Strategic decision-making is increasingly shaped by artificial intelligence (AI), yet existing theories continue to treat decision-making as an episodic managerial activity or view AI as a standalone analytical tool. This conceptual paper reframes strategic decision-making as a dynamic organizational capability enabled by AI. Drawing on dynamic capability theory and strategic decision-making research, the study develops an integrative framework explaining how AI enabled sensing, seizing, and transforming capabilities jointly shape strategic decision quality under conditions of environmental uncertainty. Rather than focusing on firm performance outcomes, the framework conceptualizes decision quality as an organizational outcome reflecting accuracy, timeliness, adaptability, and strategic alignment at the time decisions are made. By positioning AI as an embedded enabler of capability development rather than a substitute for managerial judgment, the paper explains heterogeneity in AI enabled strategic outcomes across organizations. The study contributes to theory by extending dynamic capabilities research into the domain of strategic decision-making and clarifying the organizational mechanisms through which AI reshapes strategic adaptation. Managerial implications highlight the importance of capability orchestration, decision governance, and learning mechanisms for realizing the strategic value of AI. The paper concludes by outlining directions for future research on AI enabled strategic decision-making capability, human AI collaboration, and boundary conditions shaping capability effectiveness.
Meisam Karami (Fri,) studied this question.