The increasing adoption of Human–AI teaming across the transportation sector is reshaping operational roles, organisational structures, and future workforce competencies. As artificial intelligence systems evolve from decision-support tools to collaborative teammates capable of perception, prediction, and autonomous action, transportation organisations must fundamentally rethink how they design, train, allocate, and sustain their workforce. Workforce planning—traditionally centred on staffing levels, qualification pipelines, and operational forecasts—now plays a pivotal role in ensuring that human capabilities remain aligned with the cognitive, technical, and ethical demands of future human–AI teams. This paper examines the strategic function of workforce planning in supporting the safe and effective implementation of human–AI teaming across aviation, rail, maritime, and ground transportation environments.The analysis begins by outlining the systemic transformations introduced by AI-driven operations. In contrast to earlier waves of automation, contemporary AI systems introduce non-deterministic behaviour, dynamic adaptability, and shared decision-making responsibilities. These characteristics challenge legacy workforce models premised on stable task distributions and predictable human roles. Across transportation modes, the shift toward human–AI teaming requires workforce planners to anticipate emerging competencies such as AI oversight, interpretability skills, mixed-initiative collaboration, and algorithmic risk assessment. The alignment of these competencies with recruitment, selection, training, and career progression becomes essential to preventing skill gaps, cognitive overload, and mismatches between human capabilities and system demands.The paper further examines workforce planning as a human factors instrument that supports organisational readiness. Effective planning requires an integrated understanding of future task redesign, shifts in workload dynamics, and the redistribution of responsibilities between humans, AI agents, and remote support structures. In aviation, for example, workforce planners must prepare for mixed-crew configurations, single pilot operations with AI copilots, and remote supervisory roles; in rail and maritime systems, the emergence of autonomous navigation and predictive maintenance similarly redefines operator roles. Planning must therefore incorporate scenario-based forecasting, human reliability analyses, and long-term modelling of human–machine performance interactions to ensure that human resource strategies align with technological evolution.Training and competency development are examined as critical components linking workforce planning to operational implementation. As human–AI teaming becomes central to safety-critical operations, organisations must develop training pathways that cultivate adaptive expertise, trust calibration, interpretability awareness, and resilience under automation. Workforce planning provides the structural basis for identifying training populations, sequencing AI-focused skills acquisition, and embedding competency-based training and assessment (CBTA/EBT) methods across the transportation ecosystem. The paper argues that overlooking this alignment risks creating workforces that are operationally certified but cognitively unprepared for high-automation environments.The study also addresses regulatory, cultural, and organisational constraints. Many transportation regulators have yet to establish competency frameworks for human–AI teaming, leaving workforce planners without standard definitions of required skills or acceptable performance thresholds. Organisational cultures may further influence AI acceptance, trust, and reporting behaviours, requiring workforce planning to integrate cultural readiness assessments and targeted change-management strategies.The paper concludes by proposing a workforce planning model designed to support large-scale adoption of human–AI teaming in transportation. This model integrates technological forecasting, human factors analysis, competency mapping, and AI-focused resilience strategies. The findings emphasise that technological innovation alone is insufficient; the success of human–AI teaming depends on a strategically designed workforce that is cognitively, operationally, and organisationally prepared for the transport systems of the future.
Dimitrios Ziakkas, Ibrahim Sarikaya, Konstantinos Pechlivanis et al.· AHFE International· 0 citations
Human–AI teaming is rapidly emerging as a defining paradigm in next-generation aviation operations, reshaping pilot roles, altering cockpit task distribution, and challenging established assumptions regarding expertise, decision-making, and training. As artificial intelligence systems evolve from deterministic support tools into adaptive, autonomous teammates capable of perception, prediction, and intent-driven action, the aviation training ecosystem faces a suite of unprecedented challenges. These challenges extend beyond purely technical skills and encompass deeper questions of trust calibration, cognitive adaptation, workload redistribution, ethical responsibility, and sustained human performance. This paper examines the central training challenges associated with preparing pilots, instructors, and organisational systems for effective human–AI teaming across current and expected future aviation environments.First, the paper analyses the shifting cognitive and operational landscape introduced by AI-enabled systems, including adaptive automation, predictive analytics, natural-language interfaces, and mixed-initiative control architectures. Whilst these technologies promise enhanced situational awareness, reduced workload, and strengthened predictive safety nets, they simultaneously introduce risks such as automation complacency, algorithmic over-reliance, erosion of manual competencies, and emergent forms of mode confusion. Training organisations must therefore rethink curriculum design to cultivate appropriate levels of trust in AI agents while strengthening pilots’ abilities to monitor, interrogate, and, where necessary, override AI behaviour during uncertainty or system drift. Traditional training paradigms based on linear automation logic are insufficient to address the probabilistic and at times opaque behaviour of modern AI systems.Second, the paper explores the pedagogical complexities inherent in developing joint human–AI decision-making skills. Effective teaming requires robust communication transparency, alignment of mental models, and the formation of shared situational awareness between human operators and algorithmic agents. Yet many AI systems operate as “opaque teammates,” offering outputs without interpretive depth or explainable reasoning. Training must therefore introduce strategies for evaluating machine-generated recommendations, identifying algorithmic bias, integrating AI insights with experiential human judgement, and managing discrepancies between human and AI interpretations. Scenario-based training, explainable AI (XAI) tools, and structured failure-mode exploration are presented as essential approaches for mitigating these challenges.Third, organisational, regulatory, and standardisation constraints are evaluated. The absence of harmonised human–AI competency frameworks, variability in AI system behaviour across aircraft types, and ambiguities regarding accountability pose obstacles for both initial and recurrent training. A critical need exists for evidence-based human factors methodologies that define the skills required for pilots operating in mixed-initiative or partially autonomous environments. Emerging competency-based training and assessment (CBTA/EBT) methodologies offer a promising foundation but require expansion to incorporate AI teaming competencies, error management strategies, and resilience-building mechanisms.The paper argues that training for human–AI teaming must remain fundamentally human-centric, preserving pilots’ adaptive expertise, situational awareness, and critical thinking while ensuring that AI systems remain compatible with human cognitive strengths and limitations. It concludes by proposing an integrated training model to support safe, resilient, and ethically aligned human–AI cooperation in future aviation operations.
