timofeev said.
This is especially important because voice AI is not suitable for every scenario, Timofeev noted.
Not Every Use Case Can be Fully Resolved by Voice AI
“In use cases where you need hundred percent reliability, this is still not the right technology. We’re not fully there yet,” Timofeev said, adding:
“There are some use cases where there are no definitive instructions, where you still need a human who is flexible enough and can realize that, “Iin this particular case I should not follow these instructions—for the good of the company and of the customer—I should do something else.”
The importance of identifying the correct handoff point is crucial to the design and deployment of voice AI systems.
A strong voice AI strategy should define which use cases the AI agent can handle, when escalation is needed, and what information must move with the customer when the conversation transfers to a human agent.
“At some point, the AI agent would decide to escalate to a human,” Timofeev said. “And what needs to happen at that stage is that the human needs to get the context.”
That context should include the customer’s intent, what has already been done, whether authentication has taken place, and a useful summary of the conversation, to avoid frustrating the customer with having to repeat themselves.
Answering the call is only the beginning. The real test for enterprise voice AI is whether it improves the customer journey, reduces unnecessary repetition, supports human agents with context, and resolves the issue at hand.
