AI-Driven Intelligent Automated Task Allocation System Using Context Aware Decision Modeling And Dynamic Workload Management

Manual task assignment in organizations is time-consuming, inconsistent, and leads to unbalanced workloads as team sizes grow. This paper presents an AI-driven intelligent task allocation system that automates the entire assignment process by integrating WhatsApp as the task submission interface with a Discord bot and Groq LLM. When a task message is received, the LLM evaluates the content, assigns a priority level, selects the most suitable worker from the Experts table in Supabase, and writes the structured record — containing task, priority, worker name, and worker email — directly into the Task table without any manual intervention. When all workers are occupied, the system identifies the worker nearest to completing their current task and assigns accordingly. A React frontend delivers role-specific dashboards, task tracking, pie chart reports, and inter-role chat for Super Admin, Admin, and Worker roles, while completed tasks are automatically archived into the CompletedTask table.