ARTIFICIAL INTELLIGENCE FOR PRECISION IN MANAGEMENT DECISION PROCESSES

Abstract

Achieving competitive advantages and optimizing organizational strategies depend heavily on management decision-making accuracy. With an emphasis on the use of data augmentation, filter-based feature selection techniques, and neural networks, this research explores the integration of Artificial Intelligence (AI) to improve accuracy in management decision processes. We overcome issues with unbalanced datasets and small sample sizes by utilizing data augmentation approaches, guaranteeing reliable and varied training data. To find pertinent characteristics, filter-based techniques like mutual information and correlation analysis are used. These techniques reduce noise and dimensionality while preserving important information for decision-making. The main predictive architecture makes use of neural networks, which are well known for their versatility and capacity to represent intricate relationships. In particular, structured data is handled by Feedforward Neural Networks (FNNs), which capture nonlinear interactions between components. Real-world management datasets covering situations like risk assessment, financial forecasts, and resource allocation are used to validate the suggested methodology. When compared to conventional models, experimental results show notable gains in efficiency, interpretability, and predicted accuracy. This research provides a methodical and scalable framework for accurate management decisions, highlighting the synergy of cutting-edge AI tools. The results highlight AI's revolutionary potential in promoting innovation, allowing data-driven strategies, and improving decision-making accuracy in fast-paced commercial settings.