cs.SEApr 15, 2026

Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends

Authors: João Bettencourt, Sérgio Guerreiro

Organizations: INESC-ID, R. Alves Redol 9, 1000-029 Lisbon, Portugal · Instituto Superior T´ecnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal

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Abstract

Recent advances in Generative Artificial Intelligence, particularly Large Language Models (LLMs), have stimulated growing interest in automating or assisting Business Process Modeling tasks using natural language. Several approaches have been proposed to transform textual process descriptions into BPMN and related workflow models. However, the extent to which these approaches effectively support complex process modeling in organizational settings remains unclear. This article presents a literature review of AI-driven methods for transforming natural language into BPMN process models, with a particular focus on the role of LLMs. Following a structured review strategy, relevant studies were identified and analyzed to classify existing approaches, examine how LLMs are integrated into text-to-model pipelines, and investigate the evaluation practices used to assess generated models. The analysis reveals a clear shift from rule-based and traditional NLP pipelines toward LLM-based architectures that rely on prompt engineering, intermediate representations, and iterative refinement mechanisms. While these approaches significantly expand the capabilities of automated process model generation, the literature also exposes persistent challenges related to semantic correctness, evaluation fragmentation, reproducibility, and limited validation in real-world organizational contexts. Based on these findings, this review identifies key research gaps and discusses promising directions for future research, including the integration of contextual knowledge through Retrieval-Augmented Generation (RAG), its integration with LLMs, the development of interactive modeling architectures, and the need for more comprehensive and standardized evaluation frameworks.

Figures & tables

Figure 1: The included number of paper organized by year (from authors).
Figure 2: Included results per year categorized. An increase in generative AI is noticed, while noGenAI is not evolving (from authors).
DimensionEarlier NLP / Rule-BasedLLM-Based
ModelingRule-driven pipelinesPrompt-guided, data-driven
InputStructured or simple textOpen-ended, heterogeneous text
InteractionAnalyst-driven, offlineConversational, iterative
ReliabilityDeterministic but rigidExpressive but may hallucinate
EngineeringManual rule designPrompt design and validation
ScalabilityDomain-limitedCross-domain, large-scale
Table 1: Comparison between earlier NLP/rule-based approaches and LLM-based approaches.
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