Conversational agents in business: A systematic literature review and future research directions

Abstract

The field of business shows an increasing interest in exploring conversational agents to improve service quality and market competitiveness. Furthermore, the advances in machine learning capabilities leverage the natural language processing towards natural and straightforward dialogue experiences for industries. However, in the best of our knowledge, no literature review outlines conversational agents in the business industry, primarily taking into account computational learning capabilities. This article presents a systematic literature review that encompasses these areas looking through the use of machine learning to improve the field of business. The review followed a guideline for systematic reviews to present the literature of the last decade, emphasizing business perspectives such as domains, goals, and challenges, and computational methods for self-learning, personalization, and response generation of conversational agents. As a result, the article provides the answers of three general, three focused, and two statistical questions to address the role of artificial intelligence in conversational agents applied to business domains. In this regard, the results show that no study combines self-learning, personalization, and generative-based responses for the same business solution. Additionally, the article describes the organization of the state-of-the-art, highlighting the correlation of business perspectives and machine learning methods. The contributions of this review focus on opportunities and future research directions towards human-like conversational agents for business.