Will AI replace Agricultural Technicians?
Most of the work in Agricultural Technicians still leans on things AI struggles with — research rates its theoretical AI reach at only ~32%, and real-world use lower still.
O*NET-SOC 19-4012
How your 32 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a relatively low share of this job's tasks (~32%). By late 2025, real-world AI use had reached about 1% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
The signals here line up
Theoretical reach (~32%), real-world use (~1%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 32 tasks, ratedBased on real task-level AI scores — click to collapse
- Record data pertaining to experimentation, research, or animal care.
- Prepare data summaries, reports, or analyses that include results, charts, or graphs to document research findings and results.
- Respond to general inquiries or requests from the public.
- Document and maintain records of precision agriculture information.
- Contact equipment manufacturers for technical assistance, as needed.
- Examine animals or crop specimens to determine the presence of diseases or other problems.
- Supervise pest or weed control operations, including locating and identifying pests or weeds, selecting chemicals and application methods, or scheduling application.
- Collect information about soil or field attributes, yield data, or field boundaries, using field data recorders and basic geographic information systems (GIS).
- 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.
- Identify spatial coordinates, using remote sensing and Global Positioning System (GPS) data.
- Apply precision agriculture information to specifically reduce the negative environmental impacts of farming practices.
- Create, layer, and analyze maps showing precision agricultural data, such as crop yields, soil characteristics, input applications, terrain, drainage patterns, or field management history.
- Analyze data from harvester monitors to develop yield maps.
- Analyze geospatial data to determine agricultural implications of factors such as soil quality, terrain, field productivity, fertilizers, or weather conditions.
- Program farm equipment, such as variable-rate planting equipment or pesticide sprayers, based on input from crop scouting and analysis of field condition variability.
- Draw or read maps, such as soil, contour, or plat maps.
- Prepare reports in graphical or tabular form, summarizing field productivity or profitability.
- Compare crop yield maps with maps of soil test data, chemical application patterns, or other information to develop site-specific crop management plans.
- Recommend best crop varieties or seeding rates for specific field areas, based on analysis of geospatial data.
- Divide agricultural fields into georeferenced zones, based on soil characteristics and production potentials.
- Analyze remote sensing imagery to identify relationships between soil quality, crop canopy densities, light reflectance, and weather history.
- 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.
- Identify areas in need of pesticide treatment by analyzing geospatial data to determine insect movement and damage patterns.
- Advise farmers on upgrading Global Positioning System (GPS) equipment to take advantage of newly installed advanced satellite technology.
- Participate in efforts to advance precision agriculture technology, such as developing advanced weed identification or automated spot spraying systems.
- Measure or weigh ingredients used in laboratory testing.
- Set up laboratory or field equipment as required for site testing.
- Prepare laboratory samples for analysis, following proper protocols to ensure that they will be stored, prepared, and disposed of efficiently and effectively.
- Collect animal or crop samples.
- Supervise or train agricultural technicians or farm laborers.
- 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.
- Install, calibrate, or maintain sensors, mechanical controls, GPS-based vehicle guidance systems, or computer settings.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.