TL;DR
This work offers project stakeholders a standards-aligned perspective on where NTD tends to appear in AI-enabled systems, highlighting processes that may systematically contribute to the accumulation of NTD.
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...
The article identifies three results: master data defects move through reporting and AI pipelines, S/4HANA transformations expose tolerated legacy errors, and AI readiness requires governed data quality gates before model deployment.
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.
A process-centric reference architecture designed for technical implementability, traceability, auditability, and human-supervised enterprise-scale GAI adoption is contributed.
This study provides a practical and governance-aligned model that shows how an LGU can use RPA not only to make processes faster but also to connect legacy systems with new national data standards.
A conceptual framework that connects traditional project governance, agile execution and artificial intelligence (AI)-supported decision-making within one operating model is developed and argues that AI creates value only when its outputs are connected to an existing decision process at the point where action is still...
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