4-minute read
Every year, businesses spend about $1.3 trillion addressing an estimated 265 billion customer service requests that flow into contact centers, an average of more than 700 million interactions per day. At that scale, even modest efficiency gains materially change the economics of customer service operations.
While interaction volumes vary by industry and scale, organizations of all sizes feel the pressure to deliver more responsive, consistent customer service. Experiences shaped by mobile apps, real-time chat, and always-available digital assistants, along with category leaders that have normalized near-instant resolution, have reset customer expectations around speed, simplicity, and availability at any hour.
The challenge, then, is sustaining high-quality customer care without overextending experienced agents or allowing service costs to scale linearly with demand. For many organizations, that challenge has accelerated interest in applied artificial intelligence.
AI-powered tools such as virtual agents, previously referred to as chatbots, increasingly operate alongside human agents to support both service quality and cost efficiency. Human agents remain essential for complex, high-priority interactions that require judgment and empathy, while virtual agents are well suited to handle repeatable inquiries with clearly defined resolution paths. When deployed intentionally, this division of labor accelerates response times for customers and reduces friction across the service experience.
Reducing training costs without slowing ramp-up
When AI is embedded into the agent training process, organizations can shorten ramp-up time while providing more contextual, role-specific guidance.
Faster ramp-up through real-time guidance
From the first day on the job, agents can rely on an AI-enabled resource to support them in context as questions arise. Instead of pausing work to search documentation or rely on static training materials, agents receive relevant information in real time. This approach shifts training away from memorization and toward applied learning, allowing agents to focus earlier on judgment, tone, and effective customer engagement.
Smoother transition from training to live interactions
AI-driven support can remain available as agents move from training into live customer interactions, reducing the performance drop that often accompanies this transition. By surfacing relevant knowledge base content, suggesting next best actions, and reinforcing established resolution paths, AI-powered insights help agents maintain confidence and consistency as responsibilities increase.
Lowering cost through more efficient issue resolution
Deflecting routine issues before escalation
Virtual agents can engage customers early in the service journey, resolving routine requests before human agents become involved. By applying machine learning and natural language processing, virtual agents can recognize intent across a defined set of common use cases and complete these interactions independently, reducing unnecessary escalation and shortening resolution cycles.
More accurate routing when human support is required
When an issue requires human involvement, virtual agents can use conversational context to route customers to the most appropriate agent or queue. This approach reduces misrouting and repeat transfers, helping customers reach the right support more quickly while allowing agents to begin interactions with greater context and clarity.
Increasing agent throughput without sacrificing service quality
In digital channels, human agents can manage multiple chat interactions concurrently, supported by AI-driven access to relevant information and guidance. This enhancement reduces the cognitive load associated with searching for data or navigating systems, allowing agents to focus on customer needs rather than mechanics. Compared to phone-based interactions, chat-based service enables higher throughput while still supporting personalized, context-aware responses.
Operational impact at scale
Lower-cost interactions by design
Virtual agents can deliver information quickly and consistently, particularly for routine requests with well-defined resolution paths. Looking ahead, Gartner predicts that agentic AI will autonomously resolve 80 percent of common customer service issues without human intervention by 2029, contributing to an estimated 30 percent reduction in operational costs.
Reducing repeat contacts through proactive resolution
Beyond accurate routing, virtual agents can reduce the need for follow-up interactions by anticipating related customer needs and surfacing relevant information during the initial exchange. By resolving adjacent questions proactively, organizations can limit repeat contacts and improve overall resolution efficiency.
Improving agent experience and retention
With virtual agents supporting routine interactions, human agents can spend less time on repetitive tasks and more time resolving complex issues and collaborating with peers. Many agents indicate that automation would allow them to focus on higher-value work, a shift that can improve job satisfaction and reduce attrition. Lower turnover, in turn, helps organizations avoid the high cost and disruption associated with recruiting and training new agents.
While phone interactions remain common, customers increasingly move across digital channels such as messaging, email, websites, and virtual agents, expecting continuity throughout the experience. Omnichannel service is not simply about offering multiple channels, but about maintaining a shared flow of information so that each interaction benefits from a complete view of prior customer touchpoints.
In practice, these dynamics come together when virtual agents are designed as part of the broader service model. In one engagement, Logic20/20 worked with a large U.S. telecommunications provider to deploy an OpenAI-powered virtual agent that increased call containment, reduced support costs, and expanded effective self-service across digital channels, while preserving clear escalation paths to human agents.
Industry forecasts underscore the growing role of virtual agents in reshaping customer service economics. As these capabilities mature, organizations across industries are rethinking agent training, issue resolution, and service delivery models to improve both efficiency and experience.
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Alexis Greenwood is a manager in the Logic20/20 Digital Transformation practice, focused on offerings development and innovations. In her experience as a business systems analyst, she enabled change through development of low-code platforms, including Salesforce and ServiceNow, custom applications, virtual assistants, and a variety of tools including ERPs, ITSM tools and CRMs.
Lionel Bodin is the Director of Digital Transformation at Logic20/20. He manages highly complex, multi-faceted digital programs related to CRM systems, cloud and on-prem implementations, Big Data, and more.