An Intelligent Decision Support Framework Integrating Artificial Intelligence, Business Intelligence, and Innovation for Competitive Performance in SMEs
Abstract
Artificial Intelligence (AI) and Business Intelligence (BI) have become essential technologies for enhancing organizational competitiveness in small and medium enterprises (SMEs). This study investigated the influence of AI and BI on innovation and competitive performance by proposing an integrated analytical framework that examined innovation as a mediating mechanism. A quantitative, cross-sectional research design was adopted using a synthetic dataset comprising 350 SMEs representing diverse industries and organizational characteristics. The analytical framework incorporated descriptive statistics, reliability assessment, Pearson correlation, multiple regression analysis, mediation analysis, and machine learning techniques, including Random Forest, Extreme Gradient Boosting (XGBoost), and SHAP-based feature interpretation. The findings revealed that both AI and BI positively influenced innovation, while innovation exerted the strongest effect on competitive performance. Mediation analysis demonstrated that innovation partially explained the relationship between digital intelligence capabilities and organizational performance. Machine learning models consistently identified innovation, AI adoption, and BI capability as the most influential predictors of competitive performance, supporting the robustness of the statistical findings. The study highlights the strategic importance of integrating intelligent technologies with data-driven decision-making to strengthen organizational innovation, operational efficiency, and long-term competitiveness. The proposed framework provides valuable managerial insights for SME leaders seeking to improve strategic decision-making and offers a structured foundation for future empirical validation using real-world organizational data.
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