Farm Labor Contractors
13-1074.00Median wage $58,460/yr310 employed (US)Rank #573 of 923 scored · top 62% by substitution
Recruit and hire seasonal or temporary agricultural laborers. May transport, house, and provide meals for workers.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution24
Exposure22
Augmentation34
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
7 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
14%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%22
panel mean rating 1.9/5 → substitution pressure 22/100
Technical feasibility todayw 20%22
panel mean rating 1.9/5 → substitution pressure 22/100
Cost vs. human wagew 15%22
panel mean rating 1.9/5 → substitution pressure 22/100
Adoption barriersw 20%inverted — strong barriers lower the score38
panel mean rating 3.5/5 (barrier strength) → substitution pressure 38/100
Sector adoption velocityw 10%11
panel mean rating 1.4/5 → substitution pressure 11/100
Task breakdown (7 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Pay wages of contracted farm laborers.
80CI 70–90 · exposure 92 · augmentation 63 · importance 4.5/5 · click for rater detail
Pay wages of contracted farm laborers.
80| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Agricultural labor remains relatively fragmented, with many small contractors still relying on manual methods or minimal systems; however, mid-sized and organized agricultural operations have steadily adopted cloud payroll platforms, showing moderate adoption velocity in digitized segments. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Agriculture and farm labor contracting is a low-digitization sector where many contractors still use manual or semi-manual payroll methods, lagging behind finance/professional services adoption rates. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI and modern payroll systems assist farm labor contractors by automating calculation, reducing errors, tracking compliance, and providing reporting; however, the core value is automation rather than human-AI collaboration, so augmentation is moderate rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Payroll software substantially reduces contractor time and error in wage calculation and payment while the contractor retains oversight for compliance and dispute resolution. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | This task is largely automatable: extracting time records, calculating wages per fixed rates, processing payroll, and generating checks/direct deposits can all be handled by standard payroll software with minimal setup. However, the task includes resolving disputes and compliance verification, which may require human judgment in edge cases. |
| Task automatability | claude-sonnet-5 | 5/5 | Payroll calculation and disbursement based on hours/piece-rate data is a well-structured, rules-based process that off-the-shelf payroll software already automates end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While wage payment involves compliance with labor laws (Fair Labor Standards Act, wage-hour regulations), these are increasingly handled through software and do not strictly require a licensed human to execute the payment itself; oversight and audit may remain necessary, but entry barriers are low. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Wage payment is subject to labor law compliance (minimum wage, overtime, migrant worker protections) requiring accurate recordkeeping and accountability, but no licensed human is legally required to execute the payment itself. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated payroll processing costs a small monthly fee per contractor or per transaction, orders of magnitude cheaper than hiring dedicated payroll staff to manually calculate and distribute wages. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Automated payroll processing costs a small fraction of a cent per transaction compared to manual bookkeeping labor, an order of magnitude cheaper at scale. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature payroll systems (ADP, Gusto, QuickBooks, etc.) are deployed at scale in production and reliably perform wage calculation and payment processing for agricultural operations and contractors today. |
| Technical feasibility today | claude-sonnet-5 | 5/5 | Mature payroll and HR platforms (ADP, Gusto, farm-specific labor management systems) reliably process wage payments for agricultural workers in production today. |
Direct and transport workers to appropriate work sites.
39CI 10–67 · exposure 33 · augmentation 50 · importance 4.1/5 · click for rater detail
Direct and transport workers to appropriate work sites.
39| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Agricultural and construction labor platforms are increasingly incorporating AI-driven logistics and scheduling, but adoption remains uneven: larger, more digitized operations deploy these systems while smaller and rural contractors adopt more slowly. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Agriculture is among the least digitized sectors with low AI adoption for physical labor coordination and transport tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can assist contractors by automating route planning and real-time monitoring of worker locations and job requirements, allowing human contractors to focus on worker support, problem-solving, and quality oversight. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with route planning, scheduling, or worker assignment optimization, but the core physical transport and direction activity itself sees minimal AI-driven productivity enhancement. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Route optimization, worker-to-site matching, and transport scheduling can be largely automated using AI-powered logistics and workforce management systems that integrate real-time location data, job requirements, and worker availability, achieving >50% time savings. However, some real-time exception handling and worker communication may still require human judgment. |
| Task automatability | claude-sonnet-5 | 1/5 | This task involves physical coordination, transportation logistics, and real-time direction of human workers at outdoor sites, which requires physical presence and cannot be performed end-to-end by current AI systems. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for automating dispatch and routing; however, some customer preference for human relationship-building and minor liability concerns around worker safety oversight create modest friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed in the traditional sense, farm labor contractors often need certification/registration (e.g., under FLCRA in the US) and the task requires physical presence and driving, creating structural barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven routing and scheduling systems cost far less per dispatch than a human labor contractor managing routing and transport coordination, with costs primarily in software licensing and marginal integration overhead. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical transportation and on-site direction involved, so there is no viable AI cost comparison for this core function. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Logistics and workforce management platforms with AI-assisted routing and scheduling exist in production (e.g., construction and agricultural labor platforms), but they typically require manual oversight for complex assignments and handle only a portion of the full task reliably at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product transports or physically directs farm workers to job sites; this remains a human logistics and supervisory function with no automation-in-production examples. |
Recruit and hire agricultural workers.
16CI 5–28 · exposure 13 · augmentation 25 · importance 4.3/5 · click for rater detail
Recruit and hire agricultural workers.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Farm labor contracting remains a low-digitization, fragmented sector with small operators relying on traditional networks and word-of-mouth. Adoption of formal AI recruitment tools in agriculture lags far behind professional services and tech sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Agricultural labor contracting is a low-digitization, physical, small-firm-dominated sector with minimal AI adoption for hiring workflows. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with job posting distribution and initial resume screening, but the core judgment—assessing fit, reliability, and local knowledge—still heavily rests with the contractor. Limited augmentation potential given the informal nature of agricultural labor markets. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI tools (e.g., job posting platforms, applicant tracking, translation apps) can assist with parts of recruiting like advertising or basic screening, but core hiring judgment and relationships remain manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help screen resumes and identify candidates, the interpersonal judgment required to assess worker reliability, negotiate terms, and make hiring decisions based on unstructured signals remains largely human-dependent. Recruitment involves relationship-building and contextual understanding of local labor markets that current AI cannot fully handle end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | Recruiting and hiring farm laborers involves in-person relationship building, trust, verifying work eligibility, and physical logistics (transport, housing) that current AI cannot execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Agricultural hiring is often informal and locally-rooted, creating friction for AI deployment. Some jurisdictions have employment law requirements around fair hiring practices and verification, but no strict licensing requirement prevents AI-assisted or automated recruitment in this sector. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Hiring involves legal compliance (labor law, immigration verification, contracts) typically requiring licensed human contractors to sign off, creating strong regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI recruitment tools require significant setup, integration with payroll systems, and human review of candidates and final decisions. The marginal cost savings over a farm labor contractor's existing informal networks and direct outreach is modest or comparable to current practices. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI has no viable path to replace the human networks, negotiations, and legal compliance work involved, so it offers no cost advantage over the human contractor today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some job posting and resume screening tools exist, but no deployed system reliably handles the full recruitment and hiring workflow for agricultural labor without substantial human oversight. The agricultural sector's informal hiring practices and regional variations limit the applicability of standardized AI recruitment solutions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs full recruitment and hiring of agricultural workers; this remains a human-driven, relationship- and network-based process. |
Supervise the work of contracted employees.
