The emergence of Human-AI teaming within the aviation ecosystem introduces profound implications for fatigue management, operational resilience, and safety assurance. As artificial intelligence systems become increasingly embedded in flight operations, predictive analytics, crew scheduling, and cockpit decision support, the integration of Fatigue Risk Management Systems (FRMS) takes on an expanded and critical role. This paper examines how FRMS principles can support, and must be adapted to support, the safe and effective implementation of human-AI teaming, ensuring that technological innovation enhances rather than undermines human performance in safety-critical environments. The paper situates fatigue as a persistent human factor risk that continues to shape error pathways, cognitive performance, and decision-making under operational stress. AI-enabled systems hold the potential to augment fatigue management by providing real-time physiological monitoring, predictive fatigue modelling, adaptive workload distribution, and decision-support cues during periods of reduced alertness. However, the introduction of AI also reshapes the operational landscape: as tasks shift between human and machine, the cognitive workload profile of pilots may fluctuate unpredictably. In highly automated or single-pilot contexts, AI systems may inadvertently increase fatigue risks by imposing vigilance demands, imposing excessive monitoring burdens, or providing poorly calibrated levels of assistance. These emerging risks underscore the need for FRMS frameworks capable of recognising and governing the unique human-machine interactions associated with advanced automation. The paper examines how each core component of FRMS, policy, risk assessment, data-driven monitoring, and training, contributes to the governance of human-AI teaming. FRMS Policy must explicitly acknowledge AI as both a potential fatigue mitigator and a fatigue hazard, emphasising a human-centric philosophy that prioritises pilot wellbeing alongside operational efficiency. Fatigue Risk Assessment processes must incorporate new hazard categories, including over-reliance on algorithmic alerts, cognitive underload resulting from task redistribution, and increased monitoring pressures associated with supervising autonomous functions. Safety Assurance within FRMS must transform into a continuous, adaptive process capable of monitoring AI performance, evaluating AI-human workload balance, and detecting early signs of fatigue-induced system drift. The paper proposes an integrated FRMS-AI governance model tailored to the future of human-AI teaming. This model incorporates predictive fatigue analytics, adaptive automation strategies, human-machine workload harmonisation, and AI-specific fatigue indicators within FRMS oversight. The findings emphasise that the success of human-AI teaming in aviation will depend not solely on technological progress but on the robustness of FRMS to manage evolving human cognitive vulnerabilities in increasingly automated operational ecosystems.
The integration of Human–AI teaming within the aviation ecosystem represents a transformative evolution in safety-critical operations, demanding robust organisational frameworks capable of managing emerging risks, validating new operational concepts, and sustaining human performance. As artificial intelligence becomes increasingly embedded in flight operations, maintenance, training, and safety analytics, the role of Safety Management Systems (SMS) becomes central to ensuring that human–AI collaboration is introduced, monitored, and governed in a manner consistent with international safety expectations. This paper examines how contemporary SMS principles support—and in many cases must be adapted to support—the safe and effective implementation of human–AI teaming across the aviation industry.The analysis begins by framing AI integration as a socio-technical challenge that profoundly alters hazard identification, risk modelling, and safety assurance processes. AI-enabled systems introduce unique characteristics—opacity, non-determinism, continuous learning, and probabilistic behaviour—that challenge conventional safety assumptions. SMS, traditionally grounded in predictable system performance, must expand to accommodate risks arising from algorithmic drift, data quality variability, automation bias, and human–machine misalignment. The paper argues that SMS frameworks must evolve beyond compliance-driven practices to embrace dynamic, data-rich safety monitoring capable of detecting emergent patterns of human–AI interaction.The study further explores how each component of SMS—Safety Policy, Safety Risk Management, Safety Assurance, and Safety Promotion—contributes to the governance of human–AI teaming. Within Safety Policy, organisational commitments must reflect a human-centric philosophy ensuring that AI systems complement, not replace, human cognitive strengths. Safety Risk Management must incorporate new methodologies for identifying hazards associated with collaborative automation, including unintended consequences of predictive algorithms, mismatches between AI intent and pilot expectation, and reduced redundancy in single-pilot or high-automation environments. Safety Assurance processes must evolve to include continuous performance monitoring of AI agents, explainability audits, validation of training effectiveness, and mechanisms for detecting shifts in human–AI trust relationships.Safety Promotion is examined as a crucial enabler of cultural readiness. The introduction of AI into safety-critical operations requires transparent communication, cross-disciplinary literacy, and training programmes that cultivate both confidence and critical scepticism toward AI-generated outputs. Emphasis is placed on building a safety culture that encourages reporting of anomalies involving AI systems, fosters shared understanding between technical and operational personnel, and supports learning from human–AI interaction events. The Turkish Airlines, Lufthansa Group, and FAA/EASA regulatory developments are referenced as indicative of industry movement toward SMS-driven oversight of intelligent systems.The paper concludes by proposing a strengthened SMS framework tailored to human–AI teaming. This enhanced model integrates explainable AI within risk assessment processes, adopts resilience engineering principles to manage uncertainty, incorporates AI-specific safety indicators, and emphasises adaptive training frameworks aligned with CBTA/EBT approaches. The findings suggest that the long-term success of human–AI teaming in aviation will depend not solely on technological capability but on the ability of SMS to anticipate, govern, and continuously validate the evolving dynamics of human–AI collaboration.
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