How exposed to AI is the job of Graders and Sorters, Agricultural Products?

6.5% of the weighted task load of Graders and Sorters, Agricultural Products is exposed, meaning work current AI systems can produce with little structural friction. A further 3.2% is assisted work and 90.3% is untouched. That places it 7th out of 923 occupations, rank 859, in release v2026.Q3 of the Task Exposure Index.

Will AI replace Graders and Sorters, Agricultural Products?

The index does not measure displacement. It measures what current AI systems can produce and what stands in the way. Whether exposed work becomes a lost job depends on firm economics, regulation and organisational inertia, none of which this index models.

Which tasks of a Graders and Sorters, Agricultural Products are most exposed to AI?

Of 5 tasks O*NET records for this occupation, 1 are exposed, 0 assisted and 4 untouched. The full task-by-task breakdown is published on this page.

Occupation · SOC 45-2041.00 · Job Zone 2

AI exposure: Graders and Sorters, Agricultural Products

Grade, sort, or classify unprocessed food and other agricultural products by size, weight, color, or condition.

Reading this score

computed

Graders and Sorters, Agricultural Products is among the least exposed occupations measured, at 6.5% of weighted task load, rank 859 of 923. 90.3% of this job is work current AI systems cannot produce at all. That is not a statement about skill or value. It is a statement about what these systems can and cannot do.

What holds the line here is embodiment. Across this occupation's 5 tasks it averages 2.80 out of 3, the highest of the five friction dimensions. In plain terms, the work has to happen in physical space. A language model cannot move matter. Until the robotics to do this work is both good enough and cheap enough to deploy widely, capability in software does not reach it.

The most exposed thing this job does is Record grade or identification numbers on tags or on shipping, receiving, or sales sheets, at 50.0%. The least is Grade and sort products according to factors such as color, species, length, width, appearance,..., at 0.0%. A gap of 50.0% between two parts of the same job is the reason this index publishes at task level. An occupation-wide number would have hidden both.

Within farming, fishing and forestry occupations, this one is less exposed than the median of 10.8% across the group's 12 roles, with 10 scoring higher. Being in an exposed family does not make a particular job exposed, and the reverse holds too.

What would move this score. Of 5 tasks, 1 are currently banded exposed, 0 assisted and 4 untouched. For that distribution to shift materially would take robotics cheap and reliable enough to deploy at scale, not a better language model. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.

Where the score comes from

judged

Every task is scored through the standardised work activities it maps to. These are this occupation’s averages on the six rubric dimensions. Capability is what AI can do; the other five are what stands in the way.

Dimension	Mean	Scale
Capability	0.60	0-4
Embodiment	2.80	0-3
Presence	0.00	0-3
Accountability	0.30	0-3
Context	0.70	0-3
Verification cost	0.70	0-3

Task by task

5 tasks, O*NET 31.0

Task	Exposed	Assisted	Untouched	Importance	Band
Record grade or identification numbers on tags or on shipping, receiving, or sales sheets.	50.0%	25.0%	25.0%	4.20	exposed
Place products in containers according to grade and mark grades on containers.	0.0%	0.0%	100.0%	4.59	untouched
Weigh products or estimate their weight, visually or by feel.	0.0%	0.0%	100.0%	4.51	untouched
Discard inferior or defective products or foreign matter, and place acceptable products in containers for further processing.	0.0%	0.0%	100.0%	4.44	untouched
Grade and sort products according to factors such as color, species, length, width, appearance, feel, smell, and quality to ensure correct processing and usage.	0.0%	0.0%	100.0%	4.43	untouched

Task text and importance ratings sourced from O*NET 31.0. Shares computed. The occupation score is the importance-weighted mean.

Occupations either side of this one

The four closest scores in the same occupational family, then the four closest anywhere in the index.

Read this carefully. Exposure is not displacement. A high score means current AI systems can produce this work, not that anyone will stop paying a person to do it. Adoption depends on economics, regulation and inertia that this index deliberately does not model. How the score is built.

What this means in practice

Most of this work is not reachable by current systems, so the immediate pressure is on the administrative edges of the role rather than its core: the scheduling, the reporting, the written records. That is where time is recovered.