How to Cite:
Salim Hazari, "Adaptive Cloud Platform Architecture for AI-Ready Enterprise Workloads" International Journal of Humanities Science Innovations and Management Studies, Vol. 1, No. 1, pp. 1-7, 2026.

Abstract:
The rapid adoption of artificial intelligence (AI), machine learning (ML), generative AI, and data-intensive enterprise applications is transforming the requirements of modern cloud platforms. Conventional cloud architectures are primarily optimized for scalable application hosting, but AI-ready enterprise workloads require additional capabilities involving accelerated computing, high-throughput data processing, model lifecycle management, elastic resource allocation, governance, and intelligent workload orchestration. This research proposes an Adaptive Cloud Platform Architecture for AI-Ready Enterprise Workloads (ACP-AIEW) that integrates cloud-native infrastructure, heterogeneous computing resources, data platforms, AI/ML services, intelligent workload orchestration, security, and continuous optimization. The proposed architecture dynamically adapts computing, storage, networking, and accelerator resources according to workload characteristics and performance requirements. The research methodology adopts a design science approach involving architectural design, workload classification, adaptive resource management, AI pipeline integration, and comparative evaluation. The proposed architecture is evaluated using performance indicators including resource utilization, workload completion time, scalability, latency, energy efficiency, and operational overhead. The conceptual results indicate that adaptive orchestration can improve resource utilization and reduce performance degradation compared with static cloud provisioning. The framework also establishes a foundation for integrating generative AI, MLOps, containerized AI workloads, and enterprise data platforms within a governed cloud environment. The research contributes an extensible architectural model for organizations seeking to modernize cloud infrastructure and establish scalable, secure, and intelligent platforms capable of supporting next-generation enterprise AI workloads.

Keywords: Adaptive Cloud Architecture, Artificial Intelligence, Enterprise AI, Cloud Computing, AI Workloads, Intelligent Orchestration, MLOps, Kubernetes, Resource Optimization, Cloud-Native Computing.

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