The rapid advancement of digital technologies has transformed organizational management, with Artificial Intelligence playing a central role in supporting decision making processes. Among these innovations, AI powered management systems are increasingly employed to enhance decision accuracy, reduce human bias, and improve efficiency. While theoretical discussions emphasize the benefits of AI adoption, there is limited quantitative evidence validating its actual impact on managerial decision accuracy. This study seeks to address this gap by assessing the relationship between AI powered management systems and decision accuracy through a quantitative approach. The research adopts a descriptive and correlational design, targeting managers and decision makers across selected industries. Data will be collected through a structured questionnaire using Likert-scale items to measure the extent of AI adoption, perceived decision accuracy, reduction in errors, and efficiency gains. Statistical techniques such as correlation and regression analysis will be employed to test the hypotheses and establish the strength of relationships between variables. The study is expected to provide empirical evidence that AI powered management systems positively influence decision accuracy and efficiency while reducing decision making errors. The findings will contribute to the literature on digital transformation and AI in management by offering data driven insights into how organizations can leverage AI tools for improved strategic and operational outcomes. Moreover, the study will provide practical recommendations for managers and policymakers seeking to optimize decision-making processes in an increasingly complex business environment.