Aug 2026· International Research Journal on Advanced Engineering Hub (IRJAEH)· Vol 4, pp. 5053-5061· 0 citations
TL;DR
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.
Abstract
The shift towards enterprise AI transformation driven by modern data platforms has emerged as a has become a major research and practical challenge for organizations seeking to create value from AI not just relying on standalone algorithms, but on managed, scalable, and actionable data ecosystems. This review critically evaluates peer-reviewed journal literature published in the last decade (2015-2026) related to AI capability, big data analytics capability, data governance, machine learning operations, digital transformation, and organizational value creation. The literature surveyed shows that data platforms play a role in enterprise AI transformation, by providing integrated data access, scalable analytics capabilities, establishing data governance, managing the data model lifecycle, and connecting technical architecture and enterprise change. There is, however, some empirical evidence that is not equally consistent. While previous research clearly shows correlations between analytics capability and performance, the limited number of journal articles that focus on production AI systems, platform modularity, lineage, feature management, monitoring and cross-functional operating models as coupled transformation mechanisms suggests an opportunity for further exploration. Further longitudinal studies are needed at both architectural and organizational levels. Enterprise AI transformation using modern data platforms is more of a socio-technical capability development exercise than a mere technological migration.
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
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.
Baris Ozcan· Universal Library of Enginee...· 0 citations
The rapid evolution of enterprise architecture necessitates innovative approaches to manage the increasing complexities of digital ecosystems. This paper explores the transformative potential of Artificial Intelligence (AI)-driven cloud solutions in modernizing enterprise architecture, with a focus on integrating DevOps and DataOps methodologies to achieve scalability. AI-powered tools and frameworks in cloud computing offer unparalleled scalability, operational efficiency, and real-time adaptability, enabling enterprises to remain competitive in a data-driven economy. By combining DevOps' focus on streamlining software development and operations with DataOps' emphasis on agile and automated data pipeline management, organizations can optimize workflow automation, accelerate deployment cycles, and enhance decision-making processes. AI further augments this synergy by facilitating predictive analytics, anomaly detection, and intelligent resource allocation, which are critical for achieving scalability and reliability in dynamic business environments. Case studies highlight the successful application of these technologies across various industries, showcasing measurable improvements in performance and cost efficiency. The paper also addresses challenges in adopting AI-driven cloud solutions, including data privacy, compliance, and skill gaps, offering actionable recommendations for mitigating these obstacles. Emphasis is placed on the need for collaborative strategies between IT and business teams to maximize the potential of integrated DevOps and DataOps frameworks.
Fatou Diop· International Journal of Art...· 0 citations
Modern enterprises face increasing challenges in managing data infrastructure for AI-driven analytics while maintaining governance, compliance, and cost efficiency. This paper proposes a Lean AI Data Warehouse (LAIDW) architecture that integrates the Model Context Protocol (MCP) as a unifying interface layer between AI agents and heterogeneous data sources. The proposed framework reduces redundant data movement by enabling AI models to query data sources directly through standardized MCP connectors, reducing intermediate storage overhead and improving data freshness. A complementary Data Governance Framework (DGF) enforces data quality, lineage tracking, access control, and auditability across the MCP-connected ecosystem. Comparative analysis against conventional ETL-based architectures indicates potential structural advantages in storage efficiency (estimated 60-70% reduction), query freshness (near real-time versus batch-delayed), and governance coverage (automated versus manual lineage capture), while recognizing that end-to-end latency and throughput depend on source-system performance, network conditions, query complexity, and selective materialization for complex multi-source analytical workloads. The proposed LAIDW-MCP-DGF architecture offers a scalable, maintainable approach for organizations seeking to operationalize AI workflows with minimal data infrastructure overhead.
Monsinee Keeratikrainon· 2026 7th International Confe...· 0 citations
Artificial Intelligence (AI) is transforming modern enterprises by enabling intelligent automation, predictive analytics, generative AI, autonomous decision-making, digital assistants, and real-time optimization across business processes. As organizations increasingly deploy Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), intelligent copilots, machine learning, and AI-driven business applications, governance has become one of the most critical success factors for sustainable enterprise AI adoption. While AI creates unprecedented opportunities for innovation, productivity, customer engagement, and operational excellence, it simultaneously introduces significant risks including model bias, hallucinations, explainability limitations, privacy concerns, cybersecurity threats, regulatory compliance challenges, intellectual property exposure, model drift, ethical considerations, and uncontrolled autonomous decision-making. Traditional enterprise governance models are insufficient because they were designed for conventional software systems rather than continuously learning intelligent systems operating across hybrid cloud environments. Organizations therefore require an Enterprise AI Architecture Governance and Risk Framework that integrates Enterprise Architecture, AI lifecycle management, cybersecurity, Business Knowledge, data governance, compliance, risk management, operational monitoring, and Responsible AI into a unified governance capability. This paper proposes a comprehensive governance framework built upon SAP Enterprise Architecture Framework (SAP EAF), TOGAF®, SAP Business Technology Platform (SAP BTP), SAP AI Foundation, SAP AI Core, SAP AI Launchpad, SAP Joule, SAP Business Data Cloud, SAP Datasphere, SAP HANA Cloud, SAP Integration Suite, SAP Cloud Identity Services, SAP Cloud ALM, SAP Build, SAP LeanIX, and SAP Signavio. The framework establishes governance across strategy, architecture, data, AI models, infrastructure, security, operations, compliance, and enterprise risk while embedding continuous monitoring, explainability, lifecycle governance, and policy enforcement. The proposed architecture enables organizations to build secure, scalable, transparent, explainable, compliant, resilient, and trustworthy AI ecosystems that accelerate innovation while minimizing enterprise risk and supporting long-term digital transformation.
Sanjeeve Kumar Gajadi· International Journal For Mu...· 0 citations
Business Intelligence and Analytics has transformed from static historical reporting into a dynamic, real-time discipline at the intersection of data management, machine learning, and decision science. This paper presents a comprehensive survey of the evolving BIA landscape, examining both back-end architectural innovations and front-end analytical capabilities. We trace the progression from traditional three-tier BI systems to next-generation paradigms including operational BI for real-time insights, situational BI for integrating external streaming data, and self-service BI for democratized analytics. The survey reviews enabling database technologies such as in-memory databases and hybrid OLTP/OLAP systems, and addresses critical data governance challenges arising from heterogeneous data sources and increased user participation. On the analytics front, we examine machine learning applications for time series forecasting across financial, sales, and healthcare domains, and highlight integrative approaches, particularly multiple kernel learning for combining disparate data sources to improve predictive accuracy. A healthcare case study in intensive care unit monitoring demonstrates how next-generation BIA can integrate real-time sensor data with traditional records for life-critical decision support. This survey provides researchers and practitioners with a holistic framework for understanding the converging technologies shaping the future of data-driven enterprise intelligence.
Arunkumar Yadava· International Journal of Ada...· 0 citations
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