Journal of Advanced Research in Information Technology, Systems and Management

Artificial Intelligence-Driven Decision Support Systems for Enhancing Digital Transformation in Modern Organizations

Sakshi Joshi — 2026-09-09

Artificial Intelligence (AI) has emerged as a transformative technology that is reshaping organizational decision-making processes and accelerating digital transformation. Artificial Intelligence-driven Decision Support Systems (AI-DSS) enable organizations to analyze complex data, identify patterns, predict future outcomes, and support strategic decision-making. These systems combine machine learning, data analytics, automation, and intelligent algorithms to improve operational efficiency and organizational performance.

This paper examines the role of AI-driven Decision Support Systems in enhancing digital transformation within modern organizations. The study discusses the importance of AI-based decision-making, its applications in business operations, management processes, customer analytics, and strategic planning. The paper also highlights key benefits and challenges associated with AI-DSS implementation, including data security, technological complexity, and organizational adaptation. The study concludes that AI-driven decision support systems provide valuable opportunities for organizations to improve competitiveness, innovation, and long-term digital capabilities.

Cloud Computing-Based Information Management Models for Scalable and Secure Enterprise Systems

Sakshi Joshi — 2026-09-09

Cloud computing has become a fundamental technology for modern enterprises by providing flexible, scalable, and cost-effective computing resources. Organizations increasingly depend on cloud-based information management models to store, process, and manage large volumes of digital information while maintaining security and operational efficiency. Cloud computing enables enterprises to improve accessibility, collaboration, resource optimization, and business continuity through advanced digital infrastructure.

This paper examines cloud computing-based information management models for developing scalable and secure enterprise systems. The study discusses different cloud deployment approaches, information management strategies, security mechanisms, and operational benefits associated with cloud adoption. The paper also highlights challenges including data privacy, service dependency, compliance requirements, and cloud security risks. The findings indicate that effective cloud information management requires a balanced approach combining technological innovation, governance frameworks, and security practices to support sustainable enterprise operations.

Digital Transformation Strategies Through Integrated Information Technology and Management Systems for Sustainable Organizational Growth

Sakshi Joshi — 2026-09-09

Digital transformation has become a strategic necessity for organizations seeking sustainable growth in an increasingly competitive and technology-driven environment. The integration of Information Technology (IT) and management systems enables organizations to improve operational efficiency, enhance decision-making, and develop innovative business models. Modern enterprises are adopting digital technologies such as cloud computing, artificial intelligence, data analytics, enterprise information systems, and automation tools to achieve long-term organizational sustainability.

This paper examines digital transformation strategies through integrated information technology and management systems for sustainable organizational growth. The study discusses the role of IT integration, digital management practices, intelligent information systems, and strategic technology adoption in improving organizational performance. The paper also highlights major challenges associated with digital transformation, including technological complexity, organizational resistance, cybersecurity risks, and resource limitations. The findings indicate that successful digital transformation requires alignment between technology infrastructure, business strategies, and organizational capabilities.

Advanced Cybersecurity Frameworks for Protecting Information Systems Against Emerging Digital Threats

Sakshi Joshi — 2026-09-09

The rapid growth of digital technologies, cloud computing, Internet of Things (IoT), and interconnected information systems has increased the complexity of cybersecurity challenges faced by modern organizations. Emerging digital threats such as ransomware, advanced persistent threats, phishing attacks, artificial intelligence-based cyberattacks, and data breaches require advanced cybersecurity frameworks capable of providing proactive protection and adaptive defense mechanisms.

This paper examines advanced cybersecurity frameworks designed to protect information systems against evolving digital threats. The study discusses important cybersecurity approaches, including risk-based security management, zero-trust architecture, threat intelligence, artificial intelligence-based security monitoring, and continuous system assessment. The paper highlights the importance of integrating advanced security practices with organizational policies and technological infrastructure. The findings suggest that effective cybersecurity frameworks require a combination of technological innovation, human awareness, and strategic governance to improve information system resilience.

Data Analytics and Machine Learning Approaches for Intelligent Business Process Optimization

Sakshi Joshi — 2026-09-09

The rapid advancement of digital technologies has transformed the way organizations manage and optimize their business processes. Data analytics and machine learning have emerged as powerful approaches for improving operational efficiency, predicting business outcomes, and supporting intelligent decision-making. Modern organizations generate large volumes of structured and unstructured data, creating opportunities to identify patterns, automate processes, and enhance performance through advanced analytical techniques.

This paper examines the role of data analytics and machine learning approaches in intelligent business process optimization. The study discusses analytical methods, machine learning models, predictive analytics, and their applications in improving organizational operations. The paper also highlights the benefits and challenges associated with implementing intelligent optimization systems, including data quality issues, technological complexity, and organizational readiness. The findings suggest that integrating data analytics with machine learning enables organizations to achieve more efficient, adaptive, and data-driven business processes.