Will AI Replace Riggers?
Riggers face a relatively low 26.4% AI exposure score with a 18% displacement probability. Most tasks, including operation and Control, trunk Strength, and control Precision, remain beyond current AI capabilities. Physical presence requirements and high social interaction provide partial protection.
This occupation scores below the national average of 48/100 by 21.6 points. The primary risk comes from AI's strong performance in complex problem solving, representing core functions of this role. However, physical presence and high social interaction requirements provide meaningful protection.
Which skills are most at risk?
Each skill in this occupation analyzed against current AI benchmarks. Higher scores = higher AI exposure.
The bottom line for Riggers
What's most at risk
The role's most exposed skills, specifically Problem Sensitivity, Public Safety and Security, Production and Processing, reach up to 68.8/100 on AI exposure. AI systems already match or exceed human performance on AA Intelligence Index, directly targeting these core competencies.
What provides partial protection
This role requires physical presence and involves high social interaction, such as coordinating with teams, building client trust, and navigating interpersonal dynamics in real time. These human-centric demands are significantly harder to automate and will persist even as the technical components of the role shift to AI.
Skills that remain safe
Operation and Control (8.3/100), Trunk Strength (8.3/100), Control Precision (8.8/100) are protected by physical or social barriers AI cannot replicate. Far Vision and Near Vision also sit in the augmentation zone. Workers who lean into these human-centric capabilities will be well positioned as higher-exposure tasks shift to AI.
How this compares
At 26.4/100, Riggers rank below the national average of 48/100. Among the lower-risk occupations in this cluster, safer than Woodworking Machine Setters, Operators, and Tenders, Except Sawing (26.3/100). The role sits among the bottom 30% least AI-exposed occupations.
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Based on skill overlap analysis — these occupations share core competencies with Riggers but have significantly lower automation exposure.
Common questions about Riggers and AI
Replacement is unlikely in the near term. The 18% displacement probability reflects a role where AI assists more than replaces across most dimensions. The greater risk may be workers displaced from higher-exposure roles competing for these positions; therefore, staying sharp on the skills AI can't replicate remains worthwhile.
Not imminently. The skills central to this role — especially Operation and Control and Trunk Strength — remain genuinely difficult for AI to automate. The more relevant near-term shift is AI becoming a standard productivity tool that workers in this field are expected to use fluently.
Your strongest assets are Operation and Control and Trunk Strength, representing the lowest-exposure capabilities in this profile. Double down on them. Beyond that, invest in AI tool fluency: workers who know how to direct, verify, and extend AI outputs will capture the productivity upside rather than compete against it.
Your skills transfer well to roles like Roustabouts, Oil and Gas (9.3/100 AI risk, 100% skill overlap), Helpers--Installation, Maintenance, and Repair Workers (16.1/100 AI risk, 100% skill overlap), and Industrial Truck and Tractor Operators (19.7/100 AI risk, 100% skill overlap). PathScorer can analyse your full profile and surface even more personalised matches. Try it free here.
We analyse each occupation's O*NET skill profile, covering 35+ dimensions across knowledge areas, skills, and abilities, and benchmark each against current AI capabilities (MMLU-Pro for language comprehension, τ-bench v2 for task completion, MATH-500 for mathematical reasoning, LiveCodeBench for coding, and others). Each dimension is weighted by its O*NET importance score for the occupation. Physical presence requirements and social interaction levels from O*NET work context data are also factored in. Scores are updated weekly as new AI benchmarks are published. See the full methodology →
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