AI-Based Process Mining Framework for Process Business Integration in an Enterprise System

Abstract: The accelerated progression of information technology has necessitated the adoption of more intelligent and adaptive Enterprise Systems (ES) to sustain and optimize organizational processes. The ES serve as critical infrastructures for resource management, operational efficiency, and sustaining competitive advantage; however, their deployment frequently encounters persistent challenges. These include discrepancies between modelled and actual business processes, insufficient visibility in process execution, and limited automation in the detection and optimization of workflows. To mitigate these limitations, this study advances an Artificial Intelligence (AI)-enabled Process Mining paradigm. This approach facilitates the systematic extraction, analysis, and visualization of business processes, thereby supporting the identification of deviations, the detection of anomalies, and the provision of data-driven recommendations for continuous improvement. The overarching aim of the research is to conceptualize and evaluate an enterprise system framework that integrates AI-driven Process Mining to reinforce transparency, efficiency, and effectiveness in business process management. The proposed framework aims to provide automated analytical capabilities, predictive insights, and a robust data-centric foundation to enhance the precision of strategic decision-making, thereby contributing to the advancement of adaptive and intelligent enterprise systems.

Mardhani Riasetiawan and Ahmad Ashari. “AI-Based Process Mining Framework for Process Business Integration in an Enterprise System”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.4 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170455

@article{Riasetiawan2026,
title = {AI-Based Process Mining Framework for Process Business Integration in an Enterprise System},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170455},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170455},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {4},
author = {Mardhani Riasetiawan and Ahmad Ashari}
}