Automating Root Cause Analysis in Business Process Mining with AI and Data Analysis

Abstract

Business process mining (BPM) is an essential discipline within the realm of process management and optimization, as it seeks to uncover insights from event logs to understand, analyze, and improve organizational processes. One of the most critical aspects of BPM is the identification of root causes for performance issues, inefficiencies, or bottlenecks that hinder optimal workflow execution. Traditionally, root cause analysis (RCA) has been a time-consuming and manual process, involving expert analysis of data, often leading to delayed corrective actions and suboptimal decision-making. The advent of artificial intelligence (AI) and advanced data analytics has paved the way for automating root cause analysis, significantly enhancing the efficiency and effectiveness of BPM practices. This paper explores the integration of AI and data analysis in automating RCA within BPM, focusing on the role of machine learning (ML), natural language processing (NLP), anomaly detection, and data-driven techniques in streamlining the identification of underlying issues within business processes.

The primary objective of this study is to investigate how AI-powered solutions can facilitate the automation of RCA in business process mining, enabling faster issue detection and more immediate corrective actions, particularly in sectors like customer service, IT operations, and business operations. The paper highlights key AI methodologies that are relevant to root cause analysis, including supervised and unsupervised machine learning models, deep learning, and reinforcement learning, while demonstrating their application in real-world BPM scenarios. By automating RCA, organizations can accelerate decision-making, reduce downtime, and improve overall service quality, customer satisfaction, and operational performance.

One of the central challenges in automating root cause analysis lies in processing vast amounts of heterogeneous data from diverse sources such as event logs, operational systems, and transaction databases. AI-powered systems, particularly those incorporating machine learning techniques, can identify patterns and anomalies within this data that would be nearly impossible for human analysts to detect in a timely manner. Supervised learning models can be trained to recognize specific failure patterns, while unsupervised learning algorithms can detect previously unknown anomalies. Furthermore, natural language processing (NLP) techniques enable AI systems to understand and interpret unstructured textual data, such as customer feedback, support tickets, or system logs, which further enhances the scope of analysis.

In the context of customer service, AI-based RCA tools can automate the analysis of service requests, complaints, and support tickets to identify recurring issues, bottlenecks in service delivery, or dissatisfaction drivers. In IT operations, AI can rapidly pinpoint the root causes of system downtimes, performance issues, or security breaches, significantly reducing the mean time to resolution (MTTR). Similarly, in broader business operations, AI can analyze workflow inefficiencies, cross-functional delays, and resource utilization issues, ultimately leading to more agile operations and optimized resource allocation.

The paper also discusses several case studies and practical applications where AI-driven RCA solutions have been implemented within business process mining frameworks. For example, the study examines how major corporations in the finance, healthcare, and manufacturing industries have leveraged AI to automate the identification of operational bottlenecks, defects in production processes, and delays in service delivery. These case studies underscore the transformative potential of AI in BPM by showcasing how automation not only accelerates issue resolution but also provides organizations with valuable insights that can guide long-term process improvements and strategic decision-making.

Furthermore, the paper delves into the technical aspects of integrating AI with BPM systems, outlining the challenges and considerations that organizations must address when adopting such technologies. One key challenge is ensuring the accuracy and reliability of the AI models used for RCA, as incorrect root cause identification can lead to misguided corrective actions. To mitigate this risk, the paper emphasizes the importance of model validation, continuous monitoring, and refinement to ensure that AI solutions remain accurate and relevant over time. Additionally, the paper explores the potential for hybrid approaches that combine AI with traditional expert-driven methods, thereby fostering a more comprehensive analysis of root causes and enhancing the overall effectiveness of BPM efforts.

The paper also highlights the role of explainability in AI-driven RCA systems. In business environments, it is crucial that the decisions made by AI models are transparent and understandable to human stakeholders. Explainable AI (XAI) techniques, which aim to make machine learning models more interpretable, are discussed in detail as a means of ensuring trust and accountability in AI-based RCA processes. By providing insights into the reasoning behind AI-generated conclusions, XAI can foster greater confidence in the system's outputs, ensuring that business leaders and analysts can act upon the identified root causes with a clear understanding of the rationale behind them.