AI-Enabled Decision Support for Human Decision-Making and Operational Performance: A Systematic Review

Recent advances in Artificial Intelligence have seen increasing adoption in organizations, especially in business operations to support complex decision-making processes. While prior research mainly focuses on AI automation decision-making, less attention is paid to AI decision support on human, especially in business operations contexts. This study presents a systematic literature review on how AI-enabled decision support influences human operational decision-making and improves operational performance in business operations. This systematic literature review is conducted through the PRISMA-based review protocol, and relevant studies are identified from Scopus and IEEE Xplore databases, which were later screened based on the inclusion and exclusion criteria. This resulted in 15 selected studies, which were then analyzed to identify publication year, research method, author countries, decision-making task, operational context, and effect on human operational decision-making and operational performance outcomes. The findings of the reviewed studies indicate that AI-enabled decision support influence decision-making efficiency, adaptability, situational awareness, explainability, and confidence, while operational performance impact indicate there is an improvement primarily on process efficiency, productivity, risk management, resource-related efficiency, resilience andsustainability. This findings provide insights for businesses seeking to implement AI decision support systems rather than replacing it. This study contributes by providing a synthesis of empirical evidence on AI-enabled decision support systems, highlighting the influence on human operational decision-making and organizational operational performance.