Paper Number
ECIS2025-1309
Paper Type
CRP
Abstract
Process mining adoption and its value realization in organizations is often inhibited by limited transferability of insights into actionable results: Users require extensive contextual knowledge and sophisticated skillsets to derive insights from process mining analyses. Novel generative artificial intelligence (AI) capabilities can aid users in conducting such analyses through natural language queries, thereby offering the potential to add context, assist analysis, and transform insights into actions. Hence, we conducted a design science research study to develop an artifact that facilitates AI-assisted process mining analysis. We evaluated the artifact by interviewing 14 process mining experts to determine its suitability, usefulness, and impact. We find that the artifact enables novel use cases for new and existing user groups by offering context-sensitive process mining analysis support. Our work demonstrates how generative AI can augment process mining in organizations and highlights its potential for automatically integrating contextual knowledge into process mining analyses.
Recommended Citation
Brützke, Paul; Killewald, Robin; Franzoi, Sandro; and vom Brocke, Jan, "AI-Assisted Process Mining for Context-Sensitive Analysis Support" (2025). ECIS 2025 Proceedings. 7.
https://aisel.aisnet.org/ecis2025/bpm/bpm/7
AI-Assisted Process Mining for Context-Sensitive Analysis Support
Process mining adoption and its value realization in organizations is often inhibited by limited transferability of insights into actionable results: Users require extensive contextual knowledge and sophisticated skillsets to derive insights from process mining analyses. Novel generative artificial intelligence (AI) capabilities can aid users in conducting such analyses through natural language queries, thereby offering the potential to add context, assist analysis, and transform insights into actions. Hence, we conducted a design science research study to develop an artifact that facilitates AI-assisted process mining analysis. We evaluated the artifact by interviewing 14 process mining experts to determine its suitability, usefulness, and impact. We find that the artifact enables novel use cases for new and existing user groups by offering context-sensitive process mining analysis support. Our work demonstrates how generative AI can augment process mining in organizations and highlights its potential for automatically integrating contextual knowledge into process mining analyses.
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