Will AI replace Agricultural Technicians?

Not as a job, but it is already doing parts of the work. Across the 48 official task statements scored for Agricultural Technicians (United States, SOC 19-4012), 44% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 43 out of 100 (range 38–49, band: partial). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

Which tasks in “Agricultural Technicians” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Prepare data summaries, reports, or analyses that include results, charts, or graphs to document research findings and results” (93/100, very high); “Document and maintain records of precision agriculture information” (93/100, very high); “Analyze data from harvester monitors to develop yield maps” (93/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Agricultural Technicians” stay human?

About 52% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Prepare culture media, following standard procedures” (0/100, minimal); “Prepare land for cultivated crops, orchards, or vineyards by plowing, discing, leveling, or contouring” (0/100, minimal); “Collect animal or crop samples” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Agricultural Technicians” do about AI?

Start from the ledger rather than the headline: 44% of this job's weighted core work is exposed, and roughly 52% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.

How is the AI exposure score for Agricultural Technicians calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 48 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Agricultural Technicians

preparing land for cultivated crops, applying precision agriculture information to specifically reduce the negative environmental impacts of farming practices and measuring or weighing ingredients used in laboratory testing. If that's your week, this page is about your job.

The honest answer

This job is splitting in two: documenting and maintaining records of precision agriculture information is work AI now does quickly and cheaply, and collecting information about soil or field attributes is work it can't touch.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 44%
changing shape: 4%
staying human: 52%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 43 out of 100 (38–49 allowing for uncertainty): partial exposure, across 48 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for agricultural technicians is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

Which half fills your week decides your exposure. The ledger below shows which rows you can move toward.

Your week, as this page understands it

Work with agricultural scientists in plant, fiber, and animal research, or assist with animal breeding and nutrition. Set up or maintain laboratory equipment and collect samples from crops or animals. Prepare specimens or record data to assist scientists in biology or related life science experiments. Conduct tests and experiments to improve yield and quality of crops or to increase the resistance of plants and animals to disease or insects. The job title says “agricultural technicians”. The real job is the part underneath: collecting information about soil or field attributes. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But agricultural technicians is not one task. It is 48 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is collecting information about soil or field attributes, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 44%
changing shape: 4%
staying human: 52%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

17 tasks

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Documenting and maintaining records of precision agriculture information
This is reading one thing and writing another: records of precision agriculture information in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Document and maintain records of precision agriculture information.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Keeping precision farming records is straightforward data management that software handles.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Using geospatial technology to develop soil sampling grids or identify sampling sites for testing characteristics
This is reading one thing and writing another: geospatial technology in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Use geospatial technology to develop soil sampling grids or identify sampling sites for testing characteristics such as nitrogen, phosphorus, or potassium content, pH, or micronutrients.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Designing sampling grids from mapping software is screen work software does well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Dividing agricultural fields into georeferenced zones
This is reading one thing and writing another: agricultural fields in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Divide agricultural fields into georeferenced zones, based on soil characteristics and production potentials.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Splitting fields into zones from soil and yield data is map analysis software is strong at.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Creating layer and analyzing maps showing precision agricultural data
This is reading one thing and writing another: layer in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Create, layer, and analyze maps showing precision agricultural data, such as crop yields, soil characteristics, input applications, terrain, drainage patterns, or field management history.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Building and reading layered farm maps is computer work with well-documented methods.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

3 tasks

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Advising farmers on upgrading Global Positioning System
The software now makes the first pass at farmers, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 3 · Core
Source: “Advise farmers on upgrading Global Positioning System (GPS) equipment to take advantage of newly installed advanced satellite technology.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: The technical answer is easy to look up, but farmers buy equipment advice from someone they know and trust.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Recording environmental data from field samples of soil
The software now makes the first pass at environmental data, but part of it still happens in the physical world. So the job becomes checking and deciding rather than producing.
importance 3 · Supplemental
Source: “Record environmental data from field samples of soil, air, water, or pests to monitor the effectiveness of integrated pest management (IPM) practices.” (O*NET task statement)
How this row was scored
Exposure score: 46 out of 100 (39–53 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Once samples are taken, logging the environmental readings is routine record work software handles well.
The five ratings: output a model can produce 4/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Assessing comparative soil erosion from various planting or tillage systems
The software now makes the first pass at comparative soil erosion, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 2 · Supplemental
Source: “Assess comparative soil erosion from various planting or tillage systems, such as conservation tillage with mulch or ridge till systems, no-till systems, or conventional tillage systems with or without moldboard plows.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Erosion comparisons use documented models and data, though the decisive readings come from the specific fields.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Staying human

