International Journal of Business & Computational Science
type: channel
description: The International Journal of Business & Computational Sciences (IJB&CS) is a multidisciplinary, peer-reviewed, open-access journal that publishes high-quality research in the fields of business and computational sciences. Our journal is committed to ensuring the transparency, accessibility, and integrity of scholarly research.
publisher: HERN
language: en
publication name: International Journal of Business & Computational Science
issn: 3080-3748
items: PROMPT LINEAGE AND GOVERNANCE IN LLM-ENABLED DATA ENGINEERING: A REFERENCE ARCHITECTURE
items: Large Language Models in Modern Data Engineering: A Systematic Review of Architectures, Use Cases, and Limitations
items: Application of Machine Learning for Optimizing Oil Well Production and Reservoir Management: A Simulation-Based Approach
items: A Systematic Review of Machine Learning Techniques for Predictive Maintenance In Industrial IOT (IIOT) Environments
items: Transforming Private Equity Operations Through Ai-Driven Workflow Automation: A Comprehensive Review
items: Edge AI for Energy-Efficient Computing: A Systematic Review of Strategies, Challenges, and Future Directions
items: The Role of Project Management from Idea to Execution in Entrepreneurial Product Development
items: The Effects of Government Policies On SME Growth in Developing Economies: A Comprehensive Review
items: Predictive Analytics and Its Role in Enhancing Consumer Experience in E-Commerce
items: Latency Optimization in Edge vs. Cloud Computing: A Comparative Study for Real-Time Applications
items: Sustainable Business Practices: Integrating Environmental, Social, and Governance (ESG) Metrics
items: AI-Powered Decision-Making: Balancing Automation and Human Oversight in Corporate Governance
items: Role of AI in Business Framework Revolution in Developed Countries

PROMPT LINEAGE AND GOVERNANCE IN LLM-ENABLED DATA ENGINEERING: A REFERENCE ARCHITECTURE
type: item
description: modern data engineering ecosystems to automate data transformation, quality assurance, metadata generation, and analytical reasoning. While these models enhance productivity and adaptability, they introduce significant governance challenges due to their probabilistic behavior and heavy reliance on prompts as executable control artifacts. Unlike traditional data pipelines, where logic is encoded in version-controlled code, LLM-enabled systems often embed prompts in orchestration layers without formal lifecycle management, lineage tracking, or policy enforcement. This absence of prompt governance undermines reproducibility, auditability, and regulatory compliance in enterprise data platforms. This paper proposes a reference architecture for prompt lineage and governance in LLM-enabled data engineering environments. Drawing on principles from DataOps, MLOps, metadata management, and responsible AI, the architecture treats prompts as first-class governed assets. It enables versioning, lineage tracking, metadata capture, and policy enforcement across prompt creation, deployment, and execution. The proposed architecture integrates with modern lakehouse platforms, orchestration engines, and observability tools to provide end-to-end transparency across data, prompts, models, and outputs. This study contributes a structured and practical framework to support scalable, compliant, and trustworthy adoption of LLMs in data engineering workflows.
subject: Articles
  topic: section
subject: Large Language Models
  topic: keywords
subject: Data Engineering
  topic: keywords
subject: Prompt Governance
  topic: keywords
subject: Prompt Lineage
  topic: keywords
subject: LLMOps
  topic: keywords
creator: Shambhu Adhikari
rights: Copyright (c) 2024 Shambhu Adhikari (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-08-22
publication date: 2024-08-22

