A Competency Model of the "Augmented Manager": Requirements for Managers in Hybrid Human-AI Teams

Анотація

The rapid diffusion of generative artificial intelligence (AI) into managerial work has reshaped the competency requirements imposed on managers across functions and industries. Surveys conducted between 2023 and 2025 show that the share of organisations using AI in at least one function rose from 55% to 88%, and 53% of C-level executives now report regular use of generative AI. In advanced economies, around 60% of jobs are exposed to AI, with high-skill knowledge-intensive occupations showing the highest exposure. These shifts indicate that traditional management competency models, formulated in the era of pre-AI work, no longer capture the full skill profile required of a manager who operates inside hybrid human-AI teams. The purpose of the article is to develop a structured competency model of the "augmented manager" that synthesises classical management competency theory with contemporary research on AI literacy, human-AI collaboration, and the empirical evidence of AI's effect on knowledge work. Methods. The study applies a systematic approach grounded in content analysis of established competency frameworks (McClelland; Boyatzis; DigComp 2.2; OECD AI and the Future of Skills), comparative analysis of empirical productivity studies, and conceptual modelling. The information base comprises 27 verified sources, including peer-reviewed journal articles, working papers of international organisations (IMF, OECD, World Economic Forum), and consulting research (McKinsey, BCG, Accenture). Results. The article proposes a four-cluster competency model encompassing cognitive-analytical, digital-technological, ethical-regulatory and human-relational dimensions, mapped against measurable performance outcomes in hybrid teams. Empirical evidence confirms that AI-augmented work produces productivity gains of 12-40%, with the largest gains accruing to lower-skilled workers, which has direct implications for managerial decisions on team composition and training. Conclusions. The augmented manager is conceptualised not as a replacement for traditional managerial roles, but as their extension into a context where competence in selecting, evaluating, governing and combining AI outputs with human judgement becomes the central performance variable. Directions for further empirical validation of the proposed model are outlined.