40 Questions Aerospace and Defence Should Ask Before Trusting AI
When AI generates a safety analysis report or optimisation result, the professional reading it often cannot see what the system actually examined or ignored. In aerospace and defence, that invisible reasoning can hide the failure modes that kill people.
These are suggestions. Use the ones that fit your situation.
1When ANSYS AI generated this stress concentration map, what historical failures in similar geometries was it trained on, and are those documented in your traceability log?
2The ANSYS simulation found no fatigue crack initiation at this joint under standard cycles. Did it also analyse the behaviour under the out-of-envelope load case your maintenance teams actually see?
3This safety analysis report says the system found no critical failure modes. What is the training dataset size for the component category, and how many novel failure modes appeared in your service history that would not be in that dataset?
4Siemens AI optimised the heat shield thickness and reported a 12 percent weight saving. What failure modes does thinner material enable at the extremes of your thermal envelope, and did anyone manually check those scenarios?
5The AI tool flagged three potential failure points but marked two as low probability. What was the confidence threshold it used, and who independently verified that threshold against your accident history?
6When you ran this Palantir analysis of maintenance data, how did it distinguish between a component that failed gradually and one that failed suddenly, and did the distinction hold up when your maintenance teams reviewed actual shop reports?
7This ChatGPT summary of failure modes looks complete. Which five failure modes from your last three accident investigations would have been caught if you asked the AI directly about them?
8The AI safety report covers normal operating envelope plus 10 percent margin. Does it cover the specific abuse conditions your field teams have documented, or only the ones in the original specification?
9Microsoft Azure AI recommended retiring this component class based on pattern analysis. Did it include the retrofit history of that component, or only the original design data?
10This AI analysis of your fleet found no correlation between component age and failure rate. How many components in the dataset had actually reached end-of-life, versus how many were retired early by your maintenance strategy?
Design Optimisation and Novel Failure Modes
11The Siemens AI optimisation improved aerodynamic efficiency by 8 percent. What materials or manufacturing processes changed to achieve that gain, and have you stress tested those changes against your worst-case assembly and tolerance stack?
12When ANSYS AI recommended this structural redesign, did it optimise against your actual historical loads, or against the nominal specification and test loads?
13This design optimisation increased the strength-to-weight ratio. Did it also increase sensitivity to corrosion, fatigue at different frequencies, or thermal cycling in the specific environments your aircraft will actually operate in?
14The AI tool redesigned this fuel system component for 5 percent weight reduction. Did a fuel systems engineer independently verify that the change does not create new cavitation, water ingress, or cold soak failure modes?
15This AI-optimised design passed all the tests in the training dataset. Which failure modes from your service history would have been outside that dataset, and how would you test for them without waiting for a field failure?
16The Palantir analysis recommends consolidating these two redundant systems to save weight. Did it have access to the incident reports where that redundancy actually prevented cascading failure, or only to the nominal design parameters?
17This ChatGPT summary recommends a design change based on efficiency patterns in similar platforms. Are the similar platforms actually equivalent, or do they operate in different temperature, altitude, or contamination regimes that matter here?
18The Azure AI redesign looks elegant and the weight saving is real. What is the manufacturing tolerance required to achieve that saving, and does your supply chain have proven capability at that tolerance across all vendors?
19This optimisation removed a protective feature that appeared redundant in the analysis data. Where in your maintenance history has that feature actually prevented a secondary failure, and is that scenario in the AI training set?
20The AI tool improved performance by 6 percent on the specified mission profile. How much worse does the design perform if actual mission loading is 20 percent heavier due to field modifications or environmental factors not in the specification?
Maintenance Decisions and Expert Judgment Atrophy
21This Palantir maintenance alert recommends deferring component replacement based on remaining useful life prediction. Who independently verified that prediction against the last three times this component was opened in a teardown?
22The AI tool says this component is performing normally. Does the alert system trigger on absolute failure, or on the early-stage degradation that your experienced mechanics would catch in a visual inspection?
23Azure AI recommended extending the maintenance interval on this subsystem by 200 hours based on trend analysis. Did it include the data from six months ago when you found unexpected corrosion that would not be in a simple trend curve?
24This ChatGPT maintenance summary recommends a specific repair procedure. Did you ask it to list the failure modes that would make that procedure dangerous, or only what the standard procedure says?
25The AI maintenance planner says this aircraft is ready for dispatch. Did a human maintenance lead independently walk the aircraft, or is the decision final when the tool says green?
26Siemens AI flagged this bearing for replacement in 400 hours. Does your maintenance team still have the experience to replace it correctly, or are they now dependent on the tool for all decisions?
27This Palantir alert is the fifteenth false alarm on this system in three months. When operators start ignoring the alerts because the AI cried wolf, what failure will get missed?
28The maintenance data shows this component passes all automated checks. Can your mechanics still recognise the difference between a component that is within tolerance and one that is degrading toward a hidden failure?
29Azure AI recommended a preventive replacement strategy. Did anyone compare that strategy against the failure cost and downtime of actually waiting for failure, or is the AI recommendation assumed to be optimal?
30This ChatGPT summary of maintenance best practices looks sensible. Which of your past catastrophic maintenance failures would have been prevented if this advice had been followed, and which would have happened anyway?
Certification, Accountability, and Expert Development
31This safety analysis was conducted with AI assistance and shows no critical findings. When the certification body asks who is responsible if a failure occurs in service, who is your answer?
32The design optimisation was performed by Siemens AI and validated by your engineers. If a field failure occurs that was not caught by either, can you prove the engineers performed independent critical judgment, or did they only verify the AI output?
33This Azure AI analysis is included in your certification submission. Does your civil aviation authority accept AI-generated analysis without the backup of human expert review, or will they require hand-checked work?
34Your maintenance strategy now relies on Palantir predictions for deferral decisions. If the prediction is wrong and failure occurs, are you liable, is the vendor liable, or is liability undefined?
35This ChatGPT summary of failure mode analysis was created to save time in the certification process. Did you document what a human expert would have added, or does the certification record omit that gap?
36Your junior engineers are now trained primarily on interpreting AI outputs rather than building models from first principles. How will they recognise when the AI is wrong about a scenario the training data did not cover?
37When the regulatory body asks for the engineer who takes responsibility for this safety analysis, is the answer a named person, or is it the AI tool with your organisation's name on the signature?
38This design was optimised by AI and your engineers approved it. If the approval was based on trusting the tool rather than independent calculation, what happens when a customer asks for the engineering justification?
39Your maintenance technicians are increasingly dependent on AI alerts to decide what to inspect. When one of them retires, what knowledge walks out the door that the next person will have to relearn from AI failures?
40The AI safety analysis has no gaps marked and no assumptions listed. Where is the document that says what this tool was not asked to check, so the next engineer knows what to verify independently?
How to use these questions
Ask AI tools to list their training data sources and their confidence level for each conclusion. If they cannot, do not use that conclusion without independent verification.
After any AI-assisted safety decision, write down what a domain expert would have checked that you did not, and track whether that gap ever causes a problem.
Require that junior engineers spend time on manual failure analysis and design calculation before they are allowed to interpret AI outputs. Judgment atrophies in the absence of real work.
When AI recommends a change, actively search for failure modes outside the training data rather than assuming the tool is exhaustive.
Keep a log of every time an AI tool produced a wrong answer or missed a critical issue in your operation. Review that log before trusting the tool on a safety-critical decision.