AI-Driven Data Analytics and Automation: A Systematic Literature Review of Industry Applications

Abstract

This study systematically examines the transformative role of AI-driven data analytics and automation across diverse industries, providing a comprehensive synthesis of findings from 110 high-quality peer-reviewed studies with a cumulative citation count exceeding 15,000. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, the review explores AI applications in manufacturing, healthcare, finance, retail, and governance, focusing on advancements, benefits, and challenges. The findings reveal significant improvements in operational efficiency, accuracy, and decision-making, such as a 30% increase in fault detection accuracy in manufacturing and over 90% diagnostic precision in healthcare. AI's role in fraud detection, risk assessment, customer service, personalized marketing, and resource allocation further underscores its transformative impact. However, challenges related to data quality, availability, and privacy remain persistent barriers to broader AI adoption. By consolidating insights from a large body of literature, this study provides a detailed understanding of the potential and limitations of AI-driven data analytics and automation, serving as a foundational resource for researchers, practitioners, and policymakers aiming to leverage AI for innovation and efficiency across industries.