The Role of AI in Enterprise Risk Management and Operational Efficiency

摘要

Business management in recent years has been experiencing a revolution generated by artificial intelligence, which offers innovative tools, especially in Enterprise Risk Management (ERM) and operational efficiency. This chapter explores how AI integration redefines traditional risk management methodologies and optimizes operational processes. To obtain results and a theoretical framework, the research questions were reformulated to address separately: (1) How does artificial intelligence (AI) contribute to Enterprise Risk Management (ERM)? (2) What are the strategic and operational implications of its adoption? Artificial intelligence allows risk management and improves companies’ decision-making capabilities, also through the transformation of raw data into predictive insights in financial, operational, and environmental contexts. Even in the context of ERM, AI stands out for its predictive analytics capabilities. From an operational perspective, AI automates repetitive processes through various tools, such as Robotics Process Automation (RPA) and Intelligent Process Automation (IPA), improving productivity, reducing errors, and enhancing demand forecasting and customer personalization. The literature has highlighted the gaps in how AI directly impacts project management and operational efficiency, especially in the context of digital transformation. This research aims to fill the gaps by reviewing academic outputs from 2014 to 2024 from Scopus and Wos. The aim is to ensure a comprehensive and robust analysis that is also up to date. In this regard, the theoretical implications are for improving AI-integrated ERM, while the practical ones are mostly oriented toward strategies that managers can implement to leverage AI in optimizing their operations. They also address the well-being of society while addressing all concerns related to data management and transparency. The limitations of this study include relying solely on literature-based evidence with recommendations for future research.