Riggers
49-9096.00Median wage $62,640/yr22,530 employed (US)Rank #920 of 923 scored · top 100% by substitution
Set up or repair rigging for construction projects, manufacturing plants, logging yards, ships and shipyards, or for the entertainment industry.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution5
Exposure2
Augmentation24
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
14 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%3
panel mean rating 1.1/5 → substitution pressure 3/100
Technical feasibility todayw 20%2
panel mean rating 1.1/5 → substitution pressure 2/100
Cost vs. human wagew 15%2
panel mean rating 1.1/5 → substitution pressure 2/100
Adoption barriersw 20%inverted — strong barriers lower the score20
panel mean rating 4.2/5 (barrier strength) → substitution pressure 20/100
Sector adoption velocityw 10%0
panel mean rating 1.0/5 → substitution pressure 0/100
Task breakdown (14 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.
Clean and dress machine surfaces and component parts.
24CI 15–33 · exposure 13 · augmentation 13 · importance 3.5/5 · click for rater detail
Clean and dress machine surfaces and component parts.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Manufacturing and rigger-heavy sectors adopt automation slowly for dexterous, variable tasks; this task remains largely manual and there is minimal public evidence of systematic AI or robotic displacement in this specific cleaning and dressing function. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Rigging and industrial/manufacturing trades are among the slowest sectors to adopt AI or robotics for such granular physical maintenance tasks, with minimal current investment in this narrow area. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist with inspection or defect detection on surfaces, but the core manual cleaning and dressing work offers limited scope for meaningful augmentation without direct physical robotic capability. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance for the physical act of cleaning and dressing machine surfaces, as it's a hands-on task with no digital or cognitive component to augment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Cleaning and dressing surfaces requires sensorimotor feedback, environmental adaptation, and judgment about surface condition that current AI systems cannot reliably perform end-to-end. While some surface preparation might be partially automatable in highly controlled settings, the tactile assessment and selective application needed for component parts remain beyond current robotic or vision-only automation capability. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical cleaning and finishing task requiring manual dexterity and handling of heavy machine components, which current AI systems cannot perform end-to-end without robotic embodiment far beyond available deployed capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are few formal licensing barriers, but practical adoption faces friction from the need for human judgment about surface condition and quality verification, as well as the physical and environmental adaptation required in typical workshop settings. |
| Adoption barriers | claude-sonnet-5 | 2/5 | There's no strict licensing requirement specifically for cleaning tasks, but it's embedded in a broader rigging job requiring physical presence, safety training, and equipment familiarity, creating some organizational friction against isolated automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Equipping robots or vision-guided systems to handle diverse surfaces, assess cleanliness quality, and apply dressing compounds would require significant per-deployment customization and oversight, likely exceeding the cost of a skilled rigger for this variable task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Without viable robotic automation for this variable, physical, unstructured task, AI-based approaches would be far more costly than simply having a human rigger perform the work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs general surface cleaning and dressing of arbitrary machine components with the judgment needed for production work. Specialized industrial robots exist for narrow, fixed tasks, but they lack the versatility and adaptive sensing this task demands in typical shop environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product cleans and dresses machine surfaces in rigging contexts; this remains a manual, physical trade task with no robotic automation in production for this specific niche. |
Select gear, such as cables, pulleys, and winches, according to load weights and sizes, facilities, and work schedules.
