AI-Driven Business Intelligence for Managerial Decision-Making: The Roles of Prediction Accuracy, Response Speed, Transparency, Explainability, and User Trust

Ika Rosalika (Universitas Bina Sarana Informatika)
Ibnu Akil (Universitas Bina Sarana Informatika)
Muhamad Harun (Universitas Bina Sarana Informatika)
Wahyu Indrarti (Universitas Bina Sarana Informatika)
Linggar Kiki Zakaria (Universitas Bina Sarana Informatika)

Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Vol. 4 No. 4 (2026): Oktober: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi

Digital transformation has accelerated the integration of artificial intelligence (AI) with Business Intelligence (BI) to support faster, more accurate, and data-driven managerial decision-making. This study examines the effects of Prediction Accuracy, Response Speed, Transparency, and Explainability on Managerial Decision Quality, with User Trust positioned as a mediating variable. A quantitative explanatory design was employed involving 190 respondents who had experience using AI-based systems, Business Intelligence, data analytics, or digital applications for work-related decision support. Data were collected through a structured questionnaire using a five-point Likert scale and analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that Prediction Accuracy, Response Speed, Transparency, and Explainability have positive and significant effects on User Trust. User Trust also has a positive and significant effect on Managerial Decision Quality. In addition, all four AI-driven Business Intelligence characteristics directly influence Managerial Decision Quality. Mediation analysis confirms that User Trust significantly mediates the relationships between Prediction Accuracy, Response Speed, Transparency, and Explainability and Managerial Decision Quality, indicating partial mediation. Among the examined relationships, User Trust demonstrates a particularly strong contribution to decision quality, highlighting the importance of appropriate reliance on AI-supported recommendations. These findings indicate that effective AI-driven Business Intelligence requires not only accurate predictions and rapid responses but also transparent processes and understandable explanations. Organizations should therefore develop AI-BI systems that balance technical performance with user understanding and trust to improve the quality, relevance, timeliness, and consistency of managerial decisions. This approach strengthens human-AI collaboration while preserving informed managerial judgment in organizations.