How exposed to AI is the job of Meat, Poultry, and Fish Cutters and Trimmers?

0.8% of the weighted task load of Meat, Poultry, and Fish Cutters and Trimmers is exposed, meaning work current AI systems can produce with little structural friction. A further 0.8% is assisted work and 98.4% is untouched. That places it 1th out of 923 occupations, rank 916, in release v2026.Q3 of the Task Exposure Index.

Will AI replace Meat, Poultry, and Fish Cutters and Trimmers?

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 Meat, Poultry, and Fish Cutters and Trimmers are most exposed to AI?

Of 12 tasks O*NET records for this occupation, 0 are exposed, 0 assisted and 12 untouched. The full task-by-task breakdown is published on this page.

Occupation · SOC 51-3022.00 · Job Zone 2

AI exposure: Meat, Poultry, and Fish Cutters and Trimmers

Use hands or hand tools to perform routine cutting and trimming of meat, poultry, and seafood.

Reading this score

computed

Meat, Poultry, and Fish Cutters and Trimmers is among the least exposed occupations measured, at 0.8% of weighted task load, rank 916 of 923. 98.4% 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 12 tasks it averages 3.00 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 Inspect meat products for defects, bruises or blemishes and remove them along with any excess..., at 6.2%. The least is Clean and salt hides, at 0.0%. A gap of 6.2% 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 production occupations, this one is less exposed than the median of 16.2% across the group's 107 roles, with 105 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 12 tasks, 0 are currently banded exposed, 0 assisted and 12 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	0.04	0-4
Embodiment	3.00	0-3
Presence	0.12	0-3
Accountability	0.83	0-3
Context	0.96	0-3
Verification cost	0.92	0-3

Task by task

12 tasks, O*NET 31.0

Task	Exposed	Assisted	Untouched	Importance	Band
Inspect meat products for defects, bruises or blemishes and remove them along with any excess fat.	6.2%	6.2%	87.5%	4.33	untouched
Use knives, cleavers, meat saws, bandsaws, or other equipment to perform meat cutting and trimming.	0.0%	0.0%	100.0%	4.60	untouched
Weigh meats and tag containers for weight and contents.	0.0%	0.0%	100.0%	4.48	untouched
Cut and trim meat to prepare for packing.	0.0%	0.0%	100.0%	4.28	untouched
Separate meats and byproducts into specified containers and seal containers.	0.0%	0.0%	100.0%	4.25	untouched
Process primal parts into cuts that are ready for retail use.	0.0%	0.0%	100.0%	4.36	untouched
Prepare ready-to-heat foods by filleting meat or fish or cutting it into bite-sized pieces, preparing and adding vegetables or applying sauces or breading.	0.0%	0.0%	100.0%	4.24	untouched
Clean, trim, slice, and section carcasses for future processing.	0.0%	0.0%	100.0%	4.21	untouched
Remove parts, such as skin, feathers, scales or bones, from carcass.	0.0%	0.0%	100.0%	4.11	untouched
Obtain and distribute specified meat or carcass.	0.0%	0.0%	100.0%	4.03	untouched
Produce hamburger meat and meat trimmings.	0.0%	0.0%	100.0%	4.01	untouched
Clean and salt hides.	0.0%	0.0%	100.0%	3.86	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.