Aerospace & Defense: Agentic AI Shifts From Demo to Industrialization, With MRO and Supply Chain Leading

The signal is now quantified: US aerospace and defense spending on artificial intelligence and generative AI is expected to reach US$5.8 billion by 2029, 3.5 times the 2025 level. This acceleration is not mere announcement noise. It marks the shift from siloed experiments — a copilot here, a vision model there — to structured programs where agentic AI is embedded in critical processes: decision-making, procurement, planning, logistics and maintenance. Roughly 36% of industrial manufacturing tasks could be augmented by these agents, moving the question from 'should we invest' to 'how do we industrialize without compromising flight safety'.

MRO (maintenance, repair and overhaul) concentrates the economic stakes. The global commercial aftermarket is projected to grow at 3.2% per year between 2026 and 2035, with engines accounting for 53% of demand. This is precisely where agentic AI changes the game: beyond classic predictive maintenance, value emerges from end-to-end orchestration — an agent that correlates sensor data, anticipates downtime, books the part and the shop slot, and documents the intervention for audit. Performance is no longer measured by fault-detection rates, but by reduced time on ground and the reliability of the decision chain.

The supply chain is the second front. Persistent pressure on components and the traceability of critical parts make manual planning untenable. Agents that can simulate disruptions, arbitrate between suppliers and continuously replan are already delivering notable productivity gains. But in a regulated environment, those gains are only sustainable if every recommendation stays explainable, auditable and bounded by business guardrails. Skills demand follows: data-analysis job postings in the sector are projected to rise from 9% in 2025 to nearly 14% by 2028.

Cardan-AI analysis: most organizations remain in early adoption, and rightly so, held back by operational risk and certification requirements. That is exactly where the 2026 competitive edge lies — not in access to the best model, now commoditized, but in the ability to frame, supervise and audit agents operating on processes where error carries a material and human cost. For a prime or a tier-one supplier, the first step is not another POC: it is a mapping of the MRO and supply-chain processes where agentic AI creates measurable value under regulatory constraint, followed by a governance framework that makes scaling defensible to an auditor.