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

arXiv: https://arxiv.org/abs/2610.06207 · HTML: https://mlsanity.com/paper/2610.06207/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Placed in

- Retrieval-augmented generation

## Builds on (most cited first, 2 of 2)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [AI-Enhanced Business Process Automation: A Case Study in the Insurance Domain Using Object-Centric Process Mining](https://mlsanity.com/paper/2504.17295/) (2025)

## Abstract (arXiv)

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 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 textitBPM 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.