Permissibility vs. Feasibility: AI in service from a CX perspective

Abstract

Purpose This paper aims to provide researchers and practitioners with a consumer-focused alternative for considering the integration of artificial intelligence (AI) into services. Design/methodology/approach The paper reviews and critiques the most popular frameworks for addressing AI in service. It offers an alternative approach, one grounded in social psychology and leveraging influential concepts from management and human–computer interaction. Findings The frameworks that dominate discourse on this topic (e.g. Huang and Rust, 2018) are fixated on assessing technology-determined feasibility rather than consumer-granted permissibility (CGP). Proposed is an alternative framework consisting of three barriers to CGP (experiential, motivational and definitional) and three responses (communicate, motivate and recreate). Research limitations/implications The implication of this research is that consistent with most modern marketing thought, researchers and practitioners should approach service design from the perspective of customer experience, and that the exercise of classifying service occupation tasks in terms of questionably conceived AI intelligences should be avoided. Originality/value Indicative of originality, this paper offers an approach to considering AI in services that is nearly the polar opposite of that widely advocated by e.g., Huang et al., (2019); Huang and Rust (2018, 2021a, 2021b, 2022b). Indicative of value is that their highly cited paradigm is optimized for predicting the rate at which AI will take over service tasks/occupations, a niche topic compared to the mainstream challenge of integrating AI into service offerings.