Documenting SME Processes with Conversational AI: From Tacit Knowledge to BPMN
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arXiv
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| Format: | Preprint |
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2025
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| author | Radhakrishnan, Unnikrishnan |
| author_facet | Radhakrishnan, Unnikrishnan |
| contents | Small and medium-sized enterprises (SMEs) still depend heavily on tacit, experience-based know-how that rarely makes its way into formal documentation. This paper introduces a large-language-model (LLM)-driven conversational assistant that captures such knowledge on the shop floor and converts it incrementally and interactively into standards-compliant Business Process Model and Notation (BPMN) 2.0 diagrams. Powered by Gemini 2.5 Pro and delivered through a lightweight Gradio front-end with client-side bpmn-js visualisation, the assistant conducts an interview-style dialogue: it elicits process details, supports clarifying dialogue and on-demand analysis, and renders live diagrams that users can refine in real time. A proof-of-concept evaluation in an equipment-maintenance scenario shows that the chatbot produced an accurate "AS-IS" model, flagged issues via on-diagram annotations, and generated an improved "TO-BE" variant, all within about 12-minutes, while keeping API costs within an SME-friendly budget. The study analyses latency sources, model-selection trade-offs, and the challenges of enforcing strict XML schemas, then outlines a roadmap toward agentic and multimodal deployments. The results demonstrate that conversational LLMs can potentially be used to lower the skill and cost barriers to rigorous process documentation, helping SMEs preserve institutional knowledge, enhance operational transparency, and accelerate continuous-improvement efforts. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_05122 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Documenting SME Processes with Conversational AI: From Tacit Knowledge to BPMN Radhakrishnan, Unnikrishnan Artificial Intelligence H.4.1; I.2.7; H.5.2 Small and medium-sized enterprises (SMEs) still depend heavily on tacit, experience-based know-how that rarely makes its way into formal documentation. This paper introduces a large-language-model (LLM)-driven conversational assistant that captures such knowledge on the shop floor and converts it incrementally and interactively into standards-compliant Business Process Model and Notation (BPMN) 2.0 diagrams. Powered by Gemini 2.5 Pro and delivered through a lightweight Gradio front-end with client-side bpmn-js visualisation, the assistant conducts an interview-style dialogue: it elicits process details, supports clarifying dialogue and on-demand analysis, and renders live diagrams that users can refine in real time. A proof-of-concept evaluation in an equipment-maintenance scenario shows that the chatbot produced an accurate "AS-IS" model, flagged issues via on-diagram annotations, and generated an improved "TO-BE" variant, all within about 12-minutes, while keeping API costs within an SME-friendly budget. The study analyses latency sources, model-selection trade-offs, and the challenges of enforcing strict XML schemas, then outlines a roadmap toward agentic and multimodal deployments. The results demonstrate that conversational LLMs can potentially be used to lower the skill and cost barriers to rigorous process documentation, helping SMEs preserve institutional knowledge, enhance operational transparency, and accelerate continuous-improvement efforts. |
| title | Documenting SME Processes with Conversational AI: From Tacit Knowledge to BPMN |
| topic | Artificial Intelligence H.4.1; I.2.7; H.5.2 |
| url | https://arxiv.org/abs/2512.05122 |