# Large Language Models for Business Process Management: Opportunities and Challenges

arXiv: https://arxiv.org/abs/2304.04309 · HTML: https://mlsanity.com/paper/2304.04309/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2023.

## Built on by (most cited first, 24 of 24)

- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [Large Language Models can accomplish Business Process Management Tasks](https://mlsanity.com/paper/2307.09923/) (2023)
- [PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks](https://mlsanity.com/paper/2407.13244/) (2024)
- [Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today](https://mlsanity.com/paper/2306.01499/) (2023)
- [ProMoAI: Process Modeling with Generative AI](https://mlsanity.com/paper/2403.04327/) (2024)
- [WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks](https://mlsanity.com/paper/2406.13264/) (2024)
- [How well can a large language model explain business processes as perceived by users?](https://mlsanity.com/paper/2401.12846/) (2024)
- [Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency](https://mlsanity.com/paper/2402.19366/) (2024)
- [Towards a Benchmark for Causal Business Process Reasoning with LLMs](https://mlsanity.com/paper/2406.05506/) (2024)
- [NeurDB: An AI-powered Autonomous Data System](https://mlsanity.com/paper/2405.03924/) (2024)
- [Tailoring Machine Learning for Process Mining](https://mlsanity.com/paper/2306.10341/) (2023)
- [Large Process Models: A Vision for Business Process Management in the Age of Generative AI](https://mlsanity.com/paper/2309.00900/) (2023)
- [Bridging Domain Knowledge and Process Discovery Using Large Language Models](https://mlsanity.com/paper/2408.17316/) (2024)
- [NLP4PBM: A Systematic Review on Process Extraction using Natural Language Processing with Rule-based, Machine and Deep Learning Methods](https://mlsanity.com/paper/2409.13738/) (2024)
- [ConDABench: Interactive Evaluation of Language Models for Data Analysis](https://mlsanity.com/paper/2510.13835/) (2025)
- [Conceptual Model Interpreter for Large Language Models](https://mlsanity.com/paper/2311.07605/) (2023)
- [Automated Business Process Analysis: An LLM-Based Approach to Value Assessment](https://mlsanity.com/paper/2504.06600/) (2025)
- [Evaluating the Process Modeling Abilities of Large Language Models -- Preliminary Foundations and Results](https://mlsanity.com/paper/2503.13520/) (2025)
- [On LLM-Assisted Generation of Smart Contracts from Business Processes](https://mlsanity.com/paper/2507.23087/) (2025)
- [Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency -- A Review](https://mlsanity.com/paper/2311.11159/) (2023)
- [Responsible AI in NLP: GUS-Net Span-Level Bias Detection Dataset and Benchmark for Generalizations, Unfairness, and Stereotypes](https://mlsanity.com/paper/2410.08388/) (2024)
- [Evaluation of LLMs for Process Model Analysis and Optimization](https://mlsanity.com/paper/2510.07489/) (2025)
- [Documenting SME Processes with Conversational AI: From Tacit Knowledge to BPMN](https://mlsanity.com/paper/2512.05122/) (2025)
- [BPMN4CAI: A BPMN Extension for Modeling Dynamic Conversational AI](https://mlsanity.com/paper/2608.27149/) (2026)

## Abstract (arXiv)

Large language models are deep learning models with a large number of parameters. The models made noticeable progress on a large number of tasks, and as a consequence allowing them to serve as valuable and versatile tools for a diverse range of applications. Their capabilities also offer opportunities for business process management, however, these opportunities have not yet been systematically investigated. In this paper, we address this research problem by foregrounding various management tasks of the BPM lifecycle. We investigate six research directions highlighting problems that need to be addressed when using large language models, including usage guidelines for practitioners.