Sukhetska K. Anticipative management of the development of agricultural enterprises

In the process of researching and developing the directions for the development of anticipative management, the key prerequisites for improving the measures for detecting weak signals were identified. After all, the main goal of this process in the context of anticipative management in the agricultural sector is to increase the flexibility, adaptability and sustainability of enterprises. Therefore, first of all, it will strengthen the ability to respond quickly to weak signals indicating future trends and possible risks. A system for detecting weak signals has been formed, which is a critical element in the process of managing agricultural enterprises in conditions of constant uncertainty and rapid changes in the external environment. A set of measures that analyse and interpret information to identify potential changes has been identified. These measures include market monitoring, analytical tools, and data collection tools to identify weak signals. The work presents the main parameters for assessing weak signals of changes in the conditions of the external and internal environments of an enterprise according to the area of manifestation, which will help to predict and predict the consequences of changes. After all, the constant collection and analysis of information on trends and dynamics of the relevant parameters is a key aspect in the timely detection of weak signals. The work identifies the key stages that should be followed when implementing system improvements to improve measures for detecting weak signals. Based on the proposed stages, specific ways to improve the existing measures for detecting weak signals in agricultural enterprises have been developed. The next step in developing areas for improvement was the study of the concept of «roadmap». Consideration of its key types, and goals, and a comparative analysis of the roadmap with other types of planning allowed for a thorough approach to the study and further implementation of this strategic tool in the activities of enterprises involved in the agricultural sector of Ukraine. Therefore, based on the analysed information, an example of a roadmap for anticipative management for agricultural enterprises was developed. Accordingly, the main goal of the roadmap for agricultural enterprises is to facilitate diagnostics and forecasting. After all, a roadmap for agrarians can act as a kind of instruction for overcoming possible crisis situations and have a list of measures to be taken to prevent a crisis. The work defines the concept of «artificial intelligence», its functions, advantages and principles of application. Although this digital tool is relatively new in the field of agriculture, we believe that its implementation in the activities of agricultural enterprises, in the context of anticipative management, will provide significant competitive advantages in the market. The main prejudices and common misconceptions about artificial intelligence were identified, which provided a more comprehensive understanding of its potential risks and problems when implemented in the activities of an enterprise. The study analysed the volume of global investment in artificial intelligence technologies and found that globally, private investment in AI decreased by 26.7% in 2022, while the broader corporate investment landscape, which also includes mergers and acquisitions, minority stakes, and public offerings, decreased by 31.3%. Research has shown that there are currently no clear statistics on overall AI investment in the agricultural sector in Ukraine. Currently, investments in AI in the Ukrainian agricultural sector are still insignificant. However, there is a great potential for growth of these investments in the coming years. Examples of AI applications in the process of anticipative management in agricultural enterprises based on key types of crisis situations are proposed. The research was conducted and the key goals and their value in the process of introducing artificial intelligence in agricultural enterprises were outlined: increasing competitiveness (90%), improving decision-making (87%), reducing risks (55%), improving the quality of agricultural products (50%) and increasing productivity and efficiency (68%). Keywords: agrarian sector, anticipative management, crisis, development, anticipative team, weak signals, artificial intelligence, roadmap, agricultural enterprises.