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, Kharkiv, Ukraine

ORCID: 0000-0002-7236-2954
e-mail: aleksandrgridin2015@gmail.com

Zaika Olena, PhD in Finance, Banking and Insurance, Lecturer in the Department of Management, Business and Administration, State Biotechnological University, Kharkiv, Ukraine

ORCID: 0000-0002-7921-3273
e-mail: alyona.zayika1998@gmail.com

Integration of Artificial Intelligence into the System of Management Diagnostics and Operational Management of an Enterprise

Abstract. Introduction. 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’s 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’s 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.

Purpose. 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’s 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.

Results. Management diagnostics is considered as the analytical basis for assessing an enterprise’s 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’s operational management is proposed. The model combines the enterprise’s information base, digital data processing, management diagnostics, expert analytics, management decision-making and monitoring of changes in the enterprise’s operations. It is demonstrated that this model enables the alignment of digital analysis with the professional interpretation of the data obtained and the manager’s accountability for the consequences of management decisions.

Conclusions. 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’s condition. It has been determined that digital data processing cannot replace a manager’s professional judgement, as the final management decision requires consideration of the enterprise’s 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’ readiness to use artificial intelligence in operations management and on establishing a system of management diagnostic indicators for enterprises of various types.

Keywords: operations management; artificial intelligence; management diagnostics; expert analytics; management decisions; digital management; operational processes.

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