AI-DRIVEN BUSINESS INTELLIGENCE FRAMEWORK FOR PREDICTIVE DECISION-MAKING AND STRATEGIC RESOURCE OPTIMIZATION

Authors

  • Aditya Dhanekula Abraham & Sons Leather LLC, Business Analyst, USA Author

DOI:

https://doi.org/10.63125/dkt2w457

Keywords:

Artificial Intelligence, Business Intelligence, Predictive Analytics, Decision Support Systems, Resource Optimization

Abstract

This study investigates the role of Artificial Intelligence driven Business Intelligence (AI-BI) in enhancing predictive decision-making and optimizing strategic resource management across organizations. Using a quantitative research design, data were collected through a structured survey employing five-point Likert-scale measures to examine four key constructs: AI-BI Capability, Decision-Making Effectiveness, Strategic Resource Optimization, and Organizational Performance. The analysis, conducted through Structural Equation Modeling (SEM), revealed that AI-BI Capability significantly influences both Decision-Making Effectiveness (β = 0.71, p < 0.001) and Strategic Resource Optimization (β = 0.67, p < 0.001), while Decision-Making Effectiveness, in turn, has a strong positive impact on Organizational Performance (β = 0.63, p < 0.001). Moreover, Decision-Making Effectiveness was found to partially mediate the relationship between AI-BI Capability and Organizational Performance, suggesting that the cognitive enhancement derived from AI analytics plays a crucial role in translating technological capacity into strategic outcomes. Moderation analysis further revealed that Data Quality and Data Governance significantly strengthen these relationships, highlighting the necessity of reliable, well-managed data environments. The model exhibited high predictive power, with R² values ranging from 0.64 to 0.72 and excellent model fit indices (CFI = 0.951, TLI = 0.945, RMSEA = 0.047). The findings underscore that AI-BI is not merely an analytical tool but a strategic capability that integrates machine learning, automation, and predictive intelligence to drive performance excellence. By linking cognitive decision-making mechanisms with operational intelligence, the study contributes to the theoretical discourse on the Resource-Based View (RBV) and Dynamic Capabilities Theory, positioning AI-BI as a transformative asset that fosters organizational agility, efficiency, and sustained competitive advantage in data-intensive environments.

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Published

2025-11-01

How to Cite

Aditya Dhanekula. (2025). AI-DRIVEN BUSINESS INTELLIGENCE FRAMEWORK FOR PREDICTIVE DECISION-MAKING AND STRATEGIC RESOURCE OPTIMIZATION. International Journal of Business and Economics Insights, 5(3), 1238–1270. https://doi.org/10.63125/dkt2w457

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