This paper provides the architectural foundation and experimental protocol for future quantitative benchmarking, and proposes a five-pillar conceptual framework and establishes a qualitative baseline through domain expert interviews.
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.
A Digital Process Twin (DPT) is a digital twin (DT) of a business process designed to monitor, adapt, and optimize organizational workflows to maintain the efficiency, adaptability, and competitiveness of businesses. The benefits of DPTs are many: outcome prediction, data-driven insights for optimization, and streamlin...
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A critical abstraction gap between automated and manual tasks is bridges by redefining the operational perspective, introducing a novel classification of process entities, and proposing a framework for holistic support.
Leo Poss, S. Schönig· Information Systems and E-Bu...· 0 citations
The convergence of AI and sustainability has given rise to AI-driven sustainable business environments, where intelligent technologies are leveraged to optimize resource utilization, reduce carbon footprints, enhance operational efficiency, and promote long-term value creation.
M. Sathyanarayana· International Journal of Res...· 0 citations
Capital projects in the energy sector continue to experience systemic cost overruns and schedule delays. While AI-driven predictive analytics promise improved foresight, their effectiveness is often limited by fragmented data landscapes, inconsistent reporting practices, and weak governance. This paper introduces a d...
Fuad Shalan, Sarafudheen M. Tharayil, Rawabi Aldossary et al.· GOTECH· 0 citations
A lifecycle-based conceptual framework for allocating and governing LLM use across the six stages of the BPM lifecycle, which positions LLMs as tools for turning unstructured information into preliminary process knowledge, while established BPM methods and human expertise remain responsible for validating consequential...
