Hoist and Winch Operators
53-7041.00Median wage $56,450/yr2,600 employed (US)Rank #896 of 923 scored · top 97% by substitution
Operate or tend hoists or winches to lift and pull loads using power-operated cable equipment.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution9
Exposure4
Augmentation23
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
13 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
0%
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%6
panel mean rating 1.2/5 → substitution pressure 6/100
Technical feasibility todayw 20%2
panel mean rating 1.1/5 → substitution pressure 2/100
Cost vs. human wagew 15%5
panel mean rating 1.2/5 → substitution pressure 5/100
Adoption barriersw 20%inverted — strong barriers lower the score27
panel mean rating 3.9/5 (barrier strength) → substitution pressure 27/100
Sector adoption velocityw 10%1
panel mean rating 1.0/5 → substitution pressure 1/100
Task breakdown (13 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.
Select loads or materials according to weight and size specifications.
23CI 18–28 · exposure 20 · augmentation 38 · importance 4.1/5 · click for rater detail
Select loads or materials according to weight and size specifications.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hoist and winch operation remains a manual, site-based physical task with low digitization. Adoption of autonomous load-selection systems is minimal; sectors employing these workers (construction, manufacturing, logistics) show slow adoption of such specialized automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and industrial material handling sectors have historically slow AI adoption due to physical, safety-critical, and low-digitization environments. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI vision assistants could help flag load dimensions or weight estimates to the operator in real-time via overlays or alerts, improving accuracy and speed. However, the operator must remain the decision-maker given safety criticality. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could provide some assistance via digital weight/size lookup systems or load charts, but this offers limited productivity transformation for the core physical selection task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires visual assessment of physical objects against weight/size specs and real-time judgment in a physical environment. While AI vision can identify objects and compare dimensions, the task involves real-world material handling constraints and safety considerations that current systems cannot reliably automate end-to-end without significant human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical presence at a job site, visual/manual inspection of loads, and coordination with equipment, which current AI cannot perform end-to-end; some decision logic could be automated but the physical selection process cannot be replaced by off-the-shelf AI today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations and liability frameworks require human oversight of load selection in most jurisdictions; incorrect load selection can cause injury or equipment damage, creating strong legal and insurance barriers to full automation without human sign-off. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Safety regulations around heavy equipment operation, potential liability from load-related accidents, and site-specific judgment create moderate barriers, though no strict licensing mandates a human specifically for this sub-task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI vision + integration infrastructure remains expensive relative to the cost of a hoist operator performing visual inspection. The overhead of robust systems for safety-critical load selection does not yet undercut human labor costs significantly. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Any AI-assisted system would require sensors, robotics, and integration with heavy equipment, making it currently more costly than a human operator performing this routine selection task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision systems can classify and measure objects, but deployed products for autonomous load selection in operational hoist/winch environments are not reliably in production. Existing systems lack the robustness for safety-critical material handling in varied, dynamic physical settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously selects physical loads/materials based on weight and size specs in real-world hoist/winch operations; this remains a manual, on-site task. |
Move or reposition hoists, winches, loads and materials, manually or using equipment and machines such as trucks, cars, and hand trucks.
