Sami Syed
Glob Acad J Econ Buss; 2026, 8(3): 422-431
DOI : https://doi.org/10.36348/gajeb.2026.v08i03.023
Generative AI and intelligent automation are revolutionizing enterprise process transformation through the integration of language-based reasoning, prediction, workflow orchestration, robotic process automation and governance. In this review paper, the ways in which these technologies can support the objectives outlined in Saudi Arabia’s Vision 2030 are discussed through increased productivity, service improvement, operational robustness and knowledge economy development within government and corporate organizations. Through an analysis of the most recent research from 2020 to 2025, the paper compiles key findings from academic literature on large language models, retrieval augmented generation, process mining, low-code automation, analytics governance and responsible AI. Four stages of transformation include enterprise data and process infrastructure, generative AI reasoning, intelligent automation execution and governance. The paper emphasizes that while generative AI offers great potential for organizational transformation, its strategic value comes in governance-enabled integration into the entire process chain rather than individual projects or chatbots. The study provides insights into the role that generative AI and process automation can play in helping Saudi Arabia achieve its productivity and digital government ambitions as part of a broader knowledge economy. It further identifies risk areas including data quality, model hallucinations, privacy and security concerns, worker disempowerment, vendor lock-in and inadequate change management.
Mohammed Ayman Ahmed Alshaer
Glob Acad J Econ Buss; 2026, 8(3): 412-421
DOI : https://doi.org/10.36348/gajeb.2026.v08i03.022
Road and bridge infrastructure occupies a strategic position in Saudi Arabia's Vision 2030 because it links industrial zones, ports, pilgrimage corridors, logistics platforms, urban expansion and regional development. The policy question is no longer whether public infrastructure should be delivered quickly, but how delivery can build national capability without weakening safety, durability, competition or value for money. This review examines local content and supplier qualification strategies for Saudi road and bridge projects, with emphasis on how procurement rules, contractor classification, material sourcing, technical assurance and supplier development can be aligned. A structured narrative review was conducted using scholarly and institutional sources from 2020 to 2025. The analysis synthesizes evidence on local content policy, construction procurement, public project performance, digital procurement, contractor prequalification, quality systems, sustainability and transport-sector modernization. The paper proposes an integrated framework in which local content is treated as a performance dimension across planning, design, tendering, award, delivery and closeout, rather than as a late scoring preference. The findings show that effective localization depends on credible demand aggregation, transparent qualification gates, evidence-based supplier segmentation, enforceable contract metrics and targeted capability upgrading for Saudi firms. The review also warns that poorly designed localization may create cost inflation, schedule risk or nominal compliance. A publishable agenda is therefore advanced for road agencies, clients, consultants and contractors: local content must be linked to technical competence, certified production capacity, workforce development, maintenance performance and auditable data. The study contributes a practical and research-informed model for improving infrastructure delivery while expanding domestic value creation.
Mohammed Manek Hossain
Glob Acad J Econ Buss; 2026, 8(3): 403-411
DOI : https://doi.org/10.36348/gajeb.2026.v08i03.021
Purpose: To develop an AI decision support governance framework for the SAP-enabled operations of Saudi enterprises in line with the priorities laid out for digital transformation, operational efficiency, competitiveness, and value creation through data in the Saudi Vision 2030 program. Design/methodology/approach: The study applies a systematic narrative review approach to literature and standards-related sources from 2020 to 2025, together with conceptual synthesis informed by SAP transformation practice. It follows the format of the attached SAP transformation paper by connecting the problem statement, review findings, research methodology, conceptual development of the framework, and practical implications thereof. Findings: According to the review, the SAP decision support based on AI is most beneficial if predictive analytics, business intelligence, automation, generative AI, master data governance and human accountability work as an integrated decision-making structure. Practical implications: The enterprises in Saudi Arabia could utilize the suggested governance framework for improving planning, supply chain management, reliability, finance, governance, and decision-making speeds while addressing the cybersecurity issues, bias, data quality and risk of adoption. Originality/Value: This paper contributes an AI decision support governance framework that combines enterprise architecture and SAP transformations with Vision 2030 objectives.
