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

Abstract

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. 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. 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. The framework is conceptual and requires empirical validation across sectors. 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. By foregrounding human competencies, the framework supports a human-centered path of AI adoption that preserves meaningful human agency in increasingly automated work. 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.