Authors: Zulkifli Hassan
Abstract: The rapid growth of data in modern enterprises has created both opportunities and challenges for organizations seeking actionable insights. Artificial intelligence (AI)-driven data analytics has emerged as a transformative approach for extracting meaningful patterns, making predictions, and supporting decision-making across enterprise applications. This study explores the integration of AI techniques—including machine learning, deep learning, and natural language processing—into enterprise data analytics frameworks. It highlights how AI-driven analytics enhances business intelligence, customer relationship management, supply chain optimization, and financial forecasting by enabling real-time, predictive, and prescriptive insights. The study also examines key enablers such as cloud computing, big data platforms, and data lakes that support scalable AI analytics. Additionally, it addresses critical challenges, including data quality, model interpretability, privacy concerns, and integration with legacy systems, and discusses potential solutions. Through practical applications and industry examples, the study demonstrates that AI-driven data analytics is essential for enterprises aiming to achieve operational efficiency, strategic advantage, and data-driven innovation in a competitive digital landscape.
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