Assessment of the effectiveness of implementing AI tools in business analytics of enterprises in the conditions of digital change

Authors

DOI:

https://doi.org/10.15587/2706-5448.2026.356142

Keywords:

digital transformation, business analytics, intelligent algorithms, forecasting, modelling, resilience, algorithmizing

Abstract

The object of research is the processes of formation, analysis, processing and analysis of information by business analysts of an enterprise in the conditions of changing digital environment. The problem being solved is the need to process a large amount of data to make high-quality management decisions and reduce economic costs. The relevance of the research is due to the trends in the development of the digital environment. The paper considers theoretical and applied aspects and stages of the development of business analytics under the influence of AI tools. The paper explores the fundamental principles of using AI tools in analytical processes of enterprises and assesses their effectiveness. The methodological apparatus is based on the use of a systems approach, methods of theoretical and economic and mathematical modulation. As a research result, it was determined that the AI implementation tools create a positive effect and reduce errors in forecasting (up to 90%) based on business analysis. Accordingly, a positive economic result was established, which proves the feasibility of improving business analytics. A significant increase in the efficiency of such business processes as logistics (by 20–30%), marketing and foreign economic activity of enterprises has been identified. Company management indicates a reduction in additional costs (up to 92%), which contributes to the choice of optimal development strategies. Key barriers to the development of AI have been identified, including the shortage of personnel and the ethics of using digital platforms. It is empirically presented that this is due to the use of only 16% of AI tools in enterprise management. For further effective implementation, a phased transformation of the management environment of enterprises is envisaged through the gradual introduction of digital tools on a permanent basis and the creation, as a result, of a common digital ecosystem of the enterprise in various areas of activity.

Author Biographies

Inna Riepina, Kyiv National Economic University named after Vadym Hetman

Doctor of Economic Sciences, Professor

Department of Business Economics and Entrepreneurship

Maksym Budiaiev, Kyiv National Economic University named after Vadym Hetman

PhD, Associate Professor

Department of Business Economics and Entrepreneurship

Oleksandr Nychyporuk, Kyiv National Economic University named after Vadym Hetman

PhD

Department of Business Economics and Entrepreneurship

Nataliia Yakusheva, Kyiv National Economic University named after Vadym Hetman

PhD

Department of Business Economics and Entrepreneurship

Anhelina Andriushchenko, Palladin Institute of Biochemistry, National Academy of Sciences of Ukraine

PhD Student

Department of Molecular Immunology

Andrii Blyznyuk, Kyiv University of Aviation and Information Technologies

Doctor of Public Administration, Professor

Department of Public Administration and Administration

Yuliia Mazur, Interregional Academy of Personnel Management

PhD, Associate Professor

Department of Business Management

Educational and Scientific Institute of Management, Economics and Business

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Assessment of the effectiveness of implementing AI tools in business analytics of enterprises in the conditions of digital change

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Published

2026-04-30

How to Cite

Riepina, I., Budiaiev, M., Nychyporuk, O., Yakusheva, N., Andriushchenko, A., Blyznyuk, A., & Mazur, Y. (2026). Assessment of the effectiveness of implementing AI tools in business analytics of enterprises in the conditions of digital change. Technology Audit and Production Reserves, 2(4(88), 43–54. https://doi.org/10.15587/2706-5448.2026.356142

Issue

Vol. 2 No. 4(88) (2026): Economics of enterprises. Macroeconomics

Section

Economic Cybernetics

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Copyright (c) 2026 Inna Riepina, Maksym Budiaiev, Oleksandr Nychyporuk, Nataliia Yakusheva, Anhelina Andriushchenko, Andrii Blyznyuk, Yuliia Mazur

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