15CI 5–25 · exposure 8 · augmentation 38 · importance 4.1/5 · click for rater detail
Move or reposition hoists, winches, loads and materials, manually or using equipment and machines such as trucks, cars, and hand trucks.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption is slow and sector-specific; heavy construction, mining, and warehousing show pilot deployments of autonomous vehicles, but most job sites still rely on manual operator skills and real-time judgment that AI-driven automation has not yet displaced at scale. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Physical, low-digitization industrial and construction sectors show minimal AI/robotics adoption for hands-on hoisting and repositioning tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist human operators through real-time load monitoring, motion guidance, and collision-avoidance alerts, providing useful safety and positioning help. However, the operator remains essential for final decision-making and task execution in variable field conditions. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with load planning, route optimization, or predictive maintenance alerts, but offers little direct assistance to the physical act of moving hoists and materials. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While trucks and hand trucks can be operated by autonomous systems in controlled settings, the task involves dynamic repositioning of loads and materials with safety-critical judgment. Current AI lacks robust real-world perception and manipulation for variable load conditions, unstructured environments, and obstacle avoidance at human equivalence. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical materials-handling task requiring manual manipulation of heavy equipment and vehicles; no off-the-shelf AI can perform the physical repositioning itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task carries hard safety and liability barriers: machinery operation is regulated, load positioning involves high injury/property-damage risk if automated incorrectly, and many jurisdictions require licensed or certified human operators to supervise or execute hoist and winch work. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed in the way a doctor is, heavy equipment operation often requires certification, safety training, and liability considerations that create moderate friction against non-human operation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Autonomous hoist and winch systems remain expensive to acquire, integrate, and maintain; for variable job sites and ad-hoc repositioning tasks, human operators remain cost-competitive when all integration and oversight costs are factored in. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system substitute for the physical operation; any automation would require expensive specialized robotics/heavy machinery integration exceeding human labor costs today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | Deployed autonomous systems exist only in highly structured warehouse or construction sites with pre-mapped environments; general-purpose hoist and load repositioning systems do not exist in production at scale across typical work sites. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product operates hoists, winches, or drives trucks to reposition loads in general industrial settings; this remains a physical human/robotics task, not a software/AI one. |
Observe equipment gauges and indicators and hand signals of other workers to verify load positions or depths.
14CI 5–23 · exposure 13 · augmentation 38 · importance 4.4/5 · click for rater detail
Observe equipment gauges and indicators and hand signals of other workers to verify load positions or depths.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hoist and winch operations are primarily in construction, manufacturing, and logistics—sectors with slower digital transformation and strong safety and labor regulations that favor human operators. Adoption of full automation in this space remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction, industrial, and marine sectors where hoist/winch operation occurs are among the slowest to adopt AI-driven automation due to physical, safety, and regulatory constraints. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted visual displays highlighting gauge readings or alerting operators to anomalies could modestly improve situational awareness and response time, but the human operator must remain the primary decision-maker and control authority for safety reasons. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensor-based alerts or gauge-monitoring dashboards could provide minor assistance, but there is little evidence of AI meaningfully augmenting this specific perceptual/motor task today. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can detect gauges and interpret some indicators in controlled environments, real-time observation of dynamic load positions, gauge readings, and human hand signals in outdoor/industrial settings with variable lighting and occlusion remains unreliable. No current system achieves 50% time savings for this safety-critical monitoring task. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time physical perception, hand-signal interpretation, and control of heavy machinery in dynamic environments, which off-the-shelf AI cannot perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This is a safety-critical task where load failures or miscommunication can cause serious injury or death. Regulatory standards (OSHA, industry codes) typically require a qualified human operator to actively monitor and control hoists, and liability risk is high, creating strong legal and organizational barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Heavy equipment operation involves significant safety regulation, liability exposure, and often certification requirements, plus the need for human coordination via hand signals with other workers on site. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | A dedicated vision and sensor monitoring system (cameras, processing hardware, integration, continuous oversight) costs substantial capital and maintenance; the loaded wage of an operator is relatively low, making the cost ratio unfavorable for full automation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Deploying sensors, computer vision, and robotic control systems to replace a human operator would be far more expensive than the operator's wage given current technology maturity. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision products exist for gauge reading and hand detection, but integrating them into a deployed safety-critical system that reliably interprets load depth and worker signals at production scale is rare. Most deployments remain narrow pilots rather than mature production systems bearing liability. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product autonomously operates hoists/winches by reading gauges and human hand signals in production; this remains research-stage robotics/sensor fusion territory. |
Move levers, pedals, and throttles to stop, start, and regulate speeds of hoist or winch drums in response to hand, bell, buzzer, telephone, loud-speaker, or whistle signals, or by observing dial indicators or cable marks.
11CI 5–18 · exposure 8 · augmentation 25 · importance 4.6/5 · click for rater detail
Move levers, pedals, and throttles to stop, start, and regulate speeds of hoist or winch drums in response to hand, bell, buzzer, telephone, loud-speaker, or whistle signals, or by observing dial indicators or cable marks.
11| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption is very slow in traditional heavy industries (construction, manufacturing, mining). These sectors are physical, often small-firm, with high regulatory friction and resistance to untested automation of safety-critical tasks. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This occupation is in physical, industrial settings (construction, marine, mining) with low digitization and slow AI/robotics adoption for direct equipment control tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with some aspects—e.g., automated logging of dial readings or load telemetry—but the core task of responding to real-time control signals requires human judgment and presence. Augmentation is limited because the operator must remain fully engaged for safety. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some sensor-based monitoring or dial-reading assistance could theoretically support operators, but current AI offers minimal practical augmentation to the core manual control task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could theoretically control the mechanical signals to start/stop/regulate speeds, the task requires real-time response to diverse sensory inputs (hand signals, bell, dial observation, cable marks) in physically complex environments with safety-critical consequences. Current AI lacks the robust multimodal perception and safety-validated control integration needed for reliable end-to-end automation. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time physical manipulation of levers/pedals in a physical work environment based on live sensory signals and mechanical feedback, which current AI cannot perform end-to-end without robotic embodiment that doesn't exist for this role today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong regulatory and safety barriers exist: OSHA and similar bodies require licensed or certified operators for heavy-load hoist work; liability and accident costs are severe if automation fails. Human oversight and sign-off are legally mandated in most jurisdictions for load-bearing operations. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety-critical heavy equipment operation is heavily regulated with certification requirements, and liability for load-bearing hoist/winch failures creates strong barriers to unsupervised automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI vision and control systems for this task would require significant custom integration, safety certification, and oversight. The loaded cost of deploying and maintaining such a system likely exceeds or approaches the wage of a hoist operator, especially given low-volume industrial use cases. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical control task, so any hypothetical automation would require expensive custom robotics/sensor integration far exceeding current human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform this task autonomously in production. The combination of real-time signal detection, mechanical control, and the requirement to respond to variable, sometimes ambiguous human signals (hand gestures, whistles) in industrial settings remains research-stage. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product operates hoist/winch controls autonomously based on mixed human signaling methods (bells, whistles, hand signals) in real industrial settings; this remains far from productized automation. |
Oil winch drums so that cables will wind smoothly.
10CI 5–15 · exposure 0 · augmentation 13 · importance 4.3/5 · click for rater detail
Oil winch drums so that cables will wind smoothly.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hoist and winch operations occur in laggard sectors (construction, maritime, heavy industry) with low digitization and minimal AI adoption; routine equipment maintenance remains almost entirely human-performed. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Industrial/maritime/construction equipment maintenance sectors show minimal AI or robotics adoption for hands-on lubrication tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance for the task of physically oiling winch drums; it does not improve a human operator's ability to lubricate equipment effectively. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could support predictive maintenance scheduling or reminders for when to oil the drums, but it provides no direct assistance with the physical act of lubrication. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of equipment in a mechanical/industrial setting—applying oil to rotating drums. Current AI systems cannot perform physical tasks on machinery without specialized robotic hardware that is not general-purpose or widely deployed for this specific application. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical maintenance task requiring manual application of lubricant to heavy machinery components; no current AI system can perform this manipulation.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Physical presence on a work site and direct human oversight of machinery safety are typically required in regulated industrial environments; liability for equipment failure and worker safety create strong barriers to autonomous execution without human sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier per se, but the physical, hands-on nature of equipment maintenance in industrial settings creates practical friction against any automation, robotic or AI-based. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A hoist operator or maintenance technician performing this task costs significantly less per occurrence than custom robotic or automated lubrication systems, given the infrequent scheduling of this maintenance task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven alternative, so the AI cost is effectively infinite relative to a human performing the physical lubrication. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or autonomous system reliably performs routine industrial lubrication tasks on winch drums in production environments today. This remains a human-operated maintenance task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product performs winch drum lubrication in production; this remains a manual maintenance task done by workers. |
Tend auxiliary equipment, such as jacks, slings, cables, or stop blocks, to facilitate moving items or materials for further processing.
