AI-Powered Dynamic Task Allocation for Agile Work Environments

Remote work success has created new management issues related to worker productivity as well as task assignments and team collaboration requirements especially for companies working with agile approaches. The research investigates an AI -based task allocation framework which optimizes efficiency of remote workers operating in agile work environments. The system employs Reinforcement Learning (RL) and Natural Language Processing (NLP) to distribute tasks through real-time data regarding team performance as well as workload and skillsets. Preprocessing activities prepared data obtained from Jira, Asana along with Slack and Microsoft Teams for training the AI model. The implemented system showed remarkable results by reducing average task completion time by 29.3% alongside achieving a 45.4% improvement in workload balance. The task-alignment model enabled higher team satisfaction levels of 26-28% which proved its capability to match work tasks to worker abilities and minimize inefficient processes. The AI system performed task allocation with 91.4% accuracy that exceeded traditional methods which managed only 72.1 % efficiency. A task management system that runs on AI technology helps both teams and their productivity when working from different locations.