Will AI replace Labor Relations Specialists?

Not as a job, but it is already doing parts of the work. Across the 28 official task statements scored for Labor Relations Specialists (United States, SOC 13-1075), 27% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 48 out of 100 (range 43–54, 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 “Labor Relations Specialists” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Prepare and submit required governmental reports or forms related to labor relations matters, such as equal employment opportunity (EEO) forms, new hire form…” (88/100, very high); “Write letters related to labor relations activities, such as letters to amend collective bargaining agreements, letters of dispute or conciliation, or letter…” (81/100, very high); “Prepare reports or presentations to communicate employee satisfaction or related data to management” (79/100, 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 “Labor Relations Specialists” stay human?

About 33% 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: “Provide expert testimony in legal proceedings related to labor relations or labor contracts” (6/100, minimal); “Present the position of the company or of labor during arbitration or other labor negotiations” (12/100, minimal); “Negotiate collective bargaining agreements” (13/100, 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 “Labor Relations Specialists” do about AI?

Start from the ledger rather than the headline: 27% of this job's weighted core work is exposed, and roughly 33% 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 Labor Relations Specialists 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 28 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

Labor Relations Specialists

negotiating collective bargaining agreements, advising management on matters related to the administration of contracts or employee discipline or grievance procedures and scheduling or coordinating the details of grievance hearings or other meetings. If that's your week, this page is about your job.

The honest answer

AI is already taking a real slice of the routine work here: reviewing employer practices or employee data to ensure compliance with contracts on matters. That is a slice of tasks, not of you.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 27%
changing shape: 40%
staying human: 33%

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

Whole-job exposure score 48 out of 100 (43–54 allowing for uncertainty): partial exposure, across 28 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 labor relations specialists 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.

That slice is not coming back; the core of the job, negotiating collective bargaining agreements, stays yours. The tools change hands, the accountability doesn't.

Your week, as this page understands it

Resolve disputes between workers and managers, negotiate collective bargaining agreements, or coordinate grievance procedures to handle employee complaints. The job title says “labor relations specialists”. The real job is the part underneath: negotiating collective bargaining agreements. 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 labor relations specialists is not one task. It is 28 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is negotiating collective bargaining agreements, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 27%
changing shape: 40%
staying human: 33%

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.

3 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.

Reviewing employer practices or employee data to ensure compliance with contracts on matters
This is reading one thing and writing another: employer practices in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Review employer practices or employee data to ensure compliance with contracts on matters such as wages, hours, or conditions of employment.” (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: Checking pay, hours and conditions against contract terms is the kind of data comparison software does well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Researching case law or outcomes of previous case hearings
This is reading one thing and writing another: case law in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Research case law or outcomes of previous case hearings.” (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: Searching past cases and hearing outcomes is document research, which software does quickly and thoroughly.
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.
Drafting contract proposals or counter-proposals for collective bargaining or other labor negotiations
This is reading one thing and writing another: contract proposals in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Draft contract proposals or counter-proposals for collective bargaining or other labor negotiations.” (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: Contract proposals follow familiar structures and past agreements, so software can produce a solid first draft.
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.
Scheduling or coordinating the details of grievance hearings or other meetings
This is reading one thing and writing another: the details of grievance hearings in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Schedule or coordinate the details of grievance hearings or other meetings.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (75–83 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: Scheduling hearings and meetings is routine coordination that software already automates.
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 1/4 · how much data exists 3/4.

Changing shape

10 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.

Investigating and evaluating union complaints or arguments to determine viability
The software now makes the first pass at union complaints, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Investigate and evaluate union complaints or arguments to determine viability.” (O*NET task statement)
How this row was scored
Exposure score: 57 out of 100 (50–64 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Weighing whether a grievance holds up is mostly reading the contract and the evidence, 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 1/4 · how much data exists 2/4.
Interpreting contractual agreements for employers and employees engaged in collective bargaining or other labor relations processes
The software now makes the first pass at contractual agreements, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Interpret contractual agreements for employers and employees engaged in collective bargaining or other labor relations processes.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (52–60 allowing for uncertainty): partial 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: Explaining what a clause means is reading and summarizing a written agreement, something software handles reliably.
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 1/4 · how much data exists 3/4.
Proposing resolutions for collective bargaining or other labor or contract negotiations
The software now makes the first pass at resolutions, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Propose resolutions for collective bargaining or other labor or contract negotiations.” (O*NET task statement)
How this row was scored
Exposure score: 57 out of 100 (50–64 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Drafting possible settlements is writing work, though the deal itself still has to be agreed by people.
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 2/4.

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.

