Published Paper Details:

Given the increasing speed of changes in the contemporary business environment, the problem of effective decision-making emerges in the context of the abundance of dynamic data originating from diverse sources. As a framework, this paper investigates the possibilities of AI-driven data mining for improving the decision-making method. Using sophisticated machine learning approaches and data mining strategies, firms and companies can gain predictive knowledge factors that potentially enhance company performance and competitive advantage. The first stage of the methodology section of the paper provides a brief literature review. It presents AI-based data mining with a context understanding of the dynamic data environment and the corresponding literature review focusing on examining the prospects and associated difficulties. A conceptual model on how to apply AI to data mining is suggested in the framework, consisting of data acquisition, data cleaning, and model choice. Exemplification of the implementation of data mining with the help of AI across different business fields shows its advantages, accuracy, flexibility, and cost savings. However, the paper focuses on the benefits of implementing the approach and discusses the main ethical issues and considerations that organizations can face, such as data privacy, bias, and transparency. Last but not least, rising trends like explainable AI and federated learning are also introduced, raising the scope for the future of AI-based data mining for improving the reliability and positive influence of resultant decisions. This study thus simulates the need for organizations to incorporate predictions from data mining extended from artificial intelligence to compete effectively in complex data-intensive organizations.