Enterprise AI Transformation Using Real-Time Analytics and Scalable Infrastructure Platforms
DOI:
https://doi.org/10.22399/ijcesen.5115Keywords:
Enterprise AI, real-time analytics, scalable infrastructure, AI-driven decision-making, cloud-native platformsAbstract
Enterprises are increasingly adopting artificial intelligence (AI) to enhance decision-making, operational efficiency, and responsiveness in data-intensive environments. This study investigates enterprise AI transformation through the integrated use of real-time analytics and scalable infrastructure platforms. A comprehensive methodological framework is developed that combines streaming data pipelines, AI model deployment, and elastic infrastructure scaling to evaluate system performance and decision impact under varying workload conditions. The results demonstrate that real-time analytics pipelines can sustain low-latency processing across moderate to high data velocities, while optimized AI models deliver accurate predictions without compromising responsiveness. Scalable infrastructure platforms are shown to play a critical role in maintaining system stability and minimizing decision latency as data volume and velocity increase. Furthermore, empirical findings indicate substantial improvements in decision speed, automation coverage, and organizational responsiveness following AI integration. The study highlights the strong interdependency between analytics, AI, and infrastructure layers and emphasizes the need for coordinated architectural design and adaptive scaling strategies. Overall, the research provides actionable insights for enterprises seeking to operationalize AI at scale and achieve sustained transformation through real-time, intelligence-driven systems.
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