Negotiating collective bargaining agreements
The value here is that a specific person handles collective bargaining agreements and stands behind it. That is earned, not computed.
importance 5 · Core
Source: “Negotiate collective bargaining agreements.” (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: Bargaining a union contract is settled at the table by people who trust each other, not by a document generator.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Mediating discussions between employer and employee representatives in attempt to reconcile differences
The value here is that a specific person handles discussions and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Mediate discussions between employer and employee representatives in attempt to reconcile differences.” (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: Mediating between two sides depends on being trusted by both people in the room.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Calling or meeting with union
The value here is that a specific person handles union and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Call or meet with union, company, government, or other interested parties to discuss labor relations matters, such as contract negotiations or grievances.” (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: Meetings with union, company and government people work because of the relationships in the room.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.

Show the other 18 tasks

Preparing and submitting required governmental reports or forms related to labor relations matters
shifting to AI
This is reading one thing and writing another: required governmental reports in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Prepare and submit required governmental reports or forms related to labor relations matters, such as equal employment opportunity (EEO) forms, new hire forms, or minority compensation reports.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 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: Government forms such as EEO reports have fixed formats and come straight from existing staff data.
The five ratings: output a model can produce 4/4 · 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 4/4.
Writing letters related to labor relations activities
shifting to AI
This is reading one thing and writing another: letters in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Write letters related to labor relations activities, such as letters to amend collective bargaining agreements, letters of dispute or conciliation, or letters to seek clarification of contract terms.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 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: Standard labor relations letters follow set forms, and software drafts them at least as well as most people.
The five ratings: output a model can produce 4/4 · 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 reports or presentations to communicate employee satisfaction or related data to management
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 3 · Supplemental
Source: “Prepare reports or presentations to communicate employee satisfaction or related data to management.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (72–86 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: Turning survey data into a management report is chart-and-summary work software does at least as well.
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 1/4 · how much data exists 3/4.
Developing methods to monitor employee satisfaction with policies or working conditions
shifting to AI
This is reading one thing and writing another: methods in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Develop methods to monitor employee satisfaction with policies or working conditions, including grievance or complaint 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 satisfaction surveys and complaint routes is standard method work software reproduces 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.
Developing employee health and safety policies
shifting to AI
This is reading one thing and writing another: employee health in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Develop employee health and safety policies.” (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: Health and safety policies follow well-documented models, so software drafts a usable version.
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.
Identifying alternatives to proposals of unions
changing shape
The software now makes the first pass at alternatives, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Identify alternatives to proposals of unions, employees, companies, or government agencies.” (O*NET task statement)
How this row was scored
Exposure score: 57 out of 100 (50–64 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Coming up with alternatives to a proposal is analysis against known positions and precedent, drafted well by tools.
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 2/4.
Drafting rules or regulations to govern collective bargaining activities in collaboration with company
changing shape
The software now makes the first pass at rules, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Draft rules or regulations to govern collective bargaining activities in collaboration with company, government, or employee representatives.” (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: Drafting bargaining rules follows established models, so software can produce a workable first version.
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 1/4 · how much data exists 3/4.
Monitoring company or workforce adherence to labor agreements
changing shape
The software now makes the first pass at company, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Monitor company or workforce adherence to labor agreements.” (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: Tracking whether both sides stick to the agreement is largely record checking, with some shop-floor awareness.
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.
Advising management on matters related to the administration of contracts or employee discipline or grievance procedures
changing shape
The software now makes the first pass at management, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 3 · Supplemental
Source: “Advise management on matters related to the administration of contracts or employee discipline or grievance procedures.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 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; the value is that a specific person does it.
The rating behind it: Advising managers on contract administration is explaining written rules, though managers want a person they trust.
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 2/4 · how much data exists 3/4.
Assessing risk levels associated with collective bargaining strategies
changing shape
The software now makes the first pass at risk levels, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Assess risk levels associated with collective bargaining strategies.” (O*NET task statement)
How this row was scored
Exposure score: 50 out of 100 (43–57 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Judging how risky a bargaining position is depends on local knowledge of the parties involved.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Assessing the impact of union proposals on company or government operations
changing shape
The software now makes the first pass at the impact of union proposals, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Assess the impact of union proposals on company or government operations.” (O*NET task statement)
How this row was scored
Exposure score: 50 out of 100 (43–57 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Working out what a union proposal would cost this employer needs internal detail that is not public.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Selecting mediators or arbitrators for labor disputes or contract negotiations
changing shape
The software now makes the first pass at mediators, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · Core
Source: “Select mediators or arbitrators for labor disputes or contract negotiations.” (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: mistakes that are cheap to catch.
The rating behind it: Picking the right mediator rests on reputation and past experience that is mostly word of mouth.
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 2/4.
Recommending collective bargaining strategies, goals or objectives
staying human
The value here is that a specific person handles collective bargaining strategies, goals or objectives and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Recommend collective bargaining strategies, goals, or objectives.” (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 value is that a specific person does it.
The rating behind it: Recommending a bargaining strategy needs a close read of the other side, which rarely sits in any document.
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 2/4.
Reviewing and approving employee disciplinary actions
staying human
The rules require a named, qualified person to answer for employee disciplinary actions, and that person cannot be a piece of software.
importance 3 · Core
Source: “Review and approve employee disciplinary actions, such as written reprimands, suspensions, or terminations.” (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; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Approving a warning, suspension or dismissal is a decision with consequences that a named manager has to own.
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 2/4 · how much data exists 3/4.
Training managers or supervisors on topics related to labor relations
staying human
The value here is that a specific person handles managers and stands behind it. That is earned, not computed.
importance 3 · Supplemental
Source: “Train managers or supervisors on topics related to labor relations, such as working conditions, safety, or equal opportunity practices.” (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: Training supervisors is live teaching where questions and reactions shape the session.
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.
Preparing evidence for disciplinary hearings
staying human
The value here is that a specific person handles evidence and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Prepare evidence for disciplinary hearings, including preparing witnesses to testify.” (O*NET task statement)
How this row was scored
Exposure score: 23 out of 100 (16–30 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Assembling evidence is document work, but getting a nervous witness ready to speak needs a person.
The five ratings: output a model can produce 2/4 · 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.
Presenting the position of the company or of labor during arbitration or other labor negotiations
staying human
The value here is that a specific person handles the position of the company and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Present the position of the company or of labor during arbitration or other labor negotiations.” (O*NET task statement)
How this row was scored
Exposure score: 12 out of 100 (8–16 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: Arguing the case in arbitration is a live performance in front of people who judge the person speaking.
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 3/4 · how much data exists 2/4.
Providing expert testimony in legal proceedings related to labor relations or labor contracts
staying human
This work happens in the physical world: expert testimony, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Provide expert testimony in legal proceedings related to labor relations or labor contracts.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (2–10 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Expert testimony has to be given under oath by a person who can be questioned.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 3/4 · 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: $95,420a 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: 64,810in 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: employer practices in, a record out. The rows above are exactly that shape: reviewing employer practices or employee data to ensure compliance with contracts on matters and researching case law or outcomes of previous case hearings. What it cannot do is be trusted in person, which is what collective bargaining agreements 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