Dimitrios Ziakkas, Ibrahim Sarikaya, Debra Henneberry· AHFE International· 0 citations
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.
Ibrahim Sarikaya, Dimitrios Ziakkas, Fatih Rustu Altunok· AHFE International· 0 citations
Resilience has become a central concept in contemporary aviation safety, reflecting the industry’s need to manage complexity, uncertainty, and unexpected disturbances across increasingly automated and dynamic operational environments. While flight operations depend on the capacity of individuals, teams, and organisations to anticipate, adapt, and recover from disruptions, the practical implementation of resilience remains challenging. Safety Management Systems (SMS), as mandated frameworks across global aviation, play a critical role in shaping how resilience is operationalised, monitored, and sustained. This paper examines the challenges associated with implementing resilience in flight operations and analyses how SMS can support or hinder this integration.The analysis begins by defining resilience as a multi-dimensional capability, encompassing anticipation of potential threats, monitoring of system variability, adaptation to changing conditions, and recovery from disruptions. Within flight operations, resilience extends beyond pilot decision-making to include coordination between dispatchers, maintenance personnel, air traffic controllers, and organisational structures that guide operational decisions. Despite its conceptual prominence, resilience is often poorly translated into training programmes, procedural design, and operational policies, leading to fragmented or superficial implementation.The paper identifies several systemic challenges that hinder resilience adoption. First, traditional safety approaches remain predominantly reactive, focusing on compliance and incident investigation rather than proactive monitoring of system variability and weak signals. This reactive orientation limits the ability of SMS to identify early indicators of fragility or organisational drift. Second, existing performance metrics often prioritise efficiency and procedural adherence, inadvertently discouraging the adaptive behaviours that resilience requires. Third, high automation in modern flight decks can lead to reduced pilot engagement, erosion of manual flying skills, and over-reliance on automated systems—conditions that undermine adaptive capacity during system surprises or degraded modes.Human factors challenges are also examined. Pilots and operational personnel must maintain cognitive flexibility, situational awareness, and collaborative communication under dynamic conditions, yet training programmes frequently emphasise standardisation over adaptability. Additionally, organisational cultures that struggle with Just Culture principles may inhibit the open reporting and learning necessary for resilience development.The role of Safety Management Systems is critically analysed as both an enabler and a constraint. SMS offers structured processes for hazard identification, risk assessment, safety assurance, and safety promotion—all of which can support resilience if applied through a proactive, systems-oriented lens. However, many organisations implement SMS in a compliance-driven manner that prioritises documentation over learning, thereby limiting opportunities to build adaptive capacity. The paper argues that SMS must evolve to integrate resilience engineering principles, including system variability analysis, predictive monitoring, scenario-based learning, and cross-functional coordination mechanisms. Embedding resilience within SMS requires cultural transformation, leadership commitment, and the inclusion of resilience-focused competencies within CBTA/EBT frameworks.The paper concludes by proposing a resilience-enhanced SMS model tailored for flight operations. This model incorporates continuous monitoring of operational variability, systemic learning loops, transparent reporting structures, and training designed to cultivate adaptive cognitive and teamwork skills. The findings underscore that achieving genuine resilience in flight operations requires shifting SMS from a compliance instrument to a dynamic organisational capability that sustains safety performance in the face of uncertainty.
Ibrahim Sarikaya, Dimitrios Ziakkas, Eleftherios Bokas et al.· AHFE International· 0 citations