Large Language Models in Modern Data Engineering: A Systematic Review of Architectures, Use Cases, and Limitations
type: item
description: The rapid advancement of large language models (LLMs) since 2022 has significantly reshaped modern data engineering practices. Originally developed for natural language processing tasks, LLMs are increasingly integrated into data engineering workflows, including data ingestion, schema inference, metadata generation, transformation logic synthesis, data quality monitoring, and natural-language interaction with analytical systems. This systematic review examines the role of LLMs in contemporary data engineering, focusing on architectural integration patterns, practical use cases across the data lifecycle, and inherent limitations affecting reliability and governance. Following PRISMA-informed guidelines, peer-reviewed articles, preprints, and industrial reports published between 2022 and 2025 were analyzed. The review identifies Retrieval-Augmented Generation (RAG), hybrid vector-database architectures, and agent-based orchestration frameworks as dominant deployment strategies. Evidence suggests that LLM-assisted pipelines improve developer productivity, reduce manual coding overhead, and enhance accessibility of data platforms for non-technical stakeholders. However, persistent challenges remain, including hallucination, data privacy risks, limited explainability, operational costs, and scalability constraints. The findings emphasize the need for robust architectural safeguards, evaluation benchmarks, and governance frameworks to ensure safe and effective production adoption. This review contributes a structured taxonomy of LLM-centric data engineering architectures and outlines future research directions to support trustworthy, scalable, and auditable data platforms.
subject: Articles
  topic: section
subject: Large language models
  topic: keywords
subject: Data engineering
  topic: keywords
subject: Retrieval-augmented generation
  topic: keywords
subject: Data pipelines
  topic: keywords
subject: Model governance
  topic: keywords
creator: Shambhu Adhikari
rights: Copyright (c) 2025 Shambhu Adhikari (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-16
publication date: 2025-12-16

Application of Machine Learning for Optimizing Oil Well Production and Reservoir Management: A Simulation-Based Approach
type: item
description: Background: The oil and gas industry requires efficient reservoir management and accurate production forecasting to optimize operations and reduce costs. Traditional physics-based models, though reliable, are computationally intensive and require domain expertise. Machine learning (ML) offers a data-driven approach to predict production trends, optimize operational strategies, and enhance decision-making. This study evaluates various ML models, including regression, decision trees, gradient boosting machines (GBM), and deep learning, to determine their effectiveness in oil well production forecasting. Methods: A synthetic dataset simulating reservoir conditions, production histories, and operational parameters was used to train ML models. Linear regression, decision trees, random forests, GBM, and deep learning models were tested. Performance was measured using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Hyperparameter tuning and cross-validation were applied to improve model accuracy, and feature importance analysis was conducted to identify key factors influencing production. Results: GBM achieved the highest accuracy, with an RMSE of 3.5% and an MAE of 2.1%, outperforming other models in production forecasting. Deep learning models captured complex patterns but required high computational resources. Random forests showed strong generalization, making them effective for noisy datasets, while linear regression struggled with non-linearity. Overall, ML models improved forecasting accuracy and enabled real-time optimization of reservoir operations. Conclusion: ML models significantly enhance oil well production forecasting and reservoir management. GBM proved to be the most effective, balancing accuracy and efficiency. Integrating ML into oil well operations can reduce costs and improve decision-making. Future research should focus on real-world datasets and hybrid ML approaches to further refine predictive capabilities.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Reservoir Management
  topic: keywords
subject: Oil Well Production
  topic: keywords
subject: Production Forecasting
  topic: keywords
subject: Optimization Models
  topic: keywords
creator: Mohsin Saleem
rights: Copyright (c) 2024 Mohsin Saleem (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-04
publication date: 2025-12-04

