AI NLP-GPT Models: Challenges and Prospects in Business Decision Realms

This paper represents a natural progression in the ongoing investigation into the potentials and challenges associated with the application of AI NLP GPT models for decision support. Building upon the foundation laid by the prior study titled "Conceptual Analysis and Potential Applications of DL NN Transformer and GPT Artificial Intelligence Models for the Transformation and Enhancement of Enterprise Management, EIS/ESS, and Decision Support Integrated Information Systems," the current work serves as a valuable continuum, providing further insights and validation of previous research findings. The study not only reaffirms the robustness of AI models in structured environments but also ventures into exploring their limitations when confronted with unstructured and non-pretrained scenarios inherent in real-world business decision-making. Through an in-depth conceptual analysis, the paper sheds light on the intricacies and potential shortcomings of deploying AI models in such dynamic and complex decision-making contexts. This iterative approach contributes to a more nuanced understanding of the capabilities and constraints of contemporary AI systems, providing valuable guidance for their practical implementation in diverse business scenarios. In essence, this work extends beyond a mere sequel, serving as a critical checkpoint in the continuous evolution of research in this domain. It accentuates the adaptability and real-world relevance of AI models while critically examining their applicability in less controlled decision-making environments.