Voice AI Cracks Up Outside the Lab, Klearcom Exec Warns

Voice AI that aces a scripted demo regularly fails on its first real customer call, because labs do not test packet loss, coffee-machine hiss or unfamiliar accents. That is the assessment of Neil Weldon, Chief Product Officer at Klearcom, who told CX Today that transcription and related tools "work really well in a lab environment."

"But when you start to actually take it out of a lab environment and put it into much more complex scenarios, then it sometimes starts to run into challenges," Weldon said.

The claim is worth taking seriously. It comes from a vendor whose business depends on call-path testing, so it has an obvious commercial edge. But the failure modes he names are measurable, physical properties of telephony networks, not marketing abstractions.

Why does the demo road stay empty?

Weldon offered a driving analogy: a demo resembles a test on an empty road, where watching the speedometer is enough, while production traffic brings roundabouts, lights and impatient drivers tailgating you.

The contact center equivalent is concrete. He listed four conditions the model rarely meets in testing:

• carrier degradation on the call path
• lost audio packets
• background noise, from coffee machines to train platforms
• regional accents the system has seldom encountered

Voice is also unforgiving by nature. Weldon said voice AI "requires human conversational pacing," and when packet loss and audio jitter appear, "the conversational flow can actually start to break down." The visible symptom is the bot talking over the customer. The customer will not diagnose jitter. The customer will conclude the brand's AI does not work.

What do adoption numbers actually measure?

The uncomfortable part for leaders racing to deploy agents: healthy adoption statistics can mask these failures. Weldon said some companies report huge adoption figures, but that "doesn't actually equate directly to successful outcome for the end user."

The distinction matters. Adoption counts interactions. It says nothing about whether the caller resolved anything. A deployment can look successful in a dashboard while every complicated call collapses in real time, and the loop is the worst collapse of all. The customer explains the issue, the bot says it did not understand, and the cycle repeats until someone gives up.

What is the lesson for buyers?

Weldon's message, stripped of vendor framing, is a procurement test. The model may impress, but the customer's experience depends on everything around it: the carrier path, the audio quality, the accent mix of the actual caller base. Demos rarely emulate the true customer call experience, so buyers should demand evidence gathered under degraded conditions, not clean-room benchmarks.

For teams already deployed, the priority is outcome measurement, not interaction counts. If adoption is high and resolution is not, the call path is the first place to look, and the patchy-signal train platform is the environment to test against.

Klearcom's commercial interest in this diagnosis does not make it wrong. It makes it verifiable.