Will AI replace Cost Estimators?

Not as a job, but it is already doing parts of the work. Across the 14 official task statements scored for Cost Estimators (United States, SOC 13-1051), 67% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 63 out of 100 (range 58–69, band: high). 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 “Cost Estimators” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Prepare cost and expenditure statements and other necessary documentation at regular intervals for the duration of the project” (93/100, very high); “Prepare and maintain a directory of suppliers, contractors and subcontractors” (93/100, very high); “Analyze blueprints and other documentation to prepare time, cost, materials, and labor estimates” (75/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Cost Estimators” stay human?

About 21% 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: “Visit site and record information about access, drainage and topography, and availability of utility services” (0/100, minimal); “Confer with engineers, architects, owners, contractors, and subcontractors on changes and adjustments to cost estimates” (30/100, low); “Consult with clients, vendors, personnel in other departments, or construction foremen to discuss and formulate estimates and resolve issues” (30/100, low). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Cost Estimators” do about AI?

Start from the ledger rather than the headline: 67% of this job's weighted core work is exposed, and roughly 21% 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 Cost Estimators 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 14 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

Cost Estimators

analyzing blueprints and other documentation to prepare time, preparing estimates used by management and establishing and maintaining tendering process. 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: analyzing blueprints and other documentation to prepare time. The tasks, though, are not you.

Your move: three real directions from here ↓

Where the work sits, by task weight

shifting to AI: 67%
changing shape: 12%
staying human: 21%

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

Whole-job exposure score 63 out of 100 (58–69 allowing for uncertainty): high exposure, across 14 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 cost estimators is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

It would be a lie to soften that; conferring with engineers, architects, owners, contractors and subcontractors on changes and adjustments to cost estimates is what this work rebuilds around. Your move starts there.

Your week, as this page understands it

Prepare cost estimates for product manufacturing, construction projects, or services to aid management in bidding on or determining price of product or service. May specialize according to particular service performed or type of product manufactured. The job title says “cost estimators”. The real job is the part underneath: conferring with engineers, architects, owners, contractors and subcontractors on changes and adjustments to cost estimates. 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 cost estimators is not one task. It is 14 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is conferring with engineers, architects, owners, contractors and subcontractors on changes and adjustments to cost estimates, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 67%
changing shape: 12%
staying human: 21%

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

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

1 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

9 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.

Analyzing blueprints and other documentation to prepare time
This is reading one thing and writing another: blueprints in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Analyze blueprints and other documentation to prepare time, cost, materials, and labor estimates.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reading drawings and turning them into cost, time and labour figures is document and number work software increasingly handles.
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.
Collecting historical cost data to estimate costs for current or future products
This is reading one thing and writing another: historical cost data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Collect historical cost data to estimate costs for current or future products.” (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: Gathering past cost data and applying it to new work is straightforward analysis of existing records.
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 estimates for use in selecting vendors or subcontractors
This is reading one thing and writing another: estimates in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Prepare estimates for use in selecting vendors or subcontractors.” (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: Comparing vendor and subcontractor prices is structured analysis on documented quotes.
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.
Assessing cost effectiveness of products
This is reading one thing and writing another: cost effectiveness of products in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Assess cost effectiveness of products, projects or services, tracking actual costs relative to bids as the project develops.” (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: Tracking real spend against the original bid is reporting work built on figures already in the system.
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 estimates used by management
This is reading one thing and writing another: estimates in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Prepare estimates used by management for purposes such as planning, organizing, and scheduling work.” (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: Producing planning and scheduling estimates is calculation and drafting from information already held.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

2 tasks

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Conducting special studies to develop and establish standard hour and related cost data or to reduce cost
The software now makes the first pass at special studies, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Conduct special studies to develop and establish standard hour and related cost data or to reduce cost.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 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; mistakes that are cheap to catch.
The rating behind it: Building standard time and cost figures is analysis work, though it often starts with watching how work is done.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Establishing and maintaining tendering process
The software now makes the first pass at process, 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: “Establish and maintain tendering process, and conduct negotiations.” (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: The tender paperwork can be produced automatically, but negotiating terms rests on judgement and relationships.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

Staying human

3 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.

Conferring with engineers, architects, owners, contractors and subcontractors on changes and adjustments to cost estimates
The value here is that a specific person handles engineers, architects, owners, contractors and subcontractors and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Confer with engineers, architects, owners, contractors, and subcontractors on changes and adjustments to cost estimates.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Agreeing changes with architects, owners and contractors happens through discussion where credibility matters.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Consulting with clients, vendors
The value here is that a specific person handles clients, vendors and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Consult with clients, vendors, personnel in other departments, or construction foremen to discuss and formulate estimates and resolve issues.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Estimates get shaped in conversations with clients, suppliers and site staff who hold the local detail.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Visiting site and recording information about access
This work happens in the physical world: site, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Visit site and record information about access, drainage and topography, and availability of utility services.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Recording site access, drainage and services means walking the ground in person.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.