9CI 5–14 · exposure 8 · augmentation 25 · importance 3.9/5 · click for rater detail
Tend auxiliary equipment, such as jacks, slings, cables, or stop blocks, to facilitate moving items or materials for further processing.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hoist and winch operation is concentrated in construction, manufacturing, and logistics—physical, on-site work in sectors with slower digital transformation and strong union/regulatory presence. AI adoption in this space remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Material handling and industrial rigging sectors show minimal AI/robotic adoption for this specific task; physical, unstructured tending work sees little automation penetration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could offer limited assistance through computer vision inspection of cables for wear or defects, or calculations of load capacity and rigging geometry, but the core manual tending tasks—adjustment, positioning, securing—remain operator-dependent with modest augmentation potential. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some sensor-based monitoring or IoT alerts could inform operators about load status or equipment wear, but this offers only marginal assistance to the core physical tending task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Tending auxiliary equipment involves physical setup and adjustment of jacks, slings, and cables in variable real-world conditions. While AI could assist with load calculations or inspect cable conditions via vision, the core task of manually positioning, tensioning, and securing equipment requires embodied manipulation that current robotic systems cannot reliably perform end-to-end without extensive site-specific engineering. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical task requiring hands-on manipulation of jacks, slings, cables, and stop blocks in variable industrial environments; no current AI system can perform this physical work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Heavy machinery operation and load-bearing task setup are subject to OSHA and occupational safety regulations; operators must be trained and certified, and liability for improper rigging falls on the responsible human. Legal and safety barriers strongly protect this task from unattended automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Rigging and hoisting operations are heavily governed by workplace safety regulations (e.g., OSHA) requiring trained/certified personnel, and error costs (dropped loads, injury) are severe, creating strong liability and safety barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of a capable robotic system with manipulation, vision, and safety integration would far exceed the loaded wage of a hoist operator, especially for work that is episodic and environmentally variable. AI-only (perception) has no cost advantage without the robot hardware. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-based substitute for this physical labor, so AI cost is not comparable—human labor remains the only functional option today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs the full task of tending and adjusting slings, jacks, and stop blocks in unstructured warehouse or construction environments at scale. Robotic systems capable of this level of physical manipulation and spatial reasoning remain largely in research and specialized contexts. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical rigging and tending of auxiliary lifting equipment; robotics for this specific unstructured physical task remain research-stage at best. |
Repair, maintain, and adjust equipment, using hand tools.
7CI 5–10 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail
Repair, maintain, and adjust equipment, using hand tools.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hoist and winch operation is a physical, on-site task in construction, mining, and manufacturing—sectors with slower digitization and limited robotic adoption at the operator task level. Maintenance remains heavily manual and localized. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Industrial maintenance and physical equipment repair sectors show minimal AI adoption for hands-on manual repair tasks, being low-digitization and physically demanding. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers minimal assistance for hands-on repair and adjustment work; diagnostic decision-support or visual troubleshooting aids exist but do not substantially transform the core manual labor required to physically repair equipment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostic guidance, manuals lookup, or predictive maintenance scheduling, but offers little direct help with the physical hand-tool repair work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Physical repair, maintenance, and adjustment of mechanical equipment using hand tools requires dexterity, spatial reasoning, and real-time tactile feedback in unstructured physical environments—capabilities current AI systems lack. No deployed robotic system reliably performs general equipment maintenance with hand tools across varied equipment types. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical repair, maintenance, and adjustment of hoist/winch equipment using hand tools requires manual dexterity, physical manipulation, and situational diagnosis that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Equipment repair and maintenance often require licensed technicians or operators to sign off on safety-critical work; liability for failed repairs is high and asymmetric (safety hazards). Regulatory oversight of hoist systems is substantial, creating legal and organizational friction against full substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not always formally licensed, equipment safety standards, liability for improperly maintained lifting equipment, and physical access requirements create meaningful friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of deploying general-purpose robotic systems capable of hand-tool maintenance, combined with integration and safety oversight, far exceeds the loaded wage of a skilled hoist operator performing these tasks in the field. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI system replacing this physical labor, so any AI solution (e.g., robotic manipulation) would be far more expensive than a human mechanic today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No production systems today can autonomously repair and adjust hoists and winches using hand tools. While robotic arms exist for structured tasks, they cannot reliably diagnose, access, and fix varied mechanical failures in the field without human oversight and intervention. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously repairs or adjusts industrial hoisting equipment with hand tools; this remains firmly in the domain of human technicians. |
Apply hand or foot brakes and move levers to lock hoists or winches.
