Key points are not available for this paper at this time.
Purpose Despite rapid advancements in AI and large language models (LLMs), there remains a critical gap in understanding how AI agents function as service actors and how they influence service processes and outcomes. This study addresses this gap by integrating AI agency and service experience dimensions, categorizing AI capabilities across six levels, from passive automation to fully conscious AI, and examining their impact on service workflows, human and multi-agent collaboration, and decision-making. Design/methodology/approach This research adopts a conceptual approach, drawing from literature on service experience and AI agency. It illustrates real-world applications of AI agents in service settings and outlines a future research agenda to explore the strategic and ethical implications of AI-driven service ecosystems. Findings AI agents transform service experiences by shaping action, collaboration, processes, outcomes, and learning. Automaticity AI enhances process efficiency through task automation but lacks adaptability, while Relational AI improves personalization in customer and employee engagement. Cognitive AI enables data-driven decision-making, whereas Autonomous AI optimizes workflows without human oversight. Innovator AI drives service transformation, generating novel solutions such as AI-driven drug discovery, while Conscious Organizational AI raises governance and ethical concerns for strategic decision makers. Originality/value This study advances AI agency theory in service experience, offering a structured framework to guide AI agent integration and its impact on context, process, collaboration, action, outcome and learning.
Shaikh et al. (Fri,) studied this question.