AI-driven enterprise process automation

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.