AI Automation Index

Will AI Replace Log Graders and Scalers?

Task-level automation exposure for Log Graders and Scalers: 12 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

41% weighted task index

12 of 12 tasks scored

Median Salary

$46,330 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Low

3,070 people hold this job — size, not growth

Projected 2025–35

-2.2% employment change

600 openings a year · BLS line item

Pay by state

BLS OEWS publishes 25 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$35,920 – $61,580

Highest medians

Washington $61,580

Oregon $60,250

Idaho $57,620

California $56,670

Michigan $51,810

Lowest medians

Tennessee $35,920

Texas $36,100

Virginia $36,220

North Carolina $36,850

Missouri $37,440

State figures come from the 45-4023 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 42%

Importance-weighted mean of the scored tasks. This is the headline index.

Physical work 11%

Share of weighted tasks in hands-on or machine-operating work activities.

Judgment & influence 38%

Share in analysis, decisions, teaching, directing, advising, or caring for others.

Routine administration 17%

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 12/100 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

12 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) 49%

5 tasks

Medium exposure (≥ 35%) 7%

1 tasks

Low exposure (below 35%) 45%

6 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 →

Emerging & rewritten tasks

Tasks O*NET added or rewrote for this occupation in its recent updates (05/2025 – 08/2026), with who proposed them. Additions are not in the scored task inventory — they are new work O*NET is still measuring, so they appear here and nowhere in the index. A Revision replaces a task that is scored, which is why only those move the "Rewritten by O*NET" share.

New 07/2017 · Incumbent

Move logs using heavy equipment such as log loaders.

Constituent Tasks

#9811 · Core · 60% automatable

Evaluate log characteristics and determine grades, using established criteria.

data analysis — Log analysis aided; domain interpretation human

Evaluate log quality.

#9812 · Core · 70% automatable

Record data about individual trees or load volumes into tally books or hand-held collection terminals.

data analysis — Record inventory data; field collection is human

Record agricultural or forestry inventory data.

#9814 · Core · 24% automatable

Measure felled logs or loads of pulpwood to calculate volume, weight, dimensions, and marketable value, using measuring devices and conversion tables.

physical manipulation — Hands-on measurement of agricultural products

Measure physical characteristics of forestry or agricultural products.

#9813 · Core · 10% automatable

Paint identification marks of specified colors on logs to identify grades or species, using spray cans, or call out grades to log markers.

physical manipulation — Marking forestry products is manual field work

Mark agricultural or forestry products for identification.

#9817 · Core · 60% automatable

Jab logs with metal ends of scale sticks, and inspect logs to ascertain characteristics or defects such as water damage, splits, knots, broken ends, rotten areas, twists, and curves.

data analysis — Log analysis aided; domain interpretation human

Evaluate log quality.

#9816 · Core · 60% automatable

Identify logs of substandard or special grade so that they can be returned to shippers, regraded, recut, or transferred for other processing.

data analysis — Log analysis aided; domain interpretation human

Evaluate log quality.

#21015 · Supplemental · 22% automatable

Arrange for hauling of logs to appropriate mill sites.

physical manipulation — Material handling direction is on-floor supervision

Direct material handling or moving activities.

#9820 · Supplemental · 70% automatable

Weigh log trucks before and after unloading, and record load weights and supplier identities.

data analysis — Record inventory data; field collection is human

Record agricultural or forestry inventory data.

#9815 · Supplemental · 17% automatable

Measure log lengths and mark boles for bucking into logs, according to specifications.

physical manipulation — Hands-on measurement of agricultural products

Mark agricultural or forestry products for identification. Measure physical characteristics of forestry or agricultural products.

#9819 · Supplemental · 55% automatable

Communicate with coworkers by signals to direct log movement.

interpersonal — Coordination needs live negotiation and context

Communicate with other workers to coordinate activities.

#9818 · Supplemental · 10% automatable

Drive to sawmills, wharfs, or skids to inspect logs or pulpwood.

situated mobility — Driving passenger vehicles needs real-world control

Drive passenger vehicles.

#9821 · Supplemental · 5% automatable

Saw felled trees into lengths.

physical manipulation — Chainsaw work in unpredictable forest terrain

Cut trees or logs.

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Community Sentiment

Do you agree?

Do you agree with the AI automation risk score calculated for this occupation?

Method

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

See the full method →

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