The Role of Generative AI in Managing Industry Projects: Transforming Industry 4.0 Into Industry 5.0 Driven Economy

Author:

Mohammed Moyassar Y.1,Skibniewski Mirosław J.1

Affiliation:

1. Center of Excellence in Project Management, Department of Civil and Environmental Engineering , University of Maryland College Park , MD , USA

Abstract

Abstract Industrial transformation is occurring now; the world is in the era of rapid industry movement into a collaborative robot (Cobot), empowering the human workforce, improving the project management processes, and optimizing manufacturing and supply chain procedures by utilizing the supercomputer and Generative Artificial Intelligence (GenAI) capability. GenAI is advancing and gaining traction in various economic sectors and the routine operations of businesses and people. The Fourth Industry Revolution (Industry 4.0), which focused on technical innovation, efficiency, profitability, and quality (driven by information technology (IT) and automation), gradually gave way to the Fifth Industry Revolution (Industry 5.0), which built on Industry 4.0's achievements but placed a stronger emphasis on human wellbeing, and resilience environmental sustainability. Industry 5.0 carries more societal responsibility for human-centric matters while maintaining economic growth objectives, complementing the current Industry 4.0 and together creating what is called the “Techno-Social Revolution.” The role of GenAI is positive in project management and would generate tremendous benefits to project managers, especially in large data analysis, decision-making, and automation of processes, yet GenAI is probably faced with many legal arguments against its deployment. Some of these are accountability for errors and omissions, misguided actions, data privacy, and proprietary information security. However, implementing a governance policy of tracking and auditing processes on GenAI decisions would establish proper accountability and responsibility. Additionally, adopting a multi-layered strategy that involves extensive risk assessments, strong security implementation, ongoing monitoring, training, machine learning, and evaluation of GenAI outputs mitigates these issues. Close collaboration between project managers, IT, and cybersecurity in setting up best practices and rules for employing GenAI will mitigate the legal challenges. Lastly, being open and clear with stakeholders regarding the usage of GenAI and the security and privacy precautions adopted assists in reaching agreeable legal terms and conditions.

Publisher

Walter de Gruyter GmbH

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