Businesses have experienced substantial modifications in their operations due to digital transformation, with Robotic Process Automation (RPA) and Process Mining (PM) being among the prominent technologies for improving operational efficiencies and analytical capabilities. This paper reviews the literature on Robotic Process Automation and Process Mining, exploring their development, integration, and impact on business process management. The analysis includes a detailed examination of the methodologies employed in RPA and PM, their operational synergies, the resultant enhancements in process efficiency and data-driven decision-making in various industries, and the need for a nuanced understanding of their impact on organisational performance and strategy. This study categorises existing research into thematic areas, identifies current knowledge gaps, and suggests future research directions. Significantly, it highlights how the convergence of RPA and PM can provide strategic insights within organisations, augmenting processes that traditionally require intensive manual oversight. The findings indicate that the combined application of RPA and PM enhances operational efficiency and provides strategic insights that can lead to sustainable competitive advantages

Process Mining, Process Discovery, Event Logs, Conformance Checking, Process Automation, Robotic Process Automation

Process mining and Robotic Process Automationare synergistic technologies that optimise business processes. They aid process discovery by analysing event logs during RPA bot activities, enabling organisations to identify inefficiencies and prioritise automation efforts. Process mining enhances processes by evaluating logs to pinpoint bottlenecks and deviations, helping select the most impactful automation processes. It plays a crucial role in bot discovery by identifying repetitive, rule-based, and voluminous tasks through detailed event data analysis. Once identified, RPA automates them based on the insights gained from process mining, with detailed process models guiding the bots to ensure alignment with actual process needs. Key performance indicators (KPIs) considered when using process mining and RPA include automation rate, process maturity, throughput time, cost savings, ROI, process performance KPIs, accuracy and consistency, process efficiency, deviation detection, task frequency and complexity. However, the literature on the combined use of process mining and RPA has identified several research gaps, such as the lack of standardised frameworks for task discovery, limited support for initial task suitability assessment, insufficient focus on automation objectives, lack of data-driven task selection, restricted use of event logs, need for clear criteria for automation, and limited research on cognitive RPA. More research is required to develop more standardised, data-driven, and comprehensive frameworks that leverage process mining to effectively identify and select appropriate tasks for automation

Haythem Messai, Adel Bentayeb and Leila Zemmouchi-Ghomari (2025). Bridging the Gap: A Review of Robotic Process Automation and Process Mining Integration. International Journal on Emerging Technologies, 16(1): 68–82