AI-Powered Passenger Experience in Airlines: A KPI-Driven Framework with Mixed-Methods Evidence

SeyyedAbdolHojjat  MoghadasNian,Farzaneh  GhajarGar

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Эта статья является препринтом и не была отрецензирована.
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AI-Powered Passenger Experience in Airlines: A KPI-Driven Framework with Mixed-Methods Evidence

S. MoghadasNian,

F. GhajarGar

2026-01-05

https://doi.org/10.24108/preprints-3114216

This article develops and tests a KPI-driven, Balanced-Scorecard–aligned framework that links AI modalities to enterprise outcomes across the passenger journey. The primary objective is to quantify and explain how dynamic offers, recommender systems, conversational AI, biometrics, and predictive IRROPS affect customer experience (NPS, CSAT, CES), monetization (ARPP, conversion), operations (IRROPS time, OTP), and finance/ESG (RASK, CASK, CO₂/ASK). Using a mixed-methods, explanatory-sequential design, we analyze multi-system KPIs and A/B or difference-in-differences rollouts, followed by executive interviews on governance (privacy, fairness, robustness, explainability). Findings show dynamic ancillary pricing delivers +17–58% conversion and +10–43% revenue per offer; conversational AI reduces waiting time up to 80% for routine intents but requires hybrid escalation to sustain NPS; recommenders raise CTR (~+15%) yet need stronger causal links to repeat booking and CLV; revenue-management accuracy improves +14–22%, supporting yield stability. Evidence remains limited on causal bridges to RASK/CASK and CO₂/ASK. Theoretically, we formalize a five-layer KPI architecture and position digital maturity as a measurable moderator. Practically, we recommend a Foundations → Pilot → Scale → Optimize roadmap with per-pax/per-ASK denominators, instrumentation (A/B, DiD, uplift), and responsible-AI gates (consent, bias, robustness, model cards). The framework enables airline leaders to convert AI initiatives into decision-grade, auditable value.

Ссылка для цитирования:

MoghadasNian S., GhajarGar F. 2026. AI-Powered Passenger Experience in Airlines: A KPI-Driven Framework with Mixed-Methods Evidence. PREPRINTS.RU. https://doi.org/10.24108/preprints-3114216

Список литературы

1. Accenture Technology. (2024). Responsible AI in Aviation: Governance Frameworks for Ethical Implementation. Accenture Research.

2. Aviation Week Intelligence Network. (2024). AI Implementation in Airlines: Industry Survey and Performance Benchmarks. Aviation Week Network.

3. Boston Consulting Group. (2023). The Future of Airline Customer Experience: AI-Driven Personalization and Service Excellence. BCG Travel & Tourism Practice.

4. Deloitte Consulting. (2023). Airline Digital Maturity Assessment: Benchmarking and Best Practices. Deloitte Aviation Practice.

5. Hair, J. F., Babin, B. J., Anderson, R. E., & Black, W. C. (2019). Multivariate Data Analysis (8th ed.). Cengage.

6. Harvard Business Review. (2024). Digital Customer Experience: KPI Frameworks for Service Industries. Harvard Business School Publishing.

7. Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press.

8. International Air Transport Association (IATA). (2024). Digital Transformation in Aviation: Strategic Framework and Implementation Guide. IATA Publications.

9. Jain, P. K., Srivastava, G., Lin, J. C.-W., & Pamula, R. (2022). Unscrambling Customer Recommendations: A Novel LSTM Ensemble Approach in Airline Recommendation Prediction Using Online Reviews. IEEE Transactions on Computational Social Systems, 9(6), 1777–1784. https://doi.org/10.1109/tcss.2022.3200890

10. Journal of Air Transport Management. (2023). Sustainability Metrics in Aviation: Environmental Impact of Digital Transformation. Elsevier.

11. Kolbeinsson, A., Shukla, N., Gupta, A., Marla, L., & Yellepeddi, K. (2022). Galactic Air Improves Ancillary Revenues with Dynamic Personalized Pricing. INFORMS Journal on Applied Analytics, 52(3), 233–249. https://doi.org/10.1287/inte.2021.1105

12. McKinsey & Company. (2024). AI in Aviation: Transforming Passenger Experience Through Intelligent Automation. McKinsey Global Institute.

13. MIT Technology Review. (2023). Measuring AI Impact: Attribution Models for Business Transformation. MIT Press.

14. MoghadasNian, S. (2025). AI augmented creativity in airline crisis management: Balancing novelty and strategic feasibility. 12th National Interdisciplinary Research Conference in Management & Humanistic Sciences.

15. MoghadasNian, S. (2025). AI-driven inventory optimization in airline logistics: Enhancing efficiency, sustainability, and operational performance. In Proceedings of the International Conference on “Artificial Intelligence in the Age of Digital Transformation”.