Mohammed Shakeel Miskini
Glob Acad J Econ Buss; 2026, 8(3): 392-402
DOI : https://doi.org/10.36348/gajeb.2026.v08i03.020
The integration of Radio-Frequency Identification (RFID) technology into supply chain management has emerged as a transformative enabler of inventory visibility and operational efficiency across industrial sectors. Within the oil and gas industry — where supply chain complexity, asset-intensive operations, and regulatory demands present acute challenges to inventory management — RFID offers critical capabilities that traditional barcode and manual tracking systems fail to deliver. This systematic literature review synthesises empirical, simulation-based, and conceptual research published between 2020 and 2025, drawing on 30 peer-reviewed sources indexed in Scopus and Web of Science Q1 journals, to examine the multifaceted impact of RFID technology on warehouse efficiency and inventory visibility within oil and gas supply chains. The review is guided by three principal objectives: (i) to map the current landscape of RFID adoption and deployment models in oil and gas warehousing contexts; (ii) to evaluate the quantifiable gains in inventory accuracy, stockout reduction, and replenishment efficiency attributable to RFID implementation; and (iii) to critically assess integration barriers including middleware complexity, organisational resistance, and environmental constraints inherent to upstream, midstream, and downstream oil and gas environments. Findings indicate that RFID-enabled systems consistently improve inventory accuracy by 25–40%, reduce unplanned stockouts by up to 44%, and accelerate order cycle times by 18–29% when fully integrated with enterprise resource planning (ERP) and warehouse management systems (WMS). The review further identifies an underexplored nexus between RFID, Internet of Things (IoT) convergence, and digital twin applications as a frontier for future research. The paper contributes to theory by extending the process-oriented IT value framework to the oil and gas supply chain context and offers practical implications for supply chain managers, technology architects, and policymakers.
The purpose of this review paper is to analyze the impact of the development of AI technology and financial market infrastructure on decision-making processes in Saudi Arabian debt markets, and to identify the possibility of paradigm shifts in this regard in accordance with Saudi Vision 2030. More precisely, the current paper focuses on the growing importance of instruments other than energy bonds, the development of capital markets, the innovative aspects of sukuk issues, the financing of projects on a risk-sensitive basis, and more effective decision-making regarding credit in both the public and private sectors. Unlike narrow-focused studies and case investigations, the current literature review draws on various publications on AI in finance, analytics of fixed-income securities, Islamic finance, innovative infrastructure finance, regtech technologies, and the digitalization of Saudi Arabia. The methodology applied to the research in question is the technique of narrative literature review, resembling that used when completing systematic reviews. The publications selected for the current research are mostly published between 2020 and 2025 in connection with one of the following five topics: analytics in debt markets, AI decision-making, sukuk and Shariah-compliant structuring, AI governance and responsibility, and financial market reforms under Vision 2030. The findings reveal that decision-making systems based on AI technology can positively impact surveillance in the debt market, price discipline, liquidity analysis, covenants structuring, portfolio management, and matching of issuers and investors in connection with proper data governance, decision-explanation, clarity of legislation, cybersecurity, and human oversight. The applications of AI in Saudi Arabia will include sovereign and quasi-sovereign issuance, project finance, infrastructure sukuk structuring, analytics of the secondary debt market, and treasury management.
Hybrid cloud has become the default operating model for enterprises that must combine public cloud elasticity, private cloud control, and locally governed data platforms. In this setting, mission-critical database workloads are often the most fragile layer because small deviations in wait events, query plans, replication delay, storage latency, or authentication behaviour can propagate into customer-facing outages and security exposure. This review synthesises research and industry evidence published between 2020 and 2025 on machine learning-based anomaly detection for hybrid cloud infrastructure and critical database operations. The aim is to define a practical evidence-informed framework that links telemetry collection, feature engineering, model selection, explainability, and guarded response. The review shows that no single model family is sufficient. Statistical baselines and rules remain valuable for stable service-level indicators, while tree-based, probabilistic, deep sequence, transformer, graph, and knowledge-based methods address non-linear workloads, cross-service dependencies, and root-cause reasoning. For database workloads, anomaly detection must integrate infrastructure metrics with database-native signals, including active sessions, wait classes, lock contention, query execution plans, buffer usage, redo or write-ahead logging behaviour, and replication health. The paper proposes an architecture in which observability, topology context, model governance, and operational runbooks are treated as one system rather than separate tools. It therefore argues that successful deployment should be measured not only by precision and recall, but also by mean time to detect, mean time to recover, false-alert cost, safety of automated actions, and compliance readiness. The review concludes that hybrid cloud anomaly detection is most reliable when machine learning is constrained by domain knowledge, continuous validation, human approval for high-risk interventions, and clear accountability across platform, security, and database engineering teams.
