AI-DRIVEN HRM FRAMEWORK FOR EMPLOYEE SEGMENTATION AND PERFORMANCE OPTIMIZATION USING AUTOENCODER-BASED CLUSTERING

Abstract

The rapidly evolving marketing, employee performance evaluation is unarguably a data-driven approach, thus requiring employee segmentation and performance optimization. This study proposes a full employee segmentation and performance optimization process using K-means segments. This algorithm accommodates decision-making based on key employee data, such as productivity, satisfaction, and engagement. Through soundly collecting, preparing and analyzing employee data, organizations can actively design targeted interventions toward the segments they classify, ultimately impacting all interventions directed toward improving performance and retention. The findings indicate that the K-Means clustering algorithm reveals different employee profiles, which leads to much more effective HR strategies congruent with the organization's objectives.Such a study suggests an AI-based employee segmentation and performance optimization framework through K-means clustering—a machine learning technique. By integrating artificial intelligence in HR analytics the framework facilitates wiser decision-making based on principal employee data such as productivity, satisfaction and engagement.

Keywords: HRM, human resource management, employee segmentation, performance optimization, k-means clustering, artificial intelligence, deep learning, autoencoders, machine learning, intelligent decision support, ai-driven analytics, predictive modelling.