15CI 5–25 · exposure 13 · augmentation 38 · importance 4.1/5 · click for rater detail
Supervise the work of contracted employees.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Agricultural and farm labor sectors show slower digitization and adoption of management technology compared to professional services or tech sectors. Most farm labor contracting still relies on traditional on-site management. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Agriculture is a low-digitization, physically-oriented sector with minimal AI adoption for direct labor supervision. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted attendance tracking, performance dashboards, and alert systems can help supervisors allocate attention more effectively, though the core supervisory relationship and decision-making remain human-driven. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Basic tools like scheduling apps, GPS tracking, or communication platforms can support logistics, but they offer only marginal help with the core supervisory task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Supervising contracted employees involves real-time monitoring, performance assessment, and interpersonal judgment that require human context awareness. While AI can log work hours and flag basic performance metrics, the core judgment and corrective coaching required for effective supervision remains heavily human-dependent. |
| Task automatability | claude-sonnet-5 | 1/5 | Direct on-site supervision of manual farm laborers requires physical presence, real-time observation, and interpersonal management that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Labor law, worker safety regulations, and employment law typically require a responsible human supervisor with legal accountability. Liability for worker safety, wage compliance, and working conditions creates strong legal barriers to full automation of supervisory duties. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Farm labor contractors often carry licensing/registration requirements and legal responsibility for worker safety, wages, and compliance, creating strong liability-driven barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-based monitoring and oversight tools require integration, human review, and management, making them comparably costly to retaining human supervisors, especially in lower-margin agricultural labor contexts where margins are tight. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this task, so any AI cost is additive to, not a replacement for, the human supervisor's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Existing products can track attendance and basic metrics, but deployed systems do not reliably perform end-to-end supervision in agricultural or contract labor settings. Most supervision in this domain still relies on on-site human managers despite emerging worker-tracking tools. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product supervises physical field labor crews; sensor/camera monitoring exists but does not replace active supervisory judgment and interaction. |
Furnish tools for employee use.
10CI 10–10 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Furnish tools for employee use.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Farm labor contracting is a low-digitization, physical-work sector with minimal AI adoption. Most farm operations use manual or basic paper-based systems for tool tracking and distribution, reflecting the industry's general lag in automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Agricultural labor management is a low-digitization, physical-labor-intensive sector with minimal AI/automation adoption for logistics tasks like tool distribution. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide modest assistance through inventory management software (tracking which tools are checked out or need maintenance), but the core task of physically furnishing tools to workers remains fundamentally manual and offers limited augmentation opportunity. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with inventory tracking or ordering tools via software, but it provides only marginal indirect assistance to the core physical task of furnishing tools. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Furnishing tools for employee use involves physical logistics, inventory management, and situational judgment about which tools are needed by which workers at which times. Current AI systems cannot physically procure, store, or distribute tools, nor can they reliably assess real-time field conditions to determine tool requirements without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | Furnishing physical tools is a logistical/physical procurement and distribution task that AI cannot perform end-to-end; it requires physical handling and delivery of equipment to workers in the field.</br> |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Farm operations typically require direct human management of inventory and tool distribution. Some organizational friction exists (systems must integrate with existing workflows), but no legal or licensing barriers specifically prevent automation of tool supply logistics. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically bars automation, but the inherently physical nature of sourcing, transporting, and handing out tools creates strong practical barriers to any digital-only solution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automating tool furnishing would require robotics, inventory systems, and logistics infrastructure far more expensive than the direct labor cost of a farm labor contractor assigning and distributing tools. AI software alone provides no cost advantage for the physical distribution task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for physical tool procurement/distribution, so any AI cost would be additive rather than replacing the human labor and logistics involved. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs end-to-end tool furnishing. While AI could assist with inventory tracking or ordering, the physical procurement, storage, and distribution remain entirely manual tasks that require human execution. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product physically supplies or distributes tools to farm workers; this remains a manual logistics task. |
Provide food, drinking water, and field sanitation facilities to contracted workers.
