US dataswitch to UK
Claims Adjusters, Examiners, and Investigators
examining claims forms and other records to determine insurance coverage, entering claim payments and resolving complex, severe exposure claims. If that's your week, this page is about your job.
The honest answer
This job is splitting in two: examining claims forms and other records to determine insurance coverage is work AI now does quickly and cheaply, and investigating, evaluating and settle claims is work it can't touch.
Which half fills your week decides your exposure. The ledger below shows which rows you can move toward.
Your week, as this page understands it
Review settled claims to determine that payments and settlements are made in accordance with company practices and procedures. Confer with legal counsel on claims requiring litigation. May also settle insurance claims. The job title says “claims adjusters”, “examiners” or “investigators”: officially one job, several names. The real job is the part underneath: investigating, evaluating and settle claims. 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 claims adjusters, examiners, and investigators is not one task. It is 29 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is investigating, evaluating and settle claims, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 40%
- changing shape
- 15%
- staying human
- 45%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 46 out of 100 (41–52 allowing for uncertainty): partial exposure, across 29 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 claims adjusters, examiners, and investigators 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.
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.
- One row is marked low confidence, so treat it as a ballpark rather than a fine measurement.
- 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
11 tasksTasks 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.
Examining claims forms and other records to determine insurance coverage
This is reading one thing and writing another: claims forms in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Examine claims forms and other records to determine insurance coverage.” (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: Reading a claim form against policy wording to decide what is covered is document comparison software already does well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Analyzing information gathered by investigation and reporting findings and recommendations
This is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Analyze information gathered by investigation and report findings and recommendations.” (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: Pulling investigation material together into a findings-and-recommendation report is written analysis software produces to a usable standard.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Verifying and analyzing data used in settling claims to ensure that claims are valid and that settlements are made according to company practices and procedures
This is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Verify and analyze data used in settling claims to ensure that claims are valid and that settlements are made according to company practices and procedures.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Checking claim data against company rules and settlement procedures is exactly the kind of consistency checking software does quickly.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Adjusting reserves or providing reserve recommendations to ensure that reserve activities are consistent with corporate policies
This is reading one thing and writing another: reserves in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Adjust reserves or provide reserve recommendations to ensure that reserve activities are consistent with corporate policies.” (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: Setting a reserve figure follows company rules and historical patterns, which software applies consistently.
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
4 tasksTasks 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.
Reviewing police reports, medical treatment records, medical bills or physical property damage to determine the extent of liability
The software now makes the first pass at police reports, medical treatment records, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Review police reports, medical treatment records, medical bills, or physical property damage to determine the extent of liability.” (O*NET task statement)
How this row was scored
Exposure score: 49 out of 100 (42–56 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Police reports and medical records are documents software reads well, though assessing physical damage may mean looking at the property.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Paying and processing claims within designated authority level
The software now makes the first pass at claims within designated authority level, 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 5 · CoreSource: “Pay and process claims within designated authority level.” (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; someone qualified has to answer for it.
The rating behind it: Processing a payment within set limits is rules-based work, though insurers keep a qualified handler responsible for releasing money.
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 0/4 · how much data exists 3/4.
Examining claims investigated by insurance adjusters
The software now makes the first pass at claims, 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 · CoreSource: “Examine claims investigated by insurance adjusters, further investigating questionable claims to determine whether to authorize payments.” (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; someone qualified has to answer for it.
The rating behind it: Re-checking another handler's file is document review software does well, though authorising payment stays a qualified person's responsibility.
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 0/4 · how much data exists 3/4.
Staying human
14 tasksTasks 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.
Interviewing or corresponding with claimants
The rules require a named, qualified person to answer for claimants, and that person cannot be a piece of software.
importance 5 · CoreSource: “Interview or correspond with claimants, witnesses, police, physicians, or other relevant parties to determine claim settlement, denial, or review.” (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: Getting a full picture from witnesses, doctors and police relies on live conversation and a qualified handler's judgement.
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.
Interviewing or corresponding with agents and claimants to correct errors or omissions and to investigate questionable claims
The value here is that a specific person handles agents and stands behind it. That is earned, not computed.
importance 5 · CoreSource: “Interview or correspond with agents and claimants to correct errors or omissions and to investigate questionable claims.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Chasing missing details can be drafted automatically, but probing a doubtful claim depends on how a person responds in conversation.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Investigating, evaluating and settle claims
The rules require a named, qualified person to answer for settle claims, and that person cannot be a piece of software.
importance 5 · CoreSource: “Investigate, evaluate, and settle claims, applying technical knowledge and human relations skills to effect fair and prompt disposal of cases and to contribute to a reduced loss ratio.” (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 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: The paperwork side is routine, but reaching a fair settlement involves site facts, a qualified handler and dealing with an upset claimant.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Show the other 19 tasks
Obtaining credit information from banks and other credit services
shifting to AIThis is reading one thing and writing another: credit information in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Obtain credit information from banks and other credit services.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (74–88 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: Pulling a credit report from an agency is a standard lookup already done through their systems.
