Artificial intelligence in agribusiness: research trends and gaps

Abstract

This article analyzes the evolution of scientific production, emerging themes, and research gaps related to artificial intelligence applied to decision-making in agribusiness, through a bibliometric review of scientific literature indexed in Scopus for the period 2020-2026. The introduction contextualizes the growing adoption of artificial intelligence in the agri-food sector and raises the problem of the limited systematic understanding of how research in this field has evolved. The theoretical framework articulates the concepts of artificial intelligence, organizational decision-making, agribusiness, and bibliometrics as an analytical tool. The methodology describes a bibliometric design based on Donthu et al. (2021) guidelines, with a search equation that retrieved 8,088 documents, reduced to 1,662 after applying filters for year, document type, language, and subject area. The results show accelerated growth in scientific production with a peak of 480 documents in 2025, a shift in the dominant thematic axis from decision support systems toward machine learning, and significant gaps at the intersection of artificial intelligence, agricultural business management, and Latin American scientific production. The discussion contrasts these findings with previous studies and highlights their relevance for the strategic management of organizations in the sector. It is concluded that research on artificial intelligence in agribusiness has grown rapidly but remains disconnected from the managerial needs of the sector, opening a relevant research agenda for Latin America.