Will AI replace aerospace engineers?
Updated
Aerospace engineers score 51 of 100 for AI exposure. The largest share of their task time, 68%, is work AI speeds up while they stay in charge. AI can take over 14% outright, and 18% stays with people.
- 14% AI can do it
- 68% AI helps
- 18% Needs a person
High exposure
Higher than 77% of the 923 occupations we scored.
Task by task
We graded 16 tasks, listed here from most to least exposed. AI can do 2 of them today, speeds up 11 and leaves 3 to people.
| Task | AI exposure |
|---|---|
| Maintain records of performance reports for future reference. Routine digital record keeping that software and AI handle well. | 75% AI can do it |
| Write technical reports or other documentation, such as handbooks or bulletins, for use by engineering staff, management, or customers. AI drafts and structures technical reports well; engineer reviews accuracy. | 70% AI can do it |
| Formulate mathematical models or other methods of computer analysis to develop, evaluate, or modify design, according to customer engineering requirements. AI speeds modeling code; engineer validates results and design choices. | 55% AI helps |
| Evaluate product data or design from inspections or reports for conformance to engineering principles, customer requirements, environmental regulations, or quality standards. AI can check designs against standards; engineer signs off conformance. | 55% AI helps |
| Analyze project requests, proposals, or engineering data to determine feasibility, productibility, cost, or production time of aerospace or aeronautical products. AI can analyze proposals and estimate costs; engineer confirms feasibility. | 50% AI helps |
| Evaluate biofuel performance specifications to determine feasibility for aerospace applications. AI can screen biofuel specs against aerospace requirements; engineer decides. | 50% AI helps |
| Evaluate and approve selection of vendors by studying past performance or new advertisements. AI summarizes vendor records; approval and accountability stay human. | 50% AI helps |
| Formulate conceptual design of aeronautical or aerospace products or systems to meet customer requirements or conform to environmental regulations. Ideation and trade studies aided; conceptual choices need engineer judgment. | 40% AI helps |
| Develop design criteria for aeronautical or aerospace products or systems, including testing methods, production costs, quality standards, environmental standards, or completion dates. AI drafts criteria options; engineer is responsible for cost and quality tradeoffs. | 40% AI helps |
| Diagnose performance problems by reviewing reports or documentation from customers or field engineers or by inspecting malfunctioning or damaged products. AI reviews reports for likely causes; physical inspection stays manual. | 35% AI helps |
Show the other 6 tasks
| Task | AI exposure |
|---|---|
| Design new or modify existing aerospace systems to reduce polluting emissions, such as nitrogen oxide, carbon monoxide, or smoke emissions. Simulation helps emissions design; hardware tradeoffs need engineering judgment. | 35% AI helps |
| Plan or coordinate investigation and resolution of customers' reports of technical problems with aircraft or aerospace vehicles. AI can draft triage notes; live customer coordination stays human. | 30% AI helps |
| Plan or conduct experimental, environmental, operational, or stress tests on models or prototypes of aircraft or aerospace systems or equipment. Planning and data analysis aided; physical test work stays manual. | 25% AI helps |
| Design or engineer filtration systems that reduce harmful emissions. Filter hardware design is physical; AI assists only with analysis. | 18% Needs a person |
| Direct or coordinate activities of engineering or technical personnel involved in designing, fabricating, modifying, or testing of aircraft or aerospace products. Team leadership and direction rely on people, not AI. | 12% Needs a person |
| Direct aerospace research and development programs. Leading R&D programs relies on people, strategy, and funding decisions. | 12% Needs a person |
How aerospace engineers compare
All occupations (923)
Architecture and engineering (56)
Pay and outlook in the US
- Median pay
- $134,960 a year, May 2025
- People employed
- 67,710 in the US, May 2025
- Projected change in jobs
- +8.3% from 2024 to 2034
Skills to build
- Run stress and environmental tests on physical prototypes
- Model and verify designs in CATIA and SolidWorks
- Validate AI-generated MATLAB and Python analysis code
- Sign off design conformance to customer requirements and standards
- Hold a Professional Engineer (PE) licence
Safer roles nearby
5 related occupations with lower scores.
Questions about aerospace engineers and AI
Will aerospace engineers be replaced by AI?
Partly. About 4 in 10 working hours go to tasks AI can already do, mostly maintaining performance records and writing technical reports. What stays with people is the engineering judgment behind each design choice and the sign-off on conformance.
Will AI take over aerospace engineering?
AI can already do 14% of the task time for aerospace engineers and speeds up another 68%. People still do 18% of it. The US has 67,710 aerospace engineer jobs, with a projected change of +8.3% from 2024 to 2034.
Which tasks will AI take over for aerospace engineers?
Maintaining performance reports and writing technical reports or handbooks are the most exposed, with exposure scores of 75 and 70. Software handles routine digital record keeping well, and AI drafts and structures technical reports, leaving the engineer to check accuracy.
Which tasks stay human for aerospace engineers?
Leading engineering teams and directing aerospace R&D programs score lowest, at 12, because direction and funding decisions rely on people. Filtration hardware design, at 18, is physical work where AI only assists with analysis. Running stress tests on prototypes stays manual too.
How can aerospace engineers stay valuable with AI?
Become the person who verifies AI output. Check AI-drafted technical reports and MATLAB or CATIA models against test data and customer requirements, then sign the result yourself. Physical testing stays manual, so keep those hands-on skills current.
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