Human resource management (HRM) remains predominantly organized around competency and occupation-based representations that implicitly presume relative stability in work content. Yet artificial intelligence (AI) increasingly changes work at the level of tasks, activity sequences, decision criteria, and human–tool interaction. This theory-building article introduces ATHENA (Advanced Tool for Holistic Evaluation and Nurturing of Abilities), a facet-based framework that reconceptualizes competence as an emergent, context-bound configuration of mobilizable human resources rather than a stable entity attached to job titles. ATHENA proposes an intermediate analytical layer through facets specified at developmental mastery levels to connect activity-based work analysis with recruitment, learning design, internal mobility, and strategic workforce planning under task volatility. The framework is organized around five interdependent dimensions: cognition, conation, knowledge, emotion, and sensorimotor resources. These dimensions are decomposed into nineteen sub-dimensions and sixty facets, each interpretable through four progressive mastery levels. We clarify how the framework was theoretically derived, distinguish it from competency models, KSAO (Knowledge, Skills, Abilities, Other Characteristics) approaches, ability requirement scales, work analysis, psychometrics, and skills-based HRM, and specify how different AI modalities alter activity demands and facet configurations. The article provides a worked example of an AI-augmented analyst role, operationalizes five testable propositions, and defines boundary conditions, governance requirements, and a research agenda. ATHENA is not presented as a validated measurement instrument; rather, it is a conceptual and methodological scaffold for empirical validation and responsible organizational experimentation.
