End-to-End Data Intelligence in Multi-Sector Environments Using Generative AI and Google Cloud Services

Abstract

This study examines the strategic integration of Generative Artificial Intelligence (GenAI) and Google Cloud Services to enable end-to-end data intelligence across multiple sectors. It aims to critically explore how these emerging technologies support the automation, augmentation, and acceleration of data-driven processes, enabling real-time decision-making and scalable analytics. Utilizing a conceptual-exploratory methodology, this research reviews existing literature and synthesizes case-based insights from various sectors, including healthcare, finance, and education. The study examines the technical architectures, capabilities, and contextual implications of implementing AI-driven data pipelines through services such as Google Cloud’s Vertex AI, BigQuery, and AutoML. Findings suggest that the convergence of Generative AI with Cloud-native infrastructure offers robust support for data ingestion, transformation, modeling, and visualization, delivering intelligence at scale. The results underscore an increasing shift toward Cloud-first AI architectures, with sector-specific adaptations influenced by data privacy concerns, legacy systems, and regulatory requirements. This paper contributes to the academic discourse by offering a cross-sectoral framework for understanding how generative capabilities enhance data intelligence systems. The research concludes by emphasizing the potential and limitations of these technologies, and proposes a roadmap for future applications and governance in data-centric enterprises.