Leveraging AI and Machine Learning for Advanced Predictive Analytics in Workforce Management

This paper shows how predictive workforce management using AI and ML may improve worker engagement, productivity at work, and resource allocation. We conduct a detailed analysis of the performance of numerous prediction models, even Linear Regression, Random Forest, XGBoost, SVM, and Long Short-Term Memory (LSTM), with the LSTM model reaching the greatest accuracy of 95%. Findings reveal major gains in worker engagement, with most employees expressing ratings over 80%, with a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$15-20 {\%}$</tex> savings in operating expenditures following implementation. Additionally, the favorable effects of AI/ML integration, including a 30% jump in productivity and an additional 25% decrease in resource consumption, were obvious across numerous organizational KPIs. The research stresses the scalability and adaptability of these technologies, bringing substantial insights into their adoption across varied labor situations. Notwithstanding difficulties concerning encryption of knowledge in order model candor and the results demonstrate the strategic relevance of AI and ML in modern labor management. Present studies will need to focus on mixtures of artificial and AI explicable to increase the performance of these tools in real applications.