Will AI replace Urban and Regional Planners?

Not as a job, but it is already doing parts of the work. Across the 25 official task statements scored for Urban and Regional Planners (United States, SOC 19-3051), 37% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 46 out of 100 (range 41–53, 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 “Urban and Regional Planners” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Prepare, maintain and update files and records, including land use data and statistics” (93/100, very high); “Keep informed about economic or legal issues involved in zoning codes, building codes, or environmental regulations” (83/100, very high); “Research, compile, analyze and organize information from maps, reports, investigations, and books for use in reports and special projects” (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 “Urban and Regional Planners” stay human?

About 37% 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: “Conduct interviews, surveys and site inspections concerning factors that affect land usage, such as zoning, traffic flow and housing” (7/100, minimal); “Hold public meetings with government officials, social scientists, lawyers, developers, the public, or special interest groups to formulate, develop, or addr…” (7/100, minimal); “Mediate community disputes or assist in developing alternative plans or recommendations for programs or projects” (10/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Urban and Regional Planners” do about AI?

Start from the ledger rather than the headline: 37% of this job's weighted core work is exposed, and roughly 37% 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 Urban and Regional Planners 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 25 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

Urban and Regional Planners

designing, promoting, discussing with planning officials the purpose of land use projects and keeping informed about economic or legal issues involved in zoning codes. If that's your week, this page is about your job.

The honest answer

This job is splitting in two: keeping informed about economic or legal issues involved in zoning codes is work AI now does quickly and cheaply, and holding public meetings with government officials is work it can't touch.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 37%
changing shape: 25%
staying human: 37%

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

Whole-job exposure score 46 out of 100 (41–53 allowing for uncertainty): partial exposure, across 25 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 urban and regional planners 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.

Which half fills your week decides your exposure. That is more in your control than it sounds.

Your week, as this page understands it

Develop comprehensive plans and programs for use of land and physical facilities of jurisdictions, such as towns, cities, counties, and metropolitan areas. The job title says “urban” or “regional planners”: officially one job, two names. The real job is the part underneath: holding public meetings with government officials. 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 urban and regional planners is not one task. It is 25 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is holding public meetings with government officials, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 37%
changing shape: 25%
staying human: 37%

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.

4 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

10 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.

Creating, preparing or requisition graphic or narrative reports on land use data
This is reading one thing and writing another: requisition graphic in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Create, prepare, or requisition graphic or narrative reports on land use data, including land area maps overlaid with geographic variables, such as population density.” (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: Mapping software and AI already turn land use data into clear maps and written reports that need only light checking.
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.
Keeping informed about economic or legal issues involved in zoning codes
This is reading one thing and writing another: informed in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Keep informed about economic or legal issues involved in zoning codes, building codes, or environmental regulations.” (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: Keeping track of changes in codes and regulations is reading and summarizing, which AI 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 4/4.
Determining the effects of regulatory limitations on land use projects
This is reading one thing and writing another: the effects of regulatory limitations in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Determine the effects of regulatory limitations on land use projects.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 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: Working out what written rules allow on a site is documented legal reading that AI 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.
Reviewing and evaluating environmental impact reports pertaining to private or public planning projects or programs
This is reading one thing and writing another: environmental impact reports in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Review and evaluate environmental impact reports pertaining to private or public planning projects or programs.” (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: Reviewing a long impact report against standards is document work AI can draft for a planner to confirm.
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.

Changing shape

6 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.

Designing, promoting or administering government plans or policies affecting land use, zoning, public utilities, community facilities, housing or transportation
The software now makes the first pass at government plans, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · Core
Source: “Design, promote, or administer government plans or policies affecting land use, zoning, public utilities, community facilities, housing, or transportation.” (O*NET task statement)
How this row was scored
Exposure score: 44 out of 100 (37–51 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: AI can draft plan and policy wording, but which rules suit a particular community depends on local politics and knowledge.
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 0/4 · how much data exists 2/4.
Recommending approval, denial or conditional approval of proposals
The software now makes the first pass at approval, denial or conditional approval of proposals, 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: “Recommend approval, denial, or conditional approval of proposals.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Writing a recommendation follows documented policy tests, though an accountable officer must sign it.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Evaluating proposals for infrastructure projects or other development for environmental impact or sustainability
The software now makes the first pass at proposals, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Evaluate proposals for infrastructure projects or other development for environmental impact or sustainability.” (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: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Environmental review follows published methods and standard paperwork, though local ecology and judgment still need a planner.
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 0/4 · how much data exists 3/4.

Staying human

9 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.

