From Gut Feeling to Algorithmic Thinking: AI-Driven Decision-Making in Strategic Management

Abstract

In today’s rapidly evolving business environment marked by data proliferation and digital transformation, strategic decision-making is experiencing a profound shift, from intuition-driven judgments to algorithmically informed strategies. This paper explores the transition from traditional managerial reliance on “gut feeling” to the integration of Artificial Intelligence (AI), particularly predictive analytics, in strategic management. Drawing on theoretical models, contemporary case studies, and empirical evidence, the study employs a mixed-methods approach: regression analysis (SPSS) on survey data and thematic coding (NVivo) of expert interviews to uncover the structural and behavioral mechanisms influencing this paradigm shift. The findings reveal that leadership support, organizational readiness, and AI adoption levels are significant predictors of enhanced decision- making effectiveness. At the same time, the qualitative data exposes a nuanced interplay between human intuition and machine intelligence, emphasizing trust, ethical ambiguity, and resistance to automation. While AI offers compelling benefits such as increased predictive accuracy, objectivity, and speed, it also raises concerns about data bias, algorithmic opacity, and over-reliance on automation. Grounded in a critical realist paradigm, the study argues that AI should not replace human judgment but should augment it, creating a hybrid decision-making model that leverages both machine learning capabilities and human insight. The paper concludes with targeted recommendations for managers, educators, and policymakers, advocating for digital literacy, ethical AI governance, and organizational cultures of continuous learning. As organizations move from gut feeling to algorithmic thinking, a synergistic model of hybrid intelligence emerges as essential for sustainable strategic advantage in the AI-augmented era.