Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle.
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）
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。
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）
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）
