Conduct quantitative failure analyses of operational data.

Conduct quantitative failure analyses of operational data. is a detailed work activity in O*NET — a concrete unit of work shared across 6 occupations and seen in 8 occupation-specific tasks. It rolls up into the broader work activity Analyze performance of systems or equipment. in Analyzing Data or Information .

Detailed work activities are the most granular shared layer in O*NET's work-activity hierarchy (Generalized → Intermediate → Detailed → occupation-specific task). The figures below describe how this activity shows up across the economy and what independent studies measure about AI and this kind of work — not a prediction that the work will be automated.

AI exposure

Of the 8 tasks under this activity that the OpenAI / Eloundou “GPTs are GPTs” study rated, 7 (88%) are flagged as directly exposed to language models (E1) or exposed via model-powered tools (E2).

Exposure estimates overlap with model capabilities — whether a model could speed the task up — not whether the work will be done by software. Observed AI use is augmentation and assistance today, not jobs replaced.

Member tasks

Occupation-specific tasks O*NET maps to this detailed work activity, most important first.

• Analyze product failure data and laboratory test results to determine causes of problems and develop solutions. · Materials Engineers · importance 4.3 · exposure with tools
• Perform failure, variation, or root cause analyses. · Automotive Engineers · importance 4.2 · exposure with tools
• Participate in out-of-specification and failure investigations and recommend corrective actions. · Quality Control Analysts · importance 3.9 · exposure with tools
• Conduct post-service or failure analyses, using electromechanical diagnostic principles or procedures. · Fuel Cell Engineers · importance 3.8 · exposure with tools
• Test or perform failure analysis for optomechanical or optoelectrical products, according to test plans. · Photonics Technicians · importance 3.7 · no direct exposure
• Conduct analyses addressing issues such as failure, reliability, or yield improvement. · Microsystems Engineers · importance 3.5 · exposure with tools
• Conduct failure analyses, document results, and recommend corrective actions. · Mechanical Engineering Technologists and Technicians · importance 3.4 · exposure with tools
• Perform root cause analysis on wind turbine tower component failures. · Wind Energy Engineers · importance 3.0 · exposure with tools

Occupations that perform this

• Materials Engineers
• Automotive Engineers
• Quality Control Analysts
• Photonics Technicians
• Microsystems Engineers
• Mechanical Engineering Technologists and Technicians

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

• O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
• “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic

Data compiled June 2, 2026. Figures are estimates, not advice.

@misc{singulariki-conduct-quantitative-failure-analyses-of-operational-data,
  title  = {Conduct quantitative failure analyses of operational data.},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130. Accessed June 7, 2026},
  url    = {https://singulariki.com/detailed-activities/conduct-quantitative-failure-analyses-of-operational-data}
}