Will AI replace Operations Research Analysts?

Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Operations Research Analysts (United States, SOC 15-2031), 64% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 59 out of 100 (range 53–65, 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 “Operations Research Analysts” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Review research literature” (83/100, very high); “Formulate mathematical or simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their nume…” (75/100, high); “Perform validation and testing of models to ensure adequacy, and reformulate models, as necessary” (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 “Operations Research Analysts” stay human?

About 25% 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: “Collaborate with senior managers and decision makers to identify and solve a variety of problems and to clarify management objectives” (23/100, low); “Collaborate with others in the organization to ensure successful implementation of chosen problem solutions” (23/100, low); “Observe the current system in operation, and gather and analyze information about each of the component problems, using a variety of sources” (25/100, 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 “Operations Research Analysts” do about AI?

Start from the ledger rather than the headline: 64% of this job's weighted core work is exposed, and roughly 25% 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 Operations Research Analysts 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.

US dataswitch to UK

Operations Research Analysts

presenting the results of mathematical modeling and data analysis to management or other end users, formulating mathematical or simulation models of problems and analyzing information obtained from management to conceptualize and defining operational problems. If that's your week, this page is about your job.

The honest answer

Most tasks in this job are the kind AI has learned to do: performing validation and testing of models to ensure adequacy. The tasks, though, are not you.

Your move: three real directions from here ↓

Where the work sits, by task weight

shifting to AI: 64%
changing shape: 11%
staying human: 25%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 59 out of 100 (53–65 allowing for uncertainty): partial 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 operations research analysts 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 2026-08-04. Read the full method.

It would be a lie to soften that; collaborating with others in the organization to ensure successful implementation of chosen problem solutions is what this work rebuilds around. The routes below start from it.

Your week, as this page understands it

Formulate and apply mathematical modeling and other optimizing methods to develop and interpret information that assists management with decisionmaking, policy formulation, or other managerial functions. May collect and analyze data and develop decision support software, services, or products. May develop and supply optimal time, cost, or logistics networks for program evaluation, review, or implementation. The job title says “operations research analysts”. The real job is the part underneath: collaborating with others in the organization to ensure successful implementation of chosen problem solutions. 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 operations research analysts 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 collaborating with others in the organization to ensure successful implementation of chosen problem solutions, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 64%
changing shape: 11%
staying human: 25%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

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 2026-08-04. 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.

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

11 tasks

Tasks 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.

Presenting the results of mathematical modeling and data analysis to management or other end users
This is reading one thing and writing another: the results of mathematical in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Present the results of mathematical modeling and data analysis to management or other end users.” (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: Software can build the charts and write the story; the meeting itself still wants a person in the room.
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.
Performing validation and testing of models to ensure adequacy
This is reading one thing and writing another: validation in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Perform validation and testing of models to ensure adequacy, and reformulate models, as necessary.” (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: Testing a model and refitting it is code against data the workplace already holds.
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.
Formulating mathematical or simulation models of problems
This is reading one thing and writing another: mathematical in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Formulate mathematical or simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 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: Building an optimization or simulation model is well-documented technical work, though the real business detail comes from colleagues.
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.
Defining data requirements and gathering and validating information, applying judgment and statistical tests
This is reading one thing and writing another: data requirements in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Define data requirements, and gather and validate information, applying judgment and statistical tests.” (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: Pulling data together and running the statistical checks is the kind of work software already handles end to end.
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

2 tasks

Tasks 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.

Analyzing information obtained from management to conceptualize and defining operational problems
The software now makes the first pass at information, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Analyze information obtained from management to conceptualize and define operational problems.” (O*NET task statement)
How this row was scored
Exposure score: 50 out of 100 (43–57 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Software can draft a problem definition, but naming the real issue depends on inside knowledge of the organization.
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 2/4.
Educating staff in the use of mathematical models
The software now makes the first pass at staff, 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 4 · Core
Source: “Educate staff in the use of mathematical models.” (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 write the training material, but staff learn faster from a colleague who answers questions live.
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

4 tasks

Tasks 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.

Collaborating with others in the organization to ensure successful implementation of chosen problem solutions
The value here is that a specific person handles this work and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Collaborate with others in the organization to ensure successful implementation of chosen problem solutions.” (O*NET task statement)
How this row was scored
Exposure score: 23 out of 100 (16–30 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Getting a solution actually adopted depends on working with colleagues, which software can support but not do.
The five ratings: output a model can produce 1/4 · 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 2/4.
Observing the current system in operation
This work happens in the physical world: the current system, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Observe the current system in operation, and gather and analyze information about each of the component problems, using a variety of sources.” (O*NET task statement)
How this row was scored
Exposure score: 25 out of 100 (18–32 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: Watching a real operation run means being there, and much of what matters is never written down.
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 0/4 · how much data exists 2/4.
Collaborating with senior managers and decision makers to identify and solve a variety of problems and to clarify management objectives
The value here is that a specific person handles senior managers and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Collaborate with senior managers and decision makers to identify and solve a variety of problems and to clarify management objectives.” (O*NET task statement)
How this row was scored
Exposure score: 23 out of 100 (16–30 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Pinning down what senior managers actually want happens in conversation, and much of it is never written down.
The five ratings: output a model can produce 1/4 · 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 2/4.
Designing, conducting and evaluating experimental operational models in cases where models cannot be developed from existing data
The ratings behind this row put experimental operational models well outside what today's tools can do on their own.
importance 4 · Core
Source: “Design, conduct, and evaluate experimental operational models in cases where models cannot be developed from existing data.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: When there is no existing data, someone has to set up and run the real experiment.
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 2/4.

