Share and intensity of work current AI systems can materially affect.
SOC 51-2092
Team Assemblers AI displacement risk
Assembly line work is the historical heart of factory automation, and collaborative robots keep taking repetitive stations. Line balancing, quality checks, changeover judgment, and rotation across tasks keep human assemblers present in most plants.
Likely potential for exposed tasks to move to software after workflow integration.
Highly standardized electronics and automotive assembly automates first. Mixed-model, low-volume, and final-assembly work with frequent changeovers keeps human flexibility economically superior for longer.
Distribution
Where Team Assemblers sits across 620 tracked roles
Displacement pressure 48 — higher than 78% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
11 O*NET task statements matched to SOC 51-2092. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $41,490 (Fallback estimate; May 2025 median unavailable, US national). BLS does not publish an exact current median for this occupational split, so the page retains a clearly labeled fallback estimate.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Team Assemblers
SOC 51-2092 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 48/100 role score and are not an occupation forecast.
+1.1% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +1.6% GDP and 3.9% unemployment.
+5.9% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +8.3% GDP and 4.6% unemployment.
+33.6% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Team Assemblers
The current evidence import matched 11 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks11
SOC51-2092
- Core task / ID 20157
Perform quality checks on products and parts.
- Core task / ID 20160
Review work orders and blueprints to ensure work is performed according to specifications.
- Core task / ID 8468
Rotate through all the tasks required in a particular production process.
- Core task / ID 8469
Determine work assignments and procedures.
- Core task / ID 20163
Supervise assemblers and train employees on job procedures.
- Core task / ID 20159
Shovel, sweep, or otherwise clean work areas.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Perform assembly tasks in rotation
Exposure 46, automation 38%, augmentation 18%.
Check product quality
Exposure 52, automation 36%, augmentation 34%.
O*NET evidence: Perform quality checks on products and parts. (ID 20157)
Review work orders and blueprints
Exposure 56, automation 34%, augmentation 48%.
O*NET evidence: Review work orders and blueprints to ensure work is performed according to specifications. (ID 20160)
Maintain production equipment
Exposure 34, automation 20%, augmentation 36%.
O*NET evidence: Maintain production equipment and machinery. (ID 20162)
TaskExposureAutomationAugmentation
Perform assembly tasks in rotation4638%18%
Check product quality5236%34%
Review work orders and blueprints5634%48%
Maintain production equipment3420%36%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
ModerateProduction Team Lead
Training horizon: 1-3 months. Skill overlap 78. Wage preservation signal 124.
- Own line balancing
- Track station quality metrics
- Train rotating team members
credentialed transition
ModerateManufacturing Technician
Training horizon: 6-12 months. Skill overlap 58. Wage preservation signal 134.
- Learn PLC and controls basics
- Document process improvements
- Practice equipment troubleshooting
Comparison guides
Compare the next move before you commit
1-3 months
Team Assemblers to Production Team Lead
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Team Assemblers into Production Team Lead.
6-12 months
Team Assemblers to Manufacturing Technician
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Team Assemblers into Manufacturing Technician.
What the AI risk score means for Team Assemblers
The displacement pressure score for Team Assemblers is 48. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Perform assembly tasks in rotation carries 38% automation pressure, while Review work orders and blueprints carries 48% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $41,490 (Fallback estimate; May 2025 median unavailable, US national). Employment context: One of the largest manufacturing occupations. Typical education: No formal educational credential.
Wage vulnerability is 70, while transition feasibility is 62. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Moderate displacement pressure
- Cobots take repetitive stations
- Wage vulnerability is high
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Team Assemblers, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Station flexibility
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Quality awareness
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Robot collaboration
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Safety discipline
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Production Team Lead, such as own line balancing.
- By 90 days, compare internal openings and external postings for Production Team Lead or Manufacturing Technician and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Team Assemblers
Will AI replace Team Assemblers?
Assembly line work is the historical heart of factory automation, and collaborative robots keep taking repetitive stations. Line balancing, quality checks, changeover judgment, and rotation across tasks keep human assemblers present in most plants. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Team Assemblers work are most exposed to AI?
Perform assembly tasks in rotation and Check product quality show the strongest automation pressure in this model. Review work orders and blueprints and Maintain production equipment are better treated as AI-augmented work.
What should Team Assemblers learn next?
Start with Station flexibility, Quality awareness, Robot collaboration. The most practical adjacent paths in this model are Production Team Lead and Manufacturing Technician.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureOECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics