Eastern-European Journal of Enterprise Technologies | 2026
Authors: Nurlanuly A.-K.; Serikbayev S.; Aidaraliyeva A.; Akhmetzhanova N.; Stecenko I.; Saktayeva A.; Kirichok O.
DOI: 10.15587/1729-4061.2026.352883
Journal: Eastern-European Journal of Enterprise Technologies
Year: 2026
Publisher: Technology Center
Document Type: Article
Open Access: All Open Access; Gold Open Access; Green Open Access
Cited by: 0
The object of the study is a complex of management practices and organizational mechanisms, which ensure implementation of data analysis and artificial intelligence technologies into airline operations. The study deals with the problem of quantitative evaluation of the impact from the extent and quality of data & artificial intelligence solutions on the key indexes of airline managerial efficiency. The following results have been obtained: – analysis of the digital maturity level with financial and operational key performance indicators of airlines has identified a considerable intercluster differentiation; – a one-point increase in artificial intelligence digital maturity is associated with the growth of operating margin by 1.98%, whereas the 1% increase of data investment share contributes to its growth by 1.12%; – two standard models of data & artificial intelligence innovation project management, which demonstrated various outputs in studied institutional contexts. The produced findings can be explained by the fact that translation of technology investments into financial outcomes is mediated by the quality of management system, which includes strategic alignment, coordinating organizational changes and a system of investment efficiency evaluation. The specifics of obtained results possess a dual nature: on the one hand, they confirm the universally positive effect from data & artificial intelligence implementation; on the other hand, they highlight the critical significance of context-dependent, cluster-specific management strategy. The practical significance of this study lies in the formation of evidence base for making justified decisions by airline management, as well as producing defined tools for maximizing the output from investment in digital technology Copyright © 2026, Authors.
artificial intelligence in aviation; cluster analysis; data; digital transformation; operational efficiency; panel regression