US dataswitch to UK
Dredge Operators
moving levers to position dredges, starting power winches that draw in or let out cables to change positions of dredges and pumping water to clear machinery pipelines. If that's your week, this page is about your job.
The honest answer
AI changes the edges of this job, not the middle: moving levers to position dredges is work software can't reach.
What shifts is the routine end of the work: the paper around the work, not the work.
Your week, as this page understands it
Operate dredge to remove sand, gravel, or other materials in order to excavate and maintain navigable channels in waterways. The job title says “dredge operators”. The real job is the part underneath: moving levers to position dredges. That is the thing someone has to be right about.
The exposed part of this job is specific, and we won’t pretend it is coming back. But dredge operators is not one task. It is 6 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is moving levers to position dredges, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 0%
- changing shape
- 0%
- staying human
- 100%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 2 out of 100 (0–8 allowing for uncertainty): minimal exposure, across 6 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.
How we know this
What is measured: Every published task statement for dredge operators is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.
How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.
Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.
Your job, task by task
These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.
- 2 rows are marked low confidence, so treat them as a ballpark rather than a fine measurement.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
0 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.
Changing shape
0 tasksTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.
Staying human
6 tasksTasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.
Moving levers to position dredges
This work happens in the physical world: levers, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Move levers to position dredges for excavation, to engage hydraulic pumps, to raise and lower suction booms, and to control rotation of cutterheads.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Working the levers that swing the dredge, run the pumps and turn the cutterhead means sitting at the controls.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Starting and stopping engines to operate equipment
This work happens in the physical world: engines, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Start and stop engines to operate equipment.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Starting and stopping the engines is done at the machine itself.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Starting power winches that draw in or let out cables to change positions of dredges
This work happens in the physical world: power winches, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Start power winches that draw in or let out cables to change positions of dredges, or pull in and let out cables manually.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Running the winches or hauling cables by hand to shift the dredge is physical work aboard.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Pumping water to clear machinery pipelines
This work happens in the physical world: water, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Pump water to clear machinery pipelines.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Pumping water through the pipelines to clear them is done at the controls on the dredge.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Directing or assisting workers placing shore anchors and cables
This work happens in the physical world: workers placing shore anchors, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Direct or assist workers placing shore anchors and cables, laying additional pipes from dredges to shore, and pumping water from pontoons.” (O*NET task statement)
How this row was scored
Exposure score: 5 out of 100 (0–17 allowing for uncertainty): minimal exposure, low confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Instructions can be written down, but placing anchors and pipes involves working alongside the crew on the water.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 1/4.
Lowering anchor poles to verify depths of excavations
This work happens in the physical world: anchor poles, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Lower anchor poles to verify depths of excavations, using winches, or scan depth gauges to determine depths of excavations.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (0–20 allowing for uncertainty): minimal exposure, low confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Survey records can suggest the depth, but confirming it means lowering poles or reading gauges on board.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
What this job pays, and how many people do it
- Median pay
- $49,640a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
- People doing this job
- 1,040in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)
What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.
Why this is shifting
The reason is boringly specific. Almost none of this job is reading one thing and writing another (the shape today's tools are built for), because the work turns on levers, which happens with people and things rather than on a screen. The rows above are the evidence rather than the reassurance: moving levers to position dredges and starting and stopping engines to operate equipment. The parts that are changing are the paperwork and the tools around the job, not the middle of it, which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
Start with what does not change: moving levers to position dredges is the middle of this job, and the evidence on this page says it stays with a person.
So, given all that: 0% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 100% in rows it is nowhere near. That is the position, measured across 6 scored tasks. It is not a forecast about you.
So the thing worth your attention is not the job going away. It is the layer around it. The routine end of the work is the part turning into software, and being the person who understands that layer is worth money.
This week: one thing
Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to the routine work, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.
- What you end up holding
- a straight answer about what is actually being rolled out, and when
- How long it takes
- ten minutes
If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether levers are in scope or not. Nothing to log into, no license needed.
Over the next 90 days
Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near moving levers to position dredges. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.
Over the next 12 months
On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to dredge operators (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was gas compressor and gas pumping station operators: only about 7% of its durable work is work you already do. And on the numbers you do not need one. This job scores 2/100 here, with only 0% of the task list in the top band, and “move levers to position dredges for excavation, to engage hydraulic pumps, to…” is not work that hands over cleanly. None of them beats deepening what you already have.
How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.
3 moves I checked and rejected
These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.
Gas Compressor and Gas Pumping Station Operators
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already start and stop engines to operate equipment, and their equivalent is to move controls and turn valves to start compressor engines, pumps, and auxiliary equipment. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step.
Pump Operators, Except Wellhead Pumpers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already pump water to clear machinery pipelines, and their equivalent is to connect hoses and pipelines to pumps and vessels prior to material transfer, using…. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step.
Roustabouts, Oil and Gas
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already pump water to clear machinery pipelines, and their equivalent is to lay gas and oil pipelines. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step.
What I’d stop worrying about
A friend tells you what not to spend fear on. This is that list.
The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 0% of its task weight, across 6 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like moving levers to position dredges arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Mobile machine drivers and operatives n.e.c. is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
In UK official statistics this job is counted as Mobile machine drivers and operatives n.e.c.. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
Why there is no community here
Collab365, who build this site, run paid Spaces for a small number of subjects, and none of them is built for this job. We are not going to point you at the nearest one and call it a fit.
So the free services listed on this page are the whole answer, and it is the same answer we would give a friend.
Noted, and thank you. We’ll email you if a Space for dredge operators launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.
No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace Dredge Operators?
- Not as a job, but it is already doing parts of the work. Across the 6 official task statements scored for Dredge Operators (United States, SOC 53-7031), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 2 out of 100 (range 0–8, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Dredge Operators” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Lower anchor poles to verify depths of excavations, using winches, or scan depth gauges to determine depths of excavations” (8/100, minimal); “Direct or assist workers placing shore anchors and cables, laying additional pipes from dredges to shore, and pumping water from pontoons” (5/100, minimal); “Move levers to position dredges for excavation, to engage hydraulic pumps, to raise and lower suction booms, and to control rotation of cutterheads” (0/100, minimal). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Dredge Operators” stay human?
- About 100% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Pump water to clear machinery pipelines” (0/100, minimal); “Start power winches that draw in or let out cables to change positions of dredges, or pull in and let out cables manually” (0/100, minimal); “Start and stop engines to operate equipment” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
- What should someone working in “Dredge Operators” do about AI?
- Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 100% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
- How is the AI exposure score for Dredge Operators calculated?
- Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 6 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
Worth knowing about these figures
- 2 rows are marked low confidence, so treat them as a ballpark rather than a fine measurement.
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
Using these figures?
Cite this
Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.
Plain text
Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).
BibTeX
@misc{collab365futureproof2026q41,
title = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
author = {{Collab365}},
year = {2026},
url = {https://futureproof.collab365.com/data/2026-q4.1},
note = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.