An increasing number of organizations are deploying Large Language Models (LLMs) for a wide range of tasks. Despite their general utility, LLMs are prone to errors, ranging from inaccuracies to hallucinations. To objectively assess the capabilities of existing LLMs, performance benchmarks are conducted. However, these benchmarks often do not translate to more specific real-world tasks. This paper addresses the gap in benchmarking LLM performance in the Business Process Management (BPM) domain. Currently, no BPM-specific benchmarks exist, creating uncertainty about the suitability of different LLMs for BPM tasks. This paper systematically compares LLM performance on four BPM tasks focusing on small open-source models. The analysis aims to identify task-specific performance variations, compare the effectiveness of open-source versus commercial models, and assess the impact of model size on BPM task performance. This paper provides insights into the practical applications of LLMs in BPM, guiding organizations in selecting appropriate models for their specific needs.
Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning
The Wharton School, University of Pennsylvania（宾夕法尼亚大学沃顿商学院）；Carnegie Mellon University（卡内基梅隆大学）；Harvard Business School, Harvard University（哈佛大学哈佛商学院）
Large Process Models: A Vision for Business Process Management in the Age of Generative AI
SAP（思爱普）；University of Vienna（维也纳大学）；University of Melbourne（墨尔本大学）；Technical University of Munich（慕尼黑工业大学）；Fraunhofer Gesellschaft（弗劳恩霍夫协会）；Humboldt-Universität zu Berlin（柏林洪堡大学）
Harnessing the Potential of Large Language Models in Modern Marketing Management: Applications, Future Directions, and Strategic Recommendations
Iran University of Science & Technology（伊朗科学技术大学）；Iran University of Medical Sciences（伊朗医科大学）；Shahid Beheshti University of Medical Sciences（沙希德贝赫什提医科大学）；Islamic Azad University（伊斯兰阿扎德大学）；Alborz Vocational Technical University（厄尔布尔士职业技术大学）
Business as Rulesual: A Benchmark and Framework for Business Rule Flow Modeling with LLMs
Zhejiang University of Technology（浙江工业大学）；Zhejiang Key Laboratory of Visual Information Intelligent Processing（浙江省视觉信息智能处理重点实验室）；University of Aberdeen（阿伯丁大学）；University of Birmingham（伯明翰大学）
Recent Advances in Data-Driven Business Process Management
University of Bayreuth（拜罗伊特大学）；FIM Research Center（FIM研究中心）；University of Applied Sciences Augsburg（奥格斯堡应用技术大学）；Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）；Friedrich-Alexander University Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University of Regensburg（雷根斯堡大学）
AI-Augmented Business Process Management Systems: A Research Manifesto
University of Tartu（塔尔图大学）；Apromore；IBM Research - Haifa（IBM海法研究院）；Sapienza University of Rome（罗马第一大学（萨皮恩扎大学））；Free University of Bozen-Bolzano（博岑-波尔扎诺自由大学）；University of Mannheim（曼海姆大学）；Eindhoven University of Technology（埃因霍温理工大学）；Technion（以色列理工学院）
Empowering Business Transformation: The Positive Impact and Ethical Considerations of Generative AI in Software Product Management -- A Systematic Literature Review
Capitol Technology University（卡皮托尔理工大学）
本文通过系统性综述2016至2023年文献，分析生成式 AI 在软件产品管理中的应用、收益、局限与伦理问题，指出其可提升效率与体验但仍需解决可靠性挑战。
Agentic Business Process Management: A Research Manifesto
Free University of Bozen-Bolzano（博尔扎诺自由大学）；University of Oxford（牛津大学）；University of Tartu（塔尔图大学）；IBM Research（IBM研究院）；Umeå University（于默奥大学）；paluno (Ruhr Institute for Software Technology), University of Duisburg Essen（帕卢诺（鲁尔软件技术研究所），杜伊斯堡-埃森大学）；University of Ottawa（渥太华大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Saarland University（萨尔大学）；The University of Melbourne（墨尔本大学）；TUM School of Computation, Information, and Technology, TU Munich（慕尼黑工业大学计算、信息与技术学院）；RMIT University（皇家墨尔本理工大学）；Meta；University of St. Gallen（圣加仑大学）
Formal Foundations of Agentic Business Process Management
University of Oxford, UK（牛津大学，英国）；SAP, Germany（德国SAP公司）；Umeå University, Sweden（乌梅拉大学，瑞典）；University of Mannheim, Germany（曼海姆大学，德国）；Free University of Bozen-Bolzano, Italy（博兹纳自由大学，意大利）
Business and Regulatory Responses to Artificial Intelligence: Dynamic Regulation, Innovation Ecosystems and the Strategic Management of Disruptive Technology
Risk Analysis in Customer Relationship Management via Quantile Region Convolutional Neural Network-Long Short-Term Memory and Cross-Attention Mechanism
Longyan University（龙岩学院）；National Central University（中央大学）；University of Portsmouth（朴茨茅斯大学）；Institute of Semiconductors, Chinese Academy of Sciences（中国科学院半导体研究所）；Northeastern University（东北大学）
