How A&D manufacturers can harness AI for innovation and resilience

We’ve all heard the hype about artificial intelligence (AI) and the last two years have seen a shift from AI experimentation to execution. The challenge has never been about whether AI could add value but about moving from pilot programs to company-wide implementations.

The aerospace and defense (A&D) sector's relationship with AI is complex. The U.S. Department of Defense increased its fiscal year 2024 budget to $842 billion, with a focus on integrating AI and automation into defense systems. The UK Ministry of Defence spent approximately £60.2 billion on defense in fiscal year 2024/25 with future planned increases including allocations towards new technologies. However, adoption patterns show a different picture. While AI spending is increasing, the industry faces distinct challenges compared to other sectors. The regulatory, security and mission-critical nature of A&D operations means that AI implementation needs more validation and testing than in other applications.

Where does AI deliver the most impact in the A&D industry?

• Predictive maintenance and asset management: AI can analyze large sensor data from aircraft systems and then detect anomalies and failures and trigger maintenance action before any issue occurs. This often results in reduced unplanned downtime and extended asset lifecycles.
• Manufacturing optimization and quality control: AI has revolutionized the manufacturing process by helping improve factory operation and supply chains, reduce delays, improve customer experiences, lower costs and increase productivity. In quality control, AI-powered visual inspection systems can spot defects that might get missed by human inspection, mainly during repetitive tasks.
• Supply chain intelligence: A&D supply chains are known for thousands of components, global supplier networks, long lead times and countless regulations. AI systems can analyze multiple variables at the same time, like supplier performance, demand patterns, geopolitical factors and production schedules to identify any potential disruptions.

How should A&D manufacturers approach AI implementation?