AI决策检查点用于AI增强的业务流程管理：框架与教育实例化
AI-Decision Checkpoints for AI-Augmented Business Process Management: Framework and Educational Instantiation
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中文总结 AI 辅助
本文提出AI决策检查点框架，通过教学案例实例化，帮助流程开发者将AI作为一等设计元素，区分任务级自动化与流程级价值。
中文摘要 AI 辅助
大型语言模型（LLMs）、检索增强生成（RAG）和AI智能体日益嵌入到运营业务流程中。然而，业务流程管理（BPM）的课程和框架在很大程度上仍将人工智能（AI）视为一种附加技术，使得毕业生（作为潜在的未来流程开发者）未能准备好将AI视为端到端流程的一等设计元素进行推理。本文通过提出“AI决策检查点”来弥补这一差距：这些是流程开发轨迹中的明确时刻，流程开发者在此识别AI候选子流程，评估对时间、成本、质量和灵活性的预期影响，考虑法律和组织约束，并记录关于采用、限制或拒绝特定AI组件的理性决策。这些检查点通过一个虚构的客户入职流程作为《BPM教学案例》进行实例化，嵌入在一个涵盖六个模块的生命周期驱动框架中，该框架结合了流程建模、仿真、与AI智能体的工作流执行以及流程挖掘，每个模块的输出作为下一个模块的输入。一项初步的形成性反思基于讲师观察、提交的工件以及从学习管理系统日志中发现的流程地图。这些探索性观察表明，该方法支持了任务级自动化与流程级价值之间的更清晰区分。
英文摘要
Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents are increasingly embedded in operational business processes. Yet Business Process Management (BPM) curricula and frameworks still largely treat artificial intelligence (AI) as an add-on technology, leaving graduates (as potential future process developers) unprepared to reason about AI as a first-class design element of end-to-end processes. This paper addresses that gap by proposing \emph{AI-decision checkpoints}: explicit moments in a process development trajectory where process developers identify AI-candidate sub-processes, assess expected effects on time, cost, quality, and flexibility, consider legal and organisational constraints, and document a reasoned decision to adopt, constrain, or reject specific AI components. The checkpoints are instantiated through a fictitious customer onboarding process as a \textit{BPM Teaching Case}, embedded in a lifecycle-driven framework spanning six modules that combine process modeling, simulation, workflow execution with AI agents, and process mining, with each module's output serving as the next module's input. A preliminary formative reflection draws on instructor observations, submitted artifacts, and discovered process maps from learning-management-system logs. These exploratory observations suggest that the approach supported clearer distinctions between task-level automation and process-level value.
发表机构
- Stockholm University（斯德哥尔摩大学）
机构由 AI 辅助整理，请以论文原文为准。