Hassan Ahmed Atiah
Glob Acad J Econ Buss; 2026, 8(3): 362-370
DOI : https://doi.org/10.36348/gajeb.2026.v08i03.017
Deep excavations underpin transit-oriented districts, airport-linked complexes, high-rise mixed-use precincts, and underground utility corridors. In these dense settings, wall movements, ground loss, drawdown, and construction sequencing interact with vulnerable urban assets in ways that are difficult to observe and harder to predict. Digital twin technology is increasingly proposed as a response because it links field measurements with continuously updated virtual models for interpretation, forecasting, and intervention. This review synthesizes literature published between 2020 and 2025 on digital twins, geotechnical monitoring, deep excavation risk analytics, and urban digital infrastructure. Using a PRISMA-informed review design, the paper maps how sensing, BIM-GIS integration, physics-based simulation, machine learning, and uncertainty management are combined to support wall deflection control, settlement warning, groundwater management, and protection of adjacent buildings. The review argues that the value of a digital twin lies not in visualization alone but in the creation of a closed monitoring loop in which data, models, thresholds, and response rules are aligned to excavation stages and urban exposure. Two contributions are offered: a lifecycle architecture for twin-enabled excavation monitoring and a maturity-based implementation roadmap for mega urban developments. The synthesis shows that current advances are strongest in risk prediction, virtual sensing, and automated model updating, whereas major limitations remain in data interoperability, uncertainty communication, contractual governance, and scaling from pilot projects to district-level portfolios.
Top Editors
Dr. Sumathi Kumaraswamy
Associate Editorial Board
Assistant Professor Department of Economics and Finance College of Business Administration University of Bahrain Email: skumaraswamy@uob.edu.bh
Dr. Jitender Kumar
Associate Editorial Board
Assistant Professor, Department of Management Studies, Deenbandhu Chhotu Ram University Murthal, Sonepat, Haryana, India Email: jitenderkbatra@gmail.com
Omer Allagabo Omer Mustafa
Associate Editorial Board
Assistant Professor of Economics, Banking and Finance, Deputy Secretary of Academic Affairs-Sudan Academy for Banking and Financial Sciences (SABFS), Khartoum, Sudan E-mail: omergabo78@sabfs.edu.sd
Dr. Zahoor Ahmad Parray
Associate Editorial Board
Department of Management Studies, South Campus, University of Kashmir Anantnag, Jammu and Kashmir, India Email: zahoorhs@gmail.com
Dr. Hafnida Hasan
Associate Editorial Board
Assistant professor in Applied Sciences University (ASU), Kingdom of Bahrain Email: hafnida.hasan@asu.edu.bh
Dr. Ulysses J Brown III
Associate Editorial Board
College of Business Savannah State University 3219 College Street Savannah GA 31404 Email: brownu@savannahstate.edu , brownuj@gmail.com
Dr. C. Vijai
Associate Editorial Board
Assistant Professor Department of Commerce St.Peter’s Institute of Higher Education and Research, Avadi, Chennai-600 054 Tamil Nadu, India Email: Vijaialvar@gmail.com
Prof. Mounir BELLOUMI
Associate Editorial Board
College of Administrative Sciences, Najran University, Saudi Arabia Email: mounir.balloumi@gmail.com
Prof. P Malyadri
Editor-in-Chief
ICSSR Senior Fellow Centre for Economic and Social Studies (CESS), Begumpet, Hyderabad-500016, India Email: drpm16@gmail.com
Husam Alharahsheh
Executive Editor
Lecturer in business management, University of wales Trinity Saint David - UK Senior lecturer, Several UK and EU based higher education institutions Email: husam.helmi1@yahoo.com
Dr. Mohd Shahril Bin Ahmad Razimi
Deputy Chief-Editor
Programme Coordinator Senior Lecturer, Islamic Business School Universiti Utara Malaysia Email: shahril@uum.edu.my
Aliashraf Ahmadian
Associate Editorial Board
Marketing lecturer Graduated in the Degree of Doctor of Philosophy (phD) from University of Tehran, Iran. Email: AliAshrafAhmadian@ut.ac.ir
ZULFAN NAHRUDDIN
Executive Editor
Lecturer at Muhammadiyah University of Makassar email:- zulfan.nahruddin@unismuh.ac.id
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