US dataswitch to UK
Skincare Specialists
sterilizing equipment and cleaning work areas, examining clients' skin and collaborating with plastic surgeons and dermatologists to provide patients with preoperative and postoperative skin care. If that's your week, this page is about your job.
The honest answer
AI changes the edges of this job, not the middle: sterilizing equipment and cleaning work areas is work software can't reach.
What shifts is keeping records of client needs and preferences and the services: the overhead at the edges, not the middle you trained for.
Your week, as this page understands it
Provide skincare treatments to face and body to enhance an individual's appearance. Includes electrologists and laser hair removal specialists. The job title says “skincare specialists”. The real job is the part underneath: sterilizing equipment and cleaning work areas. 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 skincare specialists is not one task. It is 18 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is sterilizing equipment and cleaning work areas, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 13%
- changing shape
- 0%
- staying human
- 87%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 19 out of 100 (16–24 allowing for uncertainty): minimal exposure, across 18 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 skincare specialists 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.
- 4 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
2 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.
Keeping records of client needs and preferences and the services
This is reading one thing and writing another: records of client needs in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Keep records of client needs and preferences and the services provided.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high 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: Keeping a client history and service log is ordinary record-keeping that software handles well.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Staying abreast of latest industry trends
This is reading one thing and writing another: abreast of latest industry trends in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Stay abreast of latest industry trends, products, research, and treatments.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very 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: Following new products, treatments and research is reading and summarizing, which software does quickly across many sources.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
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.
Sterilizing equipment and cleaning work areas
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Sterilize equipment and clean work areas.” (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: Sterilizing tools and cleaning a treatment room is physical work that has to happen in that room.
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.
Cleansing clients' skin with water
This work happens in the physical world: clients' skin, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Cleanse clients' skin with water, creams, or lotions.” (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: Cleansing a client's skin is done by hand, on the person.
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.
Demonstrating how to clean and care for skin properly and recommend skin-care regimens
This work happens in the physical world: how, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Demonstrate how to clean and care for skin properly and recommend skin-care regimens.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Writing a skincare routine is easy for software, but showing someone how to do it means being there with them.
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 2/4 · how much data exists 3/4.
Examining clients' skin, using magnifying lamps or visors when necessary, to evaluate skin condition and appearance
This work happens in the physical world: clients' skin, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Examine clients' skin, using magnifying lamps or visors when necessary, to evaluate skin condition and appearance.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (7–15 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Looking closely at skin under a lamp needs the client in the chair, even though photo analysis is improving.
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 1/4 · how much data exists 2/4.
Treating the facial skin to maintain and improve its appearance
This work happens in the physical world: the facial skin, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Treat the facial skin to maintain and improve its appearance, using specialized techniques and products, such as peels and masks.” (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: Applying peels and masks to a face is physical treatment 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.
Selecting, and applying cosmeticing products
This work happens in the physical world: products, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Select and apply cosmetic products, such as creams, lotions, and tonics.” (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: Putting creams and cosmetics on a client's face 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 1/4 · how much data exists 2/4.
Performing simple extractions to remove blackheads
This work happens in the physical world: simple extractions, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Perform simple extractions to remove blackheads.” (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: Removing blackheads is done by hand on a client who is present.
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 1/4 · how much data exists 2/4.
Referring clients to medical personnel for treatment of serious skin problems
The value here is that a specific person handles clients and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Refer clients to medical personnel for treatment of serious skin problems.” (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: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: A problem needing a doctor can be flagged from notes and photos, but the client has to trust who tells them.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Show the other 8 tasks
Determining which products or colors will improve clients' skin quality and appearance
staying humanThe value here is that a specific person handles products and stands behind it. That is earned, not computed.
importance 5 · CoreSource: “Determine which products or colors will improve clients' skin quality and appearance.” (O*NET task statement)
How this row was scored
Exposure score: 39 out of 100 (32–46 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Matching products and colors to a described skin type is something software does well, though clients want a person's judgment.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Advising clients about colors and types of makeup and instructing them in makeup application techniques
staying humanThis work happens in the physical world: clients, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Advise clients about colors and types of makeup and instruct them in makeup application techniques.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Advice on shades and products is easy to generate, but teaching application technique happens face to face.
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 2/4 · how much data exists 3/4.
Selling makeup to clients
staying humanThis work happens in the physical world: makeup, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Sell makeup to clients.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (0–14 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Selling makeup in the treatment room depends on being there with the client and their reaction to samples.
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 2/4 · how much data exists 3/4.
