Trends of artificial intelligence-driven enterprise management development: A bibliometric analysis

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Type of the article: Research Article

Abstract
Artificial intelligence (AI) has become the most eye-catching new technology in recent years, and its application is driving the transformation of enterprise management. In order to cope with the impact of new technological changes and address key issues affecting enterprise management development, it is necessary to research and clarify the basic relationship between the application of AI and the development of enterprise management. This study aims to analyze the current situation and future development direction of AI-driven enterprise management through bibliometric analysis. Scopus and Web of Science data from 2014 to July 2025 were analyzed to explore the evolutionary time, geography, and scientific landscape of this topic. The findings contribute to understanding AI’s driving role in enterprise management development. The analysis reveals exponential growth in research output on AI-driven management, accompanied by a decreasing growth rate of publications on AI-driven enterprise management since 2021. The important factors that affect research output are population and total GDP. China, the United States, and India were identified as the leading contributors, with significant research activity in this field. Keyword analysis indicates that the thematic focus is becoming more technical and universal. Thematic analysis highlights that human resource management, financial management, supply chain management, and operational decision-making are the main aspects of AI-driven enterprise management, accounting for 94% of the total number of publications. The study proposes a new direction for the development of AI-driven enterprise management, including department integration, cognitive convergence, and ethical and social responsibility.

Acknowledgments
This research has been supported by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (IRN 19680544 “Innovation infrastructure of Kazakhstan in the context of digitalization: assessment of the state and development of an atlas”).

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    • Figure 1. Research framework
    • Figure 2. Research hotspot relationship
    • Figure 3. Pearson coefficient correlation map
    • Table 1. AI application keywords
    • Table 2. Number of articles on AI in the field of management
    • Table 3. Number of articles about AI in the field of enterprise management
    • Table 4. Cluster description of keyword formation
    • Conceptualization
      Liangliang Xue, Zaira Satpayeva, Dana Kangalakova, Ercan Ozen
    • Data curation
      Liangliang Xue
    • Formal Analysis
      Liangliang Xue, Zaira Satpayeva, Dana Kangalakova
    • Funding acquisition
      Liangliang Xue, Zaira Satpayeva
    • Investigation
      Liangliang Xue, Zaira Satpayeva, Dana Kangalakova
    • Methodology
      Liangliang Xue, Zaira Satpayeva
    • Resources
      Liangliang Xue, Zaira Satpayeva
    • Software
      Liangliang Xue
    • Validation
      Liangliang Xue, Ercan Ozen
    • Visualization
      Liangliang Xue, Zaira Satpayeva
    • Writing – original draft
      Liangliang Xue, Zaira Satpayeva, Dana Kangalakova, Ercan Ozen
    • Writing – review & editing
      Liangliang Xue, Zaira Satpayeva, Dana Kangalakova, Ercan Ozen
    • Project administration
      Zaira Satpayeva
    • Supervision
      Zaira Satpayeva, Ercan Ozen