Artificial Intelligence-Driven Cloud Performance Optimization Framework for Vision 2030 Enterprises
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Abstract
Artificial Intelligence (AI) has become the analytical backbone of modern cloud ecosystems, offering enterprises predictive insights, automated resource management, and performance optimization at unprecedented scale. Yet despite the proliferation of AI-Ops tools, global organizations continue to waste an estimated US $100 billion annually (Gartner 2025) on inefficient cloud utilization. This study develops an Artificial Intelligence-Driven Cloud Performance Optimization Framework (AI-CPOF) designed to enhance efficiency, reduce operational cost, and support sustainable digital transformation within the context of Saudi Vision 2030.
Using a mixed-method approach—combining bibliometric analysis (2020–2025 Scopus dataset) with applied case evidence from Gulf cloud operations—the study identifies recurring optimization challenges related to resource elasticity, data-quality governance, and energy efficiency. It proposes a layered architecture integrating performance telemetry, AI-based predictive analytics, decision automation, and continuous-feedback control loops.
Preliminary application of the framework in representative telecom and enterprise environments shows potential performance gains between 18 and 27 percent and carbon-emission reductions of up to 14 percent through intelligent workload scheduling. The framework also aligns with the National Strategy for Data and AI (SDAIA 2023), emphasizing governance, transparency, and local capacity building. The findings underscore that AI-driven optimization is not only a technical endeavor but also a strategic requirement for sustainable competitiveness under Vision 2030’s digital-economy pillar.
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