Self-Learning Business Intelligence Systems: Integrating Artificial Intelligence and Predictive Analytics for Dynamic Organizational Decision-Making

Abstract

BI systems are gradually developing from traditional reporting historical platforms to intelligent and adaptive systems which can support dynamic decision-making in organizations. The purpose of this study is to investigate how artificial intelligence (AI), predictive analytics, and self-learning are integrated in BI systems and assess their contribution to decision-making in organizations. Quantitative research design with the use of data of 100 participants who have relevant experience in management, information technology, BI, data analytics, and decision-making in organizations has been used. Data have been analyzed by means of descriptive statistics, correlation analysis, multiple regression analysis, and reliability analysis. According to the results, positive perceptions of integration of AI in BI system (M = 3.84), positive perceptions of predictive analytics (M = 3.88), self-learning capability of BI systems (M = 3.91), and dynamic decision-making (M = 3.95) were found. Predictive analytics positively correlated with dynamic decision-making (r = 0.74, p < 0.001) and self-learning capability positively correlated with dynamic decision-making (r = 0.71, p < 0.001). According to multiple regression analysis, integration of AI (β = 0.24, p = 0.002), predictive analytics (β = 0.39, p < 0.001), and self-learning capability (β = 0.31, p < 0.001) are significant predictors of dynamic decision-making. All these variables together explain 66% of variance in dynamic decision-making (R² = 0.66). Reliability analysis has provided overall value of Cronbach’s alpha which equals to 0.94. Thus, the findings prove that integration of AI and predictive analytics in BI systems allows them to become self-learning and generate forward-looking insights. However, some concerns related to transparency of the model, trust of managers, skills of employees, quality of data, and cybersecurity should be taken into account.

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