September 12, 2025Open Access

Machine Learning Models in Strategic Management: A Comprehensive Review of Current Applications

Key Points

Machine learning significantly improves decision accuracy by 22% through hybrid models in strategic management.
Applications include high-accuracy financial prediction, competitive analysis, and resource allocation using diverse methodologies.
Key challenges encompass technical aspects like data quality, along with ethical concerns such as bias and privacy.
Methodologies range from supervised learning techniques like decision trees to deep learning and ensemble methods, each offering unique advantages.

Abstract

This paper reviews machine learning ML applications in strategic management 2020-2025, which has shifted the field from intuition-based to data-driven decision-making. Theoretical foundations include Resource-Based View, Dynamic Capabilities Theory, Strategic Decision-Making Theory, and integrative frameworks, positioning ML as a strategic resource that boosts decision accuracy by 22% via hybrid models. ML applies to strategic planning e.g., high-accuracy financial prediction with support vector machines, competitive analysis Ridge Regression for market patterns, resource allocation 70-90% forecast accuracy in HR/finance via ensemble methods, and performance evaluation. Methodologies cover supervised decision trees, random forests, unsupervised K-means, deep learning, ensemble methods, reinforcement learning, and hybrids, each with unique strengths. Key challenges: technical data quality, model interpretability, organizational cultural resistance, skill gaps, ethical bias, privacy, and legacy system integration.

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