Human-Centric AI Integration in Airline Operations: Enhancing Safety, Efficiency, and Workforce Resilience

SeyyedAbdolHojjat  MoghadasNian

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Эта статья является препринтом и не была отрецензирована.
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Human-Centric AI Integration in Airline Operations: Enhancing Safety, Efficiency, and Workforce Resilience

S. MoghadasNian

2026-01-20

https://doi.org/10.24108/preprints-3114306

This study investigates the transformative impact of generative AI on expertise hierarchies within airline operations and explores strategies for seamless human-AI collaboration in safety-critical workflows. The primary objective is to examine how AI-driven analytics reconfigures decision-making roles empowering junior staff with advanced insights while allowing senior experts to assume mentoring and oversight responsibilities. Employing a mixed-methods approach that combines qualitative case studies with quantitative KPI analysis, our research demonstrates significant operational benefits, including reduced turnaround times, enhanced predictive maintenance, and improved resource utilization. However, the study also reveals risks associated with workforce skill atrophy when AI support dominates routine tasks. The findings underscore the necessity of robust training programs, clear communication protocols, and the implementation of dual-axis KPI frameworks (e.g., Efficiency-Skill Ratio, Risk-Adjusted ROI) to balance technological gains with human expertise retention. These insights have significant implications for theory and practice in digital transformation and airline management, offering actionable recommendations for airline managers and policymakers to foster sustainable operational excellence.

Ссылка для цитирования:

MoghadasNian S. 2026. Human-Centric AI Integration in Airline Operations: Enhancing Safety, Efficiency, and Workforce Resilience. PREPRINTS.RU. https://doi.org/10.24108/preprints-3114306

Список литературы

1. European Commission. (n.d.). Industry 5.0. Research and Innovation. https://research-and-innovation.ec.europa.eu/research-area/industrial-research-and-innovation/industry-50_en

2. Huang, M., Jin, M., & Li, N. (2024). Augmenting Minds or Automating Skills: The Differential Role of Human Capital in Generative AI's Impact on Creative Tasks. ArXiv, abs/2412.03963. https://doi.org/10.48550/arXiv.2412.03963.

3. Kirwan, B. (2024). The Impact of Artificial Intelligence on Future Aviation Safety Culture. Future Transportation, 4(2), 349-379. https://doi.org/10.3390/futuretransp4020018.

4. Latino, M. E. (2025). A maturity model for assessing the implementation of Industry 5.0 in manufacturing SMEs: Learning from theory and practice. Technological Forecasting and Social Change, 214, 124045. https://doi.org/10.1016/j.techfore.2025.124045

5. Li, N., Zhou, H., & Mikel-Hong, K. (2024). Generative AI Enhances Team Performance and Reduces Need for Traditional Teams. ArXiv, abs/2405.17924. https://doi.org/10.2139/ssrn.4844976.

6. McKinsey & Company. (2024). The state of AI: Global survey. Retrieved from https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

7. Moghadasnian, S. (2022). Flight to Excellence: A Comprehensive Guide to Key Performance Indicators in the Airline Industry: Unlocking Success Through Data-Driven Strategies and Performance Metrics. Aviation and Tourism Research and Innovation Center (ATRIC). Digital Publication. Tehran, Iran & Milan, Italy.

8. Moghadasnian, S. (2023). Strategica Aeronautica: Mastering KPI-Driven Leadership Across the Airline and Tourism Ecosystem: A Comprehensive Guide for Executives: From Analytic Hierarchy Process to Zero-Based Budgeting, Navigate the Full Spectrum of Strategic Decision-Making Metrics. Aviation and Tourism Research and Innovation Center (ATRIC). Digital Publication. Tehran, Iran & Milan, Italy.

9. MoghadasNian, S. (2025a). Digital transformation in airline logistics: A data-driven approach to enhancing cost efficiency and operational performance. Paper presented at the 19th International Conference on Modern Research in Management, Economics, Accounting and Banking (ICMEAB19), Tbilisi, Georgia.

10. MoghadasNian, S. (2025b). AI-powered predictive maintenance in aviation operations. Paper presented at the 16th International Conference on Advanced Research in Science Engineering and Technology (RSETCONF17), Bern, Switzerland.

11. Subramonyam, H., Thakkar, D., Dieber, J., & Sinha, A. (2024). Content-Centric Prototyping of Generative AI Applications: Emerging Approaches and Challenges in Collaborative Software Teams. ArXiv, abs/2402.17721. https://doi.org/10.48550/arXiv.2402.17721.

12. Zhu, G., Sudarshan, V., Kow, J., & Ong, Y. (2024). Human-Generative AI Collaborative Problem Solving Who Leads and How Students Perceive the Interactions. 2024 IEEE Conference on Artificial Intelligence (CAI), 680-686. https://doi.org/10.1109/CAI59869.2024.00133.

13. Ziakkas, D., & Pechlivanis, K. (2024). The Role of Artificial Intelligence in Transportation Safety Management Systems: The Aviation Safety II Case study. AHFE International.https://doi.org/10.54941/ahfe1005296.

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