28 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Collecting information about soil or field attributes
This work happens in the physical world: information, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Collect information about soil or field attributes, yield data, or field boundaries, using field data recorders and basic geographic information systems (GIS).” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (7–21 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Field attributes and boundaries are captured by walking or driving the ground with a data recorder.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Applying precision agriculture information to specifically reduce the negative environmental impacts of farming practices
The ratings behind this row put precision agriculture information well outside what today's tools can do on their own.
importance 4 · Core
Source: “Apply precision agriculture information to specifically reduce the negative environmental impacts of farming practices.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: The analysis is computer work, but changing what actually happens in a field depends on the farmer and the ground.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Installing, calibrating or maintaining sensors, mechanical controls, GPS-based vehicle guidance systems or computer settings
This work happens in the physical world: sensors, mechanical controls, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Install, calibrate, or maintain sensors, mechanical controls, GPS-based vehicle guidance systems, or computer settings.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (7–21 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Fitting and calibrating sensors and guidance kit means working on the machine itself.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Show the other 38 tasks

Preparing data summaries, reports or analyses that include results, charts or graphs to document research findings and results
shifting to AI
This is reading one thing and writing another: data summaries, reports or analyses in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Prepare data summaries, reports, or analyses that include results, charts, or graphs to document research findings and results.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Turning results into summaries, charts and reports is exactly what software does quickly and well.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Analyzing data from harvester monitors to develop yield maps
shifting to AI
This is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Analyze data from harvester monitors to develop yield maps.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Turning harvester data into yield maps is automatic data processing already built into farm software.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Preparing reports in graphical or tabular form
shifting to AI
This is reading one thing and writing another: reports in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Prepare reports in graphical or tabular form, summarizing field productivity or profitability.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Producing productivity and profitability summaries in tables and charts is standard reporting software does well.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Comparing crop yield maps with maps of soil test data
shifting to AI
This is reading one thing and writing another: crop yield maps in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Compare crop yield maps with maps of soil test data, chemical application patterns, or other information to develop site-specific crop management plans.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Comparing yield maps with soil and chemical data to build a plan is pattern work software does well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Identifying areas in need of pesticide treatment by analyzing geospatial data to determine insect movement and damage patterns
shifting to AI
This is reading one thing and writing another: areas in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Identify areas in need of pesticide treatment by analyzing geospatial data to determine insect movement and damage patterns.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Spotting where pests are spreading from mapped data is pattern analysis software does well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Analyzing remote sensing imagery to identify relationships between soil quality
shifting to AI
This is reading one thing and writing another: remote sensing imagery in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Analyze remote sensing imagery to identify relationships between soil quality, crop canopy densities, light reflectance, and weather history.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Finding patterns in satellite imagery and weather history is exactly what image and data software is built for.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Drawing or reading maps, such as soil, contour or plat maps
shifting to AI
This is reading one thing and writing another: maps in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Draw or read maps, such as soil, contour, or plat maps.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Drawing and reading soil, contour and plat maps is well-documented mapping work software supports strongly.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Analyzing geospatial data to determine agricultural implications of factors
shifting to AI
This is reading one thing and writing another: geospatial data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Analyze geospatial data to determine agricultural implications of factors such as soil quality, terrain, field productivity, fertilizers, or weather conditions.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reading what soil, terrain and weather data mean for a field is analysis software handles capably.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Recording data pertaining
shifting to AI
This is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Record data pertaining to experimentation, research, or animal care.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Recording research and animal care data is routine record work software handles reliably once the readings exist.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Identifying spatial coordinates, using remote sensing and Global Positioning System, GPS) data
shifting to AI
This is reading one thing and writing another: spatial coordinates in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Identify spatial coordinates, using remote sensing and Global Positioning System (GPS) data.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Pulling coordinates out of satellite and mapping data is routine computer work.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Recommending best crop varieties or seeding rates for specific field areas
shifting to AI
This is reading one thing and writing another: best crop varieties in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Recommend best crop varieties or seeding rates for specific field areas, based on analysis of geospatial data.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Variety and seeding rate advice comes from published agronomy plus field data, which software combines well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Contacting equipment manufacturers for technical assistance
shifting to AI
