Strategic Decision-Making Enhanced by Machine Learning: Insights for Effective Choices
Keywords:
Machine Learning, Strategic Decision-making, Predictive Analytics, Decision Making, Gradient Boosting, XGBoost, LightGBM, CatBoostAbstract
Organisations are increasingly using machine learning (ML) to support their strategic decision-making processes in the complex and dynamic commercial environment of today. This paper explores the critical function of ML in improving and elevating strategic decision-making, illuminating the technology's transformative potential.In order to help organisations identify new trends, market dynamics, and competitive landscapes, this research investigates how machine learning (ML) algorithms and predictive analytics can use large datasets. ML enables firms to proactively adapt to changing conditions and seize new opportunities by increasing data-driven decision-making.In addition, this study explores how ML-driven predictive models might reduce risks by evaluating probable outcomes and the probabilities that go along with them. This eventually improves organisational agility by enabling decision-makers to develop more strong and resilient plans in the face of uncertainty.The paper also looks at the ethical issues surrounding the use of ML in strategic decision-making, highlighting the significance of accountability, transparency, and justice in algorithmic decision-making. This study provides a thorough review of how machine learning may transform strategic decision-making and direct businesses towards better options. Businesses can stimulate innovation, acquire a competitive edge in a world that is becoming more data-driven, and quickly react to the changing business environment by utilising the potential of ML. For CEOs, managers, and researchers looking to navigate the revolutionary world of ML-enhanced strategic decision-making, this paper is an essential resource.
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