AI-Augmented Decision Intelligence System for Real-Time Strategic Management in Dynamic Business Environments

Abstract

The dynamic nature of the business landscape worsens the difficulties of strategic decision-making in today's markets, where economic and technological disruptions are not only frequent but also happening at an ever-faster rate. In today's markets, strategic decision making is frequently hampered by market volatility, information asymmetry, and the growing rate of technological disruption. The traditional management models, which are based on analytical methods that look back at the data and rely on a human's cognitive capacity, suffer many limitations when it comes to making decisions in real-time, high complexity environments. This study introduces a multi-source data aggregation-based three-layered AI-Augmented Decision Intelligence System (ADIS) incorporating machine learning predictive engines, natural language processing modules. Oneway ANOVA and Chi-square statistical validation, applied to a corpus of 412 organizational decision events, confirms that AI systems bring about statistically significant improvements in decision accuracy (p < 0.001), decision efficiency (p < 0.001) and profitability results (p < 0.005). The multiple linear regression model, which consists of five independent variables, shows a high R² of 0.94, indicating high predictive validity. Results from the empirical study show an average enhancement of +31 percentage points in decision quality scores and a 47% decrease in strategic response latency compared to non-AI counterparts. The results have direct management implications for decision flexibility in a digital enterprise and its organisation as seen by management executives.