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AI Automation Index
Will AI Replace Manufacturing Engineers?
Task-level automation exposure for Manufacturing Engineers: 24 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
57%
weighted task index
24 of 24 tasks scored
Median Salary
$102,440 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Very High
365,740 people hold this job — size, not growth
Projected 2025–35
+12.4%
employment change
23,100 openings a year · BLS line item
Pay by state
BLS OEWS publishes 52 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$81,820 – $156,510
Highest medians
Alaska $156,510
Washington $129,050
Oregon $128,290
New Mexico $128,090
California $124,600
Lowest medians
Puerto Rico $81,820
South Dakota $93,200
Mississippi $95,830
Kentucky $96,820
North Dakota $96,940
State figures come from the 17-2112 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 57%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 4%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 72%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 9%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 15/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
24 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 45%
11 tasks
Medium exposure (≥ 35%) 50%
12 tasks
Low exposure (below 35%) 5%
1 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Constituent Tasks
#18181 · Core · 55% automatable
Troubleshoot new or existing product problems involving designs, materials, or processes.
decision support — Root-cause hypotheses; human verifies in the field
Determine causes of operational problems or failures.
#18177 · Core · 59% automatable
Investigate or resolve operational problems, such as material use variances or bottlenecks.
data analysis — Evaluates operations from data; human owns conclusions
Analyze operational data to evaluate operations, processes or products. Resolve operational performance problems.
#20960 · Core · 50% automatable
Identify opportunities or implement changes to improve manufacturing processes or products or to reduce costs, using knowledge of fabrication processes, tooling and production equipment, assembly methods, quality control standards, or product design, materials and parts.
text generation — AI drafts technical methods; expert validates feasibility
Implement design or process improvements. Develop technical methods or processes.
#18167 · Core · 50% automatable
Apply continuous improvement methods, such as lean manufacturing, to enhance manufacturing quality, reliability, or cost-effectiveness.
decision support — Operational methods aided; human decides
Determine operational methods.
#18183 · Core · 65% automatable
Provide technical expertise or support related to manufacturing.
decision support — Technical guidance needs context-specific human judgment
Provide technical guidance to other personnel.
#20961 · Core · 45% automatable
Incorporate new manufacturing methods or processes to improve existing operations.
decision support — AI models process systems; engineer validates feasibility
Design industrial processing systems.
#18180 · Core · 60% automatable
Review product designs for manufacturability or completeness.
decision support — Design quality evaluation needs engineering judgment
Evaluate designs or specifications to ensure quality.
#20962 · Core · 62% automatable
Determine root causes of failures or recommend changes in designs, tolerances, or processing methods, using statistical procedures.
decision support — Proposes design changes; human validates feasibility
Determine causes of operational problems or failures. Recommend technical design or process changes to improve efficiency, quality, or performance.
#18182 · Core · 80% automatable
Prepare reports summarizing information or trends related to manufacturing performance.
text generation — Generate operational reports from structured data
Prepare operational reports.
#18178 · Core · 78% automatable
Prepare documentation for new manufacturing processes or engineering procedures.
text generation — Draft procedural documents from provided content
Prepare procedural documents.
#18168 · Core · 55% automatable
Design layout of equipment or workspaces to achieve maximum efficiency.
text generation — Drafting diagrams aided; engineering validity needs human
Create graphical representations of industrial production systems.
#18170 · Core · 55% automatable
Communicate manufacturing capabilities, production schedules, or other information to facilitate production processes.
interpersonal — Conferring with technical staff on plans is relational work
Confer with technical personnel to prepare designs or operational plans.
#18185 · Core · 28% automatable
Supervise technicians, technologists, analysts, administrative staff, or other engineers.
interpersonal — Supervising production staff is on-site human management
Supervise production or support personnel.
#18172 · Core · 36% automatable
Design, install, or troubleshoot manufacturing equipment.
decision support — Root-cause hypotheses; human verifies in the field
Determine causes of operational problems or failures. Install production equipment or systems. Design industrial equipment.
#18175 · Core · 60% automatable
Evaluate manufactured products according to specifications and quality standards.
decision support — Usefulness assessment needs context and testing
Assess product or process usefulness.
#18174 · Core · 69% automatable
Estimate costs, production times, or staffing requirements for new designs.
data analysis — Time estimates from task data; human calibrates
Estimate operational costs. Estimate technical or resource requirements for development or production projects. Estimate time requirements for development or production projects.
#18186 · Core · 40% automatable
Train production personnel in new or existing methods.
interpersonal — Operational procedure training needs live demonstration
Train personnel on proper operational procedures.
#20963 · Core · 60% automatable
Design tests of finished products or process capabilities to establish standards or validate process requirements.
decision support — Protocols drafted; scientific validity human-owned
Devise research or testing protocols.
#19649 · Core · 70% automatable
Analyze the financial impacts of sustainable manufacturing processes or sustainable product manufacturing.
data analysis — Cost-benefit analysis from given inputs; human reviews
Analyze costs and benefits of proposed designs or projects.
#19650 · Core · 45% automatable
Develop sustainable manufacturing technologies to reduce greenhouse gas emissions, minimize raw material use, replace toxic materials with non-toxic materials, replace non-renewable materials with renewable materials, or reduce waste.
decision support — AI models process systems; engineer validates feasibility
Design industrial processing systems.
#18179 · Core · 65% automatable
Purchase equipment, materials, or parts.
decision support — Procurement drafting and comparison; human commits funds
Purchase materials, equipment, or other resources.
#19651 · Core · 55% automatable
Evaluate current or proposed manufacturing processes or practices for environmental sustainability, considering factors such as greenhouse gas emissions, air pollution, water pollution, energy use, or waste creation.
data analysis — Impact assessment aided; site-specific data human
Investigate the environmental impact of projects.
#18184 · Core · 80% automatable
Read current literature, talk with colleagues, participate in educational programs, attend meetings or workshops, or participate in professional organizations or conferences to keep abreast of developments in the manufacturing field.
information retrieval — AI tracks technical updates; human validates depth
Update technical knowledge.
#19652 · Supplemental · 52% automatable
Redesign packaging for manufactured products to minimize raw material use or waste.
text generation — Process methods drafted; materials validation human
Develop operational methods or processes that use green materials or emphasize sustainability.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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