AI and Machine Learning Advances

The employment of AI in risk management within a project management context has lately received attention as it assists with the predictive analytical component and helps make better decisions in this area. However, some potential problems, including ethical issues, technical requirements, and socio-technological integration of humans and AI, are not comprehensively studied in the empirical literature. This study explores how AI influences risk identification and estimation in project management, issues about AI integration, and the interdependence of AI and human factors in risk-driven decision making. The study employed an exploratory qualitative approach, conducting semi-structured interviews with 15 different project managers across the sectors. The findings reveal that AI integration in risk management increased probability detection, allowing the identification of possible failures at an earlier stage. It also highlights the existing challenges, technical barriers, resistance to change, and ethical issues. The study emphasized that human intervention should not be removed from the process of decision-making to ensure that both positive and negative consequences are attained. However, for the adoption of AI in risk management, there is a need for strong back end and constant model verification to ensure the predictive models achieve high accuracy and safety. In general, the study contributes to the growing literature on AI implementation in risk management and highlights the need to align AI with human capabilities, call for better AI policies, rules, and regulations, and enhance AI innovation and deployment for optimal application of AI in managing projects.