Original scientific paper
https://doi.org/10.30765/er.3428
AI-driven enterprise process automation: evaluating automation architectures for knowledge-intensive processes
Ume Rubab
orcid.org/0009-0005-4949-4165
; Faculty of Engineering and Management, Technische Hochschule Ingolstadt, Ingolstadt, Germany
*
Bernhard Axmann
; Faculty of Engineering and Management, Technische Hochschule Ingolstadt, Ingolstadt, Germany
* Corresponding author.
Abstract
Digital transformation in Knowledge-intensive Processes is shifting toward Agentic Business Process Management to overcome challenges posed by unstructured data complexity. This research-in-progress evaluates the structural tension between operational efficiency and compliance in Knowledge-intensive Processes automation by examining Generative AI extraction, database architectures, and performance trade-offs between Python and Low-Code/No-Code platforms under AI governance frameworks. Using Design Science Research, this study proposes a five-pillar conceptual framework and establishes a qualitative baseline through domain expert interviews. Initial findings reveal structural fragmentation as core barriers, driving a 20 to 40% waste premium. This paper provides the architectural foundation and experimental protocol for future quantitative benchmarking.
Keywords
agentic Business Process Management (BPM); Knowledge-Intensive Processes (KiP); Low-Code/No-Code (LCNC) vs. python orchestration; generative AI extraction; AI governance
Hrčak ID:
350092
URI
Publication date:
4.8.2026.
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