5CI 5–5 · exposure 0 · augmentation 25 · importance 4.5/5 · click for rater detail
Apply hand or foot brakes and move levers to lock hoists or winches.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hoist and winch operation occurs in construction, manufacturing, and logistics—sectors with slow digital adoption and strong reliance on human on-site operators for safety-critical load handling. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This task occurs in physical, industrial settings (construction, marine, mining) which are among the slowest sectors to adopt AI-driven automation for direct physical control tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Sensors and monitoring systems can provide operators visibility and alerts, but AI offers minimal direct assistance in the core task of manually controlling brakes and levers; the cognitive demand is low and the physical execution is mandatory. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could provide monitoring, alerts, or predictive maintenance data to assist operators, but it offers minimal direct assistance for the manual act of applying brakes or locking levers. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time physical manipulation of mechanical controls (hand/foot brakes, levers) in a direct operational context. Current AI systems cannot perform these motor-control actions in physical environments; the task is purely manual and embodied. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring real-time sensorimotor control of heavy equipment in a physical environment; no off-the-shelf AI system can execute the physical braking/lever action itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Industrial safety regulations mandate human operators in charge of hoist and winch systems; liability for load failures, worker safety, and inspection protocols create strong legal and organizational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Heavy equipment operation is often subject to safety regulations, certification requirements, and liability concerns given the risk of equipment failure or injury, creating strong barriers to unsupervised automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automating brake and lever control would require significant robotics investment (hardware, maintenance, integration) that would far exceed the hourly wage of a hoist operator. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Automating this would require custom robotics, sensors, and actuators integrated into physical machinery, which is far more expensive than a human operator performing a quick manual action. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can independently apply physical brakes or move mechanical levers. This requires robotic hardware integration, which remains experimental for industrial hoist operation at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs this physical hoist/winch braking action autonomously in production; while industrial automation and robotics exist for some hoisting, this specific manual brake/lever operation is not commercially automated at scale. |
Climb ladders to position and set up vehicle-mounted derricks.
5CI 5–5 · exposure 0 · augmentation 13 · importance 4.1/5 · click for rater detail
Climb ladders to position and set up vehicle-mounted derricks.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The construction and heavy equipment industries have low AI adoption for physical tasks; this niche specialty (vehicle-mounted derrick setup) shows minimal automation trends in public data. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and heavy equipment sectors show low AI/robotic adoption for physical field tasks like this, being among the least digitized and most manual industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers negligible assistance for the core task of climbing ladders and physically positioning derrick components; human intuition and embodied skill remain entirely necessary. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially assist with planning, safety checklists, or remote monitoring, but offers minimal direct assistance to the physical act of climbing and positioning equipment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical climbing and spatial positioning in a real-world environment with safety-critical constraints. Current AI systems cannot perform embodied climbing and precise mechanical setup in uncontrolled outdoor conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical task requiring climbing, manual positioning, and adjustment of heavy equipment in variable field conditions, which current AI systems cannot perform.dmit robotics for this niche task do not exist in deployable form. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, OSHA requirements, and licensing standards mandate human certification and sign-off for derrick operation and setup. Liability exposure is high for equipment failure, making automation legally and commercially difficult. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Physical safety regulations, equipment certification, and liability concerns around heavy equipment setup at height create strong barriers, though not a strict licensing requirement for the specific act of climbing. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Even the most advanced robotics systems capable of climbing and manipulation would cost orders of magnitude more than employing a human operator for this specialized task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-driven robotic system that can substitute for this physical task, so any hypothetical automation would be far more expensive than a human worker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously climb ladders and set up vehicle-mounted derricks in production today. This requires physical robotics capabilities far beyond current commercial offerings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercial product performs physical climbing and derrick setup autonomously; this remains firmly in the domain of human physical labor. |
Attach, fasten, and disconnect cables or lines to loads, materials, and equipment, using hand tools.
