Vol. 2 No. 1 (2022): African Journal of Artificial Intelligence and Sustainable Development
Articles

A Data-Centric Approach to Business Process Optimization: Integrating AI with Process Mining for Performance Benchmarking

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  • Amish Doshi
Amish Doshi
Lead Consultant, Excelon Solutions, USA
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Published 07-11-2022

Keywords

  • artificial intelligence,
  • process mining
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Amish Doshi, “A Data-Centric Approach to Business Process Optimization: Integrating AI with Process Mining for Performance Benchmarking”, African J. of Artificial Int. and Sust. Dev., vol. 2, no. 1, pp. 143–153, Nov. 2022, Accessed: Nov. 23, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/200

Abstract

In the rapidly evolving landscape of business process optimization, the integration of artificial intelligence (AI) with process mining presents a transformative approach for achieving enhanced performance benchmarking and continuous process improvement. This paper explores the synergies between AI and process mining, emphasizing their collective potential in the creation of data-driven performance benchmarks for organizations across sectors such as finance and e-commerce. By leveraging AI models and algorithms, process mining techniques can uncover process inefficiencies, monitor key performance indicators (KPIs) in real-time, and facilitate data-driven decision-making. The application of AI enables the automatic identification of patterns, anomalies, and bottlenecks within complex business workflows, thus allowing organizations to optimize their operations dynamically. Furthermore, the use of AI in conjunction with process mining enhances predictive analytics, offering insights into future process behaviors and performance outcomes. The study highlights the implications of this integration for developing a robust framework for continuous process optimization, wherein AI-driven insights drive the ongoing refinement of business processes. This research contributes to the growing body of knowledge on the use of AI for process management, offering a comprehensive approach to performance benchmarking that ensures adaptability and sustained operational excellence.

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