AI enhances airline pilots' abilities and decision-making processes, putting them back at the heart of the operational processes
According to Pereira et al. (2023), we are witnessing the beginning of a new industrial revolution, expecting to profoundly impact the industry (Aazam et al., 2018; Soh & Connolly, 2020; Xu et al., 2018). This new era brings the physical and digital worlds closer (Xu et al., 2018), strengthening the interactions between man and machine (Eberhard et al., 2017; Ferreira et al., 2020).
The relationship between the complexity of systems and the need for increasingly advanced technology is characterised by interdependence. The more intricate and multifaced systems become, the greater the demand for advanced technologies to implement, monitor and manage them safely and efficiently, allowing operators to handle the increased complications.
"The high efficiency level required by commercial aviation today leaves room for low margins of error, inaccuracy and unpredictability" (Belloni, 2021, p. 1).
"The future of aviation is clear: more aeroplanes in the skies and consequently reduced separations between them, new routes, closer flight levels, new airways, and new airports ever closer to each other to satisfy an increasingly stringent demand that requires widespread coverage of the territory" (Belloni, 2021, pp. 6-7).
The relentless growth in air traffic leads to the request for advanced technology to handle the complications of such a congested system.
In his 2017 paper "Complexity in the aeronautical sector", Dalla Quercia defines aviation as "a hypercomplex sector", asserting that hypercomplexity is a typical characteristic of industries with constant necessities for efficiency to achieve increasingly higher levels of effectiveness and productivity.
"One of the characteristics of industries considered to be extremely complex is that they are often subject to unexpected and unpredictable events due to a large number of variables that cannot be easily understood at a cognitive level and the solutions required may appear counterintuitive" (Dalla Quercia, 2017, p. 1).
Aviation is one of these sectors.
In recent years, commercial aviation has experienced a transformative integration of Artificial Intelligence (AI) to augment and refine the performance of commercial pilots and cope with this hypercomplexity. This paradigm shift aims to empower pilots by placing them at the forefront of decision-making processes by strategically applying cutting-edge technologies.
Implementing AI in commercial aviation could be a valuable ally for cockpit professionals, assisting them in their duties. AI might contribute to more informed and efficient decision-making, from real-time data analysis to predictive modelling. By harnessing the power of machine learning algorithms, pilots can benefit today from enhanced situational awareness, improved cognitive performances, and optimised reaction times.
One of the researchers' primary objectives today should be to find an ethical and practical balance between automation and human control, focusing on reinstating the pilot as a central figure in flight operations. AI must be considered a supportive tool that provides valuable insights, freeing mental room to conduct detailed analyses and evaluations in short time frames and leaving the final decisions to the capable experience of the operator.
Moreover, AI can facilitate adaptive learning to tailor to individual strengths, weaknesses, and decision-making patterns. This personalised approach enhances performance and fosters a symbiotic relationship between human expertise and technological advancements.
The integration of AI in commercial aviation shall not aim to replace a man but rather to elevate his options, abilities and efficiency. The commercial aviation industry strives to ensure safer and more efficient operations by leveraging artificial intelligence as an indispensable collaborative partner. As we progress, the emphasis remains on refining AI to align seamlessly with the intricate tasks performed by pilots, ultimately creating a harmonious synergy between human intuition and technological prowess in the cockpit.
References
Aazam, M., Zeadally, S., & Harras, K. A. (2018). Deploying fog computing in industrial internet of things and industry 4.0. IEEE Transactions on Industrial Informatics, 14(10), 4674–4682.
Belloni, G. (2021). Beyond Evidence-based Training. Analysis of the reasons that led to the development of an innovative training methodology suited to the aeronautical world's ever-increasing complexity. New thoughts to enhance the effectiveness and quality of pilots' training further. City of London University.
Dalla Quercia, C. (2017). Complexity in the aviation sector. Bocconi Students for Digital Consulting (BSDC).
Eberhard, B., Podio, M., Alonso, A. P., Radovica, E., Avotina, L., Peiseniece, L., Sol ́e-Pla, J. (2017). Smart work: The transformation of the labour market due to the fourth industrial revolution (I4. 0). International Journal of Business & Economic Sciences Applied Research, 10(3).
Ferreira, C. M. S., Oliveira, R. A. R., Silva, J. S., & da Cunha Cavalcanti, C. F. M. (2020). Blockchain for machine-to-machine interaction in Industry 4.0. In Blockchain technology for industry 4.0 (pp. 99–116). Springer.
Pereira V., Hadjielias E., Christofi M., Vrontis D. (2023). A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective. Human Resource Management Review 33.
Soh, C., & Connolly, D. (2020). New frontiers of profit and risk: The Fourth Industrial Revolution's impact on business and human rights. New Political Economy, pp. 1–18.
Xu, M., David, J. M., & Kim, S. H. (2018). The fourth industrial revolution: Opportunities and challenges. International Journal of Financial Research, 9(2), 90–95.