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Engineering & Architecture · Updated Aug 2026

Aerospace Engineering and Operations Technologists and Technicians

Operate, install, adjust, and maintain integrated computer/communications systems, consoles, simulators, and other data acquisition, test, and measurement instruments and equipment, which are used to launch, track, position, and evaluate air and space vehicles. May record and interpret test data.

JVS 2.0.0-phase4b
AI Exposure
59/100
Moderate

How much of this occupation’s work can be materially affected by current AI systems.

Replacement Risk
50/100
Moderate

How likely exposure is to translate into reduced human demand.

Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured

Evidence quality
Confidence83/100
Task coverage88%

Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.

Task-level evidence

What is driving the score?

Occupation scores are built from the task mix—not a single prediction about a job title.

JVS 2.0.0-phase4b
TaskImportanceAI impactExposure
Test aircraft systems under simulated operational conditions, performing systems readiness tests and pre- and post-operational checkouts, to establish design or fabrication parameters.High
71
Identify required data, data acquisition plans, and test parameters, setting up equipment to conform to these specifications.High
68
Record and interpret test data on parts, assemblies, and mechanisms.High
71
Confer with engineering personnel regarding details and implications of test procedures and results.High
71
Operate and calibrate computer systems and devices to comply with test requirements and to perform data acquisition and analysis.High
62
Inspect, diagnose, maintain, and operate test setups and equipment to detect malfunctions.High
53
Construct and maintain test facilities for aircraft parts and systems, according to specifications.High
70
Fabricate and install parts and systems to be tested in test equipment, using hand tools, power tools, and test instruments.High
34
Adjust, repair, or replace faulty components of test setups and equipment.High
23
Most exposed

Where AI can do more

Routine, digitized, and highly repeatable tasks face the greatest pressure.

  1. Test aircraft systems under simulated operational conditions, performing systems readiness tests and pre- and post-operational checkouts, to establish design or fabrication parameters.71
  2. Record and interpret test data on parts, assemblies, and mechanisms.71
  3. Confer with engineering personnel regarding details and implications of test procedures and results.71
  4. Construct and maintain test facilities for aircraft parts and systems, according to specifications.70
Hardest to automate

Where people still matter

These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.

  1. Adjust, repair, or replace faulty components of test setups and equipment.01
  2. Fabricate and install parts and systems to be tested in test equipment, using hand tools, power tools, and test instruments.02
  3. Identify required data, data acquisition plans, and test parameters, setting up equipment to conform to these specifications.03
  4. Record and interpret test data on parts, assemblies, and mechanisms.04
  5. Confer with engineering personnel regarding details and implications of test procedures and results.05
Where else this work leads

Related occupations

Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.

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Beyond AI capability

Adoption and labour-market outlook

Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.

Human dependency67
Physical dependency48
Adoption pressure57
Labour-market resilience65
Methodology & sources

O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.

Confidence83/100
CalculatedAug 21, 2026
Read methodology →