Predicting Employee Productivity Using AI-Powered Workforce Analytic

This research explores how Artificial Intelligence (AI) and Machine Learning (ML) can be used to predict employee productivity in small businesses, where access to large datasets and technical infrastructure is often limited. While most existing solutions focus on large organizations, this study aims to bridge that gap by applying a Random Forest model and demonstrating how model quantization can make AI deployment possible on low-cost devices. Using an open-source dataset of 100,000 employee records, the study followed a structured process of data preprocessing, feature engineering, model training, and evaluation. The Random Forest model achieved an accuracy of 100% in classifying productivity levels, which remained consistent even after quantization. The results highlight the feasibility of lightweight AI models for improving workforce management in smaller business environments. This study contributes to the growing field of AI-powered workforce analytics by showing that predictive insights can be both accurate and accessible without high computational demands.