Artificial Intelligence Based System for Predictive Business Decision Support and Planning

Abstract

In today's enterprises, the role of Artificial Intelligence (AI) in making business decisions and planning has become vital. This research introduces a system that supports business decisions and planning using Artificial Intelligence (AI) through a Hybrid Predictive Analytics Method (HPAM) using XGBoost algorithm and Explainable AI (XAI) developed in Python Language. The system being proposed aims to review the business information that is associated with sales, financial, customer behavior or operational activities, and create precise predictions for organizational planning. XGBoost has been designed to efficiently process large and complex datasets and to improve the accuracy of forecasting, while Explainable AI has been developed to provide transparency and interpretability in the results of the forecasts. The system was tested against traditional machine learning techniques like Decision Tree, Random Forest and Artificial Neural Networks. The experimental results showed that the proposed approach is more accurate in prediction, has less forecasting error, and works more efficiently for decision making. Additionally, the Python-based implementation offered a scalable, real-time analyses and flexible integration with business intelligence systems. The study is finally found to be a good improvement in predictive business planning and intelligent decision support.