AI-Integrated Project Management System: A Multi-Tenant Platform for Intelligent Project Monitoring and Task Coordination

Abstract

Effective project management requires coordinated handling of projects, team members, task assignments, deadlines, priorities, and progress information. This paper presents an AI-Integrated Project Management System, a web-based application developed to support project planning, task coordination, progress monitoring, and AI-assisted project interaction within an organization. The system follows a multi-tenant architecture in which users and project data are scoped using an organization identifier. Role-Based Access Control is implemented through two principal roles: Admin and Team Member. Administrators can manage projects, tasks, and team members, while team members can access project information and update the status of tasks assigned to them. The application is implemented using the MERN stack, comprising React with Vite for the frontend, Node.js and Express.js for backend services, and MongoDB with Mongoose for data persistence. JSON Web Tokens are used for authentication. AI functionality is integrated through the OpenAI API using GPT-3.5 Turbo for project summarization, contextual project assistance, and extraction of actionable tasks from meeting notes. No custom machine learning model is trained as part of the implemented system. The implementation demonstrates an application-oriented approach to combining conventional project management functionality with generative AI capabilities.

Introduction

The text presents an AI-Integrated Project Management System that combines traditional project and task management with AI-based assistance. The system helps organizations create projects, assign tasks, track progress, manage deadlines and priorities, and coordinate team members.

The main features include:

• Organization and role management: Administrators manage organizations, team members, projects, and tasks, while team members access assigned tasks and update their status.
• Project and task management: Projects contain descriptions, deadlines, assigned members, and tasks. Tasks include priorities, deadlines, assignments, and statuses such as Todo, In Progress, and Completed.
• AI integration: A large language model is used for three main functions:
  • Generating project summaries from project and task information.
  • Providing a contextual AI project assistant for answering questions about project information.
  • Extracting actionable tasks from meeting notes.
• System architecture: The application follows a client-server architecture using React/Vite for the frontend, Node.js and Express.js for the backend, MongoDB/Mongoose for data storage, and the OpenAI API for AI functionality.
• Security and isolation: The system uses organization-based data isolation and role-based access control, with JWT-based authentication.
• Workflow: The process covers organization registration, authentication, team creation, project creation, task assignment, task execution, progress tracking, and AI-assisted project interpretation.
• Implementation: The system is implemented as a MERN-based web application with modules for authentication, team management, projects, tasks, dashboards, analytics, and AI services.
• Research focus: Unlike research that develops new AI algorithms or predictive models, this work is application-oriented, focusing on integrating existing AI capabilities into practical project-management workflows. Evaluation therefore emphasizes functionality, workflow support, and system behavior.

Conclusion

This paper presented an AI-Integrated Project Management System, a web-based application designed to combine conventional project management operations with AI-assisted natural-language capabilities. The system is implemented using React with Vite, Node.js, Express.js, MongoDB, Mongoose, and JWT-based authentication. Its multi-tenant architecture scopes users and projects according to organization information, while role-based access separates administrative and team member responsibilities. The implemented system supports organization onboarding, team member management, project creation, task assignment, priority and deadline handling, task status updates, assigned-task retrieval, and dashboard-level aggregation. AI functionality is integrated through the OpenAI API using GPT-3.5 Turbo for generating project summaries, providing contextual project assistance, and extracting structured actionable tasks from meeting notes. Future work may extend the system with additional collaboration capabilities, enhanced task-level discussions, notification mechanisms, richer analytics, more advanced AI models, improved contextual retrieval, integration with external collaboration platforms, and formal usability or performance evaluations.

References

[1] I. Taboada, A. Daneshpajouh, N. Toledo-Gandarias, and T. de Vass, “Artificial Intelligence Enabled Project Management: A Systematic Literature Review,” Applied Sciences, vol. 13, no. 8, Art. no. 5014, 2023, doi: 10.3390/app13085014. [2] “How artificial intelligence will transform project management in the age of digitization: a systematic literature review,” Management Review Quarterly, 2024. [3] R. Müller, G. Locatelli, V. Holzmann, M. Nilsson, T. Sagay, et al., “Artificial Intelligence and Project Management: Empirical Overview, State of the Art, and Guidelines for Future Research,” Project Management Journal, vol. 55, no. 1, 2024, doi: 10.1177/87569728231225198. [4] A. Mohammad and B. Chirchir, “Challenges of Integrating Artificial Intelligence in Software Project Planning: A Systematic Literature Review,” Digital, vol. 4, no. 3, pp. 555–571, 2024, doi: 10.3390/digital4030028. [5] OpenAI, “GPT-3.5 Turbo Model,” OpenAI API Documentation.

Copyright

Copyright © 2026 Velagaleti Bhavan, M. Anuradha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.