AI-Driven Decision Making Transforming Management Practices With Machine Learning

Abstract

Supervised Learning (Random Forests) using the Tensor Flow tool is used to spend this research exploring on the effect of AI driven decision making on the transformation of management practices. The three business tasks that this study addresses are customers churn prediction, sales forecasting, and inventory optimization. The performance of the other machine learning techniques such as Support Vector Machines (SVM) and Gradient Boosting Machines (GBM) was outperformed by Random Forests in all tasks, which achieved the highest accuracy among them. Deploying and making real time decisions became efficient with the integration of Tensor Flow, which improved business operations. The study emphasize the need of feature importance analysis, yielding insights to the leading factors affecting the business results. Overall, the findings of the research verify that AI empowered models, particularly Random Forests, have considerable impact on automating the complicated decision making processes, enhance the efficiency and drive data centric business strategies. Thi