This is reading one thing and writing another: equipment manufacturers in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Contact equipment manufacturers for technical assistance, as needed.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Contacting a manufacturer for technical help is straightforward correspondence software can handle.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Responding to general inquiries or requests from the public
shifting to AI
This is reading one thing and writing another: general inquiries in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Respond to general inquiries or requests from the public.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Answering general public questions about farming is information work software already does well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Programing farm equipment, such as variable-rate planting equipment or pesticide sprayers
staying human
This work happens in the physical world: farm equipment, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Program farm equipment, such as variable-rate planting equipment or pesticide sprayers, based on input from crop scouting and analysis of field condition variability.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: The prescription maps can be generated automatically, but loading and checking them happens on the machine.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Providing advice on the development or application of better boom-spray technology to limit the overapplication of chemicals and to reduce the migration of chemicals beyond the fields
staying human
The ratings behind this row put advice well outside what today's tools can do on their own.
importance 4 · Core
Source: “Provide advice on the development or application of better boom-spray technology to limit the overapplication of chemicals and to reduce the migration of chemicals beyond the fields being treated.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Spray technology advice depends on hands-on knowledge of specific kit and local conditions rather than published material.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Conducting studies of nitrogen or alternative fertilizer application methods
staying human
This work happens in the physical world: studies of nitrogen, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Conduct studies of nitrogen or alternative fertilizer application methods, quantities, or timing to ensure satisfaction of crop needs and minimization of leaching, runoff, or denitrification.” (O*NET task statement)
How this row was scored
Exposure score: 29 out of 100 (22–36 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: The study design and analysis suit software, but fertiliser trials are run in real fields over real seasons.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Preparing or presenting agricultural demonstrations
staying human
This work happens in the physical world: agricultural demonstrations, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Prepare or present agricultural demonstrations.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: The demonstration can be prepared automatically, but showing farmers how something works happens in person.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Participating in efforts to advance precision agriculture technology
staying human
The ratings behind this row put efforts well outside what today's tools can do on their own.
importance 3 · Core
Source: “Participate in efforts to advance precision agriculture technology, such as developing advanced weed identification or automated spot spraying systems.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (14–28 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the five ratings behind the score, with no single dominant reason.
The rating behind it: Advancing new weed-spotting and spraying technology is development work that needs real trials and invention.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Demonstrating the applications of geospatial technology
staying human
This work happens in the physical world: the applications of geospatial technology, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Demonstrate the applications of geospatial technology, such as Global Positioning System (GPS), geographic information systems (GIS), automatic tractor guidance systems, variable rate chemical input applicators, surveying equipment, or computer mapping software.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (13–27 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Demonstrating guidance systems and sprayers means standing in the field with the kit and the farmer.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Devising cultural methods or environmental controls for plants for which guidelines are sketchy or nonexistent
staying human
The ratings behind this row put cultural methods well outside what today's tools can do on their own.
importance 4 · Supplemental
Source: “Devise cultural methods or environmental controls for plants for which guidelines are sketchy or nonexistent.” (O*NET task statement)
How this row was scored
Exposure score: 19 out of 100 (12–26 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the five ratings behind the score, with no single dominant reason.
The rating behind it: Where no guidelines exist, the answer comes from a technician's own trials and experience rather than anything written down.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Examining animals or crop specimens to determine the presence of diseases or other problems
staying human
This work happens in the physical world: animals, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Examine animals or crop specimens to determine the presence of diseases or other problems.” (O*NET task statement)
How this row was scored
Exposure score: 19 out of 100 (12–26 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Photo-based disease identification is now genuinely good, but the specimen still has to be found and handled by someone.
The five ratings: output a model can produce 3/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Supervising or training agricultural technicians or farm laborers
staying human
This work happens in the physical world: agricultural technicians, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Supervise or train agricultural technicians or farm laborers.” (O*NET task statement)
How this row was scored
Exposure score: 18 out of 100 (11–25 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Supervising farm workers means being with them, and training depends on showing rather than telling.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Supervising pest or weed control operations
staying human
This work happens in the physical world: pest, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Supervise pest or weed control operations, including locating and identifying pests or weeds, selecting chemicals and application methods, or scheduling application.” (O*NET task statement)
How this row was scored
Exposure score: 18 out of 100 (11–25 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Pesticide decisions are documented, but application is supervised on the ground by a certified person.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Conducting insect or plant disease surveys
staying human