5CI 5–5 · exposure 0 · augmentation 13 · importance 3.7/5 · click for rater detail
Attach, fasten, and disconnect cables or lines to loads, materials, and equipment, using hand tools.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hoist and winch operation remains in physically constrained sectors with low automation adoption. These are hands-on, site-specific roles in construction, manufacturing, and logistics where human presence is still dominant. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction, industrial, and materials-handling sectors where hoist/winch operation occurs are among the slowest to adopt AI/robotics for physical manipulation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with load-weight calculation or rigging plan visualization, but current systems offer minimal productivity enhancement for the core manual task of attaching and fastening cables. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no direct assistance for the physical act of attaching and disconnecting cables using hand tools. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of cables and equipment in three-dimensional space with precise hand-tool work. Current AI systems cannot perform end-to-end physical attachment, fastening, and disconnection tasks reliably or at scale today. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of cables and hand tools on variable loads in real-world environments, which is outside current AI capability without embodied robotics that don't exist at scale for this task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, OSHA compliance, and liability requirements in rigging and hoisting create significant legal and organizational barriers to full automation. Human operators typically must certify and sign off on rigging safety. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Rigging and hoisting work often falls under safety regulations and certification requirements (e.g., OSHA rigging standards), and errors can cause serious injury or death, creating strong liability and safety barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of cable handling, if they existed at scale, would be orders of magnitude more expensive than a human operator's loaded wage, plus high integration and maintenance costs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute deployed for this physical rigging task, so AI cost comparison is effectively moot—human labor remains the only real option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform cable attachment and disconnection in real operational environments. While robotics research exists, production systems capable of handling variable loads, angles, and cable types at industrial sites are not yet mature. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously attaches/disconnects rigging cables to loads today; this remains a manual, safety-critical physical task performed by trained workers. |
Start engines of hoists or winches and use levers and pedals to wind or unwind cable on drums.
3CI 0–5 · exposure 0 · augmentation 13 · importance 4.5/5 · click for rater detail
Start engines of hoists or winches and use levers and pedals to wind or unwind cable on drums.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The construction and manufacturing sectors where this task occurs have minimal adoption of AI for direct machinery control; this remains predominantly manual labor with no evidence of substantial AI displacement or production deployments. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction, marine, and industrial hoist operation sectors have historically low digitization and slow AI/robotics adoption for direct physical equipment control. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI cannot augment the core task of starting engines and operating levers/pedals, which is purely manual physical control requiring human presence and direct mechanical engagement. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could provide monitoring, predictive maintenance alerts, or load-sensing assistance, but it offers little direct help with the moment-to-moment lever/pedal operation itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires direct physical control of machinery (levers, pedals) and real-time mechanical operation in an unstructured physical environment. Current AI systems cannot reliably operate heavy industrial equipment requiring precise tactile feedback and immediate mechanical response. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical control task requiring real-time manipulation of levers and pedals in response to dynamic load conditions, which off-the-shelf AI cannot perform end-to-end without specialized robotics hardware not currently deployed for this purpose.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Heavy machinery operation is legally and organizationally restricted to certified human operators; liability and safety regulations require human supervision and accountability. OSHA and industry standards mandate licensed operators for hoisting equipment. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Heavy machinery operation involving cables and loads carries significant safety and liability risk, often requiring certified operators and adherence to workplace safety regulations (e.g., OSHA), creating strong barriers to unsupervised automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Building a custom robotic system to operate a hoist would cost orders of magnitude more than the loaded wage of a hoist operator, making any AI-based automation economically infeasible. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Automating this would require expensive bespoke robotic actuation and sensing systems integrated with existing machinery, likely costing more than a human operator especially given oversight needs for safety-critical equipment. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No commercially deployed AI system can autonomously start and operate a hoist or winch through manual controls. This remains entirely within the domain of human operators and specialized robotics, not general-purpose AI. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercially deployed AI product operates hoists or winches via levers and pedals in production settings; automation here would require custom industrial robotics/control systems, not general AI. |
