Artificial Intelligence in Top Management : A Bibliometric Performance Analysis

Abstract

Context: This study examines the integration of Artificial Intelligence (AI) into top management (TM) decision-making, emphasizing its potential to improve strategic planning, operational efficiency, and competitive dynamics.

Objective: The research traces the historical evolution of AI in TM literature, identifies key institutions, authors, and funding bodies driving innovation, and highlights interdisciplinary connections and unresolved challenges.

Method: Using a PRISMA-guided bibliometric analysis of 171 peer-reviewed articles from Scopus, the study identifies three growth phases: minimal activity (1984–2003), gradual adoption (2004–2015), and exponential growth (2016–2024).

Results and Discussion: The Chinese Academy of Sciences, West Virginia University, and scholars such as Mohaghegh and Al-Sartawi are key contributors. The U.S. and China lead, driven by corporate investments and national AI strategies. Thematic analysis reveals three core clusters: AI-driven decision support systems, strategic automation, and ethical/regulatory challenges.

Conclusion: AI enhances managerial efficiency but introduces complexities, including algorithmic bias and human-AI trust gaps. Limitations include database biases and a lack of longitudinal studies on AI’s organizational impact. The study recommends interdisciplinary collaboration, ethical AI frameworks for executive roles, and empirical validation of AI’s long-term strategic value.