US dataswitch to UK
Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders
monitoring equipment operation, gauges and panel lights to detect deviations from standards, removing products from equipment, weighing or measuring specified amounts of ingredients or materials and directing crane operators and crew members to load vessels with materials. If that's your week, this page is about your job.
The honest answer
AI changes the edges of this job, not the middle: monitoring equipment operation, gauges and panel lights to detect deviations from standards is work software can't reach.
What shifts is reading and interpreting work orders and instructions to determine work assignments. This page scores what today's tools actually do, not headlines.
Your week, as this page understands it
Operate or tend heating equipment other than basic metal, plastic, or food processing equipment. Includes activities such as annealing glass, drying lumber, curing rubber, removing moisture from materials, or boiling soap. The job title says “furnace”, “kiln”, “oven”, “drier”, “kettle operators” or “tenders”: officially one job, several names. The real job is the part underneath: monitoring equipment operation, gauges and panel lights to detect deviations from standards. 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 furnace, kiln, oven, drier, and kettle operators and tenders is not one task. It is 17 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is monitoring equipment operation, gauges and panel lights to detect deviations from standards, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 7%
- changing shape
- 0%
- staying human
- 93%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 13 out of 100 (10–18 allowing for uncertainty): minimal exposure, across 17 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 furnace, kiln, oven, drier, and kettle operators and tenders 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.
- 3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- 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
1 taskTasks 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.
Reading and interpreting work orders and instructions to determine work assignments
This is reading one thing and writing another: work orders in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Read and interpret work orders and instructions to determine work assignments, process specifications, and production schedules.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reading a work order and pulling out temperatures and cycle times is straightforward document work.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
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
16 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.
Conferring with supervisors or other equipment operators to report equipment malfunctions or to resolve production problems
This work happens in the physical world: supervisors, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Confer with supervisors or other equipment operators to report equipment malfunctions or to resolve production problems.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (14–28 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Reports can be drafted and routed by software, but sorting out a production problem is a conversation between people on shift.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Monitoring equipment operation, gauges and panel lights to detect deviations from standards
This work happens in the physical world: equipment operation, gauges and panel lights, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Monitor equipment operation, gauges, and panel lights to detect deviations from standards.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (7–21 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Readings can be tracked automatically, but watching the equipment and panel lights happens at the machine.
The five ratings: output a model can produce 2/4 · 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 3/4.
Pressing and adjusting controls
This work happens in the physical world: controls, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Press and adjust controls to activate, set, and regulate equipment according to specifications.” (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: Setting and regulating the press is done by hand 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.
Recording gauge readings, test results and shift production in log books
This work happens in the physical world: gauge readings, test results and shift production, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Record gauge readings, test results, and shift production in log books.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (34–42 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Writing readings and output into a log book is simple record keeping that systems can capture directly.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Examining or testing samples of processed substances
This work happens in the physical world: samples of processed substances, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Examine or test samples of processed substances, or collect samples for laboratory testing, to ensure conformance to specifications.” (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: Taking and testing samples of processed material is physical work.
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.
Transporting materials and products to and from work areas
This work happens in the physical world: materials, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Transport materials and products to and from work areas, manually or using carts, handtrucks, or hoists.” (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: Moving materials around the plant is physical work.
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.
Removing products from equipment, manually or using hoists and preparing them for storage, shipment or additional processing
This work happens in the physical world: products, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Remove products from equipment, manually or using hoists, and prepare them for storage, shipment, or additional processing.” (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: Taking product off the equipment and preparing it for shipping is physical work.
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.
Calculating amounts of materials to be loaded into furnaces
This work happens in the physical world: amounts of materials, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Calculate amounts of materials to be loaded into furnaces, adjusting amounts as necessary for specific conditions.” (O*NET task statement)
How this row was scored
Exposure score: 34 out of 100 (27–41 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Working out furnace charge quantities is calculation, though adjustments depend on watching conditions on the spot.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Stopping equipment and clearing blockages or jams
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Stop equipment and clear blockages or jams, using fingers, wire, or hand tools.” (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: Clearing jams by hand or with tools is physical work at the machine.
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.
Show the other 7 tasks
Weighing or measuring specified amounts of ingredients or materials
staying humanThis work happens in the physical world: specified amounts of ingredients, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Weigh or measure specified amounts of ingredients or materials for processing, using devices such as scales and calipers.” (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: Weighing and measuring materials is physical handling.
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.
