# Inspectors, Testers, Sorters, Samplers, and Weighers

> Inspect, test, sort, sample, or weigh nonagricultural raw materials or processed, machined, fabricated, or assembled parts or products for defects, wear, and deviations from specifications. May use precision measuring instruments and complex test equipment.

- **SOC code:** 51-9061.00
- **Canonical URL:** https://singulariki.com/roles/role-51-9061-00
- **Also known as:** Inspector, Quality Control Inspector (QC Inspector), Quality Inspector, Quality Technician, QA Auditor (Quality Assurance Auditor), QA Inspector (Quality Assurance Inspector), QA Technician (Quality Assurance Technician), QC Technician (Quality Control Technician)
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Discard or reject products, materials, or equipment not meeting specifications.
- Mark items with details, such as grade or acceptance-rejection status.
- Measure dimensions of products to verify conformance to specifications, using measuring instruments, such as rulers, calipers, gauges, or micrometers.
- Notify supervisors or other personnel of production problems.
- Inspect, test, or measure materials, products, installations, or work for conformance to specifications.
- Write test or inspection reports describing results, recommendations, or needed repairs.
- Recommend necessary corrective actions, based on inspection results.
- Read dials or meters to verify that equipment is functioning at specified levels.
- Make minor adjustments to equipment, such as turning setscrews to calibrate instruments to required tolerances.
- Read blueprints, data, manuals, or other materials to determine specifications, inspection and testing procedures, adjustment methods, certification processes, formulas, or measuring instruments required.
- Check arriving materials to ensure that they match purchase orders, submitting discrepancy reports as necessary.
- Inspect or test raw materials, parts, or products to determine compliance with environmental standards.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Production and Processing _(knowledge)_
- Quality Control Analysis _(transferable_skill)_
- Oral Expression _(ability)_
- English Language _(knowledge)_
- Oral Comprehension _(ability)_
- Near Vision _(ability)_
- Perceptual Speed _(ability)_
- Customer and Personal Service _(knowledge)_
- Writing _(essential_skill)_
- Critical Thinking _(essential_skill)_
- Problem Sensitivity _(ability)_
- Flexibility of Closure _(ability)_

**Skills in demand:**
- English Language _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Writing _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Mathematics _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Active Listening _(Common Skill)_
- Visualization _(Specialized Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology, in demand)_
- Microsoft Office software _(hot technology, in demand)_
- Microsoft Outlook _(hot technology, in demand)_
- Microsoft PowerPoint _(hot technology, in demand)_
- Microsoft Word _(hot technology, in demand)_
- Apache Hive _(hot technology)_
- Atlassian JIRA _(hot technology)_
- Autodesk AutoCAD _(hot technology)_
- Dassault Systemes SolidWorks _(hot technology)_
- Extensible markup language XML _(hot technology)_
- Microsoft Access _(hot technology)_
- Microsoft SharePoint _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 39th percentile (Moderate) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 47th percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 47th percentile (Moderate) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 28th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 97th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 0.0% growth (About average); 69.9k annual openings; 598k → 598.1k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $47,460; 591,180 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Automation vs augmentation:** 50% automation, 33% augmentation (usage-weighted).
- **Autonomy median:** 3.0 (higher = AI acts more independently).
- **Dominant collaboration mode:** directive.

**Tasks most handed to AI here:**
- Analyze test data, making computations as necessary, to determine test results. _(2.4% of measured AI use; directive)_
- Measure dimensions of products to verify conformance to specifications, using measuring instruments such as rulers, calipers, gauges, or micrometers. _(0.7% of measured AI use; directive)_
- Interpret legal requirements, provide safety information, or recommend compliance procedures to contractors, craft workers, engineers, or property owners. _(0.4% of measured AI use; learning)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me analyze test data, making computations as necessary, to determine test results.
- Help me measure dimensions of products to verify conformance to specifications, using measuring instruments such as rulers, calipers, gauges, or micrometers.
- Help me interpret legal requirements, provide safety information, or recommend compliance procedures to contractors, craft workers, engineers, or property owners.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-51-9061-00_