18CI 14–23 · exposure 20 · augmentation 50 · importance 4.3/5 · click for rater detail
Select gear, such as cables, pulleys, and winches, according to load weights and sizes, facilities, and work schedules.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Rigging is primarily a skilled, physical trade in construction and industrial sectors with low digitization rates. Adoption of AI tools in these sectors remains slow, with most riggers working in smaller firms or on-site with limited tech infrastructure. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and industrial rigging sectors show low AI adoption due to physical, safety-critical, and non-digitized work environments. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could usefully assist by automating load calculations, querying equipment inventory, and cross-referencing specifications against documented standards, helping a rigger work faster while the human retains responsibility for final selection decisions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based load calculators, engineering software, and digital load charts can assist riggers in verifying gear specifications and load calculations, improving accuracy and speed of decision-making. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist in matching gear specifications to documented load parameters, the task requires judgment about real-world facility constraints, safety margins, and dynamic work conditions that are difficult to fully formalize. End-to-end automation with 50% time savings is unlikely without substantial human verification. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical assessment of loads, site conditions, and equipment availability that current AI cannot directly perceive or verify; at best AI could assist with calculations but not execute the selection end-to-end reliably. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Riggers must typically be certified/licensed and bear liability for equipment selection safety. Regulations often require a qualified human to sign off on load-bearing calculations and gear selection, creating legal and organizational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Rigging involves significant safety and liability concerns, often requiring certified/qualified riggers per OSHA and industry standards, creating strong barriers to full automation of gear selection without human sign-off. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | An AI system capable of reliable gear selection would require significant domain knowledge encoding, integration with facility databases, and ongoing human oversight. The setup and maintenance costs would likely approach or exceed the labor cost of an experienced rigger performing the selection. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot perform the physical inspection, gear handling, and site-specific judgment involved, so no viable AI-only cost comparison exists; human labor remains necessary. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed production systems reliably perform this specialized selection task autonomously. Calculation tools exist for load analysis, but integrating them with facility-specific constraints, regulatory compliance, and real-time scheduling decisions remains largely manual. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously selects rigging gear in real work environments; this remains a physical, judgment-heavy task performed by skilled riggers on-site. |
Dismantle and store rigging equipment after use.
5CI 5–5 · exposure 0 · augmentation 13 · importance 4.2/5 · click for rater detail
Dismantle and store rigging equipment after use.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Rigging operations are heavily physical, site-specific, and concentrated in construction and entertainment industries with lower digital infrastructure, resulting in minimal AI/automation adoption for these tasks. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and industrial rigging sectors show low digitization and minimal AI/robotics adoption for physical equipment handling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI systems offer minimal assistance for physical dismantling and storage tasks; computer vision for inventory tracking might help marginally, but does not meaningfully augment the core manual work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could offer minor assistance via checklists, inventory tracking, or maintenance scheduling software, but it doesn't materially transform the physical dismantling and storage process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Dismantling and storing rigging equipment requires physical manipulation in variable 3D environments, assessment of equipment condition, and spatial reasoning about storage logistics. Current AI systems cannot perform these embodied manipulation tasks end-to-end with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring dexterity, strength, and situational judgment to safely dismantle and store heavy rigging equipment; no AI system can perform the physical manipulation involved. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Physical safety regulations, union rules in many jurisdictions, liability for improper equipment handling, and the requirement for on-site human judgment about equipment storage conditions create substantial barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Rigging work is safety-critical and often subject to OSHA and certification requirements, with significant liability if equipment is improperly stored, creating strong barriers to non-human execution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic systems capable of any portion of physical equipment handling are expensive to acquire, maintain, and deploy, making them far more costly than skilled human riggers for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical task, so any hypothetical automation would require expensive specialized robotics far costlier than human labor today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed production systems reliably perform physical rigging equipment dismantling and storage. This task requires mobile manipulation, environmental adaptation, and hands-on mechanical work beyond current robotic or AI capabilities in real-world sites. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical rigging dismantlement and storage; this remains purely a research-stage robotics challenge, if that, and is not commercially available. |
Manipulate rigging lines, hoists, and pulling gear to move or support materials, such as heavy equipment, ships, or theatrical sets.