Cleaning lubricate and adjusting equipment, using scrapers, solvents, air hoses, oil and hand tools
staying humanThis work happens in the physical world: lubricate, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Clean, lubricate, and adjust equipment, using scrapers, solvents, air hoses, oil, and hand tools.” (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: Cleaning, oiling and adjusting equipment is hands-on maintenance.
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.
Feeding fuel, such as coal and coke
staying humanThis work happens in the physical world: fuel, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Feed fuel, such as coal and coke, into fireboxes or onto conveyors, and remove ashes from furnaces, using shovels and buckets.” (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: Shovelling fuel and removing ash is physical work.
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.
Melting or refining metal before casting
staying humanThis work happens in the physical world: metal, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Melt or refine metal before casting, calculating required temperatures, and observe metal color, adjusting controls as necessary to maintain required temperatures.” (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: Melting metal and judging its color at the furnace is hands-on work.
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.
Replacing worn or defective equipment parts
staying humanThis work happens in the physical world: worn, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Replace worn or defective equipment parts, using hand tools.” (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: Replacing worn parts with hand tools is physical repair work.
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.
Loading equipment receptacles or conveyors with material
staying humanThis work happens in the physical world: equipment receptacles, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Load equipment receptacles or conveyors with material to be processed, by hand or using hoists.” (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: Loading material into equipment is physical handling.
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.
Directing crane operators and crew members to load vessels with materials
staying humanThis work happens in the physical world: crane operators, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Direct crane operators and crew members to load vessels with materials to be processed.” (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 value is that a specific person does it.
The rating behind it: Signalling and directing a crane crew during a lift happens live on the quayside.
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 2/4 · how much data exists 1/4.
What this job pays, and how many people do it
- Median pay
- $48,040a 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
- 14,280in 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. Most of what is shifting here is reading one thing and writing another: work orders in, a record out. The rows above are exactly that shape: reading and interpreting work orders and instructions to determine work assignments. What it cannot do is be there in the room, and that is still where equipment operation, gauges and panel lights get done. 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: monitoring equipment operation, gauges and panel lights to detect deviations from standards is the middle of this job, and the evidence on this page says it stays with a person.
So, given all that: 7% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 93% in rows it is nowhere near. That is the position, measured across 17 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. Reading and interpreting work orders and instructions to determine work assignments 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 work orders, 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 equipment operation, gauges and panel lights 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 conferring with supervisors or other equipment operators to report equipment malfunctions or to resolve production problems. 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 furnace, kiln, oven, drier, and kettle operators and tenders (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was conveyor operators and tenders: only about 16% of its durable work is work you already do and it pays 11.7% less. And on the numbers you do not need one. This job scores 13/100 here, with only 7% of the task list in the top band, and “confer with supervisors or other equipment operators to report equipment malfunctions or…” 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.
Conveyor Operators and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already examine or test samples of processed substances, or collect samples for laboratory testing…, and their equivalent is to collect samples of materials or products, checking them to ensure conformance to specifications…. Across both published task lists that is about 16% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 16% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. It is a pay cut, in those words: $42,420 against your $48,040, 11.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already press and adjust controls to activate, set, and regulate equipment according to specifications, and their equivalent is to press control buttons to activate machinery and equipment. Across both published task lists that is about 14% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 14% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already examine or test samples of processed substances, or collect samples for laboratory testing…, and their equivalent is to collect samples of materials or products for laboratory testing. Across both published task lists that is about 12% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 12% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
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: 7% of its task weight, across 17 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 monitoring equipment operation, gauges and panel lights to detect deviations from standards 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 Process 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 Process 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
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for furnace / kiln / oven / drier / kettle operators / tenders, and we are not going to point you at the nearest one and call it a fit.
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 7% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for furnace / kiln / oven / drier / kettle operators / tenders. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for furnace / kiln / oven / drier / kettle operators / tenders launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
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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 Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders?
- Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders (United States, SOC 51-9051), 7% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 13 out of 100 (range 10–18, 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 “Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Read and interpret work orders and instructions to determine work assignments, process specifications, and production schedules” (69/100, high); “Record gauge readings, test results, and shift production in log books” (38/100, low); “Calculate amounts of materials to be loaded into furnaces, adjusting amounts as necessary for specific conditions” (34/100, low). 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 “Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders” stay human?
- About 93% 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: “Direct crane operators and crew members to load vessels with materials to be processed” (0/100, minimal); “Load equipment receptacles or conveyors with material to be processed, by hand or using hoists” (0/100, minimal); “Replace worn or defective equipment parts, using hand tools” (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 “Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders” do about AI?
- Start from the ledger rather than the headline: 7% of this job's weighted core work is exposed, and roughly 93% 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 Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders 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 17 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
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- 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.