This study investigates the impact of conversational AI on BI systems, focusing on the effectiveness of a Support Vector Machine (SVM) model in enhancing data accessibility, user interaction, and decision-making processes. By systematically tuning hyperparameters, the study identified the optimal configuration for the SVM model, achieving a high training accuracy of 0.9821 and a validation accuracy of 0.9470. These results indicate robust model performance and excellent generalization capabilities, demonstrating the significant potential of conversational AI in improving BI systems. The findings are compared with results from existing literature, highlighting the advantages and implications of the SVM model. This study underscores the importance of machine learning in modern BI applications and suggests directions for future research to further enhance the integration of AI in BI.

Keywords: Conversational AI, Business Intelligence, Support Vector Machine, Machine Learning, Data Accessibility, Decision-Making, Hyperparameter Tuning, Model Evaluation, Predictive Analytics.