Customer service agents are prioritizing advanced AI technology over competitive salaries and fair working conditions, ranking automated assistant functionalities that help them understand customer needs as most important.
These were the results of a global Kore.ai survey of 1,200 customers and 600 agents, which also found despite the strong desire for intelligent virtual assistants (IVAs) contact centers are lagging in implementation, with 62% reporting a lack of AI use cases.
Agents trained in AI exhibit significantly higher job satisfaction and engagement levels, reaching 92%, compared to non-trained counterparts at 73%.
Furthermore, 71% of agents view increased automated assistant usage as mutually beneficial for consumers and agents, streamlining customer needs assessment and routing.
On the customer experience front, the survey highlights a shift in priorities, with effectiveness and accuracy now ranking higher than live agent access for the first time, indicting the growing importance of AI assistants in delivering service.
The results also revealed a narrowing gap between automated and live agent performance, particularly in the U.S., where there’s only a 4% difference in performance ratings.
Across industries, there is a growing comfort with AI-powered virtual assistants, with retail emerging as a standout sector for AI-assisted customer service, particularly in product search and purchasing.
Consumer acceptance of around-the-clock access to customer service also appears widespread, with more than three quarters of respondents (77%) acknowledging its appeal, driven by advancements in conversational voice technology and secure communication provided by enterprise-grade virtual assistants.
Michael Kropidlowski, CMO for Kore.ai, said the significant influence and adoption of AI shows it is essential to upskill contact center agents.
“AI won’t displace jobs, but agents unfamiliar with AI may be replaced with those who are proficient,” he said.
To address this, contact centers can organize training sessions focused on AI awareness, using AI-powered tools and understanding how AI can help improve productivity and job satisfaction.
“It’s also important to think about the costs—both what a center is spending per agent, call, and engagement,” he said. “This helps frame the financial benefit of bringing in IVAs, especially in terms of the time and resources they can save.”
Kropidlowski said given the gap between call center agents’ eagerness for AI-powered solutions and the actual availability of these tools, it’s important for contact centers considering IVAs to start by getting a solid handle on their call dynamics.
“They can start by looking at both daily and weekly call volumes and pinpoint the percentage of these calls that are routine or repetitive,” he said.
Implementing IVAs to handle these kinds of tasks can significantly reduce customer wait times and lighten the workload for agents, allowing them to play a more valuable, strategic role as problem solvers and relationship-builders.
Kropidlowski explained AI-assisted customer service interactions require strict security protocols to ensure that personally identifiable information (PII) or sensitive personal information (SPI) are consistently safeguarded.
He added inherent issues with large language models (LLMs) such as biases, hallucinations and response control can present challenges.
“Other obstacles can include data sharing and privacy concerns, response latency, and the high costs associated with API integrations,” he said. “Navigating AI regulations and integrating AI with legacy systems can also pose hurdles for certain enterprises.”
To address these challenges, companies can focus on identifying low-risk, high-reward AI applications that deliver significant, measurable outcomes.
“It’s crucial to adopt responsible AI practices to tackle issues like biases and ensure data privacy,” he said. “Utilizing enterprise-grade LLMs or SLMs can ensure data quality and response accuracy while reducing costs.”
Kropidlowski noted conversational voice technology continues to evolve, with advancements in automatic speech recognition (ASR) and text-to-speech (TTS) technologies enhancing the ability to accurately capture various accents and dialects.
“Utilizing commercial or fine-tuned LLMs enables more precise intent recognition and delivers human-like responses,” he said. “Also, integrating GenAI powered solutions allows for the customization and personalization of responses, making interactions more engaging for users.”
He predicted that together, these enhancements are bound to increase the acceptance and adoption of AI-powered customer service solutions across different age groups, including older generations.