Show the other 7 tasks

Reviewing research literature
shifting to AI
This is reading one thing and writing another: research literature in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Review research literature.” (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: Reading and summarizing published research is one of the things language models are strongest at.
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.
Preparing management reports defining and evaluating problems and recommending solutions
shifting to AI
This is reading one thing and writing another: management reports in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Prepare management reports defining and evaluating problems and recommending solutions.” (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: Turning analysis into a written management report is a drafting job software does 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.
Breaking systems into their components
shifting to AI
This is reading one thing and writing another: systems in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Break systems into their components, assign numerical values to each component, and examine the mathematical relationships between them.” (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: Breaking a system into parts and working out the maths between them is documented technical work.
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.
Specifying manipulative or computational methods to be applied to models
shifting to AI
This is reading one thing and writing another: manipulative in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Specify manipulative or computational methods to be applied to models.” (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: Choosing computational methods for a model is documented technical ground that software covers confidently.
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.
Developing, and applying timing and cost networks
shifting to AI
This is reading one thing and writing another: cost networks in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Develop and apply time and cost networks to plan, control, and review large projects.” (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: Building and updating project time and cost networks is standard scheduling work software does quickly.
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.
Studying and analyzing information about alternative courses of action to determine which plan will offer the best outcomes
shifting to AI
This is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Study and analyze information about alternative courses of action to determine which plan will offer the best outcomes.” (O*NET task statement)
How this row was scored
Exposure score: 68 out of 100 (61–75 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Comparing options is well suited to software, though the judgement rests on private detail about this organization.
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 2/4.
Developing business methods and procedures
shifting to AI
This is reading one thing and writing another: business methods in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Develop business methods and procedures, including accounting systems, file systems, office systems, logistics systems, and production schedules.” (O*NET task statement)
How this row was scored
Exposure score: 68 out of 100 (61–75 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Software drafts procedures well; what makes them work is knowledge of how this particular office runs.
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 2/4.

What this job pays, and how many people do it

Median pay: $88,940a 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: 108,510in 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: the results of mathematical in, a record out. The rows above are exactly that shape: performing validation and testing of models to ensure adequacy and presenting the results of mathematical modeling and data analysis to management or other end users. What it cannot do is be trusted in person, which is what with others in the organization to ensure successful implementation of chosen problem solutions runs 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: performing validation and testing of models to ensure adequacy is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 64% of this job's task weight sits in rows the software is already learning, 11% in rows that change shape rather than disappear, and 25% in rows it is nowhere near. That is the position, measured across 17 scored tasks. It is not a forecast about you.

What you have that the software does not is collaborating with others in the organization to ensure successful implementation of chosen problem solutions, 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 the results of mathematical 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 the results of mathematical, 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 performing validation and testing of models to ensure adequacy” 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 collaborating with others in the organization to ensure successful implementation of chosen problem solutions 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 operations research analysts (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was aerospace engineers: only about 6% of its durable work is work you already do. Your own job splits about 64/36: 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 “collaborate with others in the organization to ensure successful implementation of chosen…”, 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.

Aerospace Engineers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already design, conduct, and evaluate experimental operational models in cases where models cannot be…, and their equivalent is to plan or conduct experimental, environmental, operational, or stress tests on models or prototypes…. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Mathematicians
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already educate staff in the use of mathematical models, and their equivalent is to mentor others on mathematical techniques. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 2,030 of those jobs against 108,510 of yours (OEWS May 2025), 2% as many seats.
Look at that job’s page anyway →
Data Scientists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already present the results of mathematical modeling and data analysis to management or other…, and their equivalent is to deliver oral or written presentations of the results of mathematical modeling and data…. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% 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: 84% of its own task list already scores in the top exposure band (75/100 in this release), so the same software is eating it.
Look at that job’s page anyway →

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: 64% of its task weight, across 17 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: collaborating with others in the organization to ensure successful implementation of chosen problem solutions 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.

If you run a team doing this job

If you run a team doing this job, the conversation you owe them is the one on this page, and sooner than feels comfortable. Show them the ledger rather than a reassurance: the rows moving toward the software are performing validation and testing of models to ensure adequacy, and the rows that are not are where you want your people visible. Ask each of them to do the this-week move and bring the list to your next one-to-one. It turns a rumour into a piece of work, and it tells you which parts of your team's week are actually at stake. And say the thing out loud that a team lead usually leaves unsaid: a shrinking team is your exposure too, so do the move yourself as well.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Actuaries, economists and statisticians 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 Actuaries, economists and statisticians. 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:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for operations research analysts, 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 64% of the work on this page is already inside what they can do.

Try The AI Authority free →

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 operations research analysts. 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 operations research analysts 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 Space for operations research analysts yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for operations research analysts existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for operations research analysts launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

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 Operations Research Analysts?: Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Operations Research Analysts (United States, SOC 15-2031), 64% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 59 out of 100 (range 53–65, 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 “Operations Research Analysts” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Review research literature” (83/100, very high); “Formulate mathematical or simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their nume…” (75/100, high); “Perform validation and testing of models to ensure adequacy, and reformulate models, as necessary” (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 “Operations Research Analysts” stay human?: About 25% 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: “Collaborate with senior managers and decision makers to identify and solve a variety of problems and to clarify management objectives” (23/100, low); “Collaborate with others in the organization to ensure successful implementation of chosen problem solutions” (23/100, low); “Observe the current system in operation, and gather and analyze information about each of the component problems, using a variety of sources” (25/100, 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 “Operations Research Analysts” do about AI?: Start from the ledger rather than the headline: 64% of this job's weighted core work is exposed, and roughly 25% 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 Operations Research Analysts 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.
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 2026-08-04.
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.

How we score a job Download this release Look up another job

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}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. 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.