Will AI replace Social Scientists and Related Workers, All Other?

Not as a job, but it is already doing parts of the work. Across the 22 official task statements scored for Social Scientists and Related Workers, All Other (United States, SOC 19-3099), 62% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 58 out of 100 (range 53–64, 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 “Social Scientists and Related Workers, All Other” 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: “Represent jurisdictions in the legislative or administrative approval of land development projects” (8/100, minimal); “Participate in public meetings or hearings to explain planning proposals, to gather feedback from those affected by projects, or to achieve consensus on proj…” (9/100, minimal); “Design new or improved transport infrastructure, such as junction improvements, pedestrian projects, bus facilities, or car parking areas” (32/100, 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 “Social Scientists and Related Workers, All Other” do about AI?

Start from the ledger rather than the headline: 62% 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 Social Scientists and Related Workers, All Other 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 22 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

Social Scientists and Related Workers, All Other

defining regional or local transportation planning problems or priorities, interpreting data from traffic modeling software and designing transportation surveys to identify areas of public concern. 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: interpreting data from traffic modeling software. The tasks, though, are not you.

Your move: three real directions from here ↓

Where the work sits, by task weight

shifting to AI: 62%
changing shape: 18%
staying human: 20%

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

Whole-job exposure score 58 out of 100 (53–64 allowing for uncertainty): partial exposure, across 22 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 social scientists and related workers, all other 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.

It would be a lie to soften that; participating in public meetings or hearings to explain planning proposals is what this work rebuilds around. Your move starts there.

Your week, as this page understands it

All social scientists and related workers not listed separately. The job title says “social scientists”, “related workers” or “all other”: officially one job, several names. The real job is the part underneath: participating in public meetings or hearings to explain planning proposals. 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 social scientists and related workers, all other is not one task. It is 22 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is participating in public meetings or hearings to explain planning proposals, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 62%
changing shape: 18%
staying human: 20%

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 2026-08-05. 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.

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

13 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.

Preparing reports or recommendations on transportation planning
This is reading one thing and writing another: reports in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Prepare reports or recommendations on transportation planning.” (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: Turning planning analysis into a report or recommendation is document work software drafts to a standard planners accept and edit.
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.
Recommending transportation system improvements or projects
This is reading one thing and writing another: transportation system improvements in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Recommend transportation system improvements or projects, based on economic, population, land-use, or traffic projections.” (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: Recommending improvements from traffic, land-use and population projections is data 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 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Analyzing information
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: “Analyze information related to transportation, such as land use policies, environmental impact of projects, or long-range planning needs.” (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: Analyzing land use, environmental and long-range planning information is desk analysis software does 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 0/4 · how much data exists 3/4.
Interpreting data from traffic modeling software
This is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Interpret data from traffic modeling software, geographic information systems, or associated databases.” (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: Reading output from traffic models and mapping databases is well-documented data interpretation software 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.

Changing shape

4 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.

Defining regional or local transportation planning problems or priorities
The software now makes the first pass at regional, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Define regional or local transportation planning problems or priorities.” (O*NET task statement)
How this row was scored
Exposure score: 49 out of 100 (42–56 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: Naming the priorities involves local politics and competing interests, so software gets you a draft rather than an answer.
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 1/4 · how much data exists 3/4.
Collaborating with other professionals to develop sustainable transportation strategies at the local
The software now makes the first pass at other professionals, 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: “Collaborate with other professionals to develop sustainable transportation strategies at the local, regional, or national level.” (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: A shared regional strategy comes out of negotiation between organizations, not from a document alone.
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.
Developing or testing new methods or models of transportation analysis
The software now makes the first pass at new methods, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Develop or test new methods or models of transportation analysis.” (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: Software helps test new analysis methods, but inventing one that holds up still needs an expert researcher.
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.

Staying human

5 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 engineers
The value here is that a specific person handles engineers and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Collaborate with engineers to research, analyze, or resolve complex transportation design issues.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 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: Software helps with the analysis, but resolving a tricky design issue happens through back and forth with engineers.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Participating in public meetings or hearings to explain planning proposals
This work happens in the physical world: public meetings, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Participate in public meetings or hearings to explain planning proposals, to gather feedback from those affected by projects, or to achieve consensus on project designs.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: A public hearing works because a person stands up, answers questions and earns the room's confidence.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Designing new or improved transport infrastructure
The rules require a named, qualified person to answer for new or improved transport infrastructure, and that person cannot be a piece of software.
importance 3 · Core
Source: “Design new or improved transport infrastructure, such as junction improvements, pedestrian projects, bus facilities, or car parking areas.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Junction and parking designs need site knowledge and normally a licensed engineer signing off as the design is made.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Show the other 12 tasks