Your week is splitting in two, and which half fills it is the whole question. Reviewing employer practices or employee data to ensure compliance with contracts on matters is going; negotiating collective bargaining agreements is not.

So, given all that: 27% of this job's task weight sits in rows the software is already learning, 40% in rows that change shape rather than disappear, and 33% in rows it is nowhere near. That is the position, measured across 28 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 (negotiating collective bargaining agreements) 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 negotiating collective bargaining agreements, 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 labor relations specialists (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was compensation and benefits managers: only about 5% of its durable work is work you already do. Your own job splits about 27/73: 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 “investigate and evaluate union complaints or arguments to determine viability”, 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.

Compensation and Benefits Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already negotiate collective bargaining agreements, and their equivalent is to negotiate bargaining agreements. 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 →
Compensation, Benefits, and Job Analysis Specialists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already negotiate collective bargaining agreements, and their equivalent is to negotiate collective agreements on behalf of employers or workers, and mediate labor disputes…. 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. It is a pay cut, in those words: $78,210 against your $95,420, 18.0% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Human Resources Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already negotiate collective bargaining agreements, and their equivalent is to negotiate bargaining agreements and help interpret labor contracts. 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.
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: 27% of its task weight, across 28 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.

If you run a team doing this job

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

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Human resources and industrial relations officers 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 Human resources and industrial relations officers, Careers advisers and vocational guidance specialists and Human resource managers and directors. 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 labor relations specialists 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 The AI Authority, a community for non-technical managers and domain experts turning one-off AI prompts into workflows a team can trust. It overlaps with the part of your job that is growing: checking AI-drafted assessments and write-ups before they carry your name, and deciding what can safely be put into an AI in the first place. It covers no employment law, no bargaining practice and nothing about your industry. If that overlap isn't you, the free route below covers the same ground.

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.

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 labor relations specialists 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 Labor Relations Specialists?: Not as a job, but it is already doing parts of the work. Across the 28 official task statements scored for Labor Relations Specialists (United States, SOC 13-1075), 27% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 48 out of 100 (range 43–54, 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 “Labor Relations Specialists” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Prepare and submit required governmental reports or forms related to labor relations matters, such as equal employment opportunity (EEO) forms, new hire form…” (88/100, very high); “Write letters related to labor relations activities, such as letters to amend collective bargaining agreements, letters of dispute or conciliation, or letter…” (81/100, very high); “Prepare reports or presentations to communicate employee satisfaction or related data to management” (79/100, 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 “Labor Relations Specialists” stay human?: About 33% 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: “Provide expert testimony in legal proceedings related to labor relations or labor contracts” (6/100, minimal); “Present the position of the company or of labor during arbitration or other labor negotiations” (12/100, minimal); “Negotiate collective bargaining agreements” (13/100, 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 “Labor Relations Specialists” do about AI?: Start from the ledger rather than the headline: 27% of this job's weighted core work is exposed, and roughly 33% 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 Labor Relations Specialists 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 28 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.
3 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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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.