Automating Business Decision Making With Cognitive AI And Neural Networks

Abstract

In today's digitalized world, organizations must make decisions quickly based on available data in complicated situations. However, the strict and rule-based nature of the decision-making process in traditional decision systems might make them unable to produce relevant and accurate data. In this regard, this paper will address the problem through the utilization of AI and neural networks to make the decision-making process more automated and accurate. The model used in this case includes neural network modeling techniques such as deep neural networks (DNNs), long short-term memory (LSTM) networks, and cognitive reasoning that allows the system to learn from new input data. In this model, both structured and unstructured data are utilized to make adaptable decisions. The performance of the proposed AI model will be assessed by using accuracy, F1-score, and ROC AUC metrics. Compared to the traditional decision systems, the performance of the AI model is better with higher values of the above measures at 92%, 0.90, and 0.95 respectively, hence enhancing the decision-making process in businesses. Clearly, the importance of the model in terms of business value is evident due to the improvement of sales forecasting by 15%, resulting in effective resource allocation, inventory management, and customer engagement.