Automated Business Process Analysis: An LLM-Based Approach to Value Assessment
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Abstract (arXiv)
Business processes are fundamental to organizational operations, yet their optimization remains challenging due to the timeconsuming nature of manual process analysis. Our paper harnesses Large Language Models (LLMs) to automate value-added analysis, a qualitative process analysis technique that aims to identify steps in the process that do not deliver value. To date, this technique is predominantly manual, time-consuming, and subjective. Our method offers a more principled approach which operates in two phases: first, decomposing high-level activities into detailed steps to enable granular analysis, and second, performing a value-added analysis to classify each step according to Lean principles. This approach enables systematic identification of waste while maintaining the semantic understanding necessary for qualitative analysis. We develop our approach using 50 business process models, for which we collect and publish manual ground-truth labels. Our evaluation, comparing zero-shot baselines with more structured prompts reveals (a) a consistent benefit of structured prompting and (b) promising performance for both tasks. We discuss the potential for LLMs to augment human expertise in qualitative process analysis while reducing the time and subjectivity inherent in manual approaches.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 35 references that are in the map. First posted on arXiv 9 Apr 2025.
Builds on
20 of 35 references.
References · 20
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8 more
- 2023Large Language Models can accomplish Business Process Management Taskscited by 34
- 2023Large Language Models for Business Process Management: Opportunities and Challengescited by 24
- 2023Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Studycited by 20
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Citing papers per year: 2025: 2.
2 papers.