Izvorni znanstveni članak
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
orcid.org/0000-0002-0190-6547
; Faculty of Engineering and Management, Technische Hochschule Ingolstadt, Ingolstadt, Germany
* Dopisni autor.
Sažetak
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.
Ključne riječi
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
Datum izdavanja:
4.8.2026.
Posjeta: 0 *