Preview

Vestnik NSUEM

Advanced search
Open Access Open Access  Restricted Access Subscription Access

Foresight of the application of artificial intelligence technologies in business management

https://doi.org/10.34020/2073-6495-2025-1-153-178

Abstract

The article is devoted to a comprehensive analysis of the use of artificial intelligence (AI) technologies in business management based on a comparison of foresight session data and expert survey results. The study was conducted against the backdrop of rapid transformation of market conditions and increased global competition, where companies are increasingly seeking to integrate AI technologies into their activities to optimize management processes and improve decision-making mechanisms. Particular attention in the article is paid not only to the potential of AI in improving the efficiency of business processes, but also to the ethical, legal and social issues that arise. The methodological basis of the study includes the analysis and synthesis of data obtained during the Foresight session held at the Southern Federal University and survey data, which involved more than 300 experts from different regions and industries. The research emphasizes the importance of an integrated approach that takes into account both the technical aspects of AI and the human factor. Its results show that, despite the significant prospects for using AI in entrepreneurship, there are serious obstacles to this process, particularly the lack of qualified specialists, high costs of implementing and supporting technologies, as well as the lack of a clear regulatory framework. The authors offer recommendations for businesses on the integration of AI, including the development of educational programs, optimization of project financing and compliance with ethical standards. This study contributes to the understanding of both the current state and future prospects for using AI in business management and also contributes to the development of strategies to overcome existing and future problems.

About the Authors

S. V. Savin
Southern Federal University; “Resalt Region” LLC
Russian Federation

Savin Sergey V. – Postgraduate Student, Faculty of Management; General Director

Rostov-on-Don



A. D. Murzin
Southern Federal University; Don State Technical University
Russian Federation

Murzin Anton D. – Doctor of Technical Sciences, Candidate of Economic Sciences, Associate Professor, Professor of the Department of Management of Development of Spatial-Economic Systems, Faculty of Management

Rostov-on-Don



References

1. Zhdanov D.A., Mikirtichan A.G. Chelovecheskij kapital kak faktor intensivnogo razvitija kompanii [Human Capital as a Factor of the Intensive Development of the Company]. Trudy Mezhdunarodnoj nauchno-prakticheskoj konferencii «Rossija 2020 – novaja real’nost’: jekonomika i socium» (ISPCR 2020) (Velikij Novgorod 9–10 dekabrja 2020). Velikij Novgorod, 2021. doi.org/10.2991/aebmr.k.210222.087

2. Ivanova N.A. Strategicheskoe upravlenie innovacionnymi jekonomicheskimi cepjami na osnove ocenki konkurentnoj sposobnosti [Strategic management of innovative economic chains based on competitiveness assessment], Jekonomika i upravlenie: problemy, reshenija [Economics and Management: Problems, Solutions], 2023, vol. 4, no. 10, pp. 100–107. doi.org/10.36871/ek.up.p.r.2023.10.04.014

3. Abdelazeem B., Hamdallah A., Rizk M., Abbas K., El-Shahat N., Manasrah N., Mostafa M. & Eltobgy M. Does the use of monetary incentives affect survey participation? A systematic review and meta-analysis of 46 randomized controlled trials. PLOS ONE, 2023, vol. 18.

4. Beczski-Nagy P., Fazekasz B. Entrepreneurship development through public venture capital in emerging industries – Evidence from Hungary. Journal of Entrepreneurship in Developing Economies, 2023.

5. Bhatt P. & Muduli A. Artificial intelligence in learning and development: a systematic literature review. European Journal of Learning and Development, 2022.

6. Chui M., Manyika J., Miremadi M. What AI can and can’t do (yet) for your business. McKinsey Quarterly, 2018.

7. Dodd D., Hinton M. Performance measurement and evaluation: applying return on investment (ROI) to human capital investments. International Journal of Productivity and Performance Management, 2022.

8. Doshi-Veles F., Kim B. Toward a rigorous science of interpretive machine learning. Preprint arXiv arXiv:1702.08608, 2017.

9. Floridi L., Coles J., Beltrametti M., Chatila R., Chazerand P., Dignum V., ... and Wayen E. AI4People – An ethical framework for a good AI society: opportunities, risks, principles and recommendations. Minds and Machines, 2018, vol. 28, pp. 689–707.

10. Holtom B., Baruch Y., Aguinis H. & Ballinger G. Survey response rates: trends and the structure of validity assessment. Human Relations, 2022, vol. 75, pp. 1560–1584.

11. Jobin A., Jenca M., Vayena E. The global landscape of AI ethics guidelines. Nature Machine Intelligence, 2019, vol. 1, no. 9, pp. 389–399.

12. Knight W. The dark secret at the heart of AI. MIT technology review, 2017.

13. Kumari B., Kaur J., Swamy S. Implementing artificial intelligence in financial services: policy imperatives. Journal of science and technology policy management, 2022.

14. Liu L., Hu Z. Big data analysis technology for building and applying artificial intelligence-based decision-making platform. Mobile information systems, 2022.

15. Mladenich D. Invited speakers. International conference on innovation in intelligent systems and applications (INISTA), 2022.

16. Moraes C., Skolimoski J., Lambert-Torres G., Santini M., Dias A., Guerra F., Pedretti A. & Ramos M. Robotic process automation and machine learning: a systematic review. Brazilian Archives of Biology and Technology, 2022.

17. Morandini S., Fraboni F., Angelis M., Puzzo G., Giusino D. & Pietrantoni L. Evaluating artificial intelligence in skills assessment: upskilling and retraining in organizations. Informing Science International Journal of an Emerging Transdiscipline, 2023, vol. 26, pp. 39–68.

18. Popenici SAD, Kerr S. Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and practice in technology enhanced learning, 2017, vol. 12, no. 1, p. 22.

19. Sharma R., Shishodia A., Gunasekaran A., Min H., Munim Z. The role of artificial intelligence in supply chain management: territory mapping. International journal of manufacturing research, 2022, vol. 60, pp. 7527–7550.

20. Srivastav M. Barriers associated with adoption of AI in supply chain management. J. Glob. Inf. Manag., 2022, vol. 30, pp. 1–19. https://doi.org/10.4018/jgim.296725

21. Tan Y., Chau K., Lau Y. & Zheng Z. Inventory forecasting using AI models to automate cross-border e-commerce services. Applied Sci., 2023.

22. Wamba-Tagimje S., Wamba S., Kamjug J. & Wanko K. The impact of artificial intelligence (AI) on firm performance: business value of AI-based transformation projects. Business Process Management Journal, 2020, vol. 26, pp. 1893–1924.

23. McKinsey. The economic potential of generative AI: the next performance frontier. June 2023. Available at: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier


Review

For citations:


Savin S.V., Murzin A.D. Foresight of the application of artificial intelligence technologies in business management. Vestnik NSUEM. 2025;(1):153-178. (In Russ.) https://doi.org/10.34020/2073-6495-2025-1-153-178



ISSN 2073-6495 (Print)