16. MoghadasNian, S., & Amiri, A. (2024). Elevating financial audit practices in the airline industry: A KPI-driven framework for enhanced oversight and performance. In Proceedings of the 10th International and National Conference on Management, Accounting and Law Studies.

17. MoghadasNian, S., & Ardakani, A. (2024). Optimizing performance in airline catering: A KPI-driven approach. In Proceedings of the 8th National Conference on Interdisciplinary Research in Management & Medical Sciences.

18. MoghadasNian, S., & HajiBandeh, M. (2024). KPI-driven in-flight catering: Enhancing passenger experience. 8th International Conference on Applied Researches in Science & Engineering, Aachen, Germany.

19. MoghadasNian, S., & JavanMard, M. (2024). Optimizing international relations strategies: A KPI-driven approach for airline industry leaders. In Proceedings of the 8th International Conference on Management, Tourism and Technology.

20. MoghadasNian, S., & MahMoudy, M. (2025). Airline logistics AI performance framework: A 360-degree, multi-layered KPI approach for safety, sustainability, efficiency, and innovation. In Proceedings of the 20th Iranian International Industrial Engineering Conference.

21. MoghadasNian, S., & Moadab, M. (2024). Enhancing airline customer engagement through innovation: A KPI-driven framework for success. In Proceedings of the 8th National Conference on Interdisciplinary Research in Management & Medical Sciences.

22. MoghadasNian, S., & MoghadasNian, A. S. (2024). KPI-driven decision making for lounge managers: Enhancing customer experience and efficiency. In Proceedings of the 2nd International Congress on Science, Engineering & New Technologies. Hamburg, Germany.

23. MoghadasNian, S., & Mojavezi, S. (2024). KPI-driven decision making in airport services: Enhancing operational efficiency, customer satisfaction, and sustainability. In Proceedings of the 8th National Conference on Interdisciplinary Research in Engineering and Management.

24. MoghadasNian, S., & Moradi Dehkaboud, H. (2024). Optimizing airline base maintenance through KPI-driven strategies: A roadmap for operational excellence and financial sustainability. In Proceedings of the 8th National Conference on Interdisciplinary Research in Engineering and Management.

25. MoghadasNian, S., & NaziriHosseinpour, P. (2024). Legal frameworks and strategic international relations: Guiding the airline industry through regulatory compliance and diplomacy. In Proceedings of the 6th International Conference & The 7th National Conference on Law and Political Science.

26. MoghadasNian, S., & RoshanSourat, A. (2024). Enhancing airline maintenance efficiency: A KPI-driven framework for production control. In Proceedings of the 8th National Conference on Interdisciplinary Research in Engineering and Management.

27. MoghadasNian, S., & SadatSerki, Z. S. (2024). Enhancing cargo operations efficiency: A KPI-driven approach in the airline industry. In Proceedings of the 8th National Conference on Interdisciplinary Research in Engineering and Management.

28. MoghadasNian, S., & SamarRokhi, R. (2024). Empowering airline treasury strategy: A comprehensive KPI framework for chief treasury officers. In Proceedings of the 10th International and National Conference on Management, Accounting and Law Studies.

29. MoghadasNian, S., & Sarvi, N. (2024). Strategic enhancement of airline maintenance operations: A KPI-driven approach for the chief line maintenance officer. Transactions on Machine Intelligence, 7(4), 131–140. ISSN 2821-1693.

30. NIST. (2023). AI Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology.

31. Piedade, L., Martins, I., Marques, M., & O’Neill, A. (2025). Beyond boarding: the intersection of security and comfort in the digital transformation era. Journal of Law and Sustainable Development, 13(3), e04355. https://doi.org/10.55908/sdgs.v13i3.4355

32. PwC Strategy&. (2024). Revenue Management Evolution: AI-Powered Personalization in Airlines. PwC Aviation Insights.

33. Sağbaş, M., & Aydogan, S. (2025). Unveiling the Nuances: How Fuzzy Set Analysis Illuminates Passenger Preferences for AI and Human Agents in Airline Customer Service. Tourism and Hospitality, 6(1), 43. https://doi.org/10.3390/tourhosp6010043

34. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.

35. Shukla, N., Kolbeinsson, A., Marla, L., & Yellepeddi, K. (2020). From Average Customer to Individual Traveler: A Field Experiment in Airline Ancillary Pricing. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3518854

36. Shukla, N., Kolbeinsson, A., Otwell, K., Marla, L., & Yellepeddi, K. (2019). Dynamic Pricing for Airline Ancillaries with Customer Context. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2174–2182. https://doi.org/10.1145/3292500.3330746

37. Siering, M., Deokar, A. V., & Janze, C. (2018). Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews. Decision Support Systems, 107, 52–63. https://doi.org/10.1016/j.dss.2018.01.002

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