This work happens in the physical world: insect, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Conduct insect or plant disease surveys.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (7–21 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Surveying for insects and disease means walking the crop and looking closely.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Determining the germination rates of seeds planted in specified areas
staying human
This work happens in the physical world: the germination rates of seeds, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Determine the germination rates of seeds planted in specified areas.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (7–21 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Germination rates come from counting what actually came up in a specific plot.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Performing laboratory or field testing
staying human
This work happens in the physical world: laboratory, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Perform laboratory or field testing, using spectrometers, nitrogen determination apparatus, air samplers, centrifuges, or potential hydrogen (pH) meters to perform tests.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Running spectrometers, samplers and pH meters means being at the instrument with the sample.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Performing tests on seeds to evaluate seed viability
staying human
This work happens in the physical world: tests, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Perform tests on seeds to evaluate seed viability.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Seed viability testing means setting up, growing and counting real seeds.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Performing general nursery duties, such as propagating standard varieties of plant materials
staying human
This work happens in the physical world: general nursery duties, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Perform general nursery duties, such as propagating standard varieties of plant materials, collecting and germinating seeds, maintaining cuttings of plants, or controlling environmental conditions.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Propagating plants, sowing seed and taking cuttings is hand work in the nursery.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Operating farm machinery, including tractors, plows, mowers, combines, balers, sprayers, earthmoving equipment or trucks
staying human
This work happens in the physical world: farm machinery, including tractors, plows, mowers, combines, balers, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Operate farm machinery, including tractors, plows, mowers, combines, balers, sprayers, earthmoving equipment, or trucks.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Driving tractors, combines and sprayers is physical work in the field.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Performing crop production duties, such as tilling, hoeing, pruning, weeding or harvesting crops
staying human
This work happens in the physical world: crop production duties, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Perform crop production duties, such as tilling, hoeing, pruning, weeding, or harvesting crops.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Tilling, weeding, pruning and harvesting are done by hand or machine in the field.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Maintaining or repairing agricultural facilities
staying human
This work happens in the physical world: agricultural facilities, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Maintain or repair agricultural facilities, equipment, or tools to ensure operational readiness, safety, and cleanliness.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Repairing farm buildings, equipment and tools is hands-on work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Transplanting trees, vegetables or horticultural plants
staying human
This work happens in the physical world: trees, vegetables or horticultural plants, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Transplant trees, vegetables, or horticultural plants.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Transplanting trees and plants is physical work in the ground.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Measuring or weighing ingredients used in laboratory testing
staying human
This work happens in the physical world: ingredients, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Measure or weigh ingredients used in laboratory testing.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Weighing and measuring ingredients is done physically in the lab.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Setting up laboratory or field equipment as required for site testing
staying human
This work happens in the physical world: laboratory, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Set up laboratory or field equipment as required for site testing.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Setting equipment up on site or in the lab is physical work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Preparing laboratory samples
staying human
This work happens in the physical world: laboratory samples, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Prepare laboratory samples for analysis, following proper protocols to ensure that they will be stored, prepared, and disposed of efficiently and effectively.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Preparing laboratory samples is careful hand work at the bench.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Collecting animal or crop samples
staying human
This work happens in the physical world: animal, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Collect animal or crop samples.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Collecting samples means going to the animal or the crop and taking them.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Preparing land for cultivated crops
staying human
This work happens in the physical world: land, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Prepare land for cultivated crops, orchards, or vineyards by plowing, discing, leveling, or contouring.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Ploughing and levelling ground is heavy work done with machinery in the field.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Preparing culture media, following standard procedures
staying human
This work happens in the physical world: culture media, following standard procedures, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Prepare culture media, following standard procedures.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Making up culture media is careful hand work in the laboratory.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $49,630a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
People doing this job: 15,130in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: records of precision agriculture information in, a record out. The rows above are exactly that shape: documenting and maintaining records of precision agriculture information and using geospatial technology to develop soil sampling grids or identify sampling sites for testing characteristics. What it cannot do is be there in the room, and that is still where information gets done. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Your week is splitting in two, and which half fills it is the whole question. Documenting and maintaining records of precision agriculture information is going; collecting information about soil or field attributes is not.

