Integrating LLM in Business Process Management: A Conceptual Framework for Augmenting the Process Lifecycle

Abstract

Large language models (LLMs) are increasingly relevant to Business Process Management (BPM), particularly when process knowledge is dispersed across documents, conversations, and other unstructured sources. Their probabilistic outputs, however, raise questions about validation, traceability, and accountability. This paper develops a lifecycle-based conceptual framework for allocating and governing LLM use across the six stages of the BPM lifecycle. The framework separates generative interpretation from formal, empirical, and expert validation. It comprises five interdependent layers and six operational principles, implemented through a stage-risk-validation matrix, a principle-intensity map, and four evaluation dimensions. Governance requirements increase as outputs approach live execution or decisions that are difficult to reverse, with controls aligned with the NIST AI Risk Management Framework, the EU AI Act, and the GDPR. A customer complaint-handling scenario demonstrates how the framework can be applied. An illustrative stress test using the BPI Challenge 2017 event log and ten independent LLM generations instantiates the validation layer under information-asymmetric conditions. Although all generated models were structurally valid, the event log revealed incomplete activity coverage and control-flow mismatch. This illustrates the value of an external referent but does not establish comparative performance or a general difference in error detectability between LLM-generated and process-mining artefacts. The framework therefore positions LLMs as tools for turning unstructured information into preliminary process knowledge, while established BPM methods and human expertise remain responsible for validating consequential outputs.