2026· International Journal of Emerging Trends in Computer Science and Information Technology· Vol 7, pp. 337-342· 0 citations
TL;DR
The study concludes that enterprise AI-driven data engineering represents a foundational element of next-generation digital transformation strategies and organizations adopting intelligent data platforms are better positioned to leverage data assets, support real-time analytics, and achieve sustainable competitive advantage in increasingly data-centric business environments.
Abstract
The rapid proliferation of digital technologies, cloud computing, Internet of Things (IoT), big data ecosystems, and artificial intelligence (AI) has fundamentally transformed how organizations manage, process, and derive value from data. Traditional data engineering frameworks, designed primarily for structured and moderate-volume datasets, are increasingly incapable of addressing the complexity, velocity, variety, and scalability requirements of modern enterprises. Consequently, organizations are transitioning toward Enterprise AI-Driven Data Engineering (EAIDE), an advanced paradigm that integrates artificial intelligence, machine learning, automation, and intelligent orchestration into data platform architectures. This study investigates the role of AI-driven data engineering in building intelligent, secure, and scalable enterprise data platforms. The research examines key architectural components, including automated data ingestion, intelligent data pipelines, metadata management, data governance, cybersecurity integration, cloud-native infrastructure, and AI-powered analytics. Furthermore, the study evaluates the operational benefits, security implications, and organizational challenges associated with implementing AI-enabled data engineering frameworks. A conceptual research methodology based on comparative analysis of existing enterprise architectures, cloud-based platforms, and AI-driven automation techniques is adopted. Findings indicate that AI-enhanced data engineering significantly improves data quality, operational efficiency, decision-making capabilities, predictive analytics performance, and platform scalability. However, challenges related to governance, model transparency, ethical AI deployment, and cybersecurity remain critical considerations. The study concludes that enterprise AI-driven data engineering represents a foundational element of next-generation digital transformation strategies. Organizations adopting intelligent data platforms are better positioned to leverage data assets, support real-time analytics, and achieve sustainable competitive advantage in increasingly data-centric business environments.
Modern enterprises generate massive volumes of data from cloud platforms, IoT devices, enterprise applications, social media, and AI systems, creating challenges in data integration, governance, scalability, security, and real-time analytics. Traditional data management approaches often struggle to handle these complex and distributed environments. This paper proposes an Autonomous Data Fabric (ADF) architecture that combines AI/ML, metadata-driven automation, knowledge graphs, intelligent orchestration, and policy-based governance to enable seamless, self-managing enterprise data ecosystems. The framework supports automated data discovery, semantic integration, adaptive workflows, continuous monitoring, and intelligent resource optimization while ensuring data quality, security, and compliance. Experimental results demonstrate that the proposed ADF significantly improves data integration efficiency, governance, analytics performance, operational cost, and decision-making compared to conventional systems. Its scalable and self-adaptive design supports hybrid cloud, multi-cloud, edge, and on-premises environments, making it a robust solution for enterprise digital transformation and next-generation intelligent data management.
Narendra Karmarkar· International Journal of Dat...· 0 citations
The findings advocate for the integration of AI-powered pipelines within ERP systems as a transformative approach to enable scalable, intelligent, and high-fidelity data processing, essential for next- generation enterprise software resilience and performance.
