An Intelligent Platform for Role-Based Resource Configuration Using Machine Learning and Workflow Automation

Efficient resource allocation is essential for optimizing enterprise workflows. However, current solutions often rely on static configurations that do not adapt to the dynamic needs of organizations. This research introduces a machine learning-based framework that integrates with workflow automation to recommend optimal resource configurations tailored to specific roles. By utilizing synthetic data that simulates resource usage patterns—such as CPU, RAM, storage, tool usage, and work habits—the system employs AI-driven classification to accurately predict roles. It then leverages the Camunda Workflow Engine to assign resources, ensuring both efficiency and adaptability in real-time. The Random Forest Classifier demonstrates impressive performance, achieving a high cross-validation accuracy of 99.63%, which indicates robust predictions for previously unseen configurations. This paper outlines the data generation process, model development, and workflow integration, presenting a scalable and intelligent solution for optimizing enterprise resource allocation.