Purpose This paper aims to address the absence of theoretically grounded frameworks for developing AI prompting capabilities in organizations. The authors reconceptualize AI prompting as a dynamic organizational capability rather than a static technical skill, developing a hierarchical taxonomy that guides systematic capability development across organizational levels. Design/methodology/approach A systematic narrative literature review synthesizes research on organizational learning, dynamic capabilities and career development from 2017 to 2024. Thematic analysis of 67 sources identifies learning mechanisms, capability dimensions and developmental pathways. Findings The taxonomy establishes three progressive capability levels: (1) Technical Competency, foundational skills developed through 70:20:10 training approaches; (2) Strategic Understanding, collective sensemaking via Communities of Practice; (3) Dynamic Maturity, continuous adaptation through organizational learning architectures. Each level integrates distinct ethical governance mechanisms enabling organizational self-assessment. Originality/value To the best of the authors’ knowledge, this paper integrates, for the first time, the frameworks of dynamic capabilities, organizational learning and sustainable careers specifically for AI prompting capability development. By addressing critiques of dynamic capabilities theory and by embedding ethics at each taxonomy level, the authors provide actionable guidance for building competitive advantage in AI-enabled workplaces.
Sposato et al. (Thu,) studied this question.