Operate compressed air, diesel, electric, gasoline, or steam-driven hoists or winches to control movement of cableways, cages, derricks, draglines, loaders, railcars, or skips.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Operate compressed air, diesel, electric, gasoline, or steam-driven hoists or winches to control movement of cableways, cages, derricks, draglines, loaders, railcars, or skips.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hoist and winch operation occurs predominantly in construction, mining, and manufacturing—typically on physical sites with low digitization and strong workforce traditions. Adoption of autonomous or AI-driven alternatives has been negligible, and most operations remain highly manual. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction, mining, and heavy industrial sectors where this task occurs are among the slowest to adopt AI/autonomous control systems, with automation limited to pilot programs in mining rather than widespread deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited augmentation potential exists because the task is already a manual feedback loop requiring continuous operator judgment; AI could assist with load calculation or safety logging, but offers little productivity gain to the operator already monitoring the system directly. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some sensor-based assistance, load monitoring, and semi-automated controls exist to aid operators, but these are incremental improvements rather than transformative productivity gains for this specific task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Operating hoists and winches requires real-time physical control, constant situational awareness of load position and safety conditions, and immediate adaptive responses to environmental hazards. Current AI systems cannot perform the continuous sensorimotor control and safety-critical decision-making needed to operate these machines end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of heavy machinery in dynamic real-world environments, which current AI systems cannot perform end-to-end; no off-the-shelf software solution replaces the physical operation.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Heavy legal and regulatory barriers protect this task: OSHA requires certified, licensed operators for many hoist configurations; liability for dropped loads or worker injury is severe; and insurance requirements mandate human operator accountability, creating hard legal constraints on automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Heavy equipment operation is subject to safety regulations, certification/licensing requirements, and liability concerns that make full automation of hoist/winch operation heavily constrained in most jurisdictions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current industrial robotics capable of hoist operation are extremely expensive to develop, integrate, and maintain; their cost far exceeds the hourly wage of a hoist operator, especially at small-to-medium industrial sites where these machines are common. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Autonomous physical control systems for heavy equipment require expensive sensors, actuators, and safety systems, making AI substitution costlier than a human operator in most current deployments. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products autonomously operate hoists or winches in production settings. While roboticists have built experimental systems, no commercial product reliably handles the variable conditions, safety requirements, and physical precision this task demands without human oversight. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously operates hoists/winches across these varied power sources and equipment types in production settings; this remains largely a human physical-control task. |
Signal and assist other workers loading or unloading materials.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Signal and assist other workers loading or unloading materials.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Physical industries like construction, warehousing, and port operations have low digital adoption for worker coordination tasks. Human operators remain the standard and are likely to persist given safety-critical nature and regulatory requirements. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Materials handling and industrial/construction sectors have low AI/robotics adoption for physical coordination tasks like this, with automation efforts focused on equipment control rather than human signaling roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with hazard detection or warning systems (e.g., computer vision alerts for unsafe positioning), but the core signaling and coordination task fundamentally requires a present, accountable human making real-time decisions alongside workers. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some sensor-based or camera-assisted systems could support situational awareness or communication (e.g., proximity alerts), but they don't meaningfully transform the human's core signaling and coordination work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time coordination with other workers in a physical environment, reading gestures, positioning, and responding to dynamic hazards. Current AI systems cannot reliably perceive, interpret, and physically signal in unstructured warehouse or construction settings at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical presence, real-time visual coordination, and hand/voice signaling with co-located workers on a loading site; no current AI system can perform this physical coordination role end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task requires human presence for liability, safety certification, and legal responsibility. Workplace safety regulations typically mandate a licensed or trained human operator to oversee loading operations and communicate directly with workers, creating hard legal and regulatory barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety regulations, on-site liability, and the need for real-time human judgment in hazardous loading/unloading operations create strong practical and safety-driven barriers to substituting AI for this physical signaling role. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Deploying a robotic system or AI-driven signaling apparatus capable of reliably coordinating with workers would far exceed the loaded wage of a human operator performing this safety-critical task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical, in-person coordination task, so any hypothetical automation (e.g., robotics plus sensors) would cost far more than the human currently doing it. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs this task autonomously in production. While computer vision can detect people, coordinating live signaling and assistance with multiple workers requires embodied presence and real-time judgment that current systems lack. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that acts as a physical signal-person coordinating human workers during material loading; this remains outside current robotics/AI product deployment. |
Related occupations — Transportation & Material Moving
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.