THE ROLE OF AI AND BUSINESS INTELLIGENCE IN TRANSFORMING ORGANIZATIONAL RISK MANAGEMENT
Authors:   (MBA in Business Analytics), International American University. Los Angeles, California, United States, Rahman Siddikur , Islam Musfikul ,   (MBA in Business Analytics), International American University. Los Angeles, California, United States, Hossain Imran ,   (MBA in MIS), International American University. Los Angeles, California, United States, Ahmed Arifa ,   (MBA in MIS), International American University. Los Angeles, California, United States
Published: September 2024

The innovative business risks include cybersecurity and regulatory risks and therefore the expansion of the use of Artificial Intelligence and Business Intelligence technologies in the risk management processes has come as a result. This article examines the role of Advanced Intelligence and Business Intelligence in altering risk management paradigms in the context of the US Organizations with emphasis on operations management and decision-making sophistication enhancement and risk management in advance. To achieve the above objective, a cross-sectional survey of 200 risk management professionals drawn from various organizations is used to gather data on the level of AI/BI adoption, the benefits sought and the difficulties experienced. A structured online survey was used to solicit data relating to themes like integrated AI/BI, perceived enhancements in risk, challenges like high costs and data and ethical issues of AI-decision making. Categorized variables were used to present the demographics of the respondents and Pearson correlation and regression analyses tests were used to compare the impact of AI/BI adoption with enhanced risk management results. Chi-Square tests were conducted to establish the significance of the differences in the adoption and challenges by industries as well as the size of organizations. Organizations with optimally deployed AI/BI systems realize enhanced system effectiveness, increased speed of decision-making processes and improved ability to manage risks in an anticipatory manner. A significant positive correlation was established between these outcomes and the level of AI/BI integration with these outcomes supporting the disruptive promise of these technologies. However, the study also shows the following challenges to adoption, which are high costs of implementing the solutions, difficulty in handling big data and shortage of skilled personnel in a firm. Moreover, ethical issues remain critical, especially with reference to the levels of transparency with artificial intelligence alongside data protection for individuals’ information; a pressing issue of concern especially to the health and financial sectors. This study prompts further attention to the advancement of efficient AI solutions at a large scale and the generation of a set of ethical norms to incorporate into risk management particularly with reference to AI use. In future research, more efforts should be devoted to investigating the effects that the use of AI and BI has on risk management practices after a longer period of time has elapsed, as well as on how the barriers explored in this research could be efficiently mitigated for organizations, especially those small ones.