Overview: In 2026, the competition in the field of AI for civil aviation appears to be focused on areas visible to passengers, such as customer service, pricing, and delay notifications. However, the real challenge lies beneath the surface: how to validate the recommendations made by AI, and whether explanations can be provided for questions like "Why was this flight moved, delayed, or rerouted?" In June, Aerotime reported that the U.S. FAA awarded a 12-year, $875 million contract to Air Space Intelligence, with plans to use its software to assist in airspace, route, and trajectory management. As AI transitions from being a peripheral tool for airlines to playing a role in air traffic control decision-making processes, interpretability and trust are becoming key factors determining success or failure.
According to industry research by ALG, 2026 will be a pivotal year for the integration of AI technology into aviation regulations. The European Aviation Safety Agency (EASA) has been consistently promoting its AI roadmap for several years, categorizing applications from "basic assistance" to "close human-machine collaboration," and establishing structured methods for evaluation, certification, and assurance. The Federal Aviation Administration (FAA) is also developing its own adaptive strategy.
According to analysis of information within the aviation maintenance industry, the FAA's AI roadmap is a "living document" that will be updated as it gains experience in AI assessment. It will proceed with a risk-based approach: low-risk applications, such as administrative compliance tracking and document management, will be assessed first. High-risk applications that directly involve operational decision-making will be implemented later, after industry consensus and standards have matured. This approach suggests that AI recommendations in civil aviation will not be implemented immediately but will undergo tiered verification and continuous monitoring.
As an example, consider the AI software that will soon be used to assist in air traffic control decision-making in the United States. According to Aerotime, this type of tool analyzes airline flight plans, weather conditions, airport capacity, and airspace restrictions to identify locations where demand may exceed available capacity. It also provides continuously updated forecasts as conditions change, allowing FAA managers and individual airlines to view the same information.
When demand exceeds capacity, flight delays or adjustments are inevitable. For the airlines and passengers affected, there must be a traceable answer to the question, "Why me?" Having data displayed simultaneously and ensuring consistent metrics is the first step towards explainable design – at least everyone knows what the system "sees." There is also a quantifiable benefit: according to industry statistics compiled by Gitnux, machine learning-based weather forecasting can, on average, reduce flight delays by approximately 15 minutes in adverse weather conditions. The more impressive the figures, the more seriously users will question the basis of the recommendations.
When demand exceeds capacity, the allocation process – deciding which flights should be delayed and which should be prioritized – has always been a highly sensitive issue in airspace and slot management. After the introduction of AI, the prevailing approach in this safety-critical area is "algorithms provide recommendations, and humans make the final decisions." The rules must be made public in advance, the process must be traceable, and the results must be auditable, so that all parties will accept the ranking provided by the algorithm.
Meanwhile, the application of AI is also rapidly expanding within airlines. According to an analysis published in Forbes in May, predictive maintenance, personalized pricing, and passenger services are already transforming airline operations. As airline internal systems connect with the FAA's scheduling tools, aligning data interfaces and standards on both sides will become the focus of competition in the next phase.
The civil aviation industry is a prime example of a "trust-intensive" sector. The speed at which AI is adopted here doesn't depend on impressive demonstrations, but rather on whether recommendations can be validated, decisions can be explained, and consequences can be attributed. While the interface can be visually appealing, what truly determines its widespread use is the explainability and institutional framework that underpins it. For airlines and airports, instead of competing for the label of "using AI," it's more important to establish robust validation and explanation mechanisms – that's what creates a truly sustainable competitive advantage.