Customer service departments across industries are turning to AI-based software solutions to cope with increasing call volumes, high agent turnover, and changing customer expectations. According to the NVIDIA Blog, these solutions are designed to increase agent productivity, automate interactions, and gather insights to optimize operations.
Benefits of AI in Customer Service
By strategically deploying AI, enterprises can transform customer interactions through intuitive problem solving, leading to greater operational efficiency and improved customer satisfaction. AI tools leverage customer data from support interactions, FAQs, and other enterprise resources to deliver personalized service and proactive support. Customizable open-source generative AI technologies, such as Large Language Models (LLMs) and Augmented Search Generation (RAG), are accelerating the rollout of customer service AI across industries.
According to McKinsey, more than 80% of customer service executives are already investing in AI or plan to do so soon. These AI solutions can help significantly reduce operational costs and improve customer experiences by automating help desk support tickets, creating effective self-service tools, and supporting customer service agents with AI assistants.
Developing effective customer service AI
For satisfying real-time interactions, AI-based customer service software must return accurate, fast, and relevant responses. Open-source models fast-track AI development, allowing developers to tweak and improve pre-trained machine learning models. The RAG framework connects general-purpose LLMs to proprietary knowledge bases to personalize responses to customer inquiries.
Human engagement processes are essential for both AI training and live deployment. Human reviewers judge AI responses and provide feedback to ensure fairness, accuracy, and security. In production, if AI cannot resolve a customer question, the call should be routed to a human support team to ensure efficient and empathetic customer engagement.
The ROI of Customer Service AI
The return on investment (ROI) of customer service AI is primarily measured by efficiency gains and cost savings. Companies can track key metrics such as faster response times, reduced operating costs, improved customer satisfaction scores, and increased revenue from AI-enhanced services. A pilot period where a portion of call center traffic is redirected to an AI solution can help gather concrete data on performance improvements and cost savings.
Industrial Applications
Retailers, telecommunications service providers, financial institutions, and healthcare facilities are some of the industries leveraging AI to improve customer service. Retailers use conversational AI to manage omnichannel customer requests, while telecommunications service providers automate network troubleshooting. Financial institutions employ AI virtual assistants to manage inquiries and detect fraud, and healthcare facilities use AI to address staff shortages and improve patient care.
For example, CP All, which operates 7-Eleven stores in Thailand, implemented a conversational AI chatbot to handle call center inquiries, reducing the call load on human agents by 60%. Infosys developed an AI chatbot for its telecommunications partners that achieved 90% accuracy and significantly reduced network resolution times. European digital bank Bunq uses AI to identify fraud faster and enhance customer support, while Hippocratic AI’s generative AI medical advisor supports healthcare professionals in performing routine tasks.
conclusion
By integrating AI into customer service interactions, businesses can deliver more personalized, efficient, and responsive service, setting a new standard for omnichannel support experiences. NVIDIA provides tools and technologies, such as NVIDIA NIM Microservices and NVIDIA AI Enterprise, to help businesses develop and deploy effective customer service AI solutions.
For more information, visit the NVIDIA blog.
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