AI-Decision Checkpoints for AI-Augmented Business Process Management: Framework and Educational Instantiation

2026-10-05

AI Summary

This study addresses the deficiency in Business Process Management (BPM) education, where overlooking AI’s central design role leaves students ill-equipped for process-level AI decision-making. To bridge this gap, we propose the “AI Decision Checkpoint” framework, which treats AI as a first-class citizen and explicitly distinguishes task-level automation from process-level value creation. By integrating large language models, retrieval-augmented generation, intelligent agents, and process modeling and mining techniques, the framework establishes a six-module closed-loop pedagogical system. Furthermore, it embeds AI evaluation and decision-making stages into a customer onboarding case study. Preliminary validation demonstrates that this approach effectively enhances students’ clarity in AI-integrated decision-making and their comprehension of process-level value creation.

Abstract

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.

Problem

• Business Process Management
• Artificial Intelligence
• Curriculum
• Large Language Models
• Process Development

Innovation

• AI-Decision Checkpoints
• Business Process Management
• Large Language Models
• AI Agents
• Process Mining