A new study has highlighted the teething issues some contact centers face when implementing AI assistants.
Researchers from a selection of Chinese universities interviewed customer service representatives from a power grid service call center about their use of AI.
While the reps generally found the ability of AI assistants to transcribe calls in real time to be useful, they highlighted a number of shortcomings.
For instance, agents complained that the technology fails to keep track of longer, more complicated interactions, often cutting out after a certain point. As one rep explained:
"When the conversation gets more intense, like if the customer has a lot of follow-up questions or is emotionally charged about an issue, the call might go on for over 30 minutes.
But the system might only transcribe the first 10 or 15 minutes. After that, it just freezes or stops recording, and you don’t get a complete transcription of the entire call.
Another CSR reported issues with standard customer inquiries, stating:
[The] AI assistant isn’t that smart in reality. It gives phone numbers in bits and pieces, so I have to manually enter them... Sometimes it confuses homophones or only transcribes part of what was said.
Moreover, the AI frequently makes errors in transcriptions, particularly when callers have certain accents or switch between languages.
A further shortcoming is the technology’s supposed "emotion recognition features."
As the name suggests, the capability is designed to analyze the tone and inflection of a caller’s voice and characterize their emotional state.
However, those surveyed reported that the feature was not fit for purpose, with one rep claiming that it focuses too heavily on volume level, often misclassifying normal speech intensity as negative emotion.
Finally, the study, entitled: "Customer Service Representative’s Perception of the AI Assistant in an Organization’s Call Center", also found that the tool lacked sufficient tag features to adequately convey the callers’ emotions.
The combination of these shortcomings meant that reps "largely disregarded emotion analysis features" and instead relied upon themselves to infer the customer’s emotion during a call.
New AI-Induced Burdens
The study concluded that AI did reduce some manual typing for reps, but the prefilled content often needed correction or removal, creating new inefficiencies.
While it helped organize call records, the extra, unnecessary text added to their workload. The study stated: "Our findings reveal that an AI assistant can alleviate some traditional burdens, such as cumbersome and time-consuming tasks, thereby demonstrating potential for enhancing foundational efficiency, aligning with previous research in areas such as entertainment, work, education, and healthcare.
However, it also introduces new learning requirements, compliance challenges, and psychological burdens.
The study specifically outlined the following three burdens that AI implementation caused within this contact center environment:
1. Learning Burden
Reps face extra effort adapting to the AI assistant due to its limitations, such as misinterpreting phone numbers, addresses, dialects, or lengthy conversations.