INTEGRATING ARTIFICIAL INTELLIGENCE IN STRATEGIC BUSINESS DECISION-MAKING: A SYSTEMATIC REVIEW OF PREDICTIVE MODELS

Abstract

Artificial Intelligence (AI) integration into strategic business decision-making has emerged as a transformative force, reshaping how organizations navigate complexity, uncertainty, and long-term planning. This systematic review critically examines the role of AI-driven predictive models in enhancing strategic decision-making accuracy, risk mitigation, responsiveness, and organizational alignment. By analyzing 105 peer-reviewed journal articles published between 2018 and 2023, the study provides a comprehensive synthesis of methodologies, applications, and emerging challenges surrounding the deployment of machine learning (ML) and deep learning (DL) techniques in strategic business analytics. The evidence demonstrates that predictive models—including Random Forest, Support Vector Machines (SVM), Gradient Boosting Machines (GBM), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks—offer significant improvements in strategic forecasting across various domains such as customer behavior analysis, financial planning, supply chain optimization, market segmentation, and product innovation. The review reveals that AI tools empower organizations to transition from reactive to proactive decision-making by leveraging real-time and historical data to identify patterns, predict outcomes, and simulate strategic scenarios. These capabilities facilitate more informed, agile, and evidence-based decisions, ultimately enhancing organizational performance and competitive positioning. However, the review also identifies persistent barriers to AI adoption in strategic contexts, particularly the black box dilemma—where the opacity of complex models undermines trust, interpretability, and accountability. The findings underscore the importance of leadership engagement, ethical AI governance, explainability frameworks (e.g., SHAP, LIME), and integrated operating models to ensure that AI systems align with strategic objectives and generate actionable value. Overall, this review contributes to the growing body of literature on AI’s strategic impact by mapping the current landscape of AI-enhanced decision-making, identifying key opportunities and obstacles, and offering insights for researchers, executives, and policymakers. It advocates for a holistic approach to AI integration that combines technical innovation with strategic foresight, organizational readiness, and responsible deployment practices, ultimately promoting more resilient and future-oriented enterprises.