Artificial Intelligence of Big Data for Analysis in Organizational Decision-Making

This research aims to measure the impact of incorporating artificial intelligence on the flexibility of analytics in managing large volumes of data. Traditional data analytics rely on statistical tools and batch processing of databases collected over extended periods, often limiting the adaptability of insights to rapidly changing environments. However, integrating artificial intelligence enhances decision-making flexibility by scaling analytics to new levels of knowledge, allowing organizations to discover complex response patterns, whether explicit or latent, in real-time. AI-powered visualization tools contribute to greater strategic agility, enabling organizations to quickly adjust their strategies, decision-making processes, and competitiveness in response to market shifts. This transition also necessitates the development of new adaptive skills for personnel responsible for managing organizational information systems. A sample of 6917 data scientists from 52 countries, spanning 16 industries and varying levels of practical knowledge in data analytics, was analyzed to explore these dynamics. Multivariate analyses were conducted using PLS-SEM to establish relationships between variables and assess organizational characteristics. The findings indicate that organizations managing big data achieve an optimal flexibility threshold, reaching a maximum information level of 88% for the types of analysis required in decision-making. Therefore, organizations must understand the dynamic nature of their analytical needs to determine the most suitable AI applications, ensuring their strategies and decision-making processes remain adaptive and resilient in an ever-changing business landscape.