AI-Driven Data Analytics in Enterprise Systems
Authors-Devansh Kapoor
Keyword-Artificial Intelligence, Data Analytics, Enterprise Systems, Machine Learning, Deep Learning, Natural Language Processing, Predictive Analytics, Business Intelligence, Real-Time Analytics, Data Processing, Big Data, Cloud Computing, Decision Support Systems, Automation, Data-Driven Decision Making
AI-driven data analytics has become a critical component in modern enterprise systems, enabling organizations to transform vast amounts of data into actionable insights for strategic decision-making. With the rapid growth of data generated from business operations, customer interactions, and digital platforms, traditional analytics methods are no longer sufficient to extract meaningful value. Artificial intelligence enhances data analytics by incorporating machine learning, deep learning, and natural language processing techniques to identify patterns, predict trends, and automate analytical processes. This paper explores the integration of AI-driven analytics within enterprise systems, focusing on architectural frameworks, data processing pipelines, and intelli-gent decision-support mechanisms. It highlights how AI improves business intelligence, opera-tional efficiency, customer experience, and risk management across industries such as finance, healthcare, retail, and manufacturing. The study also examines key challenges including data quality, scalability, model interpretability, and security concerns. Emerging trends such as real-time analytics, cloud-based AI platforms, and automated data pipelines are also discussed. The findings emphasize that AI-driven data analytics is essential for building intelligent, adaptive, and competitive enterprise systems in the digital era.