Strategic Leadership and Digital Disruption: A Roadmap for Organizational Expansion using Adaptive Boosting (AdaBoost)

In the current era where technology is getting advanced at a rapid pace, one of the major challenges organizations are facing is how to keep up with digital disruption and still maintain their strategic leadership. This study proposes a complex framework that incorporates the Adaptive Boosting (AdaBoost) method to improve organizational decision-making, the main goal of which is to increase business development. The authors of the paper take advantage of the collective learning possibilities of AdaBoost to boost the accuracy of prediction in identifying market trends, customer behavioral patterns, and operational issues. The model that AdaBoost allows to build out of the weak classifiers that it groups is adaptive to the changing business environment. Thus, the top rank managers have the opportunity to generate flexible plans as well as active growth strategies. The system is implemented through three parallel pipelines of rapid data fantastic, the optimization of characteristic input, and the training of the model, as they change when the organizational environment changes. The transformed algorithm is evaluated through a series of experiments to confirm its improved precision and the ability to withstand noisy data, which is an indication of the successful modeling of complex decision scenarios. The proposed Adaboost-dependent decision-making model was computed by three realistic business datasets from the retail, finance, and logistics fields. The proposed system achieved an average prediction accuracy rate of 92.6%, with significant robustness to data noise. When compared to traditional ensemble-based models like Random Forests and Gradient-based Boosting, the AdaBoost implementation described a 7–10% improvement in precision levels when employed to dynamic operational data. These outcomes validate its practical utility for strategic business planning in changeable market scenarios.