Business Artificial Intelligence for Enhancing Sustainable Decision Intelligence - Choiri, Muttaqin; Pramudito, Eko Sigit; Sutisna, Felix; Sean, Rio Squire (Pandawan Sejahtera Indonesia, 2025)
Business Artificial Intelligence (BAI) has become a key driver of organizational transformation, enabling advanced analytics, intelligent automation, and data-driven strategic decision-making. However, despite rapid technological progress, empirical research explaining how AI capability, algorithmic transparency, and task–technology alignment collectively shape Sustainable Decision Intelligence (SDI) within real business environments remains limited. To address this gap, this study introduces a novel BAI–SDI framework integrating AI Capability, Algorithmic Transparency, Task-Technology Fit (TTF), Decision Quality, and Sustainable Decision Intelligence as core constructs influencing long-term strategic and sustainable decision outcomes. Using a quantitative approach with Structural Equation Modeling–Partial Least Squares (SEM–PLS), survey data were collected from 402 professionals working in AI- integrated business sectors across Indonesia. The empirical results indicate that AI Capability significantly enhances Task-Technology Fit, while Algorithmic Transparency strongly predicts Decision Quality, emphasizing the importance of interpretability and accountability in trust-driven decision processes. Furthermore, Task-Technology Fit mediates the impact of AI Capability on Decision Quality, demonstrating that effective system-task alignment is essential for maximizing organizational value. The findings provide theoretical advancements by positioning SDI as an empirical extension of decision management theory and offer practical guidance for implementing ethical, transparent, and future-ready AI strategies within business environments. Overall, this study contributes actionable insights for strengthening governance and accelerating sustainable digital transformation in increasingly competitive and AI-driven decision ecosystems.