Shopper artificial intelligence (AI) presents a striking paradox: while massive investments drive rapid expansion and increasingly sophisticated AI solutions, two-thirds of consumers express dissatisfaction with AI shopping assistants, citing frustrations with pushy upselling, poor understanding, and inaccurate recommendations. This disconnect motivates our development of the Shopper AI taxonomy. To develop our taxonomy, we synthesized insights from multiple disciplines through a design science research process with empirical validation. Grounded in customer experience management (CEM) theory, our taxonomy identifies 14 dimensions within two meta-characteristics: AI capabilities (knowledge, intelligence, autonomy, breadth of use, quality of work, data privacy) and AI parasocial skills (personalization, anthropomorphism, communications mode, emotion recognition, emotion expression, empathy, influence, engagement). The taxonomy advances service research theory in three ways. First, we extend CEM theory by revealing how AI creates value through interrelated but discrete capabilities and parasocial dimensions. Second, we identify how AI capabilities enable autonomous value creation without active customer participation, representing a new form of value pre-creation. Third, we reveal complex dimensional interactions, where improvements in one dimension can enhance or diminish others. This multidimensional taxonomy provides managers with actionable guidance for navigating dimensional trade-offs, designing efficient, balanced AI systems, identifying context-specific investment priorities, and avoiding common pitfalls.
Kennedy et al. (2026) studied this question.