5CI 5–5 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Manipulate rigging lines, hoists, and pulling gear to move or support materials, such as heavy equipment, ships, or theatrical sets.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Rigging occurs primarily in construction, maritime, theatrical, and manufacturing sectors with heavy physical infrastructure dependencies. Automation adoption in these sectors remains low; operations are largely manual with incremental mechanization rather than AI-driven autonomy. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction, entertainment, and marine industries relying on riggers show minimal AI/robotics adoption for physical load manipulation tasks, remaining a low-digitization, hands-on trade. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist with load calculation, rigging diagram generation, or safety compliance checks, but the core manipulation task is purely physical and offers limited room for AI-augmented human productivity in the field. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with load calculations, sensor-based monitoring, or planning software, but it offers minimal direct assistance to the physical act of manipulating rigging lines and gear. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Rigging requires real-time physical manipulation of lines, hoists, and pulling gear in three-dimensional space with precise force control and dynamic load assessment. No current AI system can physically execute this manipulation or reliably adapt to variable environmental conditions, anchor points, and load behaviors in the field. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring hands-on control of heavy rigging equipment in dynamic environments; no off-the-shelf AI system can perform this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Rigging work is heavily regulated by OSHA and industry standards requiring certified human riggers to inspect loads, verify rigging plans, and sign off on safety. Liability and worker safety laws effectively mandate human professional judgment and legal accountability in this task. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Rigging involves high liability (risk of catastrophic failure, injury, death), often requires certified/licensed riggers per safety regulations (e.g., OSHA), and demands human judgment for load assessment in real-time hazardous conditions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of robotic rigging systems capable of heavy lifting, plus integration and safety compliance overhead, substantially exceeds the wage cost of trained riggers for most applications, especially in varied operational contexts. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Without viable automation, there is no AI cost basis to compare; specialized robotic rigging systems, if they existed, would be far more expensive than skilled riggers for these variable tasks. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While perception and planning AI exists in research, no deployed commercial product performs end-to-end rigging task execution—determining rigging configurations, securing lines, operating hoists, and executing controlled moves—reliably in production environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed products that autonomously manipulate rigging lines, hoists, and pulling gear for heavy loads; this remains firmly in the physical/robotics research space, not production. |
Install ground rigging for yarding lines, attaching chokers to logs and to the lines.
5CI 0–10 · exposure 0 · augmentation 13 · importance 4.1/5 · click for rater detail
Install ground rigging for yarding lines, attaching chokers to logs and to the lines.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Logging and forestry remain low-digitization, physical-labor sectors with limited AI adoption. Equipment is specialized, sites are geographically dispersed, and safety culture prioritizes human expertise, resulting in minimal production deployment of automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Logging and forestry are low-digitization, physically demanding sectors with minimal AI/robotics adoption for field rigging tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited augmentation potential; AI could assist with load weight estimation or rigging plan visualization, but the core task of physically attaching chokers and knots remains almost entirely human-dependent, with little meaningful productivity lift from current AI tools. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers no meaningful real-time assistance for the physical act of attaching chokers and rigging lines in the field. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy logs and rigging equipment in unstructured outdoor environments, precise knot-tying and attachment in variable terrain, and real-time safety judgment. Current AI systems cannot reliably perform these physical actions or replace human expertise in securing loads safely. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical field task requiring manual manipulation of heavy cables, chokers, and logs in outdoor terrain; no current AI or robotic system performs this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Workplace safety regulations (OSHA, forestry standards) mandate human inspection and sign-off on rigging security to protect workers and equipment. Liability exposure for rigging failure is high, creating legal and insurance barriers to full automation without a licensed rigger's accountability. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed in the way some trades are, safety-critical rigging work in logging often requires trained/certified personnel and involves high liability for equipment failure or injury, creating real but not absolute barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom robotics and AI systems capable of log manipulation and rigging would require substantial capital investment, specialized sensors, and site-specific configuration—far exceeding the loaded wage of a skilled rigger, with little current commercial deployment. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system capable of performing this physical rigging work, so any hypothetical robotic solution would be far more expensive than human labor given current robotics costs and lack of maturity. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products perform autonomous rigging attachment end-to-end in logging environments today. Robotics capable of this task exist only in research settings and do not scale to the variability and safety requirements of actual logging operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products exist that physically rig chokers to logs and yarding lines; this remains entirely a research-stage robotics challenge at best. |
Load machines onto trucks to prepare for transportation.
5CI 5–5 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Load machines onto trucks to prepare for transportation.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Rigging and material handling sectors are among the most laggard in AI adoption; most operations remain manual or rely on basic mechanized equipment rather than intelligent autonomous systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Rigging and heavy equipment transport is a low-digitization, physical-labor sector with minimal AI/robotics adoption for this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI offers minimal assistance on this task; simple computer vision for load tracking or weight estimation exists but does not substantially transform a rigger's core productivity on the loading activity itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with load planning, route optimization, or safety checklists, but offers little direct assistance to the physical act of rigging and loading machinery. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Loading machines onto trucks requires physical manipulation, spatial judgment, and real-time adaptation to equipment conditions and terrain—capabilities current AI systems cannot execute autonomously in unstructured environments. While specialized industrial robots exist for narrow, pre-programmed tasks, no general off-the-shelf system achieves 50% time savings on this varied, physical task. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation and equipment-operation task requiring judgment about load balance, rigging attachment, and crane/forklift operation in dynamic environments—no off-the-shelf AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: the task occurs in unstructured physical environments, involves heavy equipment and safety liability, requires real-time judgment calls about weight distribution and securing, and is subject to OSHA and worker safety regulations that favor human oversight and accountability. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety regulations (OSHA), liability for load securement, and the physical dexterity/judgment required create strong barriers to automation, though not a strict licensing requirement in all jurisdictions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom or specialized industrial automation for machine loading remains capital-intensive, with high deployment and maintenance costs that exceed the loaded wage of a single rigger for most applications. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any AI cost comparison is moot—human labor with specialized equipment remains the only functional option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product autonomously performs end-to-end machine loading at production scale. Specialized robotics exist in controlled factory settings, but they are task-specific, not general solutions, and require extensive setup and infrastructure. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed products that autonomously rig and load heavy machinery onto trucks; this remains a manual, human-operated task in real work sites. |
Fabricate, set up, and repair rigging, supporting structures, hoists, and pulling gear, using hand and power tools.
