Abstract
The convergence of artificial intelligence, advanced analytics, and human decision-making offers organizations major opportunities alongside serious challenges. This paper examines how adaptive decision support systems (ADSS) can strengthen, rather than substitute, human judgment when tackling complex, poorly defined organizational and societal problems. Building on organizational decision theory, cognitive science, and current research on human-AI collaboration, we introduce the Human-AI Judgment Co-Creation (HAJCC) Model, a theoretical framework built around five interconnected elements: contextual adaptability, transparent reasoning, dynamic task allocation, preservation of user agency, and iterative learning. A mixed-methods analysis across four organizational case studies indicates that systems aligned with HAJCC principles can enhance decision quality, boost user confidence, and support organizational learning, all while safeguarding human agency and accountability. Above all, successful human-AI co-creation requires not so much algorithmic sophistication as infrastructures that enable adaptation to context, transparent reasoning, flexible division of labour, human control, and ongoing development. This research work contributes to the interdisciplinary literature in management science, AI ethics & governance, decision science, and human-computer interaction by offering conceptual frameworks and design principles for deploying AI in judgment-heavy contexts. The results suggest that the pursuit of maximal automation could devalue organized human participation and lead to augmentation solutions that maintain human-centered values.