Advising planning officials on project feasibility
The value here is that a specific person handles officials and stands behind it. That is earned, not computed.
importance 5 · Core
Source: “Advise planning officials on project feasibility, cost-effectiveness, regulatory conformance, or possible alternatives.” (O*NET task statement)
How this row was scored
Exposure score: 31 out of 100 (24–38 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: Feasibility advice can be drafted by software, though officials rely on an adviser they know and on local detail nobody wrote down.
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 2/4.
Holding public meetings with government officials
This work happens in the physical world: public meetings, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Hold public meetings with government officials, social scientists, lawyers, developers, the public, or special interest groups to formulate, develop, or address issues regarding land use or community plans.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (3–11 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Running a public meeting means being in the room and holding the trust of people who disagree.
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 1/4.
Mediating community disputes or assisting in developing alternative plans or recommendations for programs or projects
The value here is that a specific person handles community disputes and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Mediate community disputes or assist in developing alternative plans or recommendations for programs or projects.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (6–14 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Settling disputes between neighbors depends on live give and take with a mediator people trust.
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 1/4.

Show the other 15 tasks

Preparing, maintaining and updating files and records
shifting to AI
This is reading one thing and writing another: files in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Prepare, maintain and update files and records, including land use data and statistics.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Keeping land use files and records current is routine data work that systems do at least as well as people.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Researching, compiling, analyzing and organizing information from maps, reports, investigations and books for use in reports and special projects
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 not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Research, compile, analyze and organize information from maps, reports, investigations, and books for use in reports and special projects.” (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: Pulling together and organizing information from documents and maps is reading and summarizing, an AI strength.
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.
Investigating property availability for purposes of development
shifting to AI
This is reading one thing and writing another: property availability in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Investigate property availability for purposes of development.” (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: Searching property records and listings for available land is database work AI 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.
Preparing reports, using statistics, charts and graphs
shifting to AI
This is reading one thing and writing another: reports in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Prepare reports, using statistics, charts, and graphs, to illustrate planning studies in areas such as population, land use, or zoning.” (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 population and land use figures into charted reports is exactly the drafting AI 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.
Preparing, developing and maintaining maps and databases
shifting to AI
This is reading one thing and writing another: maps in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Prepare, develop and maintain maps and databases.” (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 and updating maps and databases is structured digital work that software largely handles already.
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.
Responding to public inquiries and complaints
shifting to AI
This is reading one thing and writing another: public inquiries in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Respond to public inquiries and complaints.” (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: Most public questions are answered from published rules, though some callers want a person who will listen.
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.
Identifying opportunities or developing plans for sustainability projects or programs to improve energy efficiency
changing shape
The software now makes the first pass at opportunities, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Identify opportunities or develop plans for sustainability projects or programs to improve energy efficiency, minimize pollution or waste, or restore natural systems.” (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: Sustainability options are well documented, so AI can shape a plan while a person judges what actually fits.
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.
Developing plans for public or alternative transportation systems for urban or regional locations to reduce carbon output associated with transportation
changing shape
The software now makes the first pass at plans, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Develop plans for public or alternative transportation systems for urban or regional locations to reduce carbon output associated with transportation.” (O*NET task statement)
How this row was scored
Exposure score: 44 out of 100 (37–51 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Transport plans can be drafted from data, but a workable answer depends on local streets, budgets and politics.
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 0/4 · how much data exists 2/4.
Assessing the feasibility of land use proposals and identifying necessary changes
changing shape
The software now makes the first pass at the feasibility of land use proposals, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Assess the feasibility of land use proposals and identify necessary changes.” (O*NET task statement)
How this row was scored
Exposure score: 44 out of 100 (37–51 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: AI can draft the feasibility check, but the deciding facts are often local and never written down.
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 0/4 · how much data exists 2/4.
Conducting field investigations, surveys, impact studies or other research to compile and analyze data on economic, social, regulatory or physical factors affecting land
staying human
This work happens in the physical world: field investigations, surveys, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Conduct field investigations, surveys, impact studies, or other research to compile and analyze data on economic, social, regulatory, or physical factors affecting land use.” (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: Half of this is walking the site and gathering local information; the analysis afterwards is far easier to automate.
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.
Advocating sustainability to community groups
staying human
The value here is that a specific person handles sustainability and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Advocate sustainability to community groups, government agencies, the general public, or special interest groups.” (O*NET task statement)
How this row was scored
Exposure score: 24 out of 100 (17–31 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: Advocacy material can be drafted, but persuading a community happens face to face over time.
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 3/4 · how much data exists 2/4.
Coordinating work with economic consultants or architects during the formulation of plans or the design of large pieces of infrastructure
staying human
The value here is that a specific person handles work and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Coordinate work with economic consultants or architects during the formulation of plans or the design of large pieces of infrastructure.” (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: Working alongside architects and consultants is live coordination where quick back and forth matters.
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.
Discussing with planning officials the purpose of land use projects
staying human
The value here is that a specific person handles planning officials the purpose of land use projects and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Discuss with planning officials the purpose of land use projects, such as transportation, conservation, residential, commercial, industrial, or community use.” (O*NET task statement)
How this row was scored
Exposure score: 15 out of 100 (8–22 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: This is a conversation with officials about what a project is really for, where being present matters.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Supervising or coordinating the work of urban planning technicians or technologists
staying human
The value here is that a specific person handles the work of urban planning technicians and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Supervise or coordinate the work of urban planning technicians or technologists.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (9–17 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Supervising technicians depends on watching people work and building a working relationship with them.
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 1/4.
Conducting interviews, surveys and site inspections concerning factors that affect land usage
staying human
This work happens in the physical world: interviews, in a real place. Software cannot follow it there.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Conduct interviews, surveys and site inspections concerning factors that affect land usage, such as zoning, traffic flow and housing.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (3–11 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Site inspections and doorstep interviews need somebody physically there to look and ask.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $89,320a 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: 44,230in 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: requisition graphic in, a record out. The rows above are exactly that shape: keeping informed about economic or legal issues involved in zoning codes and creating, preparing. 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