5CI 5–5 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail
Fabricate, set up, and repair rigging, supporting structures, hoists, and pulling gear, using hand and power tools.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Heavy trades and rigging remain among the slowest-adopting sectors for AI automation. Work is site-specific, hands-on, and embedded in skilled craft traditions with entrenched human labor and minimal digitization. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and industrial rigging trades are among the least digitized, physical-labor-dependent sectors with minimal AI/robotics adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide minor assistance through inspection documentation or maintenance scheduling tools, but the core task of fabricating, setting up, and repairing rigging depends on human judgment, physical skill, and immediate environmental adaptation that current AI systems cannot augment significantly. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with limited aspects like planning, load calculations, or documentation, but offers negligible assistance for the hands-on fabrication and repair work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy equipment, hand and power tools, and real-time spatial problem-solving in dynamic environments. Current AI cannot perform end-to-end physical fabrication, setup, or repair work without human operators. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical fabrication, setup, and repair task requiring manual dexterity, hand/power tool use, and physical manipulation of heavy equipment—current AI systems cannot perform physical labor and no robotics system generally handles rigging work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Rigging work involves significant liability and safety-critical functions; regulatory codes, OSHA requirements, and insurance often mandate licensed, trained human riggers to inspect, certify, and oversee load-bearing structures. Human responsibility and sign-off are legally and practically essential. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Rigging work often requires certified/licensed riggers due to serious safety and liability risks (falls, dropped loads), creating strong regulatory and safety-driven barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of rigging tasks are orders of magnitude more expensive than skilled human riggers, and integration costs remain prohibitive for most applications. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so AI cost is not comparable—human riggers remain the only viable option, making AI effectively far more 'expensive' (i.e., infeasible) for the physical work itself. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can autonomously fabricate, set up, or repair rigging and hoists. Robotics in this domain remain highly specialized, narrow-scope, and research-oriented rather than production-ready for the full scope of rigging work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs rigging fabrication or repair; this remains firmly in the domain of skilled human tradespeople with physical tools. |
Control movement of heavy equipment through narrow openings or confined spaces, using chainfalls, gin poles, gallows frames, and other equipment.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Control movement of heavy equipment through narrow openings or confined spaces, using chainfalls, gin poles, gallows frames, and other equipment.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Physical rigging in construction and manufacturing remains deeply human-operated with minimal automation adoption; industries are low-digitization, safety-conservative, and lack economic incentive or technical maturity for autonomous alternatives. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and heavy equipment rigging are physical, low-digitization sectors with minimal AI/robotic adoption for this kind of hands-on spatial task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with planning optimal equipment paths or load calculations prior to the operation, but during actual execution in confined spaces, real-time human control and judgment remain essential; augmentation value is limited to pre-task analysis. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with planning lift paths, load calculations, or sensor-based spatial monitoring, but offers little real-time assistance for the physical control of equipment through confined spaces. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy equipment through constrained spatial environments with real-time adjustment based on tactile feedback and safety assessment. Current AI systems cannot operate chainfalls, gin poles, or gallows frames in physical space, nor can they safely navigate equipment through confined spaces. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring real-time spatial judgment and manual control of rigging equipment in confined spaces; no AI system can perform this physical work end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task has hard legal and safety barriers: workplace safety regulations (OSHA, industry standards) require licensed, trained humans to operate rigging equipment and maintain accountability for load control in confined spaces. Liability and worker safety requirements are non-negotiable. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Rigging in confined spaces involves significant safety and liability risk, often requiring certified riggers and adherence to OSHA-type regulations, creating strong barriers to substitution even by future automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous systems capable of this task (specialized industrial robots with force-feedback control) would cost far more than a skilled rigger's loaded wage, and integration and safety certification would multiply costs further. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical rigging task, so any hypothetical automation (robotics) would be far more costly than skilled human labor today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can physically control heavy rigging equipment or manipulate objects through narrow openings. This remains exclusively within the domain of human-operated mechanical systems and robotics research, not production automation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that autonomously controls chainfalls, gin poles, or gallows frames to move heavy equipment through confined spaces; this remains a purely research or non-existent capability. |