A Systematic Review of Machine Learning Techniques for Predictive Maintenance In Industrial IOT (IIOT) Environments
type: item
description: Background and Purpose: Predictive Maintenance (PdM) has emerged as a critical component of smart manufacturing, driven by the proliferation of Industrial Internet of Things (IIoT) technologies that enable continuous monitoring of industrial assets. The extensive data generated through interconnected sensors and cyber-physical systems has created new opportunities for real-time equipment diagnostics, early fault detection, and improved operational reliability. Machine Learning (ML) techniques play a central role in transforming these heterogeneous data streams into meaningful insights, reducing unplanned downtime and enhancing productivity. Despite rapid advancements, significant challenges remain regarding model selection, performance evaluation, interpretability, and practical deployment in industrial environments. This study provides a comprehensive synthesis of ML techniques applied to PdM within IIoT ecosystems, examining methodological trends, strengths, limitations, and research gaps. Methods: A systematic review methodology was adopted following PRISMA 2020 guidelines. Peer-reviewed studies published between 2015 and 2025 were retrieved from IEEE Xplore, ACM Digital Library, Scopus, Web of Science, and ScienceDirect. Boolean search strategies were used to identify literature focused on ML-based PdM models applied to IIoT data, cyber-physical systems, sensor networks, and digital twins. Data extracted from eligible studies included ML algorithms, datasets, feature engineering approaches, performance metrics, deployment frameworks, and identified limitations. Comparative and thematic analyses were employed to categorize methods and evaluate their effectiveness across different industrial contexts. Findings: Sixty-two studies met the inclusion criteria. The findings show that Deep Learning (DL) architectures, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and autoencoders, predominate in contemporary PdM research due to…
subject: Articles
  topic: section
subject: Predictive Maintenance (PdM)
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: IIoT
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Systematic Review
  topic: keywords
subject: Industrial Analytics
  topic: keywords
creator: Mahmudul Islam
rights: Copyright (c) 2025 Mahmudul Islam (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-24
publication date: 2025-11-24
starting page: 55
ending page: 67

Transforming Private Equity Operations Through Ai-Driven Workflow Automation: A Comprehensive Review
type: item
description: Introduction: The private equity (PE) industry is undergoing a rapid transformation driven by technological advancements, particularly artificial intelligence (AI) and workflow automation. Historically dependent on manual data processing, human judgment, and fragmented decision systems, PE firms now face pressure to enhance efficiency, transparency, and scalability. The integration of AI-driven automation tools is revolutionizing deal sourcing, due diligence, portfolio monitoring, risk assessment, and operational management. This review explores how AI-driven workflow automation is reshaping private equity operations and its implications for competitiveness, value creation, and decision-making. Methods: A comprehensive literature review was conducted using databases including Scopus, Web of Science, and ScienceDirect. The search focused on publications from 2015 to 2025 employing combinations of keywords such as “private equity,” “AI automation,” “workflow transformation,” and “digitalization in investment management.” Both peer-reviewed articles and industry reports were included to capture academic and practical insights. The studies were screened for relevance, methodological rigor, and applicability to PE operations. Data were synthesized thematically across five operational domains: deal sourcing, due diligence, portfolio management, compliance, and exit strategies. Results: The review reveals that AI-driven workflow automation significantly enhances operational agility, data accuracy, and strategic foresight in PE firms. Automated systems employing natural language processing (NLP), predictive analytics, and robotic process automation (RPA) streamline repetitive tasks, improve risk modeling, and accelerate decision-making. Firms integrating AI-based deal sourcing tools reported up to 30 - 40% faster transaction screening, while automated due diligence platforms reduced manual workload by 50–60%. Moreover, AI-based portfolio monitoring systems improved real-tim…
subject: Articles
  topic: section
subject: Private Equity
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Workflow Automation
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Investment Management
  topic: keywords
creator: Wafaa Chmimo
rights: Copyright (c) 2025 Wafaa Chmimo (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-08
publication date: 2025-10-08
starting page: 41
ending page: 54

