Adaptive Workflow Orchestration using Neurophysiological Data in ERP and HRIS Systems

Abstract

The integration of neurophysiological data into Enterprise Resource Planning (ERP) and Human Resource Information Systems (HRIS) is an emerging area of research aimed at optimizing workflow orchestration in organizational environments. Traditional workflow management systems in ERP and HRIS often rely on static rulebased models that may not adapt efficiently to dynamic changes in user behavior, work conditions, or organizational needs. This paper explores the potential of adaptive workflow orchestration driven by neurophysiological data to create more personalized, responsive, and efficient systems. By incorporating data from brainwave activity, heart rate variability, and other biometric signals, we propose a new approach where workflows adjust in real-time based on the user's cognitive and emotional state. Using AI-driven algorithms, the system continuously analyzes these physiological signals to provide feedback and optimize task prioritization, resource allocation, and decision-making within ERP and HRIS frameworks. We demonstrate how such a system can improve user engagement, reduce cognitive load, and enhance productivity, while maintaining alignment with organizational goals. Through case studies and real-world applications, the paper highlights the effectiveness of this approach in reducing operational delays, improving system adaptability, and promoting greater employee well-being. The findings suggest that integrating neurophysiological feedback into workflow orchestration not only enhances system efficiency but also facilitates a more user-centered, adaptive organizational environment.