Excavating and Loading Machine and Dragline Operators, Surface Mining
The core of this work stays human, for now.
Most of this occupation's activities don't appear in measured AI usage. The paperwork fringe can be accelerated, but the core of the work stays human for now.
Where this occupation sits among all 785 measured. Bands are quintiles of the real distribution. Applicability measures task overlap with observed AI usage. It is not a probability of job loss.
What this job involves
Operate or tend machinery at surface mining site, equipped with scoops, shovels, or buckets to excavate and load loose materials.
Where AI overlaps this work, activity by activity
The occupation's most important work activities (O*NET weights, normalized to this set), each with its measured AI performance across 200,000 real conversations: how often AI is used for it, how well it completes it, and how much of the activity it covers.
| Work activity | Share of role | AI performance |
|---|---|---|
| Operate construction or excavation equipment. | 25.8% | Not observed 0.00 |
| Inspect commercial, industrial, or production systems or equipment. | 11.0% | Not observed 0.00 |
| Maintain current knowledge in area of expertise. | 8.9% | High overlap 0.61 |
| Signal others to coordinate work activities. | 8.7% | Not observed 0.00 |
| Communicate with others about operational plans or activities. | 7.1% | High overlap 0.61 |
| Direct organizational operations, activities, or procedures. | 6.7% | Not observed 0.00 |
| Move materials, equipment, or supplies. | 6.0% | Not observed 0.00 |
| Maintain tools or equipment. | 5.7% | Not observed 0.00 |
| Build structures. | 5.7% | Not observed 0.00 |
| Maintain vehicles in working condition. | 5.0% | Not observed 0.00 |
| Evaluate the quality or accuracy of data. | 4.7% | Moderate 0.45 |
| Measure physical characteristics of materials, products, or equipment. | 4.7% | Moderate 0.42 |
"Not observed" is a measurement, not missing data: that activity does not meaningfully appear in the usage sample. AI performance = completion × scope × coverage (user-goal view), 0–1.
What drives this score: the biggest AI-overlapped activity in this role is maintain current knowledge in area of expertise; the most important activity AI is not observed doing is operate construction or excavation equipment. The plan below is built around exactly that split.
Protect the core, automate the fringe.
Next 30 days
- Automate the fringe. The small text-and-paperwork layer of this job, Maintain current knowledge in area of expertise, is worth handing to AI now; it's the least valuable part of your week anyway.
- Confirm your reality. If your actual role involves far more screen work than the occupation average, the personalization above will say so.
Next 90 days
- Convert resilience into rate. Work like Operate construction or excavation equipment is measured as beyond current AI: supply of it doesn't scale with software. Skill up and price accordingly.
- Let AI carry your admin. Quotes, scheduling, follow-ups, records: the overhead around the core work is where the tools pay off for you.
The longer game
- Watch robotics, not chatbots. For low-exposure occupations the relevant automation frontier is usually physical or regulatory, which this dataset deliberately doesn't measure. Track your industry, not AI headlines.
- Build the customer asset. Reputation and repeat relationships are the moat generative AI cannot cross. Invest there.
Where to put your learning time
Your moat here is the activity operate construction or excavation equipment, which the data shows AI is not doing at all. Deepening that is worth more than chasing AI skills you do not need. The one useful exception is the paperwork fringe of the job: enough tool fluency to clear admin quickly and hand the time back to the core work.
- Full task, skill and pay profile for this occupation on O*NET OnLine Free
- Browse role-relevant courses on Coursera Partner link
Most Coursera courses can be audited free; payment covers graded work and certificates. The partner link earns this site a commission if you subscribe, at no extra cost to you, and it never changes a score, a ranking or a plan here.
- AI applicability score and per-activity metrics: Tomlinson, Jaffe, Wang, Counts & Suri, Working with AI: Measuring the Applicability of Generative AI to Occupations, Microsoft Research 2025 (arXiv:2507.07935), data CC BY 4.0.
- Occupation description and activity structure: O*NET 30.0 Database, USDOL/ETA, CC BY 4.0.
- Bands, personalization weights and action plans are JobRiskAI editorial layers; method and limitations on the methodology page.