Transformation Features of AI-Driven Management Decision-Making Models

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.

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Publication Details

Journal
Journal of Cross-Disciplinary Sustainability Innovations
Published
2025-05-20
DOI
https://doi.org/10.64229/51vb5t04
Primary Topic
Economic and Technological Developments in Russia
Type
article
Field-Weighted Citation Impact
0.00
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article

Transformation Features of AI-Driven Management Decision-Making Models

Na Wang
Journal of Cross-Disciplinary Sustainability Innovations
Economic and Technological Developments in Russia
article

Transformation Features of AI-Driven Management Decision-Making Models

Na Wang
article en

Abstract

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.

Journal of Cross-Disciplinary Sustainability InnovationsVol. 1(1)
Chenzhou First People's Hospital (CN)
Openalex Percentile: Top 60%
Economic and Technological Developments in Russia
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Transformation Features of AI-Driven Management Decision-Making Models — Na Wang · Journal of Cross-Disciplinary Sustainability Innovations (2025) | TGRS Research Map | TGRS