AI-driven organizational change: a competency-based framework for classifying AI integration trajectories

Abstract

Purpose This article proposes a competency-based framework for diagnosing organizational change trajectories driven by artificial intelligence (AI) integration. It extends a four-stage typology of AI-enabled organizational models by showing that each stage can be identified through the configuration of employee competencies, independently of technological indicators. Design/methodology/approach Drawing on structural-configurational, dynamic capabilities and sociomaterial perspectives, the article develops a five-stage competency assessment procedure and a classification matrix across twelve competency areas, and uses vertical coherence gaps and directional asymmetry of renewal to explain transitions between models. Findings Each organizational model type is associated with a distinct employee competency profile that serves as both a theoretical boundary marker and a diagnostic indicator. Transitions between models are not uniformly linear: their pace and stability depend on governance maturity, regulatory context and the clarity of an organization's strategic purpose regarding AI. Research limitations/implications The framework is conceptual and requires empirical validation across sectors. Practical implications The framework gives change managers and human resource (HR) practitioners a structured basis for assessing an organization's AI integration stage and mapping competency gaps against a target model. Social implications By foregrounding human competencies, the framework supports a human-centered path of AI adoption that preserves meaningful human agency in increasingly automated work. Originality/value This is the first study to link employee competency profiles systematically to discrete AI-enabled organizational model types, connecting stage models of AI adoption with competency management research.