A Survey on Generative AI Chatbots in Customer Service: Challenges, Benefits, and Use Cases

Abstract

Generative AI chatbots have become a revolutionary technology in customer service, they have progressed way beyond the functionalities of traditional rule-based systems. Initially, chatbots were heavily reliant on execution scripts and inflexible pattern-matching techniques, they usually were not capable of understanding the complexity of natural language and customer intent to large language models, generative AI chatbots can create context-aware, adaptable, and human-like responses, thus, they are more engaging and interaction quality has improved dramatically. They comprehend communication intents, follow conversational context, learn from user history, and help companies to communicate in different languages, so they can give their customers a personalized, fast, and scalable customer-support solution. These solutions also become part of a company’s strategy to reduce costs through the elimination of the repetitive tasks of customer service where workforce is greatly reduced. Unfortunately, in the face of such advantages, there are still some challenges which include data privacy, security issues, model coherence, and the requirement of an extensive high-quality training dataset. This study explores the architecture, classification, machine-learning methods, and communication abilities of generative AI chatbots, their advantages, disadvantages, and the potential of the new use cases in customer service.