用对象中心流程挖掘让AI真正落地工业流程
No AI Without PI! Object-Centric Process Mining as the Enabler for Generative, Predictive, and Prescriptive Artificial Intelligence
- 以对象为中心的流程挖掘打通数据与业务流程的连接
- 实现生成、预测、决策型AI在真实流程中的协同运作
- 适合希望提升流程智能的企业与研究者
人工智能（AI）正在改变工作方式、商业运营和研究范式，但在工业场景中，由于聚焦端到端运营流程，应用仍面临挑战。本文探讨生成式、预测性与决策型AI在诊断和优化流程时的瓶颈，指出必须依托对象中心流程挖掘（OCPM）来实现落地。流程数据具有组织特异性且高度动态，不同于文本数据。OCPM作为数据与流程之间的关键桥梁，使多种类型的AI成为可能。我们提出‘流程智能’（PI）概念，涵盖以流程为中心的数据驱动技术，可处理多种对象与事件类型，支持组织环境下的AI应用。本文阐明了为何流程智能是实现高效流程改进的必要基础，并揭示了将OCPM与生成、预测及决策型AI结合的关键机遇。
原文摘要 · Abstract (English)
The uptake of Artificial Intelligence (AI) impacts the way we work, interact, do business, and conduct research. However, organizations struggle to apply AI successfully in industrial settings where the focus is on end-to-end operational processes. Here, we consider generative, predictive, and prescriptive AI and elaborate on the challenges of diagnosing and improving such processes. We show that AI needs to be grounded using Object-Centric Process Mining (OCPM). Process-related data are structured and organization-specific and, unlike text, processes are often highly dynamic. OCPM is the missing link connecting data and processes and enables different forms of AI. We use the term Process Intelligence (PI) to refer to the amalgamation of process-centric data-driven techniques able to deal with a variety of object and event types, enabling AI in an organizational context. This paper explains why AI requires PI to improve operational processes and highlights opportunities for successfully combining OCPM and generative, predictive, and prescriptive AI.
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