Hybrid Human-AI Decision-Making Models: A Conceptual Framework for Organizational Integration and Strategic Implementation

Abstract

This paper synthesizes existing research to establish a comprehensive taxonomy of hybrid models, categorizing them by their reliance on human oversight, algorithmic automation, and collaborative dynamics. By delineating the interplay between machine efficiency and human judgment, this framework provides practitioners with a strategic toolkit to navigate the operational challenges inherent in modern socio-technical systems (Fabri et al., 2023; Rivero, 2025). Furthermore, the taxonomy emphasizes how task complexity and operational risk dictate the optimal alignment between human agency and algorithmic processing (Hillebrand et al., 2025). This synthesis identifies key paradigms, ranging from autonomous execution to collaborative exploration—that define the current landscape of human-AI interaction (Li & Tian, 2025; Sherson et al., 2023). By mapping these dynamics to specific decision typologies, the research offers a structured methodology for leaders to balance algorithmic performance with necessary human accountability (Li & Tian, 2025). Ultimately, these insights enable organizations to move beyond ad-hoc implementations toward a systematic governance approach that optimizes the division of labor between human intuition and computational precision (Fabri et al., 2023).