3CI 0–5 · exposure 0 · augmentation 13 · importance 4.5/5 · click for rater detail
Provide food, drinking water, and field sanitation facilities to contracted workers.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Agricultural labor remains low-digitization and physically dispersed; adoption of automation for facility provisioning is negligible across the sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Agricultural labor contracting is a low-digitization, physical-labor sector with minimal AI adoption for on-the-ground logistics tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI cannot meaningfully augment the physical task of delivering supplies or constructing field facilities; human judgment on sanitation standards cannot be materially enhanced by AI. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with scheduling, inventory tracking, or compliance recordkeeping around provisioning, but offers minimal assistance for the core physical task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical delivery of supplies and construction/maintenance of facilities in field settings, which demands embodied presence and coordination with human workers. Current AI systems cannot autonomously provision, distribute, or construct sanitation infrastructure. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical logistics and compliance task involving procuring, transporting, and setting up food, water, and sanitation facilities in fields—no AI system can perform these physical actions. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Federal and state labor laws (Fair Labor Standards Act, Migrant and Seasonal Agricultural Worker Protection Act) mandate that contractors provide these facilities; legal compliance is non-negotiable and cannot be delegated to autonomous systems. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Regulatory requirements (e.g., OSHA field sanitation standards) mandate that employers physically provide these facilities, creating strong compliance and liability barriers to any automation of the underlying obligation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Providing food, water, and sanitation facilities involves material costs and logistics that must be borne regardless of automation; AI offers no cost advantage over direct human provisioning and maintenance. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical delivery and setup of facilities, so any AI cost would be additive to (not a replacement for) the human labor and equipment costs already required. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs this task; it fundamentally requires physical intervention and real-world supply chain management beyond the scope of current automation systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product manages physical provisioning of food, water, or sanitation facilities on farms; this remains entirely a human/equipment logistics function. |
Employ foremen to deal directly with workers when recruiting, hiring, instructing, assigning tasks, and enforcing work rules.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Employ foremen to deal directly with workers when recruiting, hiring, instructing, assigning tasks, and enforcing work rules.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Farm labor contracting is a labor-intensive, geographically dispersed sector with low digitization, small operators, and minimal adoption of advanced automation; manual hiring and foreman-led oversight remain the norm. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Agricultural labor contracting is a low-digitization, physically dispersed sector with minimal AI adoption in direct workforce supervision roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist foremen with scheduling optimization or record-keeping, but the core tasks—recruiting, hiring, instructing, and enforcing rules—remain fundamentally human due to judgment and authority requirements, limiting meaningful augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with scheduling, recruitment postings, or compliance recordkeeping, but the core task of interpersonal supervision and rule enforcement via foremen sees little productivity transformation from AI tools. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires direct interpersonal management, negotiation, judgment calls about worker suitability, and enforcement of rules—all deeply human activities requiring social intelligence, contextual discretion, and legal accountability that current AI cannot perform end-to-end at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires hiring and managing human intermediaries (foremen) who then physically supervise agricultural workers in the field—a fundamentally human organizational and interpersonal management activity that AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Employment law, labor contracting regulations, liability for workplace safety and wage compliance, and the legal requirement for an authorized human agent to represent the employer in hiring and discipline create hard, regulatory barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Labor law compliance, worker safety, contractual employment relationships, and on-site physical supervision create strong practical and legal barriers to replacing this human management structure with AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A farm foreman's loaded wage is low relative to agricultural labor costs, while AI systems capable of even partial hiring and worker management oversight remain expensive and limited in agricultural settings with poor connectivity and low per-task value. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this coordination and personnel function, so cost comparison favors the human contractor/foreman arrangement entirely. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously recruit, hire, instruct, assign work, and enforce rules for farm laborers; these functions demand human authority, legal liability acceptance, and face-to-face judgment that existing systems are not designed or authorized to perform. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product hires, deploys, or manages human foremen to supervise field labor; this remains entirely a human organizational function. |
Related occupations — Business & Financial Operations
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.