by Alex Brooker, VP R&D at Cirium

What is AI and why is it important for aviation?

AI, or artificial intelligence, is the ability of machines to perform tasks that normally require human intelligence, such as learning, reasoning, and decision-making.

AI has the potential to transform the aviation industry in many ways, such as improving safety, efficiency, and customer experience. For example, AI can help airlines optimize their pricing strategies, predict and prevent maintenance issues, and enhance flight operations and air traffic management. AI can also help airports streamline their operations, security, and passenger services, and provide travelers with personalized and seamless journeys.

What are the main challenges for AI adoption in aviation?

However, harnessing AI in aviation is not without challenges. The aviation industry is one of the most complex and regulated sectors in the world, where safety is paramount and data is critical.

Some of the key challenges that AI faces in aviation are:

• Data management: Aviation generates huge amounts of data from various sources, such as aircraft sensors, air traffic control systems, weather reports, passenger information, and more. Integrating and harmonizing these diverse datasets into a unified and reliable source for analysis is a major challenge for AI applications.
• Scalability and safety: Aviation systems involve human decision-making potentially alongside AI, and require rigorous validation and verification processes to ensure safety and compliance. AI systems must be able to explain how they reach their decisions and recommendations, and be audited and monitored for their performance and behaviour.
• Reward functions and side effects: AI systems can be driven by complex reward functions that define their objectives and motivate their actions. However, designing the semantics that capture the desired outcomes while avoiding unintended consequences is hard in a complex environment like aviation, where there are many interrelated factors and trade-offs.
• Data distribution shift: AI models are trained on specific datasets, but may encounter different real-world data when they are deployed. This can lead to a mismatch between the expected and actual behaviour of the AI system, and compromise its accuracy and reliability. For example, an AI system that sets ticket prices based on historical demand may fail to account for second order behaviour change by the actors in the system – such as customers responding to a pricing strategy.

Despite these challenges, the drivers for change go beyond cost or productivity: The well documented acute skills shortage, exacerbated during the pandemic as many experienced professionals retied early, including Air Traffic Controllers, Pilots and other technical roles may necessitate the faster adoption of supportive automation and analytics Ai systems.

How can Cirium help overcome these challenges?

Cirium is a leading provider of data and analytics solutions for the aviation industry. Cirium leverages AI to create new insights and products that help aviation stakeholders solve their most pressing problems and seize new opportunities. For example, Cirium uses AI to infer aircraft maintenance on the ground enabling powerful predictive future utilization capabilities. This helps Cirium customers plan and predict their own but also future competitor availability and performance. Cirium also uses AI to detect and update flight delays and ETAs based on historical and real-time data, helping airlines, travel agents, and passengers communicate and coordinate more effectively.

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