AI-Driven Behaviour Monitoring and Leadership Feedback Optimization for Workforce Efficiency

Intelligent solutions are being used to monitor employee behavior and optimize leadership feedback for workforce efficiency in modern companies. Traditional solutions struggle to provide real-time, context-aware physical activity and workplace engagement information. The initiative intends to provide an AI-driven platform for multimodal behavior monitoring and intelligent leadership feedback. The goal is to improve employee productivity, engagement, and wellbeing using personalized analytics. Providing timely behavioral insights, encouraging transparency, and enabling adaptive management techniques promote leadership decision-making. The approach aims to improve organizational responsiveness by combining human-centric data with AI-driven reasoning. The technique uses the IBM HR Analytics Employee Attrition dataset from Kaggle, which models engagement using OverTime, JobSatisfaction, EnvironmentSatisfaction, and WorkLifeBalance, and the UCI Human Activity Recognition dataset. Use 561 Smartphones dataset features like tBodyAcc mean (), tBodyAcc-std (), and fBodyGyro-bands. Energy () from time and frequency domains classify physical activity. Support Vector Machines and Random Forests were used. The model predicted engagement 89.3% and recognized activity 93.7%.The findings demonstrate the framework's ability to analyze behavioral and physical data in complex workplaces. This paradigm promotes real-time behavioral feedback, evidencebased leadership, and future intelligent workforce management systems focused on adaptation, fairness, and operational excellence.