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.
Maintaining claim files, such as records of settled claims and an inventory of claims requiring detailed analysis
shifting to AIThis is reading one thing and writing another: claim files in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Maintain claim files, such as records of settled claims and an inventory of claims requiring detailed analysis.” (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: Keeping claim files and an inventory of cases needing analysis is routine record-keeping in the claims 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 reports to be submitted to company's data processing department
shifting to AIThis is reading one thing and writing another: reports in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Prepare reports to be submitted to company's data processing department.” (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 standard reports from claims data is straightforward reporting work software already does.
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.
Entering claim payments, reserves and new claims on computer system, inputting concise yet sufficient file documentation
shifting to AIThis is reading one thing and writing another: claim payments, reserves and new claims in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Enter claim payments, reserves and new claims on computer system, inputting concise yet sufficient file documentation.” (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: Entering payments and reserves and writing concise file notes is structured record-keeping that software handles well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Referring questionable claims to investigator or claims adjuster for investigation or settlement
shifting to AIThis is reading one thing and writing another: questionable claims in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Refer questionable claims to investigator or claims adjuster for investigation or settlement.” (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: Flagging a claim as questionable and routing it onward is pattern-spotting that claims software already does routinely.
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.
Conducting detailed bill reviews to implement sound litigation management and expense control
shifting to AIThis is reading one thing and writing another: detailed bill reviews in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Conduct detailed bill reviews to implement sound litigation management and expense control.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Checking legal bills line by line against agreed rates is repetitive comparison work software does thoroughly.
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.
Reporting overpayments, underpayments and other irregularities
shifting to AIThis is reading one thing and writing another: overpayments, underpayments and other irregularities in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Report overpayments, underpayments, and other irregularities.” (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: Spotting over- and underpayments is reconciliation work software does faster and more 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 0/4 · how much data exists 3/4.
Communicating with reinsurance brokers to obtain information necessary for processing claims
changing shapeThe software now makes the first pass at reinsurance brokers, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Communicate with reinsurance brokers to obtain information necessary for processing claims.” (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: Requesting and chasing information from reinsurance brokers is routine correspondence that can be drafted and tracked automatically.
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.
Supervising claims adjusters to ensure that adjusters have followed proper methods
staying humanThe rules require a named, qualified person to answer for claims adjusters, and that person cannot be a piece of software.
importance 4 · SupplementalSource: “Supervise claims adjusters to ensure that adjusters have followed proper methods.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: File checks can be automated, but supervising adjusters also means coaching individuals and carrying responsibility for their decisions.
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 1/4 · how much data exists 3/4.
Communicating with former associates to verify employment record or to obtain background information regarding persons or businesses applying
staying humanThe ratings behind this row put former associates well outside what today's tools can do on their own.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Communicate with former associates to verify employment record or to obtain background information regarding persons or businesses applying for credit.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Verification requests are easy to draft, but getting candid background from a former colleague depends on the conversation.
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 1/4 · how much data exists 2/4.
Contacting or interviewing claimants, doctors, medical specialists or employers to get additional information
staying humanThe value here is that a specific person handles claimants, doctors, medical specialists or employers and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Contact or interview claimants, doctors, medical specialists, or employers to get additional information.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Requests can be written automatically, but getting useful extra detail from a doctor or employer depends on the conversation.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Presenting cases and participating in their discussion at claim committee meetings
staying humanThe value here is that a specific person handles cases and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Present cases and participate in their discussion at claim committee meetings.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 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: The case summary is easy to prepare, but presenting and defending it to colleagues in a meeting is a live task.
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 3/4.
Investigating and assessing damage to property and creating or reviewing property damage estimates
staying humanThis work happens in the physical world: damage, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Investigate and assess damage to property and create or review property damage estimates.” (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: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Estimates can be built from photos and price tables, but assessing real damage often still means visiting the property.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Collecting evidence to support contested claims in court
staying humanThe rules require a named, qualified person to answer for evidence, and that person cannot be a piece of software.
importance 4 · CoreSource: “Collect evidence to support contested claims in court.” (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: someone qualified has to answer for it.
The rating behind it: Assembling evidence for a contested claim can be drafted, but gathering it often means going places and meeting legal standards.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Negotiating claim settlements or recommending litigation when settlement cannot
staying humanThe rules require a named, qualified person to answer for claim settlements, and that person cannot be a piece of software.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Negotiate claim settlements or recommend litigation when settlement cannot be negotiated.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (17–25 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: A settlement figure can be calculated, but agreeing it with a claimant is a negotiation resting on trust and authority.
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 3/4 · how much data exists 2/4.
Conferring with legal counsel on claims requiring litigation
staying humanThe value here is that a specific person handles legal counsel and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Confer with legal counsel on claims requiring litigation.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (13–27 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: Deciding litigation strategy with a lawyer is a professional conversation about a specific case, not a document to be produced.
