Enhancing Organizational Performance with AI Technologies Through SEM-PLS Analysis in the UAE Ministry of Interior

Abstract

This study utilizes the SEM-PLS technique in SmartPLS software to conduct a modelling analysis aimed at understanding the impact of various AI dimensions on Organizational Performance (OP) within the Ministry of Interior in the UAE. Data used was collected from 373 employees across different departments, using a simple random sampling technique to ensure representativeness. The analysis involved two main assessments: the measurement model assessment and the structural model assessment, carried out after the PLS Algorithm, bootstrapping, and blindfolding processes. The findings revealed that while Computer Vision (AIC) and Internet of Things (AII) had non-significant effects on OP, with path coefficients of 0.053 and -0.001 respectively, other AI dimensions such as Deep Learning (AID), Machine Learning (AIM), Robotics (AIO), Reinforcement Learning (AIR), and Training (TR) showed significant positive impacts. Their path coefficients were 0.226, 0.245, 0.126, 0.209, and 0.117, respectively. Moreover, the study found that OP significantly influences Satisfaction (OPS), Financial Performance (OPF), Productivity (OPP), and Quality (OPQ), with high path coefficients of 0.886, 0.946, 0.957, and 0.963, respectively. These results were used to establish an empirical framework to guide future implementations and enhancements within the Ministry. This framework offers strategic insights for leveraging AI technologies to improve operational efficiency, public satisfaction, financial sustainability, and service quality within the Ministry of Interior in the UAE.