Enhancing AI Driven Decision Taking in Business Intelligence via Self Adapting Language Models (SEAL) and Contextual Reinforcement Mechanisms

This study explores how self adapting language models (SEAL) combined with contextual reinforcement mechanisms can significantly enhance AI driven decision taking within Business Intelligence (BI) systems. The research investigates how adaptive reasoning, contextual learning loops, and dynamic optimization enable more accurate, scalable, and explainable decision processes. Findings indicate that SEAL based BI frameworks outperform conventional static models in complex, data dense decision environments by continuously refining decision policies through contextual feedback. The integrative architecture proposed in this study contributes a systematic foundation for the next generation of autonomous, context aware decision support systems.

کلیدواژه ها:

Self Adapting Language Models ، Business Intelligence ، Reinforcement Mechanisms ، Decision Taking ، Contextual AI ، Adaptive Reasoning ، Enterprise Analytics