Edge AI for Energy-Efficient Computing: A Systematic Review of Strategies, Challenges, and Future Directions
type: item
description: Background and Purpose: Edge Artificial Intelligence (AI) has emerged as a crucial solution for minimizing power consumption during real-time data processing in computing devices. Given the increasing demand for energy-efficient AI systems, this study systematically reviews Edge AI methods focused on optimizing energy efficiency. The research highlights existing challenges and explores future research directions to enhance Edge AI capabilities. Methods: This study employs a systematic review methodology, analyzing recent advancements in Edge AI, including new hardware systems, model optimization techniques, and software development tools. The review considers the interplay between model complexity, hardware constraints, security concerns, and the trade-off between performance and energy efficiency. Findings: The analysis reveals that optimizing Edge AI requires a multifaceted approach involving hardware innovations, lightweight models, and adaptive algorithms. Key challenges include balancing computational power with energy constraints, ensuring data security in edge environments, and maintaining real-time processing capabilities. Theoretical Contributions: This research identifies three significant research avenues: (1) integrating neuromorphic computing to enhance efficiency and mimic biological neural processes, (2) leveraging federated learning to improve privacy-preserving model training across distributed edge devices, and (3) developing adaptive AI architectures that dynamically adjust computational resources based on workload demands. Conclusions and Policy Implementations: To advance Edge AI, policymakers and industry leaders must prioritize the development of energy-efficient hardware, encourage research into low-power AI models, and establish regulatory frameworks for secure edge computing. Future work should explore interdisciplinary collaborations to further optimize Edge AI performance.
subject: Articles
  topic: section
subject: Edge AI
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Low-Power Computing
  topic: keywords
subject: Model Optimization
  topic: keywords
subject: Neuromorphic Computing
  topic: keywords
subject: Federated Learning
  topic: keywords
creator: Ndansi Seraphin Sigala
rights: Copyright (c) 2025 Ndansi Seraphin Sigala (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-15
publication date: 2025-05-15

The Role of Project Management from Idea to Execution in Entrepreneurial Product Development
type: item
description: Background and Purpose: Entrepreneurial product development is a complex, high-risk process that requires structured management to transform innovative ideas into market-ready products. Startups often operate under conditions of uncertainty, resource constraints, and rapid technological shifts, making project management a critical factor for success. This research examines the role of project management methodologies, including Agile, Lean Startup, and Design Thinking, in supporting entrepreneurs from idea generation to commercialization. By integrating structured frameworks with adaptive, iterative processes, this study aims to identify best practices that enhance product viability, market responsiveness, and operational efficiency in entrepreneurial ventures. Methods: A qualitative research design was employed, incorporating case study analyses and semi-structured interviews with startup founders, product managers, and project leaders. The study investigated project management practices across multiple industries, including technology, healthcare, and consumer products. Thematic analysis was used to identify recurring patterns in project execution, decision-making processes, and the effectiveness of various project management methodologies. Comparative assessments of successful and failed startups provided insights into critical factors influencing product development outcomes. Findings: Project management plays a fundamental role in mitigating risks, optimizing resource allocation, and ensuring structured product development in entrepreneurial ventures. The study finds that Agile methodologies, particularly Scrum and Kanban, enable startups to maintain flexibility through iterative cycles, allowing continuous user feedback integration. Lean Startup principles, centered around the Build-Measure-Learn framework, support rapid prototyping and market validation, reducing wasted resources. Design Thinking complements these approaches by emphasizing user-centric innova…
subject: Articles
  topic: section
subject: Entrepreneurial Product Development
  topic: keywords
subject: Project Management
  topic: keywords
subject: Agile Methodology
  topic: keywords
subject: Lean Startup
  topic: keywords
subject: Innovation Management
  topic: keywords
creator: Guerschom Landjohou
rights: Copyright (c) 2025 International Journal of Business & Computational Science https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-15
publication date: 2025-05-15
starting page: 16
ending page: 31

