A study of holistic artificial intelligence (HAI) and gaps in the concept

Abstract

Customer experience is a competitive driver of growth. By 2025, 95% of all customer interactions will use artificial intelligence (AI) technology-supported channels. The pathway to customer-centricity goes through conversational AI (CAI). CAI encompasses more and more artificial emotional intelligence (AEI) development. Machine learning, natural-language understanding, and processing help analyze customer emotions and feedback to improve customer experience. However, even holistic AI (HAI) definitions rarely encompass the AEI dimension in the fast-changing world of AI technologies. This situation hampers the standardization of natural-language understanding and processing in CAI and, therefore, the creation of emotional and ethical machines. The present study systematically explores the available AEI definitions, compares the mobilized defining attributes, and determines the core and peripheral assumptions of the AEI research to overcome this lack of AEI integration. Beginning with a review of the literature, the resulting resources are semantically analyzed to identify the specific elements included in each author’s definition of artificial intelligence. This provides a framework to enable cross-comparison of the definitions in a consistent manner.