Leading hybrid intelligence systems: a general systems theory approach to organizational leadership in hybrid intelligence knowledge ecosystems

Purpose This study aims to develop a meta-leadership framework based on the general systems theory to guide leaders in hybrid intelligence systems where human and artificial agents cooperate. Design/methodology/approach Using Torraco’s (2016) conceptual synthesis framework, this integrative review synthesizes leadership, systems theory and artificial intelligence (AI) governance literature to construct a novel meta-leadership model addressing AI–human cooperation challenges. Findings The framework comprises five functions (system architecture, boundary regulation, feedback stewardship, adaptation facilitation, ethics governance) operating through three dynamics (alignment, viability, emergence). Emerging empirical studies support the framework’s underlying assumptions and suggest potential improvements in organizational performance when structured meta-leadership approaches are applied. Originality/value This research extends leadership theory beyond anthropocentric models, offering practical guidance for governing distributed agency across human and AI components in knowledge ecosystems.