Developing computer models to address transportation planning issues
shifting to AI
This is reading one thing and writing another: computer models in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Develop computer models to address transportation planning issues.” (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: Building computer models is heavily documented programming work where AI is genuinely strong.
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.
Evaluating transportation-related consequences of federal or state legislative proposals
shifting to AI
This is reading one thing and writing another: transportation-related consequences of federal in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Evaluate transportation-related consequences of federal or state legislative proposals.” (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 out what a proposed law would mean for transport is analysis of public texts, which 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 4/4.
Defining or updating information, urban boundaries or classification of roadways
shifting to AI
This is reading one thing and writing another: information, urban boundaries or classification of roadways in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Define or update information such as urban boundaries or classification of roadways.” (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: Updating boundary and road classification records is structured data maintenance 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.
Analyzing information from traffic counting programs
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: “Analyze information from traffic counting programs.” (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: Analyzing traffic count data is a well-defined statistical job that software does to a standard planners accept.
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.
Designing transportation surveys to identify areas of public concern
shifting to AI
This is reading one thing and writing another: transportation surveys in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Design transportation surveys to identify areas of public concern.” (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: Designing a survey to surface public concerns follows established methods that software applies 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.
Reviewing development plans for transportation system effects
shifting to AI
This is reading one thing and writing another: development plans in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Review development plans for transportation system effects, infrastructure requirements, or compliance with applicable transportation regulations.” (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: Checking a development plan against written transportation rules is documented compliance review 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 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Producing environmental documents, such as environmental assessments or environmental impact statements
shifting to AI
This is reading one thing and writing another: environmental documents in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Produce environmental documents, such as environmental assessments or environmental impact statements.” (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: Environmental assessments follow a strict documented structure, so software can draft most of the text from project data.
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.
Evaluating transportation project needs or costs
shifting to AI
This is reading one thing and writing another: transportation project needs in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Evaluate transportation project needs or costs.” (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: Estimating project needs and costs works from documented rates and quantities, which software calculates and writes up well.
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.
Preparing necessary documents to obtain planned project approvals or permits
shifting to AI
This is reading one thing and writing another: necessary documents in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Prepare necessary documents to obtain planned project approvals or permits.” (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: Permit and approval paperwork follows set forms and requirements, so software can assemble most of it.
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.
Preparing or reviewing engineering studies or specifications
changing shape
The software now makes the first pass at studies, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Prepare or review engineering studies or specifications.” (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; someone qualified has to answer for it.
The rating behind it: Engineering studies and specifications normally need a licensed engineer involved while they are being produced.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Directing urban traffic counting programs
staying human
The ratings behind this row put urban traffic counting programs well outside what today's tools can do on their own.
importance 3 · Core
Source: “Direct urban traffic counting programs.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 allowing for uncertainty): low 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: The counting program can be planned on paper, but running crews and equipment in the field needs someone directing it.
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 1/4 · how much data exists 3/4.
Representing jurisdictions in the legislative or administrative approval of land development projects
staying human
This work happens in the physical world: jurisdictions, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Represent jurisdictions in the legislative or administrative approval of land development projects.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (1–15 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Speaking for a jurisdiction at a hearing is a person standing up and being accountable in the room.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $101,110a 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: 37,100in 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: transportation system improvements in, a record out. The rows above are exactly that shape: interpreting data from traffic modeling software and preparing reports or recommendations on transportation planning. What it cannot do is be there in the room, and that is still where public meetings get done. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

The exposed part of your job is the biggest part, and I am not going to dress that up: interpreting data from traffic modeling software is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 62% of this job's task weight sits in rows the software is already learning, 18% in rows that change shape rather than disappear, and 20% in rows it is nowhere near. That is the position, measured across 22 scored tasks. It is not a forecast about you.

What you have that the software does not is participating in public meetings or hearings to explain planning proposals, 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 transportation system improvements 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 transportation system improvements, 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 interpreting data from traffic modeling 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 participating in public meetings or hearings to explain planning proposals 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 social scientists and related workers, all other (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was civil engineers: only about 5% of its durable work is work you already do. Your own job splits about 62/38: 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 “define regional or local transportation planning problems or priorities”, 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.

Civil Engineers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already review development plans for transportation system effects, infrastructure requirements, or compliance with applicable…, and their equivalent is to inspect completed transportation projects to ensure safety or compliance with applicable standards or…. Across both published task lists that is about 5% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 5% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Urban and Regional Planners
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already direct urban traffic counting programs, and their equivalent is to supervise or coordinate the work of urban planning technicians or technologists. 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. It is a pay cut, in those words: $89,320 against your $101,110, 11.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Environmental Engineers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already analyze information related to transportation, and their equivalent is to assess the existing or potential environmental impact of land use projects on air…. 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.
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: 62% of its task weight, across 22 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: participating in public meetings or hearings to explain planning proposals 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 Social and humanities scientists 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 Social and humanities scientists, Construction project managers and related professionals, Authors, writers and translators and Health and safety managers and officers. 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:
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Anywhere in the US:
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No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for social scientists / related workers / all other, 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 62% 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 social scientists / related workers / all other. 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 social scientists / related workers / all other launches. Nothing else.

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No Space for social scientists / related workers / all other 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 social scientists / related workers / all other 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 social scientists / related workers / all other 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 Social Scientists and Related Workers, All Other?: Not as a job, but it is already doing parts of the work. Across the 22 official task statements scored for Social Scientists and Related Workers, All Other (United States, SOC 19-3099), 62% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 58 out of 100 (range 53–64, 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 “Social Scientists and Related Workers, All Other” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Develop computer models to address transportation planning issues” (83/100, very high); “Interpret data from traffic modeling software, geographic information systems, or associated databases” (83/100, very high); “Evaluate transportation-related consequences of federal or state legislative proposals” (83/100, very 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 “Social Scientists and Related Workers, All Other” 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: “Represent jurisdictions in the legislative or administrative approval of land development projects” (8/100, minimal); “Participate in public meetings or hearings to explain planning proposals, to gather feedback from those affected by projects, or to achieve consensus on proj…” (9/100, minimal); “Design new or improved transport infrastructure, such as junction improvements, pedestrian projects, bus facilities, or car parking areas” (32/100, 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 “Social Scientists and Related Workers, All Other” do about AI?: Start from the ledger rather than the headline: 62% 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 Social Scientists and Related Workers, All Other 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 22 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 2026-08-05.
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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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.