Tilt, dip, and turn suspended loads to maneuver over, under, or around obstacles, using multi-point suspension techniques.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Tilt, dip, and turn suspended loads to maneuver over, under, or around obstacles, using multi-point suspension techniques.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Rigging remains a manual, site-specific occupation in construction and manufacturing with minimal AI adoption; most work is physical and requires in-person judgment under variable conditions. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and industrial rigging are low-digitization, physical-world sectors with minimal AI/robotic adoption for dynamic load handling. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with pre-planning load paths or hazard identification through computer vision, but the core task of tilting and maneuvering requires human operator control, limiting augmentation value in practice. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with load calculation, path planning software, or sensor-based collision warnings, but offers limited real-time assistance for the actual physical maneuvering. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time spatial reasoning, dynamic load physics, and physical manipulation of suspended heavy equipment in live environments—capabilities far beyond current AI systems. No AI can currently perform end-to-end crane operation with safety guarantees. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, safety-critical manipulation task requiring real-time spatial judgment and fine motor control of heavy suspended loads; no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Rigging is heavily regulated and requires licensed operators (union, OSHA, state certifications) to perform load handling legally. Liability for dropped loads is severe, creating hard legal and safety barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Rigging work is safety-regulated (OSHA), often requires certified riggers, and involves high liability for load drops or accidents, creating strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems capable of this task do not exist at production scale, making cost comparison moot; human riggers remain the only viable option, making AI more expensive (nonexistent) than human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this task, so any hypothetical system would require far more capital and engineering than the human labor cost it replaces. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs unsupervised rigging of suspended loads. This remains a task requiring licensed human operators with direct physical control and real-time decision-making. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously maneuvers suspended loads through obstacle courses; this remains far beyond current robotics/AI capability in production. |
Attach loads to rigging to provide support or prepare them for moving, using hand and power tools.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Attach loads to rigging to provide support or prepare them for moving, using hand and power tools.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Physical rigging remains concentrated in construction, manufacturing, and logistics sectors with relatively low digital infrastructure and high reliance on manual expertise. Adoption of any automation is minimal and limited to niche, highly controlled environments. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and industrial rigging is a low-digitization, physically demanding sector with minimal AI/robotic adoption for manipulation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could provide some assistance via load-calculation software, inspection planning, or documentation, but current systems offer minimal augmentation to the core manual task of physically attaching and securing loads. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with load calculation, sensor-based monitoring, or safety alerts, but offers little direct enhancement to the physical act of attaching rigging hardware. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy loads, precise spatial positioning, and real-time safety judgment in variable physical environments. Current AI systems cannot operate robotic hands reliably enough to handle diverse rigging configurations and weight distributions at the speed and safety level required. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity, judgment about load balance, and use of hand/power tools in variable environments—current AI (software or robotics) cannot perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Safety regulations, worker certification requirements, and liability for load failures create hard legal and contractual barriers. Most jurisdictions require a licensed, physically present rigger to inspect, attach, and sign off on loads for movement due to catastrophic failure consequences. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Rigging work is safety-critical and often governed by OSHA and certification requirements, with significant liability for load-drop failures, creating strong barriers even if automation were technically feasible. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized rigging robots capable of this task remain extremely expensive to acquire, maintain, and integrate, with ongoing human oversight costs. The loaded cost of such automation far exceeds the wage of a trained rigger performing the work directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this task, so any hypothetical automation would be far more expensive than a human rigger given required specialized robotics and safety systems. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs full load attachment and rigging in real-world construction or industrial settings. While some robotic systems exist in controlled environments, they lack the adaptability and safety assurance needed for production use in variable rigging scenarios. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously attaches loads to rigging in real-world construction/industrial settings; this remains far beyond commercial robotic manipulation capability. |