The Effects of Government Policies On SME Growth in Developing Economies: A Comprehensive Review
type: item
description: Background and Purpose: Small and Medium Enterprises (SMEs) are fundamental to economic development in developing economies, contributing significantly to employment generation, poverty reduction, and GDP growth. Despite their potential, SMEs face numerous constraints, including limited financial access, regulatory burdens, inadequate infrastructure, and challenges in trade integration. This study provides a comprehensive review of the effects of government policies on SME growth, assessing the effectiveness of various policy interventions while identifying the key challenges and sectoral variations that influence their impact. Methods: A systematic literature review methodology was adopted, focusing on scholarly articles, policy reports, and empirical case studies from 2005 to 2025. The study utilized electronic databases such as Scopus, Web of Science, JSTOR, and Google Scholar. Boolean search techniques were applied to retrieve relevant literature, and thematic analysis was used to categorize the findings into distinct policy areas. The review applied comparative analysis to examine policy effectiveness across different regions and economic contexts. Inclusion criteria were set to ensure relevance, prioritizing studies that explicitly examined the intersection of government policies and SME development within developing economies. Findings: Government policies have played a critical role in shaping the SME landscape through financial inclusion initiatives, regulatory reforms, infrastructure development, trade and export promotion, digitalization policies, and sustainability measures. Financial policies, including credit guarantee schemes, microfinance programs, and interest rate subsidies, have improved access to capital. However, challenges such as high collateral requirements, inefficiencies in fund allocation, and the exclusion of informal enterprises persist. Regulatory reforms aimed at simplifying business registration and tax compliance have facilitated SME…
subject: Articles
  topic: section
subject: Small and Medium Enterprises (SMEs)
  topic: keywords
subject: Government Policies
  topic: keywords
subject: Infrastructure Development
  topic: keywords
subject: Financial Inclusion
  topic: keywords
subject: Economic Growth
  topic: keywords
creator: Guerschom Landjohou
rights: Copyright (c) 2025 International Journal of Business & Computational Science https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-15
publication date: 2025-05-15
starting page: 1
ending page: 15

Predictive Analytics and Its Role in Enhancing Consumer Experience in E-Commerce
type: item
description: In today’s digital world, where e-commerce platforms have become an integral part of the consumer landscape, businesses are constantly seeking innovative ways to differentiate themselves in an intensely competitive market. One powerful tool in this quest is predictive analytics. By leveraging data and sophisticated algorithms, predictive analytics enables e-commerce businesses to forecast consumer behavior, personalize shopping experiences, optimize inventory management, and ultimately enhance overall customer satisfaction. This editorial explores the role of predictive analytics in transforming the consumer experience in e-commerce and its implications for the industry. What is Predictive Analytics? Predictive analytics involves using historical data, statistical algorithms, machine learning models, and artificial intelligence (AI) to predict future outcomes based on past patterns. By identifying trends, behaviors, and preferences, businesses can forecast what will happen in the future, allowing them to make more informed decisions. In the context of e-commerce, predictive analytics is used to understand customer behavior, forecast demand, and enhance personalization in a way that elevates the overall consumer experience (1,2). Personalization: Tailoring the Shopping Experience One of the most significant ways predictive analytics enhances the consumer experience in e-commerce is through personalization. Today’s consumers expect personalized experiences that cater to their individual needs and preferences. A one-size-fits-all approach no longer suffices, and e-commerce businesses must use data-driven insights to create more customized experiences. Predictive analytics helps businesses tailor product recommendations to individual customers by analyzing previous purchase history, browsing behavior, and preferences (3,4). For example, Amazon’s recommendation engine is a prime example of predictive analytics at work. The platform analyzes user behavior, such as past pu…
subject: Editorial
  topic: section
subject: Predictive Analytics
  topic: keywords
subject: Enhancing Consumer Experience
  topic: keywords
subject: E-Commerce
  topic: keywords
creator: Abdul Wahab Mirza
rights: Copyright (c) 2024 International Journal of Business & Computational Science https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-15
publication date: 2025-05-15
doi: 10.0786/r89sxx65

