From Artificial Intelligence Adoption to Decision Advantage: A Human–AI Governance Framework

Abstract

Artificial Intelligence (AI) is increasingly embedded in managerial decisions, but adoption alone does not ensure superior outcomes. The central challenge is organizational: deciding when AI should lead, when it should assist, when human judgment should dominate, and how accountability should be retained. This article develops an integrative conceptual framework combining human–AI complementarity, algorithmic trust, dynamic capabilities, and governance research. The synthesis yields four contingent decision modes, AI-led with human audit, AI-assisted with approval gates, human-led with AI challenge, and human-led with dual control, selected according to task uncertainty and decision consequence. It also specifies a layered governance system and a recursive capability cycle linking sensing, seizing, learning, and reconfiguration. The framework shows that decision advantage depends not on model accuracy alone but on the fit among task characteristics, authority, verification, feedback, and organizational learning. The study contributes a practical architecture for aligning productivity, accountability, legitimacy, and capability development. For managers, sustainable value arises when AI is governed as part of a decision system rather than deployed as a stand-alone tool.