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# Will AI replace automation engineers?

Updated October 2026

Industrial engineers score 55 of 100 for AI exposure. The largest share of their task time, 79%, is work AI speeds up while they stay in charge. AI can take over 16% outright, and 5% stays with people.

-   **16%** AI can do it
-   **79%** AI helps
-   **5%** Needs a person

High exposure

Higher than 85% of the 923 occupations we scored.

## Task by task

We graded 20 tasks, listed here from most to least exposed. AI can do 3 of them today, speeds up 16 and leaves 1 to people.

| Task | AI exposure |
| --- | --- |
| Complete production reports, purchase orders, and material, tool, and equipment lists. Production reports and purchase and equipment lists are routine documents AI generates. | 75% AI can do it |
| Record or oversee recording of information to ensure currency of engineering drawings and documentation of production problems. Routine documentation updates from logs and reports are highly automatable. | 65% AI can do it |
| Apply statistical methods and perform mathematical calculations to determine manufacturing processes, staff requirements, and production standards. Statistical calculations and standards math are well suited to software and AI. | 65% AI can do it |
| Review production schedules, engineering specifications, orders, and related information to obtain knowledge of manufacturing methods, procedures, and activities. AI can summarize schedules and specifications; engineer builds process knowledge. | 60% AI helps |
| Formulate sampling procedures and designs and develop forms and instructions for recording, evaluating, and reporting quality and reliability data. AI drafts sampling plans and recording forms; engineer validates statistical design. | 60% AI helps |

## How industrial engineers compare

All occupations (923)

Architecture and engineering (56)

**Industrial engineers score 55**, marked by the dark line. Each dot is a score, darker where more occupations share it. The short marks show the averages, 37 for all occupations and 47 for architecture and engineering.

## Pay and outlook in the US

Median pay

$102,440 a year, May 2025

People employed

365,740 in the US, May 2025

Projected change in jobs

+12.4% from 2024 to 2034

## Skills to build

-   Validate AI-drafted sampling plans in MATLAB
-   Audit AI cost models against SAP cost records
-   Hold the Certified Quality Engineer credential
-   Lead disposition of defective and discrepant parts
-   Write production schedules in Microsoft Project
-   Pull production data with SQL queries

## Safer roles nearby

5 related occupations with lower scores.

-   [Industrial engineering technicians43 out of 100](https://www.aigentcy.com/ai-exposure/industrial-engineering-technicians/)
-   [Manufacturing engineers52 out of 100](https://www.aigentcy.com/ai-exposure/manufacturing-engineers/)
-   [Industrial production managers44 out of 100](https://www.aigentcy.com/ai-exposure/industrial-production-managers/)
-   [Validation engineers49 out of 100](https://www.aigentcy.com/ai-exposure/validation-engineers/)
-   [Mechanical engineers47 out of 100](https://www.aigentcy.com/ai-exposure/mechanical-engineers/)

## Questions about industrial engineers and AI

Will automation engineers be replaced by AI?

Partly. About 5 in 10 working hours go to tasks AI can already do, mostly completing production reports and purchase orders, and keeping engineering drawings and problem logs current. What stays with people is the floor work, setting standards with stakeholders, judging equipment accuracy, and deciding how defective parts are disposed of.

Which industrial engineer tasks is AI taking over?

Paperwork and calculations go first. Completing production reports, purchase orders, and equipment lists scores 75 on exposure, because AI fills them from fixed fields. Statistical methods for production standards and staffing score 65, since the math is routine for software.

Which industrial engineer tasks stay human?

Shop floor supervision stays with people. Directing inspectors and testers scores 15 on exposure, the lowest in the job, because it means leading hands-on work on the floor. Live talks with clients, vendors, and management score 25, and checking production equipment precision scores 30 because it takes physical measurement with gauges on the machine.

How can industrial engineers stay valuable?

Take responsibility for the decisions that happen on the floor. Validate AI-drafted sampling plans with your statistical design in MATLAB before they reach quality control. Test AI cost models against actual SAP cost records before management review. Lead the disposition of defective parts and the stakeholder meetings that set production standards.

## Related jobs

-   [Electrical engineers47 out of 100](https://www.aigentcy.com/ai-exposure/electrical-engineers/)
-   [Electrical engineering technicians45 out of 100](https://www.aigentcy.com/ai-exposure/electrical-engineering-technicians/)
-   [Project managers53 out of 100](https://www.aigentcy.com/ai-exposure/project-managers/)
-   [Electronics engineers53 out of 100](https://www.aigentcy.com/ai-exposure/electronics-engineers/)

## Next steps for industrial engineers

-   ### Check your job
    
    Your job title is a rough guide. Your actual tasks decide your score. Paste your job description or CV and see which of your tasks AI can do.
    
    [Check your job](https://www.aigentcy.com/tools/ai-exposure-checker/) [Check your CV](https://www.aigentcy.com/tools/cv-checker/)
    

### Check which roles in your team AI can take on

We map your team's tasks, show what AI can do today and build the automations that pay back first.

[Book an AI audit](https://www.aigentcy.com/audit/)

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-   [**Jobs A to Z**Every job we publish, by first letter.](https://www.aigentcy.com/ai-exposure/jobs/)
-   [**AI exposure by industry**Average scores for 22 occupation groups.](https://www.aigentcy.com/ai-exposure/industries/)
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-   [**How we score AI exposure**The grading, the weights and every source.](https://www.aigentcy.com/ai-exposure/methodology/)

Occupation data from the O\*NET 30.3 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) and modified by Aigentcy. O\*NET® is a trademark of USDOL/ETA. Wage, employment and projection data from the U.S. Bureau of Labor Statistics. EU occupation names from ESCO, European Commission. AI usage signals from the Anthropic Economic Index (CC BY) and Microsoft Research (CC BY 4.0). [Methodology and sources](https://www.aigentcy.com/ai-exposure/methodology/). Scores © Aigentcy. Quote them with a link to the page.
