###### Abstract  
This paper presents a quantitative vision of the study of artificial intelligence risk assessment in business based on a bibliometric analysis of the most relevant publications. The main goal is to determine whether the risk assessment of artificial intelligence systems used in businesses is really a subject of increasing interest and to identify the most influential and productive sources of scientific research in this area. Data were collected from the Web of Science Core Collection, one of the most complete and prestigious databases. Regarding the temporal evolution of publications and citations this study evidences, this research subject shows rapid growth in the number of publications (at a compound annual rate of 31.20% from 2018 to 2024 inclusive), showing its high attraction for researchers, responding to the need to implement systematic risk assessment processes in the organizations using AI to mitigate potential harms, ensure compliance with regulations, and enhance artificial intelligence systems' trust and adoption. Especially after the surge of large language models like ChatGPT or Gemini, AI is revolutionizing the dynamics of human-computer interaction using natural language, video, and audio. However, as the scientific community initiates rigorous studies on AI risk assessment within organizational contexts, it is imperative to consider critical issues such as data privacy, ethics, bias, and hallucinations to ensure the successful integration and interaction of AI systems with human operators. Furthermore, this paper constitutes a starting point, including for any researcher who wants to be introduced to this topic, indicating new challenges that should be dealt by researchers interested in AI and hot topics, in addition to the most relevant literature, authors, and journals about this research subject. artificial intelligence; bibliometrics; risk assessment; risk; impact assessment; AI  
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Also, there are concerns about the ethical aspects of AI implementation [2; 3], especially in some fields like healthcare [4]. That is the reason we can find recent and interesting works on explainable (or interpretable) AI [5; 6]. On the other hand, AI is a promising technology, and businesses should see how this technology allows them to compete in a better way, in terms of quality improvement or cost reduction. This paper aims to identify and study the state-of-the-art research on the assessment of these risks introduced by artificial intelligence systems on businesses because this could be an emerging theme with interesting development opportunities. For this, we will use these research hypotheses:  
1. The state-of-the-art developments in this research subject are characterized by a concentration of highly cited publications, predominantly authored by leading researchers from top-tier institutions and published in high-impact journals. 2. The number of publications on AI risk assessment in businesses has significantly increased over the last seven years, reflecting a growing global emphasis on cybersecurity and explainable AI. 3. Collaborative research involving multiple institutions is undertaken frequently and generates a scientific impact on the research subject of AI risk assessment in businesses. Just to clarify what we are talking about, risk assessment is defined in the ISO 31000:2018 standard [7] as \"the overall process of risk identification, risk analysis, and risk evaluation\", so it is a more general concept than risk analysis or risk evaluation. Actually, these concepts are part of the assessment process. This risk assessment should be done systematically, iteratively, and collaboratively, according to ISO, and so it should also be done in the AI context. Besides that, official references from the government about risk assessment are present in the European AI Regulation, which was recently published [8], or in the NIST AI risk management framework [9].