Doing Business with Artificial Intelligence in socio-economic perspective: benefits and risks

Degtiareva Olga
Dr. Sci. in Economics, Professor, Department of Enterprise Economics and Organization of Entrepreneurial Activity, Odessa National Economic University, Ukraine, e-mail: degtiareva@gmail.com, ORCID ID: https://orcid.org/0000-0003-1276-334X

Cite this article:

Degtiareva, О. (2023). Doing Business with Artificial Intelligence in socio-economic perspective: benefits and risks [Sotsialno-ekonomichni aspekty zastosuvannia shtuchnoho intelektu v bisnesseredovyshchi: perevahy ta ryzyky], Socio-economic research bulletin, Vìsnik socìal’noekonomìčnih doslìdžen’ (ISSN 2313-4569), Odessa National Economic University, Odessa, No. 1-2 (84-85), pp. 118-130.

Abstract

The article is dedicated to challenges of doing business with artificial intelligence (AI). The current speed development of the AI technologies leads to significant benefits for those who use them. The variety of the AI technologies was classified according to purpose, technology, functionality, and capabilities. The simplest AI is already widely used in manufacturing enterprises, but the application of the higher level AI-technologies and functions requires innovative design of business processes. Because of the AI-origin and machine learning features the innovative AI-oriented design of business processes is case-dependent, rule-driven and BigData-integrated. The innovative reengineering of business processes can be carried out according to two schemes: 1) an artificial intelligent agent supports the manager in making management decisions, providing the necessary information and/or recommendations; 2) an artificial intelligent agent replaces a manager when making management decisions. The current technology development leads to AI-adoption of a certain range of management decisions, organization of interaction between various production processes without the company’s personnel. The classification of risks arising from the complex AI-integration into production and management processes has been carried out, as well as the causes and consequences of their occurrence have been analyzed. To prevent or minimize AI risks, it is proposed to use a risk-controlling system, which is an effective tool for removing enterprises from the high-risk zone and improving the efficiency of any activity.The special control loop is a controlling solution for risk-management and control over AI and the risks associated with them. Management Cockpit and/or Controlling Cockpit are recommended to organize adequate control over AI-decissions. Thus, there are important conclusions of the research: 1) AI has a very ambitious perspectives for its further implementation in the business environment; 2) causes and consequences of the AI-risks within organization can harm operational activity, business security, financial performance and reputational integrity; 3) AI risk-management needs constant control that can be provided by the control loop and Management Cockpit and/or Controlling Cockpit. Further research should be conducted to detail the functionality of the given control loop, namely: controlling solutions for the effective and safe use of AI at an manufacturing enterprise.

Keywords

artificial intelligence (AI); risk-controlling/management control; innovative design of business processes; managerial decision; manufacturing enterprise.

JEL classification:L230; M150; O330; DOI:https://doi.org/10.33987/vsed.1-2(84-85).2023.118-130

UD classification: 007.5:004.89.330

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

1. Mashliy, G., Mosiy, O., Pelcher, M. (2019). Information provided for labor relationship management as compositional social responsibility of enterprises [Doslidzhennia upravlinskykh aspektiv vykorystannia shtuchnoho intelektu], Galician economic bulletin (Tern.), Vol. 57, No. 2, s. 80–89 DOI: https://doi.org/10.33108/galicianvisnyk_tntu2019.02.080 [in Ukrainian]
2. Strategy of the artificial intelligence development in Ukraine: monograph (2023). Ed. by A. I. Shevchenko [Stratehiia rozvytku shtuchnoho intelektu v Ukraini: monohrafiia; za red. A. I. Shevchenko], Instytut problem shtuchnoho intelektu MON i NAN Ukrainy, Kyiv, 305 s. [in Ukrainian]
3. Azmuk, N. А. (2019). Artificial intelligence in labor processes in conditions of digital economics: new challenges and opportunities [Shtuchnyi intelekt u protsesi pratsi u tsyfrovii ekonomitsi: novi vyklyky i mozhlyvosti], Ekomomichnyi visnyk Donbasu, No. 3 (57), s. 137–145. DOI: 10.12958/1817-3772-2019-3(57)-137-145 [in Ukrainian]
4. Yanenkova, I. G. (2020). Advantages and risks of artificial intelligence using in Ukraine and in the world [Perevagy ta ryzyky vykorystannia shtuchnoho intelektu v Ukraini ta sviti], Efektyvna ekonomika, Vol. 4. DOI: 10.32702/2307-2105-2020.4.19 [in Ukrainian]
5. Koehler, J. (2018). Business Process Innovation with Artificial Intelligence: Levering Benefits and Controlling Operational Risks. European Business & Management, Vol. 4, Issue 2, pp. 55–66. DOI: 10.11648/j.ebm.20180402.12.
6. Raimondo, G. M., & Locascio, L. E. (2023). The Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology & U.S. Department of Commerce. 42 p. DOI: https://doi.org/10.6028/NIST.AI.100-1.
7. Cheatham, B., Javanmardian, K., & Samandari, H. (2019). Confronting the risks of artificial intelligence. McKinsey Quarterly. Retrieved from: https://www.mckinsey.com/capabilities/ quantumblack/our-insights/confronting-the-risks-of-artificial-intelligence.
8. Horváth, P., & Michel, U. (2016). Digital Controlling & Simple Finance. Die Zukunft der Unternehmenssteuerung. 1. Auflage, Schäffer, Poeschel, 240 p.
9. Reichmann, T., Kißler, M., & Baumöl, U. (2017). Controlling mit Kennzahlen – Die systemgestützte Controlling-Konzeption, 9. Auflage, München: Vahlen, 890 p.
10. Degtiareva, О. О. (2020). Transformation of controlling in the context of digitalization [Trasformatsiia kontrolinhu v umovakh dyhitalizatsii], Prychornomorski ekonomichni studii, Issue 54/2020, s. 104–107. DOI: http://doi.org/10.32843/bses.54-16 [in Ukrainian]
11. Fadeyeva, I. G., & Gryniuk, O. I. (2019). Fuzzy logic as a risk-controlling instrument in the context of proactive management of the oil and gas producing enterprises [Nechitka lohika yak instrument ryzyk-kontrolinhu v konteksti proaktyvnoho upravlinnia naftohazovydobuvnymy pidpryiemstvamy], Biznes Іnform, No. 4, s. 212–220. DOI: https://doi.org/10.32983/2222-4459-2019-4-212-220 [in Ukrainian]
12. Turing, A. (1950). Computing Machinery and Intelligence. Mind, LIX (236), pp. 433–460. DOI: 10.1093/mind/LIX.236.433.
13. Generative AI could raise global GDP by 7%. Goldman Sachs Research, 2023. Retrieved from: https://www.goldmansachs.com/insights/pages/generative-ai-could-raise-global-gdp-by- 7-percent.html.
14. Marchenko, К. M., Oryshaka, О. B., Marchenko, А. K., & Melnyk, А. M. (2022). Risks of implementing artificial intelligence in computer systems [Ryzyky vprovadzhennia shtuchnoho intelektu v kompiuterni systemy], Tsentralnoukrainskyi naukovyi visnyk, Seriia: Tekhnichni nauky, Issue 5 (36), Ch. 1, s. 119–124 [in Ukrainian]
15. Tereshchenko, О. О. (2005). Anti-crisis management of enterprise finances: dissertation abstract [Antykryzove upravlinnia finansamy pidpryiemstv: avtoreferat dysertatsii doktora ekon. nauk], Kyiv, 34 s. [in Ukrainian]