AI-Driven Strategic Planning and Decision-Making in Management

Abstract

Artificial intelligence systems have enabled the formulation of multiple strategic frameworks with considerable operational, financial, and organizational benefits. Advances on data-driven modeling are challenging traditional conceptions of management and decision-making, and in the process, opening up windows of opportunity for strengthening the analytical capabilities associated with strategic planning. As little is known about where AI-based decision support is gaining momentum beyond predictive analytics and optimization models, the purpose of this study is to map in what domains of management it is perceived to gain traction. Drawing on data from Analytic Hierarchy Process and regression–correlation analysis in organizational case studies, we identify a long tail of decision areas and planning processes in which a total of 47 unique managerial applications operate, including contexts such as investment evaluation, resource allocation, and performance monitoring. Our findings reveal a strong, positive correlation coefficient (r = 0.82) between regression-based forecasting and AHP-derived prioritization. However, managers do not passively comply. Rather, their preferences and judgments are integrated into the architecture of decision-making. The article concludes by identifying methodological implications, reflecting on the application of AI-driven planning in the field of management, and proposing suggestions for future organizational adoption. The empirical insights enrich understandings of the workings of artificial intelligence in experiences of strategy and governance.