Aug 2026· Universal Library of Engineering Technology· 0 citations· 10 references
TL;DR
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.
Abstract
The study examines master data quality as a constraint on artificial intelligence integration in large enterprise systems. Corporate AI projects often begin with model choice, automation scenarios, and platform design, while weak Business Partner and Material Master records remain inside ERP, SAP S/4HANA, BW, and analytical layers. The article shifts attention to that data foundation. Its aim is to explain how duplicated, incomplete, outdated, and poorly mapped master data restricts reliable AI use during ERP modernization. The review uses ten sources published from 2021 to 2025 and combines master data management, governance maturity, data-centric AI, enterprise AI adoption, and SAP migration literature. Comparative source analysis, conceptual synthesis, and typologization support the argument. 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. The proposed logic supports ERP, data governance, and AI implementation teams.
This review critically evaluates peer-reviewed journal literature published in the last decade related to AI capability, big data analytics capability, data governance, machine learning operations, digital transformation, and organizational value creation.
Saurabh Mishra· International Research Journ...· 0 citations
Digital transformation has become essential for organizations competing in a data-driven economy, driven largely by the integration of Artificial Intelligence (AI) and advanced data platforms. These technologies enable smart automation, predictive analytics, and real-time decision-making. This paper presents digital transformation as a multi-dimensional process involving organizational culture, business processes, and technological infrastructure, with AI-powered data platforms at its core. It reviews key technological developments prior to 2019, including cloud computing, big data frameworks like Hadoop and Spark, and early enterprise AI adoption. Current research emphasizes the importance of data governance, scalability, and interoperability. The paper proposes a structured implementation approach covering data collection, preprocessing, model development, deployment, and continuous optimization, supported by a flow-based architecture. Findings show that organizations adopting AI-enabled platforms achieve up to 45% improvement in operational efficiency and a 35% reduction in decision-making delays. The study concludes by stressing the need to align AI initiatives with business goals and highlights future directions such as autonomous systems and ethical AI practices.
S. Rahman· International Journal of Art...· 0 citations
Engineering data migration and Product Lifecycle Management (PLM) integration are major challenges in mergers and acquisitions (M&A), particularly when organizations consolidate heterogeneous CAD systems, metadata, product structures, and engineering practices. This paper presents a scalable, validation-centered framework developed through a qualitative case study of a global CAD-to-PLM migration program. Its six phases cover data assessment, preparation, transformation, phased migration, multi-level validation, and deployment governance. Project artifacts and implementation observations indicate that the framework supported migration control, cross-functional coordination, lifecycle traceability, and deployment readiness. By treating migration as a governed engineering transformation rather than a one-time conversion, the framework offers a repeatable foundation for future integration programs and further validation through quantitative measures, automation, analytics, and AI-assisted controls.
Ramkumar Gandhi· World Journal of Advanced Re...· 0 citations
The proposed framework offers a real-world action plan for sustainable AI transformation and a theoretical understanding of the phenomenon of AI transformation by offering a structured, risk-mitigated pathway toward high-maturity, AI-enabled enterprise operations.
M. Abuhaimed· Journal of Intelligent Decis...· 0 citations
The integration of Enterprise Resource Planning (ERP) systems with Artificial Intelligence (AI) and data analytics-based predictive maintenance is a pillar of industrial digital transformation in the industry 4.0 and 5.0 era. However, a comprehensive understanding of integration patterns, AI methods, benefits, and challenges remains scattered across the literature. This study conducts a Systematic Literature Review (SLR) using the PRISMA protocol to map the current state of ERP integration with AI-based predictive maintenance. A search was performed on the Scopus database, yielding 59 initial articles that were reduced to 10 final open-access articles published between 2024 and 2026. The results show that the dominant AI methods are Long Short-Term Memory (LSTM) and ensemble learning (XGBoost, Random Forest), often combined with Digital Twin technology. ERP integration patterns range from decision support to full bidirectional integration via OPC-UA and REST API protocols. Key benefits include up to 40% improvement in mean time to failure (MTTF), a 30% reduction in maintenance costs, and a return on investment (ROI) of 42.5%. The main challenges include data quality, legacy system interoperability, cybersecurity, and limited multi-site validation.
