AI-Driven Strategic Decision-Making: Integrating Machine Learning into Financial Modeling and Corporate Strategy

Abstract

Machine learning (ML) and artificial intelligence (AI) will be substituting financial modeling and corporate strategy and will offer an opportunity to make data-driven, adaptive, and predictive decisions. This review examines the application of machine learning processes to financial analytics and strategic management by mentioning that AI has the potential to enhance the accuracy of forecasts, risk evaluation, and flexibility. In the work, a synthesis of the literature at hand is given to demonstrate that AI-based systems are of significant advantage over traditional econometric approaches, in particular, in the processing of nonlinear and high-frequency, large-scale financial data. Nevertheless, the concerns of problematic issues also encompass interpretability of models, concerns of data quality, governance, and complexity of integrating AI in business decision models, which were also characterized in the review. Furthermore, the article introduces a theoretical framework that connects the ability of AI, the quality of financial modeling, and the results of strategic decisions with the emphasis on the mediating and moderating variables, like explainability, organizational preparedness, and ethical governance. Additionally, the future research directions are also mentioned in the review and can be explained as explainable AI, hybrid modeling techniques, real-time analytics, and regulatory frameworks. On the whole, the present research can be added to the existing knowledge base as it offers a systematic insight into the ways AI can be successfully used to experience sustainable competitive advantage in finance and corporate strategy. The results apply to both academics and practitioners interested in the changing nature of AI in terms of strategic decision processes.