Resumo

YARANGA VITE, Italo Paul; TRELLES SUCA, José Luis  e  PIZARRO PRIETO, Percy Paul. Integration of artificial intelligence and data science for decision-making in companies: a bibliometric study. Revista InveCom [online]. 2026, vol.6, n.2, e602071.  Epub 30-Set-2025. ISSN 2739-0063.  https://doi.org/10.5281/zenodo.16755702.

This research aims to analyze the evolution and scientific trends surrounding the integration of Artificial Intelligence and Data Science applied to business decision-making, through a bibliometric study of academic production between 2015 and 2025. Based on a systematic review in databases such as Scopus and Google Scholar, 155 documents were selected using inclusion criteria such as language, accessibility, and type of publication. The results show sustained growth in scientific production, highlighting the United States (25.8%), the United Kingdom (16.1%), and China (12.9%) as the most productive countries. On the other hand, the field of computer science was the most relevant academic area (40%), while the most cited author was Secinaro, S. with 611 citations and the most prominent source was the IEEE Access journal with 10 publications. Through keyword co-occurrence analysis, recurring terms such as data analytics, machine learning, and artificial intelligence were identified, strongly linked to the business environment. Thematic visualizations, generated with VOSviewer, allowed for the grouping of the main research approaches and the identification of thematic gaps. The study concludes that the integration of artificial intelligence and data science represents a key strategic axis in business digital transformation, facilitating the generation of useful knowledge for decision-making. This work provides a solid empirical basis for future research aimed at developing predictive technologies and intelligent organizational support systems.

Palavras-chave : artificial intelligence; data science; business.

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