Align, level, and anchor machinery.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Align, level, and anchor machinery.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of automation in rigging is minimal; the work is physically distributed, site-specific, and involves bespoke machinery configurations that resist standardization or remote/autonomous control. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and industrial rigging is a low-digitization, physical-labor sector with minimal AI/robotic adoption for this specific task compared to information-based industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide limited assistance via computer vision feedback or digital leveling aids to help a human rigger verify alignment, but current tools offer only modest augmentation to a task that is fundamentally hands-on and judgment-intensive. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based sensors, laser leveling tools, or digital measurement aids can assist with alignment precision, but this offers only modest assistance to the core physical task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy machinery in varied on-site conditions, with tactile feedback and real-time spatial adjustment that current AI systems cannot perform autonomously. No general-purpose system can reliably align, level, and anchor machinery without human intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical task requiring manual manipulation of heavy machinery, precise physical alignment, and anchoring—no current AI system can perform these physical actions end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task has hard barriers: it requires licensed riggers or certified operators in many jurisdictions, and workplace safety regulations typically mandate a qualified human sign-off on machinery alignment and anchoring to prevent hazardous failures. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Safety-critical physical work often requires certified riggers, adherence to OSHA and industry safety standards, and liability concerns around heavy machinery placement create strong barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic or AI-augmented solutions for machinery alignment are capital-intensive and require significant setup, making them far more expensive than the labor cost of trained riggers for typical jobs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical labor involved, so AI cost is not comparable—robotics capable of this remain expensive, unreliable, and far from cost-competitive with human riggers. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs autonomous machinery alignment and anchoring in production. While robotic systems exist for specialized industrial tasks, they require extensive custom engineering and do not generalize to the diverse machinery and environmental contexts riggers encounter. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical rigging, leveling, or anchoring of machinery; this remains firmly in the domain of skilled human labor and specialized equipment. |
Attach pulleys and blocks to fixed overhead structures, such as beams, ceilings, and gin pole booms, using bolts and clamps.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Attach pulleys and blocks to fixed overhead structures, such as beams, ceilings, and gin pole booms, using bolts and clamps.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Rigging is a specialized skilled trade performed primarily on jobsites by unions and trade workers; sectors performing this task show minimal AI adoption and remain heavily dependent on human expertise and physical presence. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and industrial rigging trades show minimal AI/robotics adoption for physical installation tasks; this sector lags significantly in automation of manual physical work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with planning and visualization (e.g., suggesting optimal pulley configurations or structural load analysis), but meaningful augmentation is limited given the task's inherent requirement for hands-on physical manipulation and real-time spatial judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with load calculations, safety checklists, or structural planning beforehand, but offers little direct assistance during the physical attachment task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in three-dimensional space, custom positioning based on structural assessment, and safe handling of heavy equipment. Current AI cannot perform the mechanical installation, bolting, and clamping work end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of heavy equipment at height, climbing, positioning, and manual fastening—no current AI system can perform this physical task end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Significant legal, safety, and liability barriers exist: rigging work is often governed by safety regulations, insurance requirements, and union agreements that mandate human certification and sign-off on load-bearing installations. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Rigging work often requires certified riggers due to safety regulations (OSHA), high liability for equipment failure, and physical presence, creating strong barriers even if automation were technically feasible. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of specialized robotic systems capable of overhead installation work, combined with integration, safety validation, and oversight, far exceeds the loaded wage of a skilled rigger performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-driven equivalent to compare cost against; robotic manipulation at heights with bolts/clamps for rigging is not commercially available, making AI substitution effectively infinitely costly or nonexistent. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems can autonomously attach pulleys and blocks to overhead structures; this remains firmly in the domain of human skilled labor with potential robotic research prototypes only. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product installs pulleys and blocks on overhead structures; this remains a purely manual physical rigging task performed by skilled workers. |
Test rigging to ensure safety and reliability.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.6/5 · click for rater detail