Collaborating with plastic surgeons and dermatologists to provide patients with preoperative and postoperative skin care
staying humanThis work happens in the physical world: plastic surgeons, in a real place. Software cannot follow it there.
importance 5 · SupplementalSource: “Collaborate with plastic surgeons and dermatologists to provide patients with preoperative and postoperative skin care.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (0–14 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Working alongside surgeons on before-and-after skin care is hands-on care with a doctor accountable for the plan.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Providing facial and body massages
staying humanThis work happens in the physical world: facial, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Provide facial and body massages.” (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: A massage is physical contact; nothing at a screen replaces it.
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 body and facial hair by applying wax
staying humanThis work happens in the physical world: body, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Remove body and facial hair by applying wax.” (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: Waxing is hands-on removal of hair from a person.
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.
Applying chemical peels to reduce fine lines and age spots
staying humanThis work happens in the physical world: chemical peels, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Apply chemical peels to reduce fine lines and age spots.” (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: Applying a chemical peel is a hands-on treatment on the client's face.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Tinting eyelashes and eyebrows
staying humanThis work happens in the physical world: eyelashes, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Tint eyelashes and eyebrows.” (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: Applying tint to someone's lashes and brows is hands-on work on a person.
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 1/4 · how much data exists 2/4.
What this job pays, and how many people do it
- Median pay
- $45,330a 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
- 72,890in 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: records of client needs in, a record out. The rows above are exactly that shape: keeping records of client needs and preferences and the services and staying abreast of latest industry trends. What it cannot do is be there in the room, and that is still where equipment gets 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: sterilizing equipment and cleaning work areas is the middle of this job, and the evidence on this page says it stays with a person.
So, given all that: 13% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 87% in rows it is nowhere near. That is the position, measured across 18 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. Keeping records of client needs and preferences and the services 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 records of client needs, 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 clients' skin is 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 sterilizing equipment and cleaning work areas. 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 skincare specialists (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was barbers: only about 10% of its durable work is work you already do, it pays 15.7% less and there are far fewer of those jobs than of yours. And on the numbers you do not need one. This job scores 19/100 here, with only 13% of the task list in the top band, and “sterilize equipment and clean work areas” 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.
Barbers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already sterilize equipment and clean work areas, and their equivalent is to clean and sterilize scissors, combs, clippers, and other instruments. Across both published task lists that is about 10% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 10% 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: $38,210 against your $45,330, 15.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 15,000 of those jobs against 72,890 of yours (OEWS May 2025), 21% as many seats.
Makeup Artists, Theatrical and Performance
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already cleanse clients' skin with water, creams, or lotions, and their equivalent is to cleanse and tone the skin to prepare it for makeup application. 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. The pay gap is the market pricing a barrier: $97,150 against your $45,330 is 2.14× (OEWS May 2025 (both)), and you would be crossing it holding about 7% of their durable work. A gap that size with an overlap that small is a wish, not a route. And it is a narrow door: about 2,340 of those jobs against 72,890 of yours (OEWS May 2025), 3% as many seats.
Dermatologists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already determine which products or colors will improve clients' skin quality and appearance, and their equivalent is to perform skin surgery to improve appearance, make early diagnoses, or control diseases. 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. The pay gap is the market pricing a barrier: $328,730 against your $45,330 is 7.25× (OEWS May 2025 (both)), and you would be crossing it holding about 7% of their durable work. A gap that size with an overlap that small is a wish, not a route. And it is a narrow door: about 11,370 of those jobs against 72,890 of yours (OEWS May 2025), 16% as many seats.
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: 13% of its task weight, across 18 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 sterilizing equipment and cleaning work areas 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 Beauticians and related occupations 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 Beauticians and related occupations. 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 skincare specialists, 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 13% 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 skincare specialists. 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 skincare specialists launches. Nothing else.
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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 Skincare Specialists?
- Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for Skincare Specialists (United States, SOC 39-5094), 13% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 19 out of 100 (range 16–24, 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 “Skincare Specialists” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Keep records of client needs and preferences and the services provided” (93/100, very high); “Stay abreast of latest industry trends, products, research, and treatments” (83/100, very high); “Determine which products or colors will improve clients' skin quality and appearance” (39/100, 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 “Skincare Specialists” stay human?
- About 87% 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: “Tint eyelashes and eyebrows” (0/100, minimal); “Apply chemical peels to reduce fine lines and age spots” (0/100, minimal); “Remove body and facial hair by applying wax” (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 “Skincare Specialists” do about AI?
- Start from the ledger rather than the headline: 13% of this job's weighted core work is exposed, and roughly 87% 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 Skincare Specialists 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 18 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.
- 4 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.