US dataswitch to UK
Computer and Information Research Scientists
analyzing problems to develop solutions involving computer hardware and software, designing computers and the software that runs them and conducting logical analyses of business. If that's your week, this page is about your job.
The honest answer
This job is splitting in two: analyzing problems to develop solutions involving computer hardware and software is work AI now does quickly and cheaply, and meeting with managers, vendors and others to solicit cooperation and resolve problems is work it can't touch.
Which half fills your week decides your exposure. The ledger below shows which rows you can move toward.
Your week, as this page understands it
Conduct research into fundamental computer and information science as theorists, designers, or inventors. Develop solutions to problems in the field of computer hardware and software. The job title says “computer” or “information research scientists”: officially one job, two names. The real job is the part underneath: meeting with managers, vendors and others to solicit cooperation and resolve problems. 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 computer and information research scientists is not one task. It is 15 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is meeting with managers, vendors and others to solicit cooperation and resolve problems, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 49%
- changing shape
- 31%
- staying human
- 20%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 54 out of 100 (48–59 allowing for uncertainty): partial exposure, across 15 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 computer and information research scientists 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 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
7 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.
Analyzing problems to develop solutions involving computer hardware and software
This is reading one thing and writing another: problems in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Analyze problems to develop solutions involving computer hardware and software.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 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: Working through hardware and software problems to reach a solution is well documented territory where AI performs strongly.
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.
Conducting logical analyses of business
This is reading one thing and writing another: logical analyses of business in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Conduct logical analyses of business, scientific, engineering, and other technical problems, formulating mathematical models of problems for solution by computers.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 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: Turning a technical problem into a mathematical model a computer can solve is a documented, well-practiced skill AI does strongly.
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.
Assigning or scheduling tasks to meet work priorities and goals
This is reading one thing and writing another: tasks in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Assign or schedule tasks to meet work priorities and goals.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (60–68 allowing for uncertainty): 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: Matching tasks and schedules to priorities is a planning problem software solves well.
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 1/4 · how much data exists 3/4.
Evaluating project plans and proposals to assess feasibility issues
This is reading one thing and writing another: project plans in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Evaluate project plans and proposals to assess feasibility issues.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): 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: Reading a project plan and assessing whether it is feasible is analysis and writing software handles well.
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 3/4.
Changing shape
5 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.
Applying theoretical expertise and innovation to create or apply new technology
The software now makes the first pass at theoretical expertise, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Apply theoretical expertise and innovation to create or apply new technology, such as adapting principles for applying computers to new uses.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: AI suggests promising new approaches, but genuinely novel technology still needs an expert to shape and prove it.
The five ratings: output a model can produce 2/4 · 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.
Designing computers and the software that runs them
The software now makes the first pass at computers, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Design computers and the software that runs them.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: AI drafts software designs well, but designing the machine itself still needs expert judgment and lab work.
The five ratings: output a model can produce 2/4 · 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.
Consulting with users, management, vendors and technicians to determine computing needs and system requirements
The software now makes the first pass at users, management, vendors and technicians, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 3 · CoreSource: “Consult with users, management, vendors, and technicians to determine computing needs and system requirements.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Software can draft requirements, but drawing out what users actually need comes from conversations with them.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Staying human
3 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.
Meeting with managers, vendors and others to solicit cooperation and resolve problems
The value here is that a specific person handles managers, vendors and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Meet with managers, vendors, and others to solicit cooperation and resolve problems.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Getting cooperation from managers and vendors depends on relationships and give and take between people.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Participating in multidisciplinary projects in areas
The value here is that a specific person handles multidisciplinary projects and stands behind it. That is earned, not computed.
importance 3 · CoreSource: “Participate in multidisciplinary projects in areas such as virtual reality, human-computer interaction, or robotics.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Contributing to a shared project depends on working alongside colleagues, and robotics or VR work involves real equipment.
The five ratings: output a model can produce 2/4 · 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.
Participating in staffing decisions and directing training of subordinates
The value here is that a specific person handles staffing decisions and stands behind it. That is earned, not computed.
importance 3 · SupplementalSource: “Participate in staffing decisions and direct training of subordinates.” (O*NET task statement)
How this row was scored
Exposure score: 17 out of 100 (10–24 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Hiring choices and training subordinates rest on judgments about people made in person.
The five ratings: output a model can produce 1/4 · 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 2/4.
Show the other 5 tasks
Approving, preparing, monitoring and adjusting operational budgets
shifting to AIThis is reading one thing and writing another: operational budgets in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Approve, prepare, monitor, and adjust operational budgets.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 allowing for uncertainty): 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: Preparing and tracking budgets is spreadsheet work software does well, though approval remains a manager's decision.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Developing and interpreting organizational goals
shifting to AIThis is reading one thing and writing another: organizational goals in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Develop and interpret organizational goals, policies, and procedures.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 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: Writing and explaining goals, policies and procedures is document work software produces to a usable standard.
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 1/4 · how much data exists 3/4.
