Help extraction craft workers, such as earth drillers, blasters and explosives workers, derrick operators, and mining machine operators, by performing duties requiring less skill. Duties include supplying equipment or cleaning work area.
Current AI systems pose low direct replacement risk (34/100) to Helpers--Extraction Workers. Even where specific software tools assist with tasks (45/100 AI Exposure), physical presence, complex manual dexterity, and unpredictable real-world environments protect the core human role.
How much of this occupation's daily workload can be materially assisted or executed by current AI systems.
How much of this occupation's AI exposure could translate into reduced human labour demand, after structural barriers to substitution are considered. A modelled index, not the probability that an individual worker will lose their job.
Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured
Confidence reflects O*NET task coverage (82%), AI mapping quality, and reliance on validated structural proxies.
An evidence-led breakdown of structural exposure and real-world replacement constraints.
For Helpers--Extraction Workers, AI Exposure is rated moderate exposure at 45/100, while overall Replacement Risk is rated low at 34/100. This indicates that AI systems can already execute or accelerate significant parts of the day-to-day workload—especially "Provide assistance to extraction craft workers, such as earth drillers and derrick operators." and "Signal workers to start geological material extraction or boring."—without necessarily eliminating the occupation entirely.
The critical barrier between software capability and worker replacement is strong human dependency (70/100) involving interpersonal negotiation, empathy, and high-stakes verification alongside substantial physical requirements (73/100) that current digital AI systems cannot perform. Tasks like "Clean up work areas and remove debris after extraction activities are complete." require tacit context and real-time adaptability that cannot be reliably offloaded to generative models or autonomous pipelines.
A score of 34/100 is not a prediction of unemployment; it represents structural pressure on how time is allocated. Professionals in Helpers--Extraction Workers should proactively adopt AI for high-velocity routine tasks while cultivating deep specialization in the judgment, client relationship, and accountability facets of their profession.
How five foundational dimensions shape this occupation's vulnerability and resilience.
45/100 exposure across 11 evaluated O*NET tasks. 0 tasks show high automation feasibility under current multimodal AI models.
Strong human dependency human reliance (70/100). Evaluates requirements for interpersonal trust, consensus-building, ethical responsibility, and direct client care.
Strong physical dependency physical dependency (73/100). Measures non-routine physical agility, spatial navigation, and unconstrained environment interaction.
Moderate adoption pressure commercial pressure (35/100). Evaluates software integration pace, cost-to-automate ratios, and enterprise tooling adoption.
Strong resilience resilience buffer (77/100). Reflects structural demand, specialization barriers, and regulatory licensure protections.
Jobs are bundles of tasks. Task exposure does not equal occupation elimination.
These tasks score lowest on automation feasibility—physical agility, accountability, and empathy resist automation.
Clients, employers, and regulators require a responsible human practitioner to stand behind decisions, verify automated outputs, and uphold professional standards.
Real-world workspaces present unpredictable physical variables that cannot be handled by screen-based AI systems or current commercial robotics.
Tasks such as "Clean up work areas and remove debris after extraction activities are complete." depend on tacit institutional knowledge, ambiguous nuance, and subjective priorities that defy algorithmic formalization.
While AI generates raw drafts and analytical calculations rapidly, human specialists are essential to detect hallucinations, ensure regulatory compliance, and align work with organizational strategy.
Practical steps to stay resilient, adopt AI tools effectively, and build on your defensible strengths as Helpers--Extraction Workers.
Helpers--Extraction Workers demonstrates strong structural resilience (34/100 Replacement Risk). Focus on adopting AI tools for productivity while deepening specialized, human-centered responsibilities.
Experiment with AI assistants for standard reporting, documentation, and research to free up time for core domain work.
Strengthen the human judgment, physical oversight, or stakeholder navigation that gives Helpers--Extraction Workers its structural resilience.
Stay aware of specialized leadership or related technical tracks that leverage your core capabilities.
Focus your energy on responsibilities that rely on interpersonal trust, physical execution, and contextual judgment.
Lower exposure: Real-world complexity, physical execution, or interpersonal nuance resist automated replacement.
Lower exposure: Real-world complexity, physical execution, or interpersonal nuance resist automated replacement.
Lower exposure: Real-world complexity, physical execution, or interpersonal nuance resist automated replacement.
Adopt generative and analytical AI tools to accelerate repeatable deliverables rather than resisting automation.
High augmentation potential: Well-suited for AI co-piloting, initial drafting, and structured analysis under human oversight.
High augmentation potential: Well-suited for AI co-piloting, initial drafting, and structured analysis under human oversight.
High augmentation potential: Well-suited for AI co-piloting, initial drafting, and structured analysis under human oversight.
These tasks have comparatively higher automation feasibility and are most likely to experience shifting workflow demands.
Notable AI exposure: Machine capabilities can assist with portions of this task mix, shifting workflow expectations.
Notable AI exposure: Machine capabilities can assist with portions of this task mix, shifting workflow expectations.
Notable AI exposure: Machine capabilities can assist with portions of this task mix, shifting workflow expectations.
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National occupational analyses reflect typical job roles. Take the Career Fit Assessment to discover careers aligned with your individual work style and verified AI resilience.
Occupations linked by shared O*NET tasks and skills.
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Compare these careers →How task-level workflow unbundling explains occupational transformation. Why AI transforms day-to-day job composition long before eliminating headcounts.
Read Research Explainer →AI is unlikely to eliminate the Helpers--Extraction Workers occupation entirely, but it is actively transforming specific tasks. With an AI Exposure score of 45/100 and a Replacement Risk score of 34/100, the profession is experiencing workflow restructuring rather than outright extinction. Tasks like "Provide assistance to extraction craft workers, such as earth drillers and derrick operators." are shifting to automated tools, while "Clean up work areas and remove debris after extraction activities are complete." remains firmly human.
AI Exposure (45/100) measures how much of the work overlaps with what current AI systems can perform technically. Replacement Risk (34/100) measures whether that capability actually threatens human employment after accounting for physical constraints (73/100), human dependency (70/100), adoption costs, and professional accountability.
No. JobsVsAI scores are index ratings on a 0–100 scale, not probabilities or unemployment percentages. A score of 34/100 indicates that Helpers--Extraction Workers exhibits low structural vulnerability relative to other occupations across the labour market.
The tasks with the highest exposure in our dataset are "Provide assistance to extraction craft workers, such as earth drillers and derrick operators." (65/100), "Signal workers to start geological material extraction or boring." (65/100), "Set up and adjust equipment used to excavate geological materials." (63/100). These responsibilities involve structured data manipulation, document drafting, pattern analysis, and routine communication.
The strongest protective factors for Helpers--Extraction Workers include "Clean up work areas and remove debris after extraction activities are complete." and "Drive moving equipment to transport materials and parts to excavation sites.", as well as interpersonal negotiation, regulatory accountability, and cross-disciplinary synthesis.
JobsVsAI analysed 11 individual tasks from O*NET 30.3, evaluating each task against 15 AI capability dimensions from our Capability Index. The model calculates capability overlap, applies environmental and human constraints, and weighs adoption pressure to produce independent Exposure and Replacement metrics with 80/100 confidence.