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

@article{1715447,
      author = {Elumalai A, Dr. Ponmozhi K},
      title = {AI-Driven Intelligent Automated Task Allocation System Using Context Aware Decision Modeling And Dynamic Workload Management},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {2101-2108},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715447.pdf},
      abstract = {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.},
      keywords = {Automated Task Allocation, Groq LLM, Discord Bot Integration, WhatsApp Task Pipeline, Dynamic Workload Balancing, Role-Based Access, Priority Classification, Expert Matching System.},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715447}
  }