Human–AI Teaming in Modern Organizations: A Framework for Collaborative Intelligence

Abstract

Artificial intelligence (AI) is increasingly transforming organizational work by enabling humans and intelligent systems to collaborate in decision-making, problem-solving, and operational activities. Despite rapid adoption, existing research remains fragmented across organizational behavior, human factors, information systems, and AI governance, providing limited guidance on how organizations can effectively design and manage human–AI collaboration. This conceptual paper develops the Collaborative Intelligence Framework (CIF) through an integrative review of peer-reviewed literature published between 2020 and 2026. The framework identifies four essential conditions for successful human–AI teaming: task interdependence, calibrated trust, role clarity, and organizational enablement. It explains how these conditions promote effective collaboration while reducing the risks of algorithmic aversion and excessive reliance on AI recommendations. The study argues that collaborative intelligence should be viewed as an organizational capability rather than merely a technological outcome, requiring deliberate management of human judgment, ethical responsibility, and organizational design. The paper contributes to the growing literature on responsible AI by providing a structured framework that can guide researchers and practitioners in designing sustainable human–AI collaboration. It concludes by discussing managerial implications, study limitations, and opportunities for future empirical validation across diverse organizational and occupational contexts. Keywords: organizational behavior; human-AI collaboration; human-AI teaming; collaborative intelligence; future of work; comple-mentarity