Utilizing the Internet of Everything and Artificial Intelligence for Real-Time Workforce Management

Abstract

The real-time pool operation through the combination of the Internet of Everything (IoE) and Artificial Intelligence (AI). The purpose of this study is to evaluate how the Internet of Everything (IoE) technologies, which connect people, processes, data, and effects, may best optimize pool effectiveness and production when paired with the predictive and logical skills of artificial intelligence (AI). The utilization of IoE detectors and bias allows for the continuous collection and analysis of data, which provides insight into hand performance, workload allocation, and functional backups. Artificial intelligence systems also take advantage of this data to provide real-time decision assistance, which enables dynamic work allocation and envisions solutions to problems. Among the significant advantages that are brought to light by the investigation are improved resource application, increased functional translucency, and enhanced hand engagement. The practical operations and problems that are associated with this integrated approach are demonstrated by case studies coming from diligence.