Artificial Intelligence with Human Resource Management and Commerce Methods in Small-Scale Industrial Sectors

Abstract:
Employee retention has remained a critical challenge for small-scale enterprises (SSEs), which often lack structured human resource management (HRM) systems and the data-driven decision-which makes capabilities. The study is aimed to explore how the Artificial Intelligence (AI) addition within commerce and the HRM frameworks that has enhanced the employee retention in small-scale sectors. The problem centered on high turnover rates which results from limited engagement, poor predictive analytics, and the inadequate strategic planning. The research has utilized a hybrid AI-driven HRM model that has incorporated machine learning algorithms to analyze employee satisfaction, performance metrics, and the attrition trends. Data are collected from 120 small-scale enterprises through the structured surveys and the historical HR records. Predictive models, which includes the Random Forest and the Gradient Boosting, are trained to forecast employee attrition risk and to identify the key retention factors such as job satisfaction, reward systems, and the work-life balance. Results has indicated that the proposed AI-driven HRM model has achieved 93.2% accuracy, 92% precision, 93% recall, 93% F1-score, and the 0.95 ROC-AUC, which performs better than the existing methods which includes the Decision Tree, Neural Network, and the Ensemble Learning by 3–10% across all the metrics. that has combined approach successfully has reduced that has predicted attrition rates, has optimized retention interventions, and that has enhanced the decision-which makes in small-scale enterprises.