Petrenko Olga, Candіdate of Economіc Scіences, Assocіate Professor of the Department of Economіc Theory and Economіcs of Enterprіses, Odessa State Agrarіan Unіversіty, Odessa, Ukraіne

ORCID: 0000-0001-9722-3785
e-mail: Leka_m@ukr.net

Shevchenko Alіsa, Candіdate of Economіc Scіences, Assocіate Professor of the Department of Economіc Theory and Economіcs of Enterprіses, Odessa State Agrarіan Unіversіty, Odessa, Ukraіne

ORCID: 0000-0002-3581-7884
e-mail: alіsochka1978@gmaіl.com

Simekchi Anastasiia, applicant for the second (master’s) level of higher education in the specialty Economics and International Economic Relations, Odessa State Agrarіan Unіversіty, Odessa, Ukraіne

ORCID: 0009-0007-8196-7836
e-mail: an.fedorovna2003@gmail.com

Digital Transformation of Pricing in Agricultural Enterprises Based on Artificial Intelligence

Abstract. The agricultural sector currently depends critically on volatile global markets and unpredictable climate changes. This requires domestic enterprises to adopt new approaches to operational decision-making. However, existing pricing practices in Ukraine’s agro-industrial complex mainly rely on traditional cost-based methods that focus on internal production costs. These methods are unable to adequately account for rapid fluctuations in global supply and demand. This methodological gap results in significant profitability losses due to delayed responses to dynamic market signals and logistical challenges. Therefore, there is an urgent need to digitize pricing policies and implement intelligent decision support systems. Using artificial intelligence tools is becoming essential for ensuring the financial stability and competitiveness of agricultural enterprises in a changing economic environment.

Purpose. This study aims to establish the theoretical and methodological basis and practical applications of digitalizing pricing in agricultural enterprises using artificial intelligence, big data, and predictive analytics. The goal is to enhance pricing policy efficiency, mitigate volatility risks, and guarantee the strategic competitiveness of agribusiness amid global economic and security challenges.

Results. This study examines the features of digital pricing transformations in agricultural enterprises based on artificial intelligence in the context of high market volatility, climate change, and global economic challenges. The necessity of transitioning from traditional cost-based pricing models to dynamic digital price management systems is substantiated. A conceptual model for transforming pricing principles in the digital age has been developed, and relevant digital principles have been identified. The integration of intelligent decision-support systems has been proven to enable agricultural enterprises to minimize volatility risks, optimize profitability, and ensure the strategic sustainability of business operations. The use of digital platforms and predictive analytics has been shown to increase the efficiency of pricing policies, automate management processes, and create competitive advantages for agricultural enterprises.

Conclusіons. The study concludes that digital transformation of pricing based on artificial intelligence is indispensable for the development of modern agribusiness. This is especially important for overcoming price parity distortions. The use of intelligent systems reduces the gap between the cost of industrial resources and agricultural products by precisely managing fuel, fertilizer, and crop protection expenses. Predictive analytics effectiveness is ensured by the synergy of five components: systematic data collection using the Internet of Things (IoT), drones, and satellites; integration; application of machine learning algorithms; visualization tools; and generation of actionable insights. Practical testing of deep learning models confirms the ability to achieve high forecasting accuracy, with an error margin of only 0.29–9.8%. This enables farmers and agricultural holdings to optimize resource allocation, maximize yields, and minimize operational risks.

Keywords: digitalization; pricing; agricultural enterprises; artificial intelligence; Big Data; predictive analytics; digital transformation; competitiveness.

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