日本語 
RightTouch Co., Ltd., a provider of AI contact center infrastructure for enterprises, announces its “AI Contact Center” concept centered around AI operators. Simultaneously, they are launching a new product, the knowledge integration platform (QANT Knowledge Hub), which will serve as the brain of this AI contact center. RightTouch Launches “QANT Knowledge Hub (β),” an AI-Ready Knowledge Integration Platform for Realizing AI Contact Centers.
The goal of this initiative is not to “automate inquiries,” but to “create a system where the accuracy of inquiry resolution continuously improves the longer it is used.”
By redesigning the entire customer touchpoint with AI in mind, we will leverage the interaction data and knowledge data accumulated in the contact center to continuously improve and enhance the customer experience (CX) through the organic collaboration of AI-driven automated responses (AI operators) and human interaction.
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Background: Structural challenges facing contact centers
In recent years, the environment surrounding contact centers has changed dramatically. With the advancement of digitalization, customer touchpoints have diversified, and the number of inquiries is increasing year by year. As a result, customer experience (CX) has become a crucial management theme that influences a company’s competitive advantage. While expectations for highly accurate and rapid responses are on the rise, the following structural challenges are becoming apparent on the ground.
Issue 1: Limitations of an operational model that relies on human resources.
For many years, contact centers have operated on a labor-intensive model, relying on increasing staff to handle the volume of inquiries. However, difficulties in recruitment, persistently high turnover rates, and rising labor costs have made it increasingly difficult to secure the necessary personnel.
On the other hand, the number of inquiries continues to increase due to the proliferation of digital channels and the increasing complexity of services, and the gap between supply and demand is only widening.
In this situation, the traditional approach of “increasing staff to cope” has reached its limits both in terms of profitability and operations. A shift to an operational model that does not rely on human resources is required.
Issue ②: Stagnation of self-resolved measures
In response to labor shortages, many companies have implemented self-service measures such as developing FAQs, improving web navigation, and introducing chatbots and voicebots. However, once these efforts reach a certain level, their effectiveness slows down, and situations persist where “implementation has been carried out but results are not improving” and “the workload on employees is not decreasing.”
The reason for this is that the tools are being operated in an individually optimized manner and are not being linked with customer data such as Voice of the Customer (VoC), web behavior and attributes, and past interactions. The operational burden remains high, the data necessary for improvement is not being utilized, and there is no mechanism in place to continuously improve accuracy.
Issue 3: Fragmentation of corporate knowledge management
Even more serious is the fragmentation of knowledge data management within companies. In many companies, knowledge is managed in a dispersed manner, such as “for FAQs,” “for operators,” and “for AI.” As a result, the burden of updating increases and information inconsistencies occur, making it difficult to provide consistent customer support.
Furthermore, the fragmentation of customer touchpoints (web, chat, phone), knowledge base, and interaction logs leads to a stagnation in the improvement cycle, preventing the overall evolution of customer service.
Against this backdrop, there is a growing need to redesign the very operation of contact centers.
SOURCE: PRTimes
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