This study examines the complex dynamics of changes in input matrix within the banking industry, with a special focus on how it affects organizational decision-making processes and performance indicators. A relevant questionnaire was prepared and solicitated many Senior management respondents from different banks, utilizing snowball sampling approaches. By utilizing component analysis, the research revealed crucial elements that contribute to the variation in the input matrix. These factors account for a significant 72.1% of the overall variance. Later, Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to thoroughly evaluate the complex connections between these elements and the outputs of the organization. The findings provide detailed insights that emphasize the important impact of elements such as inconsistencies in Artificial Intelligence (AI), frequent input of data’s, errors made by humans, and cognitive biases in determining the variance of the input matrix impacting the crucial decisions. Furthermore, the study highlights the crucial role of competent leadership in mitigating the negative consequences of fluctuations in input matrix on corporate performance indicators. This research provides useful insights for the banking sector, including concrete solutions to optimize decision-making processes and improve organizational performance in dynamic and data-driven contexts.