Latency Optimization in Edge vs. Cloud Computing: A Comparative Study for Real-Time Applications
type: item
description: Background and Purpose: The increasing demand for real-time applications such as autonomous vehicles, industrial automation, and telemedicine has driven the need for low-latency computing solutions. While traditional cloud computing offers extensive computational power, it suffers from latency due to long-distance data transmission. In contrast, edge computing reduces latency by processing data closer to the source, despite facing resource limitations. This study is designed to compare latency optimization strategies in edge versus cloud computing to support the unique demands of real-time applications. Methods: The research employs a comparative analysis framework that focuses on key performance metrics and real-world case studies. It evaluates various optimization techniques including caching, network slicing, AI-driven workload allocation, and data compression. This approach allows for a systematic assessment of the latency performance and resource efficiency of both computing paradigms. Findings: Results indicate that edge computing substantially reduces latency by minimizing the distance data must travel, though it is constrained by limited resources. In contrast, cloud computing, while offering high computational capabilities, introduces latency overhead due to extended data transmission. The findings suggest that hybrid models, which integrate edge and cloud computing, provide the most effective balance by leveraging the strengths of each paradigm to enhance overall system performance. Theoretical Contributions: This study advances the theoretical understanding of distributed computing architectures by clarifying the inherent trade-offs between latency reduction and resource availability. It contributes a novel framework for evaluating hybrid edge-cloud models, thereby enriching the academic discourse on real-time system optimization. Conclusions and Policy Implications: The research concludes that hybrid computing models offer a promising solution for real-t…
subject: Articles
  topic: section
subject: Edge computing
  topic: keywords
subject: Cloud computing
  topic: keywords
subject: Latency optimization
  topic: keywords
subject: Real-time applications
  topic: keywords
subject: Hybrid models
  topic: keywords
subject: Network slicing
  topic: keywords
subject: AI-driven workload allocation
  topic: keywords
subject: Data compression
  topic: keywords
creator: Asif Irshad
rights: Copyright (c) 2024 International Journal of Business & Computational Science https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-15
publication date: 2025-05-15
doi: 10.0786/37rr2j62

Sustainable Business Practices: Integrating Environmental, Social, and Governance (ESG) Metrics
type: item
description: Background and Purpose: Sustainability has become a crucial aspect of corporate strategy, driven by increasing stakeholder expectations, regulatory requirements, and consumer demand for responsible business practices. Environmental, Social, and Governance (ESG) metrics serve as a framework for assessing corporate sustainability and ethical impact. This study examines the role of ESG metrics in business management, their influence on corporate performance, and the challenges and opportunities associated with their adoption. Methods: The paper employs a multidisciplinary approach, integrating insights from existing literature, case studies, and empirical data to analyze the implementation of ESG metrics. It explores corporate governance structures, performance indicators, and regulatory frameworks to assess how businesses integrate ESG principles into their operational models. Findings: The study reveals that integrating ESG metrics enhances long-term business sustainability by improving risk management, stakeholder engagement, and financial performance. Companies that align their strategies with ESG principles tend to experience increased investor confidence and competitive advantage. However, challenges such as standardization issues, reporting complexities, and resistance to change hinder widespread adoption. Theoretical Contributions: This research contributes to the growing body of literature on sustainable business practices by offering a comprehensive framework for understanding ESG integration in corporate governance. It highlights the interconnectedness of ESG dimensions and their impact on business resilience and value creation. Conclusions and Policy Implications: The findings underscore the importance of regulatory support and standardized ESG reporting frameworks to enhance transparency and accountability. Policymakers and corporate leaders must collaborate to establish clear guidelines and incentives for ESG adoption, ensuring long-term sustainability an…
subject: Articles
  topic: section
subject: Sustainability
  topic: keywords
subject: ESG metrics
  topic: keywords
subject: corporate governance
  topic: keywords
subject: business performance
  topic: keywords
subject: stakeholder engagement
  topic: keywords
subject: risk management
  topic: keywords
subject: regulatory frameworks
  topic: keywords
creator: Muhammad Arslan Aslam
rights: Copyright (c) 2024 International Journal of Business & Computational Science https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-15
publication date: 2025-05-15
doi: 10.0786/0148z389

