Comparative analysis of russian and foreign methodologies for assessing the artificial intelligence technologies market in the agro-industrial complex

Issue № 6, 2026, article № 3, pages 18-31

Section: Digitalization in agrarian and industrial complex

Language: Russian

Original language title: СРАВНИТЕЛЬНЫЙ АНАЛИЗ ОТЕЧЕСТВЕННЫХ И ЗАРУБЕЖНЫХ МЕТОДИК ОЦЕНКИ РЫНКА ТЕХНОЛОГИЙ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В АГРОПРОМЫШЛЕННОМ КОМПЛЕКСЕ

Keywords: ARTIFICIAL INTELLIGENCE, AGRO-INDUSTRIAL COMPLEX, ASSESSMENT METHODOLOGIES, DIGITALIZATION OF AGRICULTURE, ECONOMIC EFFICIENCY, COMPARATIVE ANALYSIS, AI INVESTMENTS, IMPLEMENTATION BARRIERS

Abstract: The article presents a comparative analysis of methodological approaches to assessing the artificial intelligence technology market in the agro-industrial complex as applied in Russian and international practice. The relevance of the study stems from the need to establish a unified system for evaluating the effectiveness of AI solutions in agriculture against the backdrop of rapid growth in the global agtech market. The analysis of foreign methodologies reveals their macroeconomic orientation, whereas Russian domestic developments focus primarily on the micro level. The comparative analysis yields a key insight for Russia: neither approach works in its pure form. Foreign methodologies ensure comparability and standardization but ignore Russian agricultural specificities – soil and climatic zones, farm structures, and skills shortages. Domestic methodologies are deeply adapted to local conditions but remain fragmented and non‑standardized. Harmonization, therefore, becomes not an academic wish but a prerequisite for building a national pre‑investment assessment system, which most agricultural enterprises currently lack. Drawing on current statistical data (Ministry of Agriculture of the Russian Federation, RANEPA, Ministry of Digital Development, NAURR), the paper provides a quantitative assessment of the potential for AI technology adoption in Russian agriculture and identifies the main barriers to its scaling. The study also outlines directions for further integration of domestic methodologies with international standards, thereby laying the groundwork for a national monitoring and performance evaluation system for AI projects in the agro-industrial complex.

Authors: Sibirskaia Elena Viktorovna, Vladyko Irina IUrevna