Aerospace engineers
Aerospace engineers research develop and design aircraft, spacecraft and their components.
Task Bundle Breakdown
How AI interacts with this occupation's task bundle
Task-by-Task Breakdown
Every task scored individually — sorted highest to lowest AI exposure
analyses test data; undertakes research and advises on all aspects of aircraft, spacecraft and their systems
AI and machine learning tools can rapidly process large test datasets, identify anomalies, and surface patterns (e.g., using MATLAB AI toolboxes or Python-based ML pipelines), though expert engineers must interpret results and make technical recommendations.
conceives and develops engineering designs from product ideas in aerospace engineering
AI tools like Ansys AI and generative design platforms (e.g., Autodesk Fusion 360) can generate and evaluate design concepts, but human engineers must validate feasibility, safety, and innovation within regulatory and physical constraints.
organises and plans projects, arranges work schedules, carries out inspection work and plans maintenance control
AI-powered project management tools like Microsoft Copilot in Project can optimise scheduling and flag risks, but the coordination of complex multi-team aerospace projects still requires experienced human judgment and stakeholder management.
ensures that equipment, operation and maintenance comply with design specifications and safety standards
AI can assist with documentation review and compliance checking against standards databases, but physical inspection, regulatory sign-off, and safety-critical judgments require qualified human engineers with legal accountability.
inspects completed aircraft maintenance work to certify that it meets standards and the aircraft is ready for operation
AI can support checklist management and flag documentation gaps, but physical inspection of completed maintenance work and airworthiness certification is a legally mandated human responsibility that cannot be delegated to AI.
ROI Calculator
Aerospace engineers
This is not a pay rise — it's 468 hours of productive capacity that AI could return to you, valued at your current rate.
The portion of your current salary tied to tasks AI can automate or augment. Not a prediction of job loss — a signal of where your role is changing fastest.
How is this calculated?▾
Task weights — each task is weighted by its O*NET frequency rating. Where frequency data is absent, equal weighting is applied.
AI time reduction — tasks scored as "AI Automates" assume 80% time saving; "AI Assists" assumes 40%; "Human-Led" assumes 5%.
Salary equivalent — annual hours saved × (salary ÷ 1,800 working hours/year).
Value at risk — salary × share of role time in AI-automate or AI-assist tasks.
Figures are indicative estimates for planning purposes, not guarantees of productivity gain or job loss risk.