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Breakthroughs Information Technology
Published by Generate Digital Publishing
ISSN : -     EISSN : 31098495     DOI : 10.70764/gdpu-bit
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering
BIT is an open-access journal which means that all content is freely available at no cost to the user or the institution. The scope of the journal includes empirical and theoretical articles relating to all aspects of information science, engineering and technology. It focuses on the biggest breakthroughs in the technology arena, with particular concentration on accelerating principles, concepts and applications, informatics and cultural informatics, high-performance computing, and reports on the continuous evolution of information science and technology as a whole.
Arjuna Subject : Umum - Umum
Articles 12 Documents
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Distributed Systems Analysis for Artificial Intelligence in Cloud Computing: A Comprehensive Review of AI-Based Applications and Services Muhammad Ubaidurrohman
Breakthroughs Information Technology Vol 2 No 1 (2026)
Publisher : Generate Digital Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-bit.2026.2(1)-01

Abstract

Objective: This study aims to examine the development and application of Artificial Intelligence (AI) in distributed cloud environments and identify emerging technologies, research trends, benefits, challenges, and future directions of AI-driven cloud services.Research Design & Methods: A literature review was conducted using the Scopus database. The search focused on publications related to Artificial Intelligence, Distributed Cloud, Cloud Environment, and Applications and Services.Findings: The results indicate a growing research interest in AI-enabled distributed cloud environments during the last five years. The literature reveals three major technological pillars: Optimization-Based AI, Machine Learning-Based AI, and Deep Learning-Based AI. AI has been widely adopted for resource management, workload scheduling, anomaly detection, cybersecurity, and autonomous cloud operations. Emerging trends include cloud-native architectures, multi-cloud systems, edge-cloud computing, Explainable AI (XAI), federated learning, and multi-agent reinforcement learning. However, challenges remain regarding infrastructure heterogeneity, scalability, security, explainability, and the generalization capability of AI models across diverse cloud environments.Implications: The findings highlight the importance of integrating intelligent and adaptive AI mechanisms to improve efficiency, security, scalability, and operational automation in distributed cloud infrastructures.Contribution & Value Added: This study provides a comprehensive synthesis of recent AI developments in distributed cloud environments and proposes a structured classification of AI approaches. The results offer insights into current research gaps and future opportunities for developing autonomous, trustworthy, and sustainable AI-driven cloud ecosystems.
AI-Driven Enterprise Systems: A Framework for Digital Transformation, Intelligent Integration, and Future Business Operations Madhulika Chaudhuri; Manar Shatara
Breakthroughs Information Technology Vol 2 No 1 (2026)
Publisher : Generate Digital Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70764/gdpu-bit.2026.2(1)-02

Abstract

Objective: This study aims to develop a comprehensive conceptual framework for Artificial Intelligence-Driven Enterprise Systems (AI-ES) by synthesizing recent literature on the integration of Artificial Intelligence (AI) within Enterprise Systems and examining its role in enabling intelligent business operations and digital transformation. Research Design & Methods: A Systematic Literature Review (SLR) was conducted following a structured review protocol. The selected studies were analyzed using thematic synthesis and classified into four interconnected analytical layers: AI Technology Layer, Intelligent Enterprise System Layer, Business Intelligence Layer, and Business Outcome Layer. Findings: The review indicates that AI technologies including Machine Learning, Deep Learning, Natural Language Processing, Large Language Models, Predictive Analytics, IoT, and cloud computing serve as the technological foundation for intelligent enterprise systems. Their integration into ERP, CRM, PLM, and enterprise platforms enhances intelligent process automation, predictive decision support, supply chain optimization, and real-time data integration. These capabilities collectively strengthen business intelligence by improving analytics, forecasting, operational visibility, and adaptive decision-making, ultimately leading to operational excellence, organizational agility, innovation capability, competitive advantage, and sustainable business performance. Implications: The proposed framework provides practical guidance for organizations seeking to implement AI-enabled Enterprise Systems by emphasizing the importance of technological readiness, enterprise integration, data governance, interoperability, and organizational capabilities as prerequisites for successful digital transformation. Contribution & Value Added: This study contributes by proposing a holistic AI-Driven Enterprise Systems Framework that integrates AI technologies, intelligent enterprise platforms, business intelligence capabilities, and business outcomes into a unified conceptual model. The framework extends existing Enterprise Systems literature by offering a structured foundation for future empirical research and practical AI adoption strategies in digital enterprises.

Page 2 of 2 | Total Record : 12

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