Varava Larysa, Doctor of Economic Sciences, Professor, Department of Management and Administration, Kryvyi Rih National University, Kryvyi Rih, Ukraine.

ORCID: 0000-0003-1069-1645
e-mail: lvarava@knu.edu.ua

Stets Roman, PhD Student, Department of Management and Administration, Kryvyi Rih National University, Kryvyi Rih, Ukraine.

ORCID: 0009-0005-0861-4060
e-mail: stets.roman96@gmail.com

Artificial Intelligence as a Tool for Improving Organizational Management: Systematizing AI Applications and the Compensatory Direction

Introduction.Artificial intelligence (AI) is rapidly becoming integrated into management, yet its value is often overstated. AI meaningfully transforms management when it provides capabilities that go beyond classical algorithms. However, the leading integrative frameworks are condition-neutral and do not account for turbulence and involuntary resource loss. This creates a gap that is particularly relevant for Ukrainian enterprises.

Purpose. This study aims to systematize the applications of AI in organizational management, clarify the conditions under which AI genuinely transforms management, develop compensatory strategies, and identify the research gaps left by condition-neutral frameworks. A structured, critical review of international and Ukrainian literature from 2017 to 2026 was conducted using analysis, synthesis, comparison, classification, and typological generalization.

Results. AI creates managerial value when at least one of the following three conditions is met: unstructured input; a substantial reduction in the cost of prediction; or a reconfigurable tool that replaces multiple specialized systems. Five applications of AI are organized along an environmental axis (normal conditions versus turbulence). The key contribution is identifying a compensatory direction whereby AI replaces involuntarily lost capabilities to maintain organizational continuity. This direction provides continuity benefits but introduces new dependencies related to connectivity, energy supply, external vendors, and data control. It is also associated with risks such as lock-in, loss of balance, and erosion of tacit knowledge.

Conclusions. AI improves management by restructuring decision-making rather than through automation alone. Its benefits depend on the task type, the reliable zone of the technology, and complementary investments. For organizations experiencing involuntary capability loss, AI can compensate for the loss, albeit at the cost of increased dependence. The absence of a condition-sensitive and risk-aware framework for such cases is an important area for future research.

Keywords: artificial intelligence; organizational management; managerial decision-making; compensatory AI use; organizational resilience; turbulence.

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