Implementation of human teaming in aviation industry: The Turkish Airlines case study

Abstract

The rapid digital transformation of commercial aviation has shifted organisational emphasis toward human–AI teaming models capable of enhancing operational efficiency, safety, and resilience. While global carriers are investing in artificial intelligence to optimise decision-making, training, and operational planning, the practical implementation of human–AI collaboration varies significantly across organisations. This paper presents an in-depth case study of Turkish Airlines, examining how one of the world’s largest network carriers has approached the integration of human–AI teaming across flight operations, training systems, and organisational decision structures. The study evaluates both the opportunities unlocked by AI-enabled capabilities and the human performance, cultural, and regulatory considerations that shape successful implementation.The analysis begins with an overview of Turkish Airlines’ digital transformation strategy, highlighting its investment in predictive maintenance, flight operations optimisation algorithms, crew rostering systems, passenger behaviour modelling, and data-driven safety programmes. While these systems are not yet fully autonomous, they increasingly act as collaborative partners—providing complex probabilistic forecasts, adaptive recommendations, and real-time decision-support inputs. This dynamic has begun to redefine the roles and cognitive demands placed upon flight crews, dispatchers, safety analysts, and operational managers, prompting the organisation to rethink how humans and AI systems jointly contribute to operational outcomes.The paper then examines the human factors and training implications associated with this transition. Interviews and document analysis reveal that the success of AI implementation hinges predominantly on the human element—specifically, trust calibration, mental model alignment, interpretability of algorithmic outputs, and the integration of AI-generated insights into high-stakes operational decisions. Within Turkish Airlines’ operational ecosystem, pilots and dispatchers express a dual dependency: appreciation for AI-driven efficiency gains and heightened concern regarding transparency, explainability, and potential loss of authority. These findings underscore the need for training approaches that go beyond procedural instruction and cultivate deeper cognitive skills in critical evaluation, cross-checking of AI outputs, and adaptive cooperation with intelligent systems.Furthermore, the study highlights organisational and cultural considerations unique to large network carriers. Turkish Airlines, operating in a highly multicultural and rapidly expanding environment, illustrates how cultural factors influence trust in automation, communication patterns, and acceptance of AI-driven recommendations. Organisational interviews indicate that a human-centric implementation requires harmonisation between technological innovation, training design, safety culture, and regulatory compliance. The absence of standardised human–AI teaming competency frameworks across regulators presents an additional challenge, particularly for multinational carriers operating across ICAO, EASA, and national oversight environments.The paper concludes with a proposed model for the aviation industry that draws on lessons from the Turkish Airlines case: (1) implementing explainable AI tools to support transparency and trust; (2) integrating AI-focused competencies within CBTA/EBT frameworks; (3) aligning training with human cognitive strengths; and (4) fostering organisational cultures that promote shared responsibility between humans and AI systems. The case study demonstrates that successful human–AI teaming in aviation is not driven by technology alone, but by the ability to adapt training, communication, and organisational culture to ensure safe and resilient collaboration.

Keywords: Human–AI Teaming, Turkish Airlines, Aviation Human Factors, AI Integration, Organisational Culture, Trust In Automation, Flight Operations

DOI: 10.54941/ahfe1007830