Published Paper Details:

Generative artificial intelligence (AI) is revolutionizing how businesses leverage vast data lakes to support critical decision-making processes. Data lakes, characterized by their ability to store diverse and large-scale datasets, pose significant challenges in extracting actionable insights efficiently. Generative AI, encompassing technologies such as generative adversarial networks (GANs) and large language models (LLMs), addresses these challenges by automating complex data management tasks, enhancing data quality, and generating predictive insights. This literature review investigates recent advancements and applications of generative AI within large-scale data lakes, highlighting its transformative impact on business analytics, risk management, and operational optimization. Through synthesizing findings from current research, the review explores practical implementations, evaluating benefits such as accelerated insight generation, improved scalability, and enhanced interpretability of complex datasets. Moreover, this paper discusses critical considerations including data governance, ethical implications of AI-generated insights, and integration challenges within existing enterprise systems. Emphasis is placed on how generative AI facilitates more responsive and agile decision-making frameworks, ultimately enhancing competitive advantage and strategic responsiveness in dynamic market conditions. The synthesis aims to provide a comprehensive understanding for business leaders, data scientists, and IT professionals on effectively deploying generative AI capabilities to maximize value extraction from extensive corporate data resources.