AI has become a buzzword synonymous with transformation, revolution, and innovation. Some industries are closer to effectively implementing AI solutions than others, due to the nature of their work and the complexity of the analyses involved. Aerospace is a leader in AI implementation because of the tangible near-term improvements it can deliver to the industry. With passenger numbers reaching all-time highs and air cargo yields increasing by nearly 40% in just seven years, commercial aviation is growing and thriving. Still, the industry faces challenges in categories vital to operations.
In recent years, we’ve seen supply chain strains, staffing constraints, maintenance mishaps, human errors, and technology issues that caused significant setbacks. Improving the processes and systems in these areas is well within reach using AI. Market players are betting on this, with aerospace and defense AI spending projected to reach $5.8 billion by 2029, a 3.5x increase from 2025.
AI an improve efficiency in mission planning and command-and-control. The complexities of flight scheduling across enterprise fleets, especially when disruptions affect available assets, can be addressed in real time. AI can coalesce data to identify potential routes, aircraft, and staff, improving commercial travel logistics even on short notice, reducing delays and cancellations. It can predict consumer demand, enabling better resource allocation. In defense, AI can similarly analyze large datasets to identify strategies and options that might otherwise be missed. This enables leadership to simulate more scenarios, ensuring the mission is faster, more efficient, and better prepared for all contingencies. AI can also aid air traffic control by evaluating factors such as traffic load and environmental conditions to streamline aircraft flow into and out of airports. It can analyze historical data to find new efficiencies or identify potential problem areas. Finally, it can act as an intelligent warning system if potential problems should arise.
Maintenance is paramount when operating aircraft, and in recent years, fleets have aged as manufacturers fall behind on production. This age necessitates greater diligence with older, stressed parts. The global commercial aftermarket maintenance, repair and overhaul demand is projected to grow at 3.2% CAGR between 2026 and 2035, with a focus on engines. AI systems can integrate databases of aircraft flight metrics, repair logs, inspection data, and more to deliver predictive maintenance analytics. These large datasets can identify gaps in existing human coverage or flag areas that might require more detailed inspection. This could be the difference in preventing disasters like the UPS MD-11 crash in Kentucky last November, which was caused by cracked engine mounts that were missed by routine maintenance. AI Digital twin technology enables virtual representations of aircraft to simulate maintenance scenarios, providing training and procedure optimization without requiring an actual aircraft. Augmented reality can assist with active maintenance tasks by providing real-time information and step-by-step instructions. All of which will improve outcomes in a space where missteps can be catastrophic.
This technology can enable aircraft to fly autonomously or significantly aid a human pilot behind the yolk. AI will guide precision munitions and autonomous aircraft in a far more complex manner: adapting for collision avoidance, geospatial targeting, and more. The military hopes to use autonomous aircraft as a force multiplier, deploying low-cost aircraft with advanced sensors and weapons ahead of human-occupied jets to see the landscape first, take risks, and potentially shoot first. Meanwhile, commercial aviation aims to develop autonomous air taxis for human and cargo transport, and reduce pilot workload through obstacle detection, hazardous weather prediction and air traffic control communication analysis.
These applications are in various stages of development, but many have working prototypes currently in operation. With increasing funding and expanding capabilities, this emerging sector offers ample opportunities for innovation. Companies should consider intellectual property protection for their AI innovations and weigh the benefits of applying for patent protection versus opting for trade secret protection. For example, technology that is easy to reverse-engineer would be a good candidate for patenting, while technology that is hidden from end users may be better protected as a trade secret.
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