Automation risk assessment
Industrial Engineers: AI exposure and automation risk
Industrial Engineers score 61% on the Eloundou et al. LLM exposure measure (high exposure). 18 of 20 rated tasks are exposed. BLS projects +12.4% employment, 2025–35.
Data compiled 2026-10-09 · BLS 2025–35 · O*NET 31.0 · AEI May 2026
LLM exposure (β)
61%
High exposure
BLS projects +12.4% employment for industrial engineers from 2025 to 2035.
Eloundou et al., Science 2024
Exposure scores
How much of this occupation's task list large language models could speed up
- 61%
High exposure (β, human-rated)
Share of this occupation's tasks that annotators judged an LLM could do at least 50% faster at the same quality, counting tasks that need extra software at half weight.
Eloundou et al., Science 2024
- 53%
Same measure, rated by GPT-4
The study's model-rated β for comparison. Directly exposed tasks alone (α) score 32%; all exposed tasks (ζ) score 90%.
Eloundou et al., Science 2024
- 4%
Observed exposure in real AI usage
How much of the theoretically exposed work actually shows up as automated, work-related Claude usage.
Anthropic observed exposure (Mar 2026)
Exposed and unexposed tasks
Human ratings of 20 O*NET task statements, Eloundou et al., Science 2024
Of 20 rated tasks, 7 are directly exposed (E1), 11 are exposed once complementary software is built (E2), and 2 are not exposed (E0).
Exposed tasks
Estimate production costs, cost saving methods, and the effects of product design changes on expenditures for management review, action, and control.
E2 · Exposed with added software
Plan and establish sequence of operations to fabricate and assemble parts or products and to promote efficient utilization.
E1 · Directly exposed
Analyze statistical data and product specifications to determine standards and establish quality and reliability objectives of finished product.
E2 · Exposed with added software
Communicate with management and user personnel to develop production and design standards.
E2 · Exposed with added software
Evaluate precision and accuracy of production and testing equipment and engineering drawings to formulate corrective action plan.
E2 · Exposed with added software
Tasks not exposed
Confer with clients, vendors, staff, and management personnel regarding purchases, product and production specifications, manufacturing capabilities, or project status.
E0 · Not exposed
Recommend methods for improving utilization of personnel, material, and utilities.
E0 · Not exposed
Job outlook
U.S. projections, BLS Employment Projections 2025–35
- +12.4%
Projected employment change, 2025–35
From 365,100 jobs in 2025 to 410,600 in 2035. All occupations: +3.5%.
BLS Employment Projections 2025–35
- 23,100
Openings per year, 2025–35 average
Includes openings from growth and from workers who retire or change occupations.
BLS Employment Projections 2025–35
- $102,440
Median annual wage, 2025
BLS Employment Projections 2025–35
- Bachelor's degree
Typical education for entry
BLS Employment Projections 2025–35
What BLS expects to change
Demand change - share increases as increasingly complex manufacturing processes, logistics and supply chains drive demand for industrial engineers to design and develop them.
BLS note on factors affecting industrial engineers employment, Table 1.12, BLS Employment Projections 2025–35.
Primary profiles: BLS Occupational Outlook Handbook · O*NET OnLine
How AI is used on this work
Claude.ai conversations on this occupation's tasks, Anthropic Economic Index (May 2026)
Augmentation and automation are shares of conversations with a classified collaboration pattern, excluding unclassified conversations. The individual pattern shares include them. 61.4% of all conversations on these tasks were for work. Low-volume occupation: 0.02% of all conversations (under 0.05%), so treat these shares as indicative. See the full collaboration breakdown
Sources & methodology
Every figure on this page comes from the public datasets below. We do not edit the source values; derived figures are explained here.
- Exposure is the human-annotator β score from Eloundou et al.: the share of an occupation's O*NET tasks that a large language model could do at least 50% faster at equal quality, counting tasks that need extra software (E2) at half weight. We label β under 0.25 low, 0.25–0.5 moderate, and 0.5 or more high; these bands are ours, not the authors'.
- Task labels (E0 not exposed, E1 directly exposed, E2 exposed with complementary software) are the study's aggregated human ratings of each O*NET task statement for the occupation. We show up to five tasks per group.
- Observed exposure (Anthropic, March 2026) combines that theoretical feasibility with real Claude usage on the occupation's tasks, weighting automated and work-related use more heavily than augmentative use, then averaging by time spent on each task. Values run from 0 to 1.
- Employment, growth, openings, wage, and education are the BLS 2025–35 projections as published; BLS utilization notes are quoted verbatim.
- Collaboration shares come from Anthropic Economic Index data for Claude.ai conversations in May 2026, classified to the O*NET tasks they match. "Automation" groups directive and feedback-loop conversations; "augmentation" groups task iteration, learning, and validation. Both buckets are shares of conversations with a classified collaboration pattern, excluding the unclassified "none" pattern; the six individual pattern shares include unclassified conversations. They describe how people use Claude on this occupation's tasks — not what share of workers in the occupation use AI.
- BLS occupations are matched to O*NET-SOC codes with the BLS O*NET-to-NEM crosswalk; where a BLS occupation spans several O*NET occupations, we average their values.
- Exposure measures what AI could help do, not a forecast of job loss.
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2024). "GPTs are GPTs: Labor market impact potential of LLMs." Science, 384(6702), 1306–1308. doi:10.1126/science.adj0998. Occupation- and task-level exposure data from the authors' public repository (openai/GPTs-are-GPTs).
Vintage: Published June 21, 2024; human annotations of O*NET tasks collected 2023. License: Data repository: MIT License. Retrieved 2026-10-09.
Anthropic observed exposure (Mar 2026)
Massenkoff, M., & McCrory, P. (2026). "Labor market impacts of AI: A new measure and early evidence." Anthropic, March 5, 2026. Occupation-level observed exposure (Anthropic Economic Index, labor_market_impacts/job_exposure.csv).
Vintage: Published March 5, 2026. License: CC BY (data). Retrieved 2026-10-09.
BLS Employment Projections 2025–35
U.S. Bureau of Labor Statistics, Employment Projections program. Occupational projections, 2025–35, and worker characteristics, 2025 (Table 1.2); fastest growing occupations (Table 1.3); factors affecting occupational utilization (Table 1.12).
Vintage: 2025–35 projections, released August 27, 2026. License: Public domain (U.S. federal government work). Retrieved 2026-10-09.
Anthropic Economic Index (May 2026)
Massenkoff, M., Lyubich, E., Sacher, S., Hitzig, Z., Zhang, S., Heller, R., & McCrory, P. (2026). "Anthropic Economic Index report: Cadences." Anthropic, June 26, 2026. Claude.ai usage metrics by occupation, global, May 2026.
Vintage: Release of June 26, 2026; Claude.ai conversations from May 2026. License: CC BY (data). Retrieved 2026-10-09.
This page includes information from the O*NET 31.0 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA. Metaintro has modified all or some of this information. USDOL/ETA has not approved, endorsed, or tested these modifications.
Vintage: O*NET 31.0, August 2026 release. License: CC BY 4.0. Retrieved 2026-10-09.
Keep exploring
Related pages built on the same dataset