Mostly Resilient
Last Update: 8/30/2026
AI Resilience Score for Farming Supervisors:
61.2%
Median Score
Meaningful human contribution
Measures the parts of the occupation that still require a human touch. This score averages data from up to four AI exposure datasets, focusing on the role’s resilience against automation.
Med
Long-term employer demand
Predicts the health of the job market for this role through 2034. Using Bureau of Labor Statistics data, it balances projected annual job openings (60%) with overall employment growth (40%).
Med
Sustained economic opportunity
Measures future earning potential and career flexibility. This score is a blend of total projected labor income (67%) and the role’s inherent ability to adapt to economic and technological shifts (33%).
High
This reflects the reliability of your score based on the number of data sources available for this career and how closely those sources agree on the outlook. A higher confidence means more consistent evidence from labor experts and AI models.
Most data sources align, with only minor variation. This is a well-supported result.
Contributing sources
AI Resilience Report forFirst-Line Supervisors of Farming, Fishing, and Forestry Workers
$59,320 median salary•7,200 annual openings•SOC Code: 45-1011.00
First-Line Supervisors of Farming, Fishing, and Forestry Workers are somewhat more resilient to AI impacts than most occupations, according to our analysis of 6 sources.
This career earns a "Mostly Resilient" label because the heart of the work, which includes training crews, operating equipment in unpredictable outdoor conditions, and making real-time judgment calls about animals, crops, and forests, is genuinely hard for AI to replicate. Where AI is making the biggest moves is in the more routine, desk-based tasks like managing records, scheduling, and analyzing sensor data, so expect those parts of the job to shrink or get much easier over time.
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This career earns a "Mostly Resilient" label because the heart of the work, which includes training crews, operating equipment in unpredictable outdoor conditions, and making real-time judgment calls about animals, crops, and forests, is genuinely hard for AI to replicate. Where AI is making the biggest moves is in the more routine, desk-based tasks like managing records, scheduling, and analyzing sensor data, so expect those parts of the job to shrink or get much easier over time.
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View analysisChat with CoachLatest newsMore career infoAnalysisChatNewsMoreAnalysis of Current AI Resilience
Farming Supervisors
Updated Quarterly
How is AI changing Farming Supervisors jobs?
Here's the good news: the parts of this job that require human hands, animal care instincts, and on-the-ground judgment are the hardest for AI to replicate. A patent analysis published in AAEA's Choices Magazine found that cognition & learning accounts for the largest share (68.2%) of AI-related agricultural innovation, with particularly high activity in planning and control. Perception technologies represent 20.7% of AI innovation in agriculture.
Finally, the execution domain—which encompasses AI hardware and robotic control systems—accounts for 11.1% of AI activity. In plain English, most farm AI helps supervisors think and plan rather than physically replace workers driving tractors or treating sick animals.
That pattern shows up in real tools. At the 2026 USDA Ag Outlook Forum [1], experts predicted generative AI will handle "tedious farm shop tasks like managing spreadsheets, making sense of collected sensor data, scheduling, minimizing fuel usage, optimizing labor costs and sorting supplier information"—exactly the higher-automation tasks in your role (records, inventory, payroll). In aquaculture, a 2026 Frontiers review [2] notes AI-driven tools have improved biomass estimation, behavior tracking, disease detection, and feed optimization.
In forestry, Stora Enso's Precision Forestry project uses AI [3] to combine remote sensing and laser scanning to spot bark beetle damage before it's visible. The American Farm Bureau [4] highlights AI that distinguishes weeds from tomato plants and can reduce herbicide use by 70%, framing AI as a partner: farmers "should be leading that conversation."
Sources
How fast is AI adoption growing for Farming Supervisors?
Adoption is accelerating fast because of a persistent labor crunch. Drovers/Farm Journal reports [5] that by automating repetitive data tasks, AI lets farm teams spend less time behind screens and more on husbandry and fieldwork. The World Economic Forum [6] links this urgency to feeding nearly 10 billion people by 2050.
But adoption also has real brakes: adoption remains constrained by affordability, digital literacy, infrastructure limitations, and data interoperability barriers. Ethical concerns related to transparency, cybersecurity, privacy, and equitable access to data further underscore the need for adaptive governance mechanisms. Animal agriculture is adopting AI more slowly than crops because of biological complexity, and Choices researchers stress that many agricultural AI tools remain narrow in scope, depend on extensive data infrastructure, or still require human oversight.
