Orchestrating Human–AI Work Systems: Leadership, HPWS, and Agentic Intelligence

Abstract

As artificial intelligence (AI) systems evolve from passive tools to agentic collaborators, organizations must redesign work systems to integrate human and machine intelligence effectively. This article develops a conceptual framework for orchestrating Human–AI work systems by synthesizing research on leadership, High-Performance Work Systems (HPWS), and agentic intelligence. We argue that leadership plays a central coordinating role in aligning AI capabilities with human expertise, shaping psychological safety, trust, and adaptive learning climates. Extending HPWS theory, we propose that practices such as selective staffing, continuous training, participatory decision-making, and performance-based incentives must be reconfigured to accommodate AI agents as semi-autonomous contributors. We introduce the concept of agentic intelligence orchestration, emphasizing dynamic role allocation, human oversight, and ethical governance as key mechanisms for value creation. The article advances theory by integrating sociotechnical systems thinking with strategic HRM and AI governance, offering a multilevel model linking leadership behaviors, HPWS configurations, and system-level outcomes including innovation, resilience, and responsible AI use. Implications for research and practice highlight how leaders can cultivate hybrid intelligence ecosystems that enhance performance while preserving human agency and accountability.

Keywords: Human–AI collaboration; High-Performance Work Systems (HPWS); agentic intelligence; AI leadership; sociotechnical systems; hybrid intelligence; organizational design; digital transformation; AI governance; strategic HRM.