Sustainable Digital Transformation through Gradient Boosting Machines

Digital transformation has become a critical aspect for organizations to thrive in the era of rapid technological advancements. However, achieving sustainable digital transformation remains a challenge due to the complexity and dynamism of digital ecosystems. Current research efforts primarily focus on utilizing traditional machine learning techniques for digital transformation, but face limitations in capturing the non-linear and intricate relationships within digital data. This paper addresses this gap by proposing a novel approach utilizing Gradient Boosting Machines (GBM) to enhance the sustainability of digital transformation initiatives. The study demonstrates the effectiveness of GBM in optimizing digital processes, identifying patterns, and predicting future trends with high accuracy and efficiency. By incorporating GBM into the digital transformation framework, this research contributes to advancing the field by providing a more robust and adaptive solution for sustainable digital innovation.