Enhancing AI-Human Collaborative Decision-Making in Industry 4.0 Management Practices

Abstract

Artificial intelligence (AI) systems have emerged as a powerful tool for management decision-making. However, there is a lack of a comprehensive framework that aims to bridge the gap between AI systems and human decision-makers. The proposed approach provides a novel framework, stepping towards the enhancement of AI-human communication with real-time feedback, iterative refinements, and user-centric interface designs. The framework is based on design principles of modularity, scalability, user-centricity, and adaptability, which were conceptualized to make it robust, flexible, and highly effective. Finally, it is expected that the indicated case studies and application scenarios will show the applicability and effectiveness of the framework in different contexts of industry, and therefore, provide concrete examples of how the benefits of the framework might be in practice. Simulation results show that the proposed mechanism has been significantly adopted, demonstrating a 10-20% increase in efficiency, user satisfaction, and feedback responsiveness compared to the existing mechanisms such as EHIDM and HCADMR. These results underscore the potential of the proposed framework to significantly enhance interaction dynamics between AI systems and human users.