Yuvaraj Kavala· International Journal of Com...· 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
The rapid evolution of Property Technology (PropTech) has transformed enterprise real estate software from isolated operational applications into intelligent digital ecosystems that integrate cloud computing, artificial intelligence, enterprise software, and large-scale data platforms. Traditional property management systems were primarily designed to record transactions, automate administrative workflows, and support operational reporting. Contemporary organizations, however, increasingly require platforms capable of orchestrating intelligent decision-making across leasing, facility management, asset performance, sustainability, financial operations, customer engagement, and strategic portfolio management. This article argues that the future of PropTech should be understood through the perspective of enterprise architecture rather than software development alone. It proposes a strategic architectural framework that integrates business capabilities, cloud-native infrastructure, AI platform services, enterprise applications, governance mechanisms, and digital operations into a unified enterprise ecosystem. Unlike conventional software-centric approaches, the proposed framework emphasizes interoperability, architectural scalability, operational resilience, explainable artificial intelligence, and continuous organizational evolution. The study further demonstrates how artificial intelligence should function as an enterprise capability embedded throughout digital operations instead of existing as an isolated technological feature. Recent developments in enterprise architecture similarly emphasize that future competitive advantage will depend less on individual AI models and more on organizations' ability to integrate intelligence, cloud infrastructure, governance, and enterprise workflows into coherent operational ecosystems.
D. Tuncay· International Journal of Res...· 0 citations
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
Abstract
The rapid advancement of artificial intelligence (AI), cloud-native computing, software-defined networking, and intelligent automation is fundamentally transforming enterprise cloud ecosystems and redefining the principles of cloud network governance. Modern organizations increasingly operate complex hybrid and multi-cloud environments that integrate distributed infrastructures, cloud-native applications, enterprise data platforms, and adaptive cybersecurity mechanisms. Although existing governance frameworks provide valuable guidance for cloud management, cybersecurity, compliance, and enterprise architecture, they generally address these domains independently and offer limited support for integrated AI-driven governance, autonomous operations, enterprise data excellence, and adaptive cyber defense. This fragmentation creates significant challenges in achieving operational resilience, governance transparency, regulatory compliance, and intelligent decision-making, particularly within rapidly evolving digital ecosystems such as those envisioned under Saudi Arabia's Vision 2030.This study proposes a Next-Generation AI-Powered Cloud Network Governance Framework that integrates autonomous cloud operations, enterprise data excellence, adaptive cyber defense, AI governance, regulatory compliance, and intelligent decision support into a unified governance architecture. The research adopts a Design Science Research (DSR) methodology supported by a structured review of international standards, peer-reviewed literature, cloud governance frameworks, cybersecurity best practices, AI governance models, and Saudi digital transformation initiatives. The proposed framework is developed through the synthesis of governance principles derived from ISO/IEC standards, the NIST Artificial Intelligence Risk Management Framework, the NIST Cybersecurity Framework, Cloud Security Alliance guidance, cloud-native computing practices, and Saudi national strategies for data, artificial intelligence, and cybersecurity.The proposed architecture comprises six integrated governance dimensions: Strategic AI Governance, Autonomous Cloud Operations, Enterprise Data Excellence, Adaptive Cyber Defense, Intelligent Decision Support, and Continuous Governance Improvement. Together, these dimensions establish a holistic governance model that enables continuous operational monitoring, AI-assisted policy enforcement, trusted enterprise data management, predictive risk assessment, explainable decision-making, and adaptive cybersecurity across hybrid and multi-cloud environments.As a conceptual contribution, the proposed framework provides a comprehensive governance model for organizations seeking to modernize cloud governance while aligning intelligent operations with business objectives, regulatory obligations, and national digital transformation priorities. The framework offers practical guidance for government agencies, telecommunications operators, cloud service providers, financial institutions, healthcare organizations, and smart-city initiatives pursuing secure, resilient, scalable, and intelligent cloud ecosystems. Furthermore, it establishes a foundation for future empirical validation, industrial implementation, and quantitative assessment of AI-enabled cloud governance in next-generation digital infrastructures.
Corresponding Author: Walid Abdulfattah FararjehORCID: 0009-0006-3150-6225
Keywords: Artificial Intelligence; Cloud Network Governance; Autonomous Cloud Operations; Enterprise Data Excellence; AI Governance; Adaptive Cyber Defense; Cloud Security; Multi-Cloud Governance; Intelligent Decision Support; Digital Transformation; Saudi Vision 2030.
Walid Abdulfattah Fararjeh· Technium· 0 citations
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