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Artificial intelligence in corporate governance domains: a scientometric and content analysis

Nicola Cucari et al.

Corporate Governance2026https://doi.org/10.1108/cg-05-2024-0308article
AJG 2ABDC B
Weight
0.50

Abstract

Purpose This paper aims to examine the academic literature on artificial intelligence (AI) in corporate governance (CG), defined as research at the intersection of AI techniques and governance domains, encompassing applications, integration into board processes and regulatory or ethical implications. By combining bibliometric and content analysis, the study maps key strands of scholarship and outlines future research directions, thereby advancing the discourse on the role of AI in CG. Design/methodology/approach The research uses bibliometric analyses using Bibliometrix and VOSviewer on a corpus of 122 academic papers from Scopus. The authors apply performance analysis and science mapping to scrutinise scholarly contributions and identify thematic trends. Further, manual content analysis of the 21 research papers was conducted. Findings The findings demonstrate the transformative impact of AI on CG, fundamentally reshaping decision-making processes, operational efficiency, communication and diversity within board structures. While AI has initially been used primarily as an external support tool, future research may point to its potential role as an autonomous agent. This emerging influence is driving the evolution of CG practices, signalling a shift towards an era of “artificial corporate governance”. Originality/value The study highlights the need for firms to understand the interplay between AI technologies and CG to navigate the changing landscape effectively. It provides original insights into the multifaceted impacts of AI on CG, beyond operational efficiency and data analytics and underscores the importance of further research in this field.

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https://doi.org/https://doi.org/10.1108/cg-05-2024-0308

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@article{nicola2026,
  title        = {{Artificial intelligence in corporate governance domains: a scientometric and content analysis}},
  author       = {Nicola Cucari et al.},
  journal      = {Corporate Governance},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/cg-05-2024-0308},
}

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Artificial intelligence in corporate governance domains: a scientometric and content analysis

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.