Alistair Leuchars, Deputy Director of Cyber and Digital at ADS Group Limited, discusses the transformation of civil aerospace driven by artificial intelligence in this insightful piece
Artificial intelligence (AI) is fundamentally transforming so many areas of our lives, and aerospace is no exception. As the sector faces some significant challenges from increasing passenger demand, new environmental imperatives, and the addition of new airspace actors – such as unmanned air vehicles (UAVs) – we will see more and more AI driving advancements in safety, efficiency and sustainability.
The question, however, is no longer whether AI will transform aviation, but how it can be applied responsibly, especially as aerospace innovation accelerates at a pace and scale few sectors can match.
Applying AI to aerospace
Modern airspace is a high-stakes puzzle, with thousands of flights criss-crossing the globe daily and traditional air traffic management (ATM) systems currently straining under the load. AI offers critical solutions by enabling dynamic, real-time air traffic flow management. AI-driven algorithms optimise flight paths and sequencing, reducing delays and maximising airspace utilisation. These systems can also enhance resilience, supporting controllers and pilots with automated decision tools during disruptions, thereby improving safety margins.
Predictive maintenance is another significant area of advancement. Imagine knowing if a component will fail before it does. By analysing the multitude of sensor data from an aircraft system, predictive maintenance forecasts wear and tear, enabling airlines to act before problems ground their fleets. This approach reduces unscheduled downtime and operational costs and could be especially useful in areas where aircraft usage doesn’t follow a repeatable pattern – such as in military aircraft.
Aviation operations generate and require vast amounts of data, from weather pattern monitoring and flight plans to fuel usage and emission measurements. AI excels at processing this sort of information, transforming it into usable insights for both airlines and airports. Real-time weather integration enables dynamic rerouting around adverse conditions, enhancing passenger comfort and safety, as well as aircraft longevity. Predictive models can help anticipate congestion in airspace and airports, allowing for proactive traffic flow management that minimises delays and emissions.
Furthermore, AI-driven route optimisation also improves fuel efficiency by minimising unnecessary deviations and optimising flight profiles, supporting the industry’s sustainability goals. Enhanced collaboration and data sharing between airlines, airports, and network managers – facilitated by AI – creates greater shared situational awareness for all airspace users.
The influence of AI extends to emerging domains such as advanced air mobility (AAM) and urban air mobility (UAM). The integration of electric vertical take-off and landing (eVTOL) aircraft and autonomous drones into existing airspace presents significant challenges across many areas of society. AI-enabled unmanned traffic management (UTM) systems could be essential for safely and efficiently managing large volumes of low-altitude operations.
Navigating the headwinds
However, it’s not all plain sailing, and increased automation certainly introduces challenges for the human-machine interface. Operators must maintain situational awareness and be prepared to intervene, much like cars with autopilot modes on the road. Regulatory frameworks still have some way to go to address certification and safety assurance for autonomous systems, ensuring oversight and accountability remain robust, without stifling this innovation.
As AI capabilities grow, the risk of over-reliance on machine outputs becomes more pronounced. Effective training on human-machine teaming is essential to ensure proper use and to build resilience in a potential scenario where automation fails. Crews must be able to manage unexpected behaviours and exercise manual control to ensure safe outcomes in any situation. Human factors and training will remain vital to air operations, with AI augmenting, not replacing, human expertise. In a similar light, there is a need to determine and define accountability in automated air operations, ensuring there is legal liability and accountability.
The cybersecurity of automated air platforms is paramount. The risk of a hostile takeover, either physically or digitally, of an automated aircraft should never be underestimated, similar to that of traditional surface vehicles. At perhaps a less mortal threat level, dataset poisoning or algorithmic sabotage could also play a role in future commercial competition or hostile action.
AI will prove to be a powerful enabler in civil aerospace, offering significant gains in efficiency, safety, and sustainability. This won’t be a simple or rapid transition and will rely on cooperation and collaboration throughout complex supply chains, air operation environments and jurisdictions. By embracing AI thoughtfully, the aerospace industry can achieve a future that is efficient, safe, equitable, and sustainable.
