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{"id":22965,"date":"2026-10-07T15:48:57","date_gmt":"2026-10-07T12:48:57","guid":{"rendered":"https:\/\/modecon.mnau.edu.ua\/?p=22965"},"modified":"2026-10-09T10:02:16","modified_gmt":"2026-10-09T07:02:16","slug":"integration-of-artificial-intelligence-into","status":"publish","type":"post","link":"https:\/\/modecon.mnau.edu.ua\/en\/integration-of-artificial-intelligence-into\/","title":{"rendered":"Zaika S., Hridin O., Zaika O. Integration of Artificial Intelligence into the System of Management Diagnostics and Operational Management of an Enterprise"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text]<\/p>\n\n\n\n
JEL Classification:<\/strong> M10; M11; O33; C88<\/td>\nDOI<\/b>: https:\/\/doi.org\/10.31521\/modecon.V58(2026)-16<\/a><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/vc_column_text][vc_column_text]Zaika Svitlana, <\/strong>Candidate of Economic Sciences, Professor, Professor in the Department of Management, Business and Administration, State Biotechnological University, Kharkiv, Ukraine<\/p>\n

ORCID:<\/strong> 0000-0001-8132-7643<\/span><\/a>
\ne-mail: <\/strong>zaika.svitlana1975@gmail.com<\/span><\/a><\/p>\n

Hridin Oleksandr,<\/strong> Candidate of Economic Sciences, Associate Professor, Associate Professor in the Department of Management, Business and Administration, State Biotechnological University, Kharkiv, Ukraine<\/p>\n

ORCID:<\/strong> 0000-0002-7236-2954<\/span><\/a>
\ne-mail: <\/strong>aleksandrgridin2015@gmail.com<\/span><\/a><\/p>\n

Zaika Olena,<\/strong> PhD in Finance, Banking and Insurance, Lecturer in the Department of Management, Business and Administration, State Biotechnological University, Kharkiv, Ukraine<\/p>\n

ORCID:<\/strong> 0000-0002-7921-3273<\/span><\/a>
\ne-mail: <\/strong>alyona.zayika1998@gmail.com<\/span><\/a><\/p>\n

Integration of Artificial Intelligence into the System of Management Diagnostics and Operational Management of an Enterprise<\/strong><\/p>\n

Abstract. Introduction. <\/em><\/strong>This study examines the integration of artificial intelligence into management diagnostics and operational management at an enterprise. The need for digital processing of data on resources, costs, quality, personnel, supplies, work completion deadlines and the enterprise\u2019s actual performance indicators has been substantiated. It has been established that the increasing volume of management information complicates the timely assessment of the operational system\u2019s status, the identification of the causes of deviations and the preparation of management decisions using traditional means of analysis. Artificial intelligence is considered as a means of analytical support for management, enabling the processing of large data sets, the identification of recurring patterns, the forecasting of changes and the formation of an information basis for expert assessment.<\/em><\/p>\n

Purpose<\/em><\/strong>. The aim of this article is to substantiate the theoretical principles and develop a structural-functional model for integrating artificial intelligence into a system of management diagnostics and operational management within an enterprise. To achieve this aim, the role of management diagnostics in assessing an enterprise\u2019s operational activities has been defined, the possibilities of digital processing of management information have been explored, and a sequence for the transition from data analysis to management decision-making has been established.<\/em><\/p>\n

Results<\/em><\/strong>. Management diagnostics is considered as the analytical basis for assessing an enterprise\u2019s operational activities. It has been established that its use makes it possible to identify deviations, determine the causes of management problems, assess the links between decisions taken and actual performance indicators, and prepare a diagnostic report. Areas for the application of artificial intelligence in operational management have been identified, specifically the analysis of data on resources, costs, quality, personnel, supply, completion times, forecasting potential changes, comparing decision options, and preparing information for expert analysis. A structural-functional model for integrating artificial intelligence into managerial diagnostics and the enterprise\u2019s operational management is proposed. The model combines the enterprise\u2019s information base, digital data processing, management diagnostics, expert analytics, management decision-making and monitoring of changes in the enterprise\u2019s operations. It is demonstrated that this model enables the alignment of digital analysis with the professional interpretation of the data obtained and the manager\u2019s accountability for the consequences of management decisions.<\/em><\/p>\n