AI-Powered Decision-Making: Balancing Automation and Human Oversight in Corporate Governance
type: item
description: Background and Purpose: The integration of Artificial Intelligence (AI) in corporate governance has significantly transformed decision-making processes, enhancing efficiency, transparency, and predictive accuracy. AI-driven analytics, machine learning algorithms, and robotic process automation (RPA) have enabled organizations to optimize risk management, regulatory compliance, and strategic planning. However, concerns regarding ethical accountability, algorithmic biases, and the diminishing role of human oversight necessitate a critical examination of AI’s role in governance. This study aims to explore the benefits and risks associated with AI adoption in corporate governance and highlight the need for a balanced approach that combines automation with human judgment. Methods: This paper employs a multidisciplinary approach, drawing from recent literature, case studies, and empirical analyses to assess the impact of AI on corporate governance. It examines the applications of AI in governance structures, identifies key challenges, and evaluates existing regulatory frameworks governing AI adoption in corporate decision-making. Findings: The study finds that AI-driven governance enhances operational efficiency, reduces compliance risks, and improves data-driven decision-making. However, over-reliance on AI introduces ethical concerns, including biases embedded in algorithms, lack of transparency in decision-making, and the potential marginalization of human judgment in critical corporate affairs. The findings underscore the necessity of hybrid governance models that integrate AI-driven insights with human expertise to mitigate risks and ensure responsible decision-making. Theoretical Contributions: This paper contributes to corporate governance and AI ethics literature by proposing a framework for responsible AI adoption that balances automation with human oversight. It extends existing discussions on algorithmic accountability and governance ethics by emphasizing the r…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Corporate Governance
  topic: keywords
subject: AI Ethics
  topic: keywords
subject: Algorithmic Bias
  topic: keywords
subject: Regulatory Frameworks
  topic: keywords
subject: Hybrid Governance Model
  topic: keywords
subject: Ethical Decision-Making
  topic: keywords
subject: Responsible AI Adoption
  topic: keywords
creator: Fariha Ambreen Chaudhry
rights: Copyright (c) 2024 International Journal of Business & Computational Science https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-15
publication date: 2025-05-15
doi: 10.0786/eng9jb68

Role of AI in Business Framework Revolution in Developed Countries
type: item
description: Background and Purpose: The integration of Artificial Intelligence (AI) into business frameworks has transformed operations, decision-making, and customer engagement, particularly in developed economies. This study explores the profound impact of AI on businesses by examining advancements in automation, data analytics, and strategic decision-making. It also investigates AI-driven business models, associated challenges, ethical considerations, and the evolving regulatory landscape. Methods: This research employs a qualitative approach, utilizing case studies of AI adoption across various industries. It analyzes secondary data from academic literature, industry reports, and regulatory guidelines to assess AI's role in enhancing business efficiency and competitiveness. Findings: The study reveals that AI significantly improves business performance by automating processes, optimizing data-driven strategies, and enabling innovative business models. However, challenges such as data privacy concerns, algorithmic bias, and regulatory complexities persist. The research highlights how businesses navigate these challenges while leveraging AI to gain a competitive advantage. Theoretical Contributions: This paper contributes to the existing literature by providing a comprehensive framework for understanding AI’s role in business transformation. It integrates perspectives from technology adoption theories, competitive strategy, and regulatory compliance, offering a holistic view of AI’s impact on business sustainability and growth. Conclusions and Policy Implications: The study concludes that AI is a critical driver of business innovation and efficiency but requires balanced regulatory oversight to address ethical and operational risks. Policymakers must develop adaptive frameworks that encourage AI-driven growth while ensuring responsible implementation. Businesses should invest in AI governance and ethical AI practices to sustain long-term success in evolving market landscapes.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Business Transformation
  topic: keywords
subject: Automation
  topic: keywords
subject: Data Analytics
  topic: keywords
subject: AI Ethics
  topic: keywords
subject: Regulatory Frameworks
  topic: keywords
subject: Strategic Decision-Making
  topic: keywords
subject: Innovation
  topic: keywords
creator: Arif Irshad
rights: Copyright (c) 2024 International Journal of Business & Computational Science https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-15
publication date: 2025-05-15
doi: 10.0786/vsq1mm74