Subject: Information System Science
Title: LLM-Powered Business Process Modelling in Small and Medium Enterprises: Benefits,
Success Factors and Implementation Challenges
Abstract:
Small and Medium Enterprises (SMEs) face significant barriers in adopting traditional Business Process
Modelling (BPM) due to resource constraints, expertise requirements, and complex notation systems. Large
Language Models (LLMs) offer potential solutions by generating process models from natural language
descriptions, yet empirical evidence of their effectiveness in real SME contexts remains limited. This research
investigates the benefits, success factors, and failure factors when implementing LLM-powered BPM in SMEs.
Existing literature demonstrates clear BPM organizational benefits but identifies expertise requirements and
resource constraints as primary SME adoption barriers (Papademetriou & Karras, 2017; Viegas & Costa,
2022). Recent AI-powered BPM research shows technical feasibility for generating BPMN-compliant models
from textual descriptions (Grohs et al., 2023; Kourani et al., 2024) but lacks empirical investigation of
organizational adoption factors in real business contexts. The study employs the Technology-Organization-
Environment (TOE) framework to analyse adoption factors, combined with established BPM quality
assessment frameworks (SEQUAL for multi-dimensional quality evaluation, 7PMG for objective diagram
assessment) to create a comprehensive evaluation approach for AI-generated process models. A qualitative
multiple case study approach examines three French SMEs across different industries (IT consulting,
manufacturing, perfume production) with varying digital maturity levels. Using GPT-4 mini, BPMN 2.0
compliant process models were generated from organizational documentation and evaluated using the
established quality frameworks. Semi-structured interviews with key stakeholders captured organizational
perceptions, adoption challenges, and value recognition patterns. The technical assessment revealed consistent
strengths in activity labelling and gateway selection, alongside universal weaknesses including multiple
start/end event violations and excessive element proliferation. Stakeholder evaluation demonstrated a
fundamental dichotomy between communication effectiveness and operational completeness. While all
participants recognized value for external communication and training purposes, semantic gaps rendered
models insufficient for internal process management. The most significant finding involved universal
requirements for human verification despite AI accessibility benefits, creating capability demands that
potentially exceeded SME resources. The research contributes a Multi-Factor Alignment Framework
organizing success factors across technical, organizational, and environmental dimensions. The study
concludes that LLM-powered BPM represents a transformation from operational tool to communication
medium, requiring hybrid approaches leveraging AI for communication while maintaining traditional methods
for operational requirements.
Key words: Business Process Modelling, Large Language Models, SME Digital Transformation,
AI Adoption, Process Management, BPMN.