2021· International Journal of Artificial Intelligence & Digital Transformation· 0 citations
TL;DR
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.
Abstract
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.
Digital transformation has become essential for organizations competing in a data-driven economy, driven largely by the integration of Artificial Intelligence (AI) and advanced data platforms. These technologies enable smart automation, predictive analytics, and real-time decision-making. This paper presents digital transformation as a multi-dimensional process involving organizational culture, business processes, and technological infrastructure, with AI-powered data platforms at its core. It reviews key technological developments prior to 2019, including cloud computing, big data frameworks like Hadoop and Spark, and early enterprise AI adoption. Current research emphasizes the importance of data governance, scalability, and interoperability. The paper proposes a structured implementation approach covering data collection, preprocessing, model development, deployment, and continuous optimization, supported by a flow-based architecture. Findings show that organizations adopting AI-enabled platforms achieve up to 45% improvement in operational efficiency and a 35% reduction in decision-making delays. The study concludes by stressing the need to align AI initiatives with business goals and highlights future directions such as autonomous systems and ethical AI practices.
S. Rahman· International Journal of Art...· 0 citations
The accelerating pace of digital transformation has compelled organizations to adopt intelligent technologies that enhance operational agility, improve decision-making, and create sustainable competitive advantages in increasingly dynamic business environments. Among these technologies, cloud computing and artificial intelligence have emerged as complementary enablers that facilitate scalable, data-driven, and automated business operations. This study examines the role of cloud-based AI automation models in supporting smart digital business transformation by integrating advanced computational capabilities with flexible cloud infrastructures to optimize enterprise processes across diverse organizational functions. The research investigates how cloud-enabled AI services, including machine learning, intelligent process automation, natural language processing, computer vision, and predictive analytics, contribute to improving operational efficiency, customer engagement, resource utilization, and strategic decision-making. Particular emphasis is placed on the ability of cloud-based platforms to provide real-time access to computational resources, centralized data management, scalable application deployment, and collaborative digital ecosystems that enable organizations to respond rapidly to evolving market demands. The study further explores the integration of AI-driven automation within finance, supply chain management, customer relationship management, human resource administration, marketing, and manufacturing operations, highlighting its contribution to minimizing manual intervention, reducing operational costs, enhancing service quality, and accelerating business innovation. In addition, the research evaluates critical implementation factors, including cloud security, data privacy, interoperability, governance frameworks, organizational readiness, workforce capability, and regulatory compliance, all of which influence the successful adoption of intelligent cloud solutions. The findings indicate that enterprises implementing cloud-based AI automation models experience measurable improvements in operational responsiveness, business continuity, predictive decision-making, process accuracy, and organizational scalability. Furthermore, the study demonstrates that sustainable digital transformation depends not only on technological adoption but also on strategic alignment, effective data governance, leadership commitment, and continuous organizational learning. By combining the elasticity of cloud computing with the adaptive intelligence of AI-driven automation, organizations can establish resilient and agile digital business ecosystems capable of supporting innovation, optimizing enterprise performance, and delivering superior value to customers and stakeholders. The study concludes that cloud-based AI automation models represent a fundamental technological framework for enabling intelligent digital transformation, empowering organizations to achieve long-term operational excellence, competitive resilience, and sustainable business growth within an increasingly interconnected and data-intensive global economy.
Shi-Hu Gan· International journal of com...· 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 fast interwoven of the world of artificial intelligence (AI) and cloud computing changes the manner in which the business ecology is created, grown, and maintained. This article discusses a Abstract- The rapid integration of artificial intelligence (AI) and cloud computing is transforming how enterprise ecosystems are created, scaled, and sustained. This paper presents an AI-integrated cloud enterprise platform designed to support entrepreneurship and innovation through intelligent automation, scalable digital infrastructure, data-driven decision-making, and adaptive service orchestration. The proposed framework combines cloud-native architecture with AI services such as predictive analytics, recommendation systems, adaptive resource management, and decision intelligence to improve operational agility and innovation support. Unlike traditional enterprise systems, the platform provides real-time guidance, dynamic workflow adaptation, personalized support for entrepreneurs, and seamless integration with third-party tools and APIs. The framework also supports rapid prototyping, experimentation, collaboration, and market responsiveness within entrepreneurial ecosystems. Key architectural aspects such as multi-tenant cloud environments, security, privacy, scalability, interoperability, and governance are also discussed. The proposed work contributes a unified and adaptive platform that integrates decision intelligence, service orchestration, and innovation enablement within a single scalable architecture.
Shandilya Avadhanam Venkat Krishna Sastry, Pasuluri Bindu Swetha, S. Kishore et al.· International Conference on...· 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
The rapid development of Artificial Intelligence (AI) has significantly transformed the operations and strategies of digital businesses. Initially, AI adoption focused on efficiency, automation, and extracting insights from large-scale data, but the lack of systematic innovation frameworks limited its full integration into enterprise ecosystems. This paper provides an in-depth analysis of AI-based innovation models for smart digital firms, focusing on foundational approaches. AI-driven innovation frameworks utilize machine learning, data analytics, cognitive computing, and automation to enhance business processes, customer interaction, and decision-making, enabling scalable and responsive enterprise systems. It also highlights the role of data-centric architectures, cloud computing, and algorithmic intelligence in digital transformation. The study examines the evolution from traditional IT systems to predictive, automated smart ecosystems and reviews key frameworks such as data-driven models, knowledge-based systems, and enterprise intelligence architectures. A multi-layered AI innovation model is proposed, including data acquisition, processing, intelligence, and business integration, supported by feedback mechanisms and continuous learning for adaptability and scalability. Results indicate that AI frameworks improve efficiency, reduce costs, and enhance decision accuracy and innovation. The paper concludes that structured AI frameworks are essential for sustainable innovation in digital enterprises and highlights future directions, including deep learning and autonomous systems.
Tharindu Jayasinghe, Nimal Perera· International Journal of Art...· 0 citations
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