Test rigging to ensure safety and reliability.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Rigging and related trades remain low-digitization, physically-grounded sectors where adoption of AI agents is minimal. Safety-critical roles and union/regulatory constraints slow any AI integration; most organizations still rely on human inspection protocols. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction, entertainment, and industrial rigging sectors have low digitization and minimal AI adoption for physical safety-critical inspection tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI vision tools could assist by flagging obvious defects or documenting inspection data, but the core judgment—whether a rig is safe to load—remains a human responsibility. Augmentation is limited because safety decisions cannot be delegated; AI is a minor assistant, not a productivity multiplier. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with recordkeeping, predictive maintenance alerts, or flagging inspection schedules, but offers little direct help with the physical act of testing rigging integrity. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Testing rigging for safety requires physical inspection, hands-on manipulation, weight-bearing verification, and judgment calls about structural integrity that current AI cannot perform end-to-end. While vision systems might inspect some visual defects, they cannot replicate the tactile feedback, load testing, and real-time decision-making essential to this safety-critical task. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically testing rigging setups (load testing, inspecting hardware, verifying rated capacities and knots/hitches) requires hands-on manipulation and sensory judgment in real-world physical environments that current AI cannot perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Testing rigging safety is heavily regulated and often legally requires sign-off by a certified rigger or qualified inspector. OSHA and industry standards mandate human accountability for load-bearing decisions, creating a hard legal barrier to full substitution. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Rigging safety testing is governed by strict occupational safety regulations (e.g., OSHA) requiring qualified/certified personnel to inspect and certify rigging, creating hard legal and liability barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current AI vision and inspection tools, combined with human oversight and remediation, would cost more than a trained rigger performing the task directly. The safety criticality and liability exposure make any partial automation require extensive human verification, eroding cost savings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical test, so any AI cost is irrelevant—human labor with specialized equipment remains the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs comprehensive rigging safety testing in production environments today. Isolated computer vision applications for defect detection exist in research, but full end-to-end safety validation remains a skilled human domain with no mature alternative product. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical rigging safety tests; this remains a manual inspection task requiring certified human riggers on site. |
Signal or verbally direct workers engaged in hoisting and moving loads to ensure safety of workers and materials.
0CI 0–0 · exposure 0 · augmentation 25 · importance 4.6/5 · click for rater detail
Signal or verbally direct workers engaged in hoisting and moving loads to ensure safety of workers and materials.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and heavy industrial sectors—where riggers work—remain low-adoption for AI agents. Safety liability and physical unpredictability of work sites create structural resistance; automation adoption in these sectors lags far behind information and finance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and industrial rigging is a low-digitization, physical-labor sector with minimal AI adoption for real-time safety-critical coordination tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist a rigger by flagging detected obstacles or load imbalances via vision analytics, but the core task—live safety direction of workers—cannot be meaningfully augmented without removing the human from the decision loop, defeating the safety purpose. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Sensors, cameras, and load-monitoring systems can supplement situational awareness, but AI does not meaningfully transform the moment-to-moment verbal/signal communication central to this task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires real-time spatial awareness, dynamic decision-making in safety-critical situations, and immediate verbal/gestural communication with workers in physical space. Current AI systems lack the embodied presence, environmental sensing, and liability-bearing authority to replace a human rigger's safety-critical signaling role. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time physical presence, visual assessment of load dynamics, and verbal/hand signaling coordinated with human crane operators and workers in a hazardous physical environment—far beyond current AI capabilities to perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Safety regulations and OSHA standards explicitly require a qualified human spotter or signaler to direct hoisting operations; liability and worker safety law create hard legal barriers to removing human oversight from this task. A licensed or certified human must sign off on load movement safety. |
| Adoption barriers | claude-sonnet-5 | 5/5 | OSHA and industry safety regulations require qualified, often certified riggers/signal persons to be physically present and legally responsible for hoisting safety, creating hard regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any AI system that could plausibly perform this task would require significant hardware (sensors, cameras, audio systems on-site), processing, and integration costs that would exceed the wage cost of a single rigger, given that a rigger is already physically present at the location. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI system performing this task, so cost comparison favors the human rigger entirely; any hypothetical robotic/sensor system would require significant capital investment exceeding wage costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs human-equivalent safety signaling and worker direction for hoisting operations in production environments. While computer vision could theoretically detect load positions, converting that into real-time, authoritative safety directives with live worker coordination remains research-stage only. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product directs live rigging/hoisting operations; this remains a physically embodied, safety-critical human task with no commercial substitute in production. |
Related occupations — Installation, Maintenance & Repair
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.