Enhancing Workforce Productivity through Machine Learning: An Analysis of Adaptive Training Models

The study aims to determine whether machine learning (ML)-based or conventional training approaches are more effective at boosting worker productivity, skill retention, and engagement. It used the mixed-method research approach where the data is collected from 150 employees working within 10 organizations that adopt technology in various sectors of work, with the intent of evaluating productivity, skill retention, and performance. Training with the aid of ML results in increased productivity by 40% and retention of information by 30%. The employee engagement rate increased remarkably by 90% during the end of the training than the outcomes of the conventional training methods. Such results, therefore, significantly enhance skill development and labor optimization through proof that models based on ML can be applied in modern work environments. The paper further suggests applications of AI-based models for staff development aside from the rapidly increasing number of studies demonstrating the potential of ML for corporate training.