Abstract:

In the data-driven economy of today, artificial intelligence (AI) enables speedy analysis of large datasets, therefore revealing valuable insights that direct strategic choices. In many sectors, this approach accelerates the recognition of trends, deviations, and patterns. However, traditional data analysis methods are often slow, entail tremendous human work, and struggle with scalability when faced with high-volume, high-velocity data. This study offers a fresh perspective to overcome these limitations: Fast Trend Discovery and Insight Extraction from Business or Social Data Applied using AutoML Tools (AMLT). Modern AutoML technologies used here automates the end-to- end data analysis pipeline—data preparation, model selection, and insight generation. The proposed method finds user behavior patterns, market trends, and attitude changes by use of real-world data from business intelligence systems and social media analytics. Results reveal that AMLT accelerates decision-making compared to manual analysis techniques, reduces analysis time by more than 60%, and increases model consistency. The framework looks to be fairly useful for non-technical users especially as it offers scalability insight generating. The proposed method achieves the data analysis time by 35%, model accuracy by 97.4%, decision making by 98.3%.