Your week is splitting in two, and which half fills it is the whole question. Keeping informed about economic or legal issues involved in zoning codes is going; holding public meetings with government officials is not.

So, given all that: 37% of this job's task weight sits in rows the software is already learning, 25% in rows that change shape rather than disappear, and 37% in rows it is nowhere near. That is the position, measured across 25 scored tasks. It is not a forecast about you.

The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.

This week: one thing

Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.

What you end up holding: your own week, on one page, sorted into what is shifting and what is not
How long it takes: about twenty minutes

If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.

Over the next 90 days

Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (holding public meetings with government officials) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.

Over the next 12 months

Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles holding public meetings with government officials, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. 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 urban and regional planners (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was landscape architects: only about 2% of its durable work is work you already do and it pays 10.6% less. Your own job splits about 37/63: 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 “design, promote, or administer government plans or policies affecting land use, zoning…”, 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.

Landscape Architects
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already advocate sustainability to community groups, government agencies, the general public, or special interest…, and their equivalent is to present project plans or designs to public stakeholders. 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: $79,870 against your $89,320, 10.6% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Social Scientists and Related Workers, All Other
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already supervise or coordinate the work of urban planning technicians or technologists, and their equivalent is to direct urban traffic counting programs. 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 →
Civil Engineering Technologists and Technicians
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already respond to public inquiries and complaints, and their equivalent is to respond to public suggestions and complaints. 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: $64,950 against your $89,320, 27.3% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
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: 37% of its task weight, across 25 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
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 Chartered architectural technologists, planning officers and consultants 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 Chartered architectural technologists, planning officers and consultants. 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 urban / regional planners, 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 37% 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 urban / regional planners. 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 urban / regional planners 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 urban / regional planners 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 urban / regional planners 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 urban / regional planners 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 Urban and Regional Planners?: Not as a job, but it is already doing parts of the work. Across the 25 official task statements scored for Urban and Regional Planners (United States, SOC 19-3051), 37% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 46 out of 100 (range 41–53, 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 “Urban and Regional Planners” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Prepare, maintain and update files and records, including land use data and statistics” (93/100, very high); “Keep informed about economic or legal issues involved in zoning codes, building codes, or environmental regulations” (83/100, very high); “Research, compile, analyze and organize information from maps, reports, investigations, and books for use in reports and special projects” (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 “Urban and Regional Planners” stay human?: About 37% 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: “Conduct interviews, surveys and site inspections concerning factors that affect land usage, such as zoning, traffic flow and housing” (7/100, minimal); “Hold public meetings with government officials, social scientists, lawyers, developers, the public, or special interest groups to formulate, develop, or addr…” (7/100, minimal); “Mediate community disputes or assist in developing alternative plans or recommendations for programs or projects” (10/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Urban and Regional Planners” do about AI?: Start from the ledger rather than the headline: 37% of this job's weighted core work is exposed, and roughly 37% 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 Urban and Regional Planners 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 25 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
4 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt-with-imputed)
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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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.