Abstract
Organizations increasingly adopt artificial intelligence, yet most struggle to integrate it meaningfully into strategic decision-making, a phenomenon we term the “assimilation gap.” This conceptual article addresses this gap by introducing the Hybrid Intelligence Configuration Matrix (HICM), a novel theoretical framework for designing proposed human-AI collaboration in organizational decision-making. Synthesizing insights from team decision-making theory, human-AI interaction research, and organizational paradox theory, the HICM moves beyond simplistic substitution or augmentation narratives to offer a dynamic, configurational perspective. We propose a three-dimensional decision space, Cognitive Complexity, Social-Emotional Complexity, and Temporal Pressure, that generates eight distinct configuration zones, each specifying proposed human-AI role allocation. The framework introduces the Human Necessity Index (HNI), a multi-attribute preliminary diagnostic heuristic for quantifying required human involvement based on decision characteristics and ethical stakes. Further, we advance a phase-specific reconfiguration model, demonstrating how proposed collaboration shifts dynamically across problem framing, analysis, evaluation, and implementation. We also identify three inherent paradoxes, Efficiency-Empathy, Consistency-Creativity, and Scalability-Personalization, that emerge when AI is treated as a team member and must be managed rather than resolved. By bridging fragmented literatures and providing testable propositions, the HICM offers scholars a coherent foundation for future research and equips organizations with a diagnostic toolkit for architecting responsible, adaptive, and strategically valuable human-AI collaboration. However, as a conceptual framework, these proposed benefits require systematic empirical validation before definitive claims about organizational value can be made.