Developing performance standards and evaluating work in light of established standards
shifting to AIThis is reading one thing and writing another: performance standards in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Develop performance standards, and evaluate work in light of established standards.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (60–68 allowing for uncertainty): 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: Setting performance standards and measuring work against them is a documented, largely paper-based exercise.
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 1/4 · how much data exists 3/4.
Maintaining network hardware and software
changing shapeThe software now makes the first pass at network hardware, but part of it still happens in the physical world. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Maintain network hardware and software, direct network security measures, and monitor networks to ensure availability to system users.” (O*NET task statement)
How this row was scored
Exposure score: 41 out of 100 (37–45 allowing for uncertainty): partial 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: Network monitoring and security are heavily automated already, though replacing hardware still needs someone on site.
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 4/4.
Directing daily operations of departments
changing shapeThe software now makes the first pass at operations of departments, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 3 · SupplementalSource: “Direct daily operations of departments, coordinating project activities with other departments.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Running a department day to day is largely coordination between colleagues.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
What this job pays, and how many people do it
- Median pay
- $140,300a 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
- 37,200in 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: logical analyses of business in, a record out. The rows above are exactly that shape: analyzing problems to develop solutions involving computer hardware and software and conducting logical analyses of business. What it cannot do is be trusted in person, which is what managers, vendors run on: someone specific doing it and standing behind 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
The exposed part of your job is the biggest part, and I am not going to dress that up: analyzing problems to develop solutions involving computer hardware and software is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 49% of this job's task weight sits in rows the software is already learning, 31% in rows that change shape rather than disappear, and 20% in rows it is nowhere near. That is the position, measured across 15 scored tasks. It is not a forecast about you.
What you have that the software does not is meeting with managers, vendors and others to solicit cooperation and resolve problems, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.
This week: one thing
Sit on the machine's side of the desk. Pick one real piece of logical analyses of business you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.
- What you end up holding
- a written list of the machine’s mistakes, in your handwriting
- How long it takes
- an evening, or an hour if you pick one job rather than one client
If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of logical analyses of business, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.
Over the next 90 days
Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is analyzing problems to develop solutions involving computer hardware and software” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.
Over the next 12 months
Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of meeting with managers, vendors and others to solicit cooperation and resolve problems you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. 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 computer and information research scientists (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was computer and information systems managers: only about 28% of its durable work is work you already do. Your own job splits about 49/51: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “apply theoretical expertise and innovation to create or apply new technology”, and let the exposed end go.
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.
Computer and Information Systems Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already meet with managers, vendors, and others to solicit cooperation and resolve problems, and their equivalent is to meet with department heads, managers, supervisors, vendors, and others, to solicit cooperation and…. Across both published task lists that is about 28% of the durable work in that job.
Why I am not recommending it: It is closer than most, and still not close enough: about 28% of that job's durable work is already yours, against the 35% I want to see before I will call something a route.
Computer Network Support Specialists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already maintain network hardware and software, direct network security measures, and monitor networks to…, and their equivalent is to install new hardware or software systems or components, ensuring integration with existing network…. 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. I will not move you off one melting floe onto another: 66% of its own task list already scores in the top exposure band (66/100 in this release), so the same software is eating it. It is a pay cut, in those words: $76,220 against your $140,300, 45.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Network and Computer Systems Administrators
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already maintain network hardware and software, direct network security measures, and monitor networks to…, and their equivalent is to design, configure, and test computer hardware, networking software and operating system software. Across both published task lists that is about 8% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 8% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 65% of its own task list already scores in the top exposure band (60/100 in this release), so the same software is eating it. It is a pay cut, in those words: $99,130 against your $140,300, 29.3% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
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: 49% of its task weight, across 15 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: meeting with managers, vendors and others to solicit cooperation and resolve problems is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
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 IT business analysts, architects and systems designers 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
The other groups this work is counted across:
In UK official statistics this job is counted as IT business analysts, architects and systems designers and Information technology professionals n.e.c.. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.
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 computer / information research scientists, 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 49% 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 computer / information research scientists. 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 computer / information research scientists 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 Computer and Information Research Scientists?
- Not as a job, but it is already doing parts of the work. Across the 15 official task statements scored for Computer and Information Research Scientists (United States, SOC 15-1221), 49% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 54 out of 100 (range 48–59, band: partial). 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 “Computer and Information Research Scientists” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Analyze problems to develop solutions involving computer hardware and software” (83/100, very high); “Conduct logical analyses of business, scientific, engineering, and other technical problems, formulating mathematical models of problems for solution by comp…” (83/100, very high); “Evaluate project plans and proposals to assess feasibility issues” (75/100, high). 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 “Computer and Information Research Scientists” stay human?
- About 20% 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: “Meet with managers, vendors, and others to solicit cooperation and resolve problems” (13/100, minimal); “Participate in staffing decisions and direct training of subordinates” (17/100, minimal); “Participate in multidisciplinary projects in areas such as virtual reality, human-computer interaction, or robotics” (30/100, low). 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 “Computer and Information Research Scientists” do about AI?
- Start from the ledger rather than the headline: 49% of this job's weighted core work is exposed, and roughly 20% 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 Computer and Information Research Scientists 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 15 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.
- 2 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.
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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.