Among the causes of the low success rate of the projects (around 35% of the total) is the low level of maturity of the technologies available for the management of the projects themselves. However, today many researchers, startups and innovative companies are starting to apply artificial intelligence (AI), machine learning and other advanced technologies to the field of project management. By 2030 the industry will undergo significant changes. By using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol this paper explores the intersection of project risk management and AI. The study highlights how AI-driven methodologies and tools can revolutionize the way project risks are managed throughout the project lifecycle. Specifically, 215 papers have been analysed to explore how the scientific community has been moving so far on the topic. Besides, a cross-sectional investigation of the PM processes and AI categories/tools was carried out to identify any path that is prevalent, where the prevalence comes from, and for which PM process or sector it is most successful. Finally, from this study several gaps emerged that scientific research would have to fill to effectively implement AI in PM and that have been turned into opportunities for future research in the form of a research agenda.

In the era of Industry 4.0, the integration of intelligent systems with human elements presents both opportunities and challenges. This study explores this interplay through the application of an industrial engineering technique to a real process issue, demonstrating originality in problem selection and solution tools, as well as the relevance of the results. An operational framework is proposed to drive digital transformation in manufacturing by balancing automated systems efficiency with the complexity of human activities, which include decision-making flexibility, adaptability, tacit knowledge and collaborative interaction. It examines Industry 4.0 domains to find solutions that use smart technology while enhancing human experience. A key element is the use of discrete-event simulation to create a digital replica of the existing process. This enabled a detailed analysis and the development of innovative, validated approaches through what-if scenarios. The implemented solutions led to a significant annual increase in productivity, the result of an overall improvement in process efficiency, which was also achieved through the identification and resolution of key process bottlenecks, confirming the method’s effectiveness. The research offers a scalable model for various sectors, emphasizing the need to integrate human aspects into intelligent systems. It highlights how technological progress should enrich, not overshadow, human contribution, contributing to a deeper understanding of digital transformation in intelligent manufacturing and service systems, where technology and humanity evolve together.