So, given all that: 44% of this job's task weight sits in rows the software is already learning, 4% in rows that change shape rather than disappear, and 52% in rows it is nowhere near. That is the position, measured across 48 scored tasks. It is not a forecast about you.

The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.

This week: one thing

Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.

What you end up holding: your own week, on one page, sorted into what is shifting and what is not
How long it takes: about twenty minutes

If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.

Over the next 90 days

Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (collecting information about soil or field attributes) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.

Over the next 12 months

Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles collecting information about soil or field attributes, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest US occupations to agricultural technicians (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was biological technicians: only about 6% of its durable work is work you already do. Your own job splits about 44/56: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “collect information about soil or field attributes, yield data, or field boundaries…”, and let the exposed end go.

How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

Biological Technicians
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already examine animals or crop specimens to determine the presence of diseases or other…, and their equivalent is to examine animals and specimens to detect the presence of disease or other problems. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Physical Scientists, All Other
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already identify spatial coordinates, using remote sensing and Global Positioning System (GPS) data, and their equivalent is to set up or maintain remote sensing data collection systems. Across both published task lists that is about 5% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 5% of the durable side of that job. That is a different job, not a next step. The pay gap is the market pricing a barrier: $122,570 against your $49,630 is 2.47× (OEWS May 2025 (both)), and you would be crossing it holding about 5% of their durable work. A gap that size with an overlap that small is a wish, not a route.
Look at that job’s page anyway →
Farmworkers and Laborers, Crop, Nursery, and Greenhouse
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already compare crop yield maps with maps of soil test data, chemical application patterns…, and their equivalent is to record information about crops. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $35,660 against your $49,630, 28.1% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 44% of its task weight, across 48 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Biological scientists is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

The other groups this work is counted across:

In UK official statistics this job is counted as Biological scientists, Farmers, Laboratory technicians and Horticultural trades. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for agricultural technicians, and we are not going to point you at the nearest one and call it a fit.

There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 44% of the work on this page is already inside what they can do.

Try The AI Authority free →

7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.

The AI Authority is a general community about working with AI, not a course for agricultural technicians. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.

Noted, and thank you. We’ll email you if a Space for agricultural technicians launches. Nothing else.

That did not look like an email address, so nothing was saved. Have another go below.

We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.

No Space for agricultural technicians yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for agricultural technicians existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for agricultural technicians launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Agricultural Technicians?: Not as a job, but it is already doing parts of the work. Across the 48 official task statements scored for Agricultural Technicians (United States, SOC 19-4012), 44% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 43 out of 100 (range 38–49, band: partial). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Agricultural Technicians” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Prepare data summaries, reports, or analyses that include results, charts, or graphs to document research findings and results” (93/100, very high); “Document and maintain records of precision agriculture information” (93/100, very high); “Analyze data from harvester monitors to develop yield maps” (93/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Agricultural Technicians” stay human?: About 52% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Prepare culture media, following standard procedures” (0/100, minimal); “Prepare land for cultivated crops, orchards, or vineyards by plowing, discing, leveling, or contouring” (0/100, minimal); “Collect animal or crop samples” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Agricultural Technicians” do about AI?: Start from the ledger rather than the headline: 44% of this job's weighted core work is exposed, and roughly 52% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Agricultural Technicians calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 48 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

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Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

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https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.