Although AI’s implementation into CCaaS environments is progressing rapidly, recent research on its use in a utility company’s operations is beginning to cast doubt on its supposed efficiencies.
The recent findings point to a disconnect between the theoretical benefits of AI and the practical challenges that affect workers using it.
Although major consultancies have been forecasting substantial workforce reductions through AI automation, the academic study’s findings suggest these predictions may be overly optimistic.
The initial evidence from the study shows that the path to effective AI integration in contact centers is proving more complex than initially expected.
Issues with AI’s Implementation
A study published on arXiv—a digital repository where researchers can share their latest findings – by researchers affiliated with a Chinese power utility and several Chinese universities examined how customer service representatives actually interact with AI assistance during their daily work.
Its aims were to examine AI being used to augment workers’ experience instead of the customer’s. Based on 13 semi-structured interviews with service representatives – including team leaders, shift supervisors, and those responsible for handling phone inquiries – the findings reveal significant gaps between AI capabilities and real-world performance.
A key finding of the study demonstrated that AI transcription accuracy remains a persistent challenge. Customer service representatives reported that AI systems frequently struggled with caller accents, pronunciation variations, and speech speed, leading to inaccurate text conversion. This becomes particularly problematic when dealing with critical information such as phone numbers, which AI systems often fragmented into incomplete segments that required manual reconstruction.
The research also highlighted problems with AI’s ability to handle homophones – words that sound identical but have different meanings or spellings. Additionally, the AI’s emotion recognition system demonstrated poor performance, misclassifying normal speech patterns as negative emotions and inappropriately linking volume levels to customer attitude. For AI systems that try to ascertain tone so that the agent can better mimic the customer’s sentiment, this could result in widely different approaches to customer interactions.
This led researchers to conclude that while AI reduced basic typing tasks, it simultaneously introduced what they termed “inefficiencies in information processing.” Representatives reported that AI-generated content frequently required manual correction or deletion, creating additional work rather than reducing it. Text summaries of calls, while potentially useful, often needed substantial editing and failed to capture key information consistently.
This issue is critical, as one of the main ways AI is being advertised to improve the agent side of the equation is by reducing the manual administration associated with calls. Not being able to accurately transcribe may force workers to revisit conversations to look for errors, eliminating the time savings AI promises.
This led the research to conclude that AI implementation actually increased the learning burden on customer service representatives, who must adapt to new systems while maintaining service quality, far from the supposed efficiencies AI is meant to bring to workers.
This mismatch between technological expectations and actual implementation reflects a common oversight among technology designers who overestimate efficiency gains while underestimating the human cost of system adaptation.
Mixed Results for AI Integration
Current industry statistics reveal a complex landscape where AI adoption is widespread but its effectiveness remains questionable.
Recent research from Calabrio indicates that 98% of contact centers are now using AI technology in some capacity, demonstrating the technology’s ubiquity across the industry.
However, this widespread adoption has not delivered the seamless experience many organizations expected. The same research reveals that 61% of contact center leaders report that customer conversations have become more challenging since AI implementation. This could go a long way toward explaining the struggles AI had in detecting tone in the Chinese study.
Industry predictions have also shifted significantly as real-world implementation challenges become apparent. Gartner’s recent forecast revision represents a notable change in industry expectations.
The consultancy now predicts that by 2027, 50% of organizations that initially planned to significantly reduce their customer service workforce will abandon these plans.
This is illustrated by fintech company Klarna, which made news by pushing to swap much of its human workforce with AI replacements, but has begun hiring again after reports of declining service quality and customer satisfaction.
The revised outlook acknowledges that the human touch remains irreplaceable in many interactions, requiring organizations to balance technology implementation with human skills.
Despite these challenges, some technology providers continue to report strong growth in AI usage.
NICE’s CXone Mpower Autopilot has experienced a 400% increase in usage, highlighting the rapid adoption of AI in Contact Center as a Service (CCaaS) environments. This growth suggests that while implementation challenges exist, organizations continue to invest in AI solutions as they seek to improve operational efficiency and customer service delivery.
Balancing Innovation With Practical Realities
The recent study on arXiv, although accepted to the 28th ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW), does not need peer review for publication on the website. Therefore, the results do not necessarily represent AI implementation across contact centers as a whole. However, it does provide food for thought on the perception versus the reality of some AI adoption rollouts.
While AI adoption continues to accelerate in the contact center sphere, the evidence suggests that successful implementation currently requires a step-by-step approach that evaluates each implementation—this may not only save money, but prevent associated loss of efficiencies.
Rather than viewing AI as an immediate solution to operational challenges, contact center leaders would benefit from treating it as an evolving technology that requires careful integration, ongoing refinement, and realistic expectations about its current limitations.
