Leveraging the wealth of structured data owned and ingested from various areas, including hereditary transactional/customer databases, customer database, financial records, and sensor data, presents significant obstacles when it comes to systematic and accurate knowledge and actionable insight. Most processes and tools fail to articulate the knowledge embedded in static and transactional structured datasets, which are the central issues that make it difficult to maximize the value of strategic decision-making opportunities with data. Inherent in these institutional challenges is the need for new processes, methodologies, and tools that can help organizations and practitioners get better at data integration, pattern recognition, and analysis. This manuscript primarily assesses the accuracy of structured data analysis in a few major frameworks, Lang Chain, Pandas AI, and many other AI models. This research focuses on the ability of these methodologies to extract knowledge or actionable insights from a few structured datasets to improve and streamline decision-making and increase the usefulness and value of structured datasets. In this manuscript, we offer a systematic assessment of these new methods in order to understand their potential use for converting structured data into strategic and tactical knowledge.