Conclusions<\/em><\/strong>. It has been established that artificial intelligence broadens the analytical basis of operational management, accelerates the processing of management information and facilitates an informed assessment of the enterprise\u2019s condition. It has been determined that digital data processing cannot replace a manager\u2019s professional judgement, as the final management decision requires consideration of the enterprise\u2019s condition, the acceptable level of risk, the organisational structure, staff competence and the conditions for implementing the decision. The proposed model can be used for operations planning, resource utilisation, quality control, supply chain management and the preparation of well-founded management decisions. Further research should focus on developing a methodology for assessing enterprises\u2019 readiness to use artificial intelligence in operations management and on establishing a system of management diagnostic indicators for enterprises of various types. <\/em><\/p>\n

Keywords: <\/em><\/strong>o<\/em>perations management; artificial intelligence; management diagnostics; expert analytics; management decisions; digital management; operational processes<\/em>.<\/em><\/strong><\/p>\n

References<\/b>:<\/strong><\/p>\n

    \n
  1. Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: An empirical investigation. Annals of Operations Research, 333(2), 627-652. https:\/\/doi.org\/10.1007\/s10479-021-03956-x.<\/li>\n
  2. Cannas, V. G., Ciano, M. P., Saltalamacchia, M., & Secchi, R. (2024). Artificial intelligence in supply chain and operations management: A multiple case study research. International Journal of Production Research, 62(9), 3333-3360. https:\/\/doi.org\/10.1080\/00207543.2023.2232050.<\/li>\n
  3. Di Vaio, A., Palladino, R., Hassan, R., & Escobar, O. (2020). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 121, 283-314. https:\/\/doi.org\/10.1016\/j.jbusres.2020.08.019.<\/li>\n
  4. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., \u2026 Williams, M. D. (2021). Artificial intelligence, business and society. International Journal of Information Management, 57, Article 101994. https:\/\/doi.org\/10.1016\/j.ijinfomgt.2019.08.002.<\/li>\n
  5. Helo, P., & Hao, Y. (2022). Artificial intelligence in operations management and supply chain management: An exploratory case study. Production Planning & Control, 33(16), 1573-1590. https:\/\/doi.org\/10.1080\/09537287.2021.1882690.<\/li>\n
  6. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586. https:\/\/doi.org\/10.1016\/j.bushor.2018.03.007.<\/li>\n
  7. Mundlos, P. (2025). The impact of artificial intelligence on managerial attention allocation for discontinuous change: A conceptual framework. Management Review Quarterly, 75, 1-45. https:\/\/doi.org\/10.1007\/s11301-024-00409-0.<\/li>\n
  8. Rocha, I. F., & Kissimoto, K. O. (2022). Artificial intelligence and internet of things adoption in operations management: Barriers and benefits. RAM. Revista de Administra\u00e7\u00e3o Mackenzie, 23(4), Article eRAMR220119. https:\/\/doi.org\/10.1590\/1678-6971\/eRAMR220119.en.<\/li>\n
  9. Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502-517. https:\/\/doi.org\/10.1016\/j.jbusres.2020.09.009.<\/li>\n
  10. Urbanovi\u010d, M., & Holub\u010d\u00edk, M. (2026). Artificial intelligence in managerial decision-making for sustainable business models: A systematic literature review. Systems, 14(3), Article 245. https:\/\/doi.org\/10.3390\/systems14030245.<\/li>\n
  11. Zaika, S. O., Hridin, O. V., & Sahachko, Y. M. (2024). The essence of expert analytics as a basis for managerial decision-making in operational management. Economics of Systems Development, 6(1), 76-83. https:\/\/doi.org\/10.32782\/2707-8019\/2024-1-8.<\/li>\n<\/ol>\n

    [\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]Received:<\/strong>07 July 2026<\/p>\n

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    Zaika S., Hridin O., Zaika O. (2026). Integration of Artificial Intelligence into the System of Management Diagnostics and Operational Management of an Enterprise. Modern Economics, 58(2026), 129-134. DOI: https:\/\/doi.org\/10.31521\/modecon.V58(2026)-16.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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    [vc_row][vc_column][vc_column_text] JEL Classification: M10; M11; O33; C88 DOI: https:\/\/doi.org\/10.31521\/modecon.V58(2026)-16 [\/vc_column_text][vc_column_text]Zaika Svitlana, Candidate of Economic Sciences, Professor, Professor in the Department of Management, Business and Administration, State Biotechnological University, Kharkiv, Ukraine ORCID: 0000-0001-8132-7643 e-mail: zaika.svitlana1975@gmail.com Hridin Oleksandr, Candidate of Economic Sciences, Associate Professor, Associate Professor in the Department of Management, Business and Administration, State Biotechnological University,
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