The five ratings: output a model can produce 1/4 · 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.
Examining titles to property to determine validity and act as company agent in transactions with property owners
staying humanThe rules require a named, qualified person to answer for titles, and that person cannot be a piece of software.
importance 4 · SupplementalSource: “Examine titles to property to determine validity and act as company agent in transactions with property owners.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (8–32 allowing for uncertainty): low exposure, low confidence.
Why it sits in this group: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Title records can be read on screen, but acting as the company's agent with a property owner is a person's role.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Resolving complex, severe exposure claims, using high service oriented file handling
staying humanThe rules require a named, qualified person to answer for complex, severe exposure claims, and that person cannot be a piece of software.
importance 4 · CoreSource: “Resolve complex, severe exposure claims, using high service oriented file handling.” (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: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Large, messy claims turn on the handler's judgement and on keeping a worried customer's confidence throughout.
The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Attending mediations or trials
staying humanThis work happens in the physical world: mediations, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Attend mediations or trials.” (O*NET task statement)
How this row was scored
Exposure score: 3 out of 100 (0–7 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: Attending a mediation or trial means a person physically representing the insurer in the room.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 3/4 · how much data exists 1/4.
What this job pays, and how many people do it
- Median pay
- $78,000a 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
- 324,230in 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: claims forms in, a record out. The rows above are exactly that shape: examining claims forms and other records to determine insurance coverage and analyzing information gathered by investigation and reporting findings and recommendations. What it cannot do is be answerable: settle claims need a named person the rules will accept, and software cannot be that person. 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. Examining claims forms and other records to determine insurance coverage is going; investigating, evaluating and settle claims is not.
So, given all that: 40% of this job's task weight sits in rows the software is already learning, 15% in rows that change shape rather than disappear, and 45% in rows it is nowhere near. That is the position, measured across 29 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 (investigating, evaluating and settle claims) 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 investigating, evaluating and settle claims, 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 claims adjusters, examiners, and investigators (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was insurance sales agents: only about 3% of its durable work is work you already do and it pays 20.2% less. Your own job splits about 40/60: 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 “review police reports, medical treatment records, medical bills, or physical property damage…”, 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.
Insurance Sales Agents
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already verify and analyze data used in settling claims to ensure that claims are…, and their equivalent is to confer with clients to obtain and provide information when claims are made on…. 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: $62,280 against your $78,000, 20.2% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Arbitrators, Mediators, and Conciliators
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already interview or correspond with agents and claimants to correct errors or omissions and…, and their equivalent is to interview claimants, agents, or witnesses to obtain information about disputed issues. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 9,210 of those jobs against 324,230 of yours (OEWS May 2025), 3% as many seats.
Administrative Law Judges, Adjudicators, and Hearing Officers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already examine claims investigated by insurance adjusters, further investigating questionable claims to determine whether…, and their equivalent is to authorize payment of valid claims and determine method of payment. 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. And it is a narrow door: about 16,370 of those jobs against 324,230 of yours (OEWS May 2025), 5% as many seats.
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: 40% of its task weight, across 29 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 Estimators, valuers and assessors 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 Estimators, valuers and assessors. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
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:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
A nearby route
There's no Space built for claims adjusters and examiners 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.
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 insurance law, no policy interpretation and nothing about your claims system. If that overlap isn't you, the free route below covers the same ground.
- Problem: “I can use AI, but I can’t turn it into a workflow my team can trust”
- Problem: “I can’t hand off AI work without it falling apart”
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 claims adjusters and examiners launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
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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 Claims Adjusters, Examiners, and Investigators?
- Not as a job, but it is already doing parts of the work. Across the 29 official task statements scored for Claims Adjusters, Examiners, and Investigators (United States, SOC 13-1031), 40% 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 41–52, 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 “Claims Adjusters, Examiners, and Investigators” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Obtain credit information from banks and other credit services” (81/100, very high); “Maintain claim files, such as records of settled claims and an inventory of claims requiring detailed analysis” (75/100, high); “Prepare reports to be submitted to company's data processing department” (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 “Claims Adjusters, Examiners, and Investigators” stay human?
- About 45% 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: “Attend mediations or trials” (3/100, minimal); “Resolve complex, severe exposure claims, using high service oriented file handling” (13/100, minimal); “Examine titles to property to determine validity and act as company agent in transactions with property owners” (20/100, 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 “Claims Adjusters, Examiners, and Investigators” do about AI?
- Start from the ledger rather than the headline: 40% of this job's weighted core work is exposed, and roughly 45% 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 Claims Adjusters, Examiners, and Investigators 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 29 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
About the data on this page
- One row is marked low confidence, so treat it as a ballpark rather than a fine measurement.
- 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 2026-08-04.
- 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.
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}
}Data as of release 2026-q4.1, published . 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.