The Potential of Predictive Analysis for Improving Strategic Decision Making

Abstract

This thesis investigates the potential of predictive analysis for improving strategic decision making by replacing retrospective observation and managerial intuition with proactive, data-driven foresight. Utilizing the Dynamic Capabilities framework alongside secondary data from Eurostat and McKinsey, the study objectively evaluates real world technological adoption rates and their subsequent financial outcomes. The findings reveal a persistent "adoption-payoff gap" demonstrating that while artificial intelligence utilization is expanding, the majority of companies fail to translate these digital investments into tangible enterprise-level profitability. to successfully bridge this gap, organizations must fundamentally redesign their operational workflows, secure direct executive leadership, and prioritize the internal re skilling of their workforce to cultivate analytics translator. Furthermore, the research concludes that extracting sustainable value from predictive models requires strict ethical governance and continuous human in the loop oversight safely mitigate algorithmic and regulatory risks.