Show the other 4 tasks

Preparing cost and expenditure statements and other necessary documentation at regular intervals for the duration of the project
shifting to AI
This is reading one thing and writing another: cost in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Prepare cost and expenditure statements and other necessary documentation at regular intervals for the duration of the project.” (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: Regular cost statements are repeatable reports that software can generate from project data.
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.
Preparing and maintaining a directory of suppliers
shifting to AI
This is reading one thing and writing another: a directory of suppliers in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Prepare and maintain a directory of suppliers, contractors and subcontractors.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Keeping a supplier and subcontractor directory current is routine data maintenance.
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.
Reviewing material and labor requirements to decide whether it is more cost-effective to produce or purchase components
shifting to AI
This is reading one thing and writing another: material in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Review material and labor requirements to decide whether it is more cost-effective to produce or purchase components.” (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: Make-or-buy comparisons are arithmetic on known costs, though supplier realities still need 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.
Setting up cost monitoring and reporting systems and procedures
shifting to AI
This is reading one thing and writing another: cost in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Set up cost monitoring and reporting systems and procedures.” (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: Designing cost monitoring and reporting routines is documented process work done at a screen.
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.

What this job pays, and how many people do it

Median pay: $78,740a 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: 224,220in 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: historical cost data in, a record out. The rows above are exactly that shape: analyzing blueprints and other documentation to prepare time and collecting historical cost data to estimate costs for current or future products. What it cannot do is be trusted in person, which is what engineers, architects, owners, contractors and subcontractors run on: someone specific doing it and standing behind it. Which is why this page talks about your tasks changing, not your job ending.

Your move

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

The exposed part of your job is the biggest part, and I am not going to dress that up: analyzing blueprints and other documentation to prepare time is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 67% of this job's task weight sits in rows the software is already learning, 12% in rows that change shape rather than disappear, and 21% in rows it is nowhere near. That is the position, measured across 14 scored tasks. It is not a forecast about you.

What you have that the software does not is conferring with engineers, architects, owners, contractors and subcontractors on changes and adjustments to cost estimates, 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 historical cost data 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 historical cost data, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.

Over the next 90 days

Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is analyzing blueprints and other documentation to prepare time” 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 conferring with engineers, architects, owners, contractors and subcontractors on changes and adjustments to cost estimates 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 cost estimators (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 8% of its durable work is work you already do and there are far fewer of those jobs than of yours. I am not going to pretend that is comfortable news: 67% of your own task list is already in the top exposure band. But the answer on this evidence is not a sideways jump into a job with the same problem. It is to walk toward the end of this one that answers for things. “confer with engineers, architects, owners, contractors, and subcontractors on changes and adjustments…” is the part that stays with a person, and spending a year getting formally recognised for it beats a standing start somewhere else.

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 prepare estimates for use in selecting vendors or subcontractors, and their equivalent is to manage the work of subcontractors to ensure quality control. Across both published task lists that is about 8% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 8% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 19,600 of those jobs against 224,220 of yours (OEWS May 2025), 9% as many seats.
Look at that job’s page anyway →
Interior Designers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already confer with engineers, architects, owners, contractors, and subcontractors on changes and adjustments to…, and their equivalent is to coordinate with other professionals. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $67,190 against your $78,740, 14.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Logisticians
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already prepare estimates for use in selecting vendors or subcontractors, and their equivalent is to manage subcontractor activities, reviewing proposals, developing performance specifications, and serving as liaisons between…. Across both published task lists that is about 1% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 1% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 74% of its own task list already scores in the top exposure band (66/100 in this release), so the same software is eating it.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 67% of its task weight, across 14 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: conferring with engineers, architects, owners, contractors and subcontractors on changes and adjustments to cost estimates 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 Managers and directors in the creative industries 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 Managers and directors in the creative industries, Estate agents and auctioneers and Sales related occupations n.e.c.. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

Your route through this

Two honest options, and no deadline on either

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

A nearby route

There's no Space built for cost estimators yet.

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

The closest match is Microsoft 365 Report Builders, a community for people who build business reports in Excel, Power Query and Power BI without a data team behind them. It overlaps with the part of your job that is growing: the spreadsheet half of estimating - data that is clean, models that survive being reused, numbers a client can challenge. It does not cover the site knowledge an estimate rests on. If that overlap isn't you, the free route below covers the same ground.

Try Microsoft 365 Report Builders free →

7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.

After the trial it is a paid community, and you get identical data either way. If the overlap above is not your job, the moves above cost nothing and stand on their own.

Noted, and thank you. We’ll email you if a Space for cost estimators launches. Nothing else.

That did not look like an email address, so nothing was saved. Have another go below.

We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Cost Estimators?: Not as a job, but it is already doing parts of the work. Across the 14 official task statements scored for Cost Estimators (United States, SOC 13-1051), 67% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 63 out of 100 (range 58–69, band: high). 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 “Cost Estimators” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Prepare cost and expenditure statements and other necessary documentation at regular intervals for the duration of the project” (93/100, very high); “Prepare and maintain a directory of suppliers, contractors and subcontractors” (93/100, very high); “Analyze blueprints and other documentation to prepare time, cost, materials, and labor estimates” (75/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Cost Estimators” stay human?: About 21% 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: “Visit site and record information about access, drainage and topography, and availability of utility services” (0/100, minimal); “Confer with engineers, architects, owners, contractors, and subcontractors on changes and adjustments to cost estimates” (30/100, low); “Consult with clients, vendors, personnel in other departments, or construction foremen to discuss and formulate estimates and resolve issues” (30/100, low). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Cost Estimators” do about AI?: Start from the ledger rather than the headline: 67% of this job's weighted core work is exposed, and roughly 21% 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 Cost Estimators 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 14 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.
1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

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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}
}

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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.