A voice AI demo is a bit like a driving test on an empty road.
As long as you keep an eye on your speedometer, nothing can really go wrong. But when you get onto a busy main road where roundabouts and traffic lights start popping up, and other impatient drivers start getting worryingly close to the back of your car, things become a lot more difficult.
The same is true for voice AI. Demos rarely emulate the true customer call experience.
What happens if the caller is on a train platform with a patchy signal, or a coffee machine is hissing and spluttering behind them, or the agent is confronted with a regional accent the system has rarely encountered?
That is where many contact center voice AI deployments start to struggle.
Indeed, in a recent conversation with CX Today, Neil Weldon, Chief Product Officer at Klearcom, explained that "transcription and other 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.”
For leaders racing to roll out AI agents, that is an uncomfortable message. The model may be impressive, but the customer’s experience depends on everything around it.
The Call Path Is Part of the Customer Experience
So why does voice AI fail outside the lab? Often, it is because of network and audio conditions that the model has never encountered in testing.
Weldon pointed to carrier degradation, lost audio packets, background noise, and regional accents. Any of them can stop an AI agent from understanding what the customer wants.
Voice also leaves little room for error. Weldon said voice AI “requires human conversational pacing,” and when packet loss and audio jitter creep in, “the conversational flow can actually start to break down.”
This can result in the bot talking over the customer. In these instances, customers will rarely diagnose a jitter problem; they will simply decide the brand’s AI does not work.
Healthy adoption figures can hide this. Weldon said some companies report a huge amount of adoption, but it “doesn’t actually equate directly to successful outcome for the end user.”
Do Not Let the Bot Trap the Customer
The most damaging failure is the loop. The customer explains their issue, the bot says it did not understand, and the cycle repeats.