The rapid development of artificial intelligence (AI) is reshaping management decision-making models. Through big data analytics, machine learning, and intelligent algorithms, enterprises can promptly access market dynamics, enabling accurate forecasting and scientific decision-making. By building intelligent decision-making models, businesses can enhance resource allocation efficiency and improve risk warning capabilities. Managers are thus able to respond swiftly to market fluctuations, optimizing management processes and reducing operational costs. While intelligent decision-making significantly boosts efficiency, it also introduces potential risks such as data security concerns, algorithmic bias, and technological dependence. Establishing multi-layered risk prevention mechanisms and enhancing managers’ algorithmic literacy are essential strategies to address uncertainties in AI-driven decision-making and to promote a more scientific and stable management model.
Na Wang (2025) studied this question.