The takeaway for young people eyeing this career: paperwork tasks will shrink, but the human skills that matter most—training workers, operating machinery in muddy fields, and caring for living animals—remain firmly in your hands.
Sources
Will AI replace Farming Supervisors?
No. We don't think AI will replace First-Line Supervisors of Farming, Fishing, and Forestry Workers, though we do expect the job to change.
Our 61.2% AI Resilience Score reflects a role where the hardest parts, training workers, operating machinery in unpredictable conditions, and caring for living animals, are exactly what AI struggles to replicate. Most agricultural AI focuses on helping supervisors think and plan rather than replacing their hands-on judgment. Generative AI is already handling tedious tasks like managing spreadsheets, scheduling, and sorting supplier data [1], and aquaculture operations are using AI for feed optimization and disease detection [2]. That frees supervisors to focus on the work that actually requires being present.
The economic picture supports staying in this field. Earning potential scores well, and the American Farm Bureau frames AI as a partner, noting that farmers should be leading the conversation about how these tools get used [4]. Adoption is also slower than headlines suggest, held back by affordability, infrastructure gaps, and the biological complexity of working with animals and ecosystems [5]. The job is shifting, not disappearing. Supervisors who get comfortable with AI planning tools while sharpening their people and field skills will be in a strong position through the 2030s.
Sources
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Latest AI news for Farming Supervisors
These articles highlight how AI will reshape the role of First-Line Supervisors in farming, fishing, and forestry. For instance, advanced monitoring technologies and predictive analytics will enhance resource management, allowing supervisors to make more informed decisions. Additionally, while AI may automate some tasks, it also creates opportunities for supervisors to focus on strategic management and safety protocols. Understanding these changes can help students build AI resilience in their careers, ensuring they remain valuable in an evolving landscape.
AI Job Risk in Farming, Fishing & Forestry
jobriskai.com • 8/20/2026
Home / Farming, Fishing & Forestry. AI job risk in Farming, Fishing & Forestry. All 12 occupations in this field, ranked by measured AI applicability. Read more
Will AI Replace First-Line Supervisors of Farming, Fishing, and ...
jobzonerisk.com • 8/20/2026
Plans daily work schedules, assigns crew tasks, monitors crop/livestock/timber conditions, enforces safety protocols, manages equipment and supply logistics, ...
First-Line Supervisors of Farming, Fishing, and Forestry Workers | AI ...
aiworkforcereport.com • 8/20/2026
AI Impact Explanation: AI will significantly transform this role through advanced monitoring technologies, predictive analytics for crop/resource management, ...
First-Line Supervisors of Farming, Fishing, and Forestry Workers: AI ...
www.willaitakemyjob.app • 8/20/2026
Baseline AI displacement risk for First-Line Supervisors of Farming, Fishing, and Forestry Workers, aggregated from 5 research sources. What this score means.
AI impacting farm workers - Yonkers
www.facebook.com • 8/20/2026
Robots equipped with AI technology are increasingly employed for labor -intensive tasks such as planting, weeding, and harvesting.
More Career Info
Career: First-Line Supervisors of Farming, Fishing, and Forestry Workers
They oversee workers in farming, fishing, and forestry, making sure tasks are done safely and efficiently while managing schedules and equipment.
Parent Careers
Employment & Wage Data
Median Wage
$59,320
Jobs (2025)
57,600
Growth (2025-35)
+3.8%
Annual Openings
7,200
Education
High school diploma or equivalent
Experience
Less than 5 years
Source: Bureau of Labor Statistics, Employment Projections 2025-2035
Task-Level AI Resilience Scores
AI-generated estimates of task resilience over the next 3 years
1
Treat animal illnesses or injuries, following experience or instructions of veterinarians.
2
Drive or operate farm machinery, such as trucks, tractors, or self-propelled harvesters, to transport workers or supplies or to cultivate or harvest fields.
3
Train workers in spawning, rearing, cultivating, and harvesting methods, and in the use of equipment.
4
Train workers in tree felling or bucking, operation of tractors or loading machines, yarding or loading techniques, or safety regulations.
5
Direct or assist with the adjustment or repair of equipment or machinery.
6
Train workers in techniques such as planting, harvesting, weeding, or insect identification and in the use of safety measures.
7
Assign tasks such as feeding and treatment of animals, and cleaning and maintenance of animal quarters.
Tasks are ranked by their AI resilience, with the most resilient tasks shown first. Core tasks are essential functions of this occupation, while supplemental tasks provide additional context.
