Evolving

Last Update: 2/17/2026

Your role’s AI Resilience Score is

43.0%

Median Score

Changing Fast

Evolving

Stable

Our confidence in this score:
Low-medium

What does this resilience result mean?

These roles are shifting as AI becomes part of everyday workflows. Expect new responsibilities and new opportunities.

AI Resilience Report for

Shoe Machine Operators and Tenders

They run machines to make shoes, making sure everything works smoothly and fixing any issues to keep production moving.

This role is evolving

The career of Shoe Machine Operators and Tenders is labeled as "Evolving" because, while some parts of shoemaking can be automated, many tasks still need human skills. Machines can help with repetitive jobs like gluing, but people are essential for cutting, inspecting, and ensuring quality because these tasks require careful attention and creativity.

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Learn more about how you can thrive in this position

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Chat with Coach
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This role is evolving

The career of Shoe Machine Operators and Tenders is labeled as "Evolving" because, while some parts of shoemaking can be automated, many tasks still need human skills. Machines can help with repetitive jobs like gluing, but people are essential for cutting, inspecting, and ensuring quality because these tasks require careful attention and creativity.

Read full analysis

Contributing Sources

We aggregate scores from multiple models and supplement with employment projections for a more accurate picture of this occupation’s resilience. Expand to view all sources.

AI Resilience

AI Resilience Model v1.0

AI Task Resilience

Learn about this score
Stable iconStable

86.2%

86.2%

Microsoft's Working with AI

AI Applicability

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Stable iconStable

88.9%

88.9%

Will Robots Take My Job

Automation Resilience

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Changing fast iconChanging fast

1.9%

1.9%

Low Demand

Labor Market Outlook

We use BLS employment projections to complement the AI-focused assessments from other sources.

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Growth Rate (2024-34):

-3.7%

Growth Percentile:

14.1%

Annual Openings:

400

Annual Openings Pct:

3.9%

Analysis of Current AI Resilience

Shoe Machine Operators

Updated Quarterly • Last Update: 2/17/2026

Analysis
Suggested Actions
State of Automation

What's changing and what's not

Shoemaking is still hard to fully automate. Leather and fabric are flexible and each style/size can vary a lot, so experts note that “automation [is] beset by tremendous difficulties” in this industry [1] [1]. Today, most cutting and finishing steps are done by people.

For example, workers still “cut excess thread or material from shoe parts, using scissors or knives” and they “remove and examine shoes…to verify conformance” [2] [2]. Some machines have become smarter: in a recent U.S. pilot factory a few robots apply glue and position pieces, letting one line (with only 10 people) produce ~150,000 pairs per year – down from 60 people on an older line [3] [3]. Even so, many tasks remain manual.

Reading job tags and studying specifications are still done by hand [2], and inspecting stitches for quality mostly relies on a human eye. In short, only parts of the process (like precise gluing on one line) are automated or AI‐augmented today; detailed trimming, tagging, and fine inspection still need workers’ skills [1] [3].

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AI Adoption

AI in the real world

Big changes will likely be gradual. Right now, most shoe‐assembly happens where labor is cheap – about 99% of U.S. athletic shoes are imported [3] – so factories only invest in robots when the economics really change. For example, tariffs or demand for faster delivery could make local automation worthwhile [3].

One experiment found that robots could cut staff from 60 to 10 per line if glue‐applying and assembly were automated [3] [3], but even Adidas tried a U.S. robot factory and ultimately sent production back overseas because it was still cheaper abroad [3]. In sum, AI and robots offer benefits like steadier quality and less hard labor, but the machines are expensive and best for large batches. As a result, adoption has been slow.

Importantly, human skills remain crucial: knowing how shoes are made, spotting subtle defects, and adapting on the fly are tough for AI. Young workers can be hopeful—machines tend to handle repetitive steps, while creative problem‐solving and skilled handwork stay in human hands [1] [3], at least for the foreseeable future.

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More Career Info

Career: Shoe Machine Operators and Tenders

Employment & Wage Data

Median Wage

$38,160

Jobs (2024)

4,100

Growth (2024-34)

-3.7%

Annual Openings

400

Education

High school diploma or equivalent

Experience

None

Source: Bureau of Labor Statistics, Employment Projections 2024-2034

Task-Level AI Resilience Scores

AI-generated estimates of task resilience over the next 3 years

1

60% ResilienceCore Task

Perform routine equipment maintenance such as cleaning and lubricating machines or replacing broken needles.

2

60% ResilienceSupplemental

Hammer loose staples for proper attachment.

3

55% ResilienceSupplemental

Turn setscrews on needle bars, and position required numbers of needles in stitching machines.

4

55% ResilienceSupplemental

Turn knobs to adjust stitch length and thread tension.

5

50% ResilienceSupplemental

Align parts to be stitched, following seams, edges, or markings, before positioning them under needles.

6

50% ResilienceSupplemental

Switch on machines, lower pressure feet or rollers to secure parts, and start machine stitching, using hand, foot, or knee controls.

7

50% ResilienceSupplemental

Test machinery to ensure proper functioning before beginning production.

Tasks are ranked by their AI resilience, with the most resilient tasks shown first. Core tasks are essential functions of this occupation, while supplemental tasks provide additional context.

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