US dataswitch to UK
Court, Municipal, and License Clerks
evaluating information on applications to verify completeness and accuracy and to determine whether applicants are qualified to obtain desired licenses, examining legal documents submitted to courts for adherence to laws or court procedures and participating in the administration of municipal elections. If that's your week, this page is about your job.
The honest answer
Most tasks in this job are the kind AI has learned to do: evaluating information on applications to verify completeness and accuracy and to determine whether applicants are qualified to obtain desired licenses. The tasks, though, are not you.
It would be a lie to soften that; verifying the authenticity of documents is what this work rebuilds around. The routes below start from it.
Your week, as this page understands it
Perform clerical duties for courts of law, municipalities, or governmental licensing agencies and bureaus. May prepare docket of cases to be called; secure information for judges and court; prepare draft agendas or bylaws for town or city council; answer official correspondence; keep fiscal records and accounts; issue licenses or permits; and record data, administer tests, or collect fees. The job title says “court”, “municipal” or “license clerks”: officially one job, several names. The real job is the part underneath: verifying the authenticity of documents. 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 court, municipal, and license clerks is not one task. It is 30 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is verifying the authenticity of documents, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 58%
- changing shape
- 31%
- staying human
- 11%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 63 out of 100 (57–68 allowing for uncertainty): high exposure, across 30 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 court, municipal, and license clerks 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.
- 2 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
17 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.
Evaluating information on applications to verify completeness and accuracy and to determine whether applicants are qualified to obtain desired licenses
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: “Evaluate information on applications to verify completeness and accuracy and to determine whether applicants are qualified to obtain desired licenses.” (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: Checking an application against written eligibility rules is what software is good at.
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.
Recording case dispositions, court orders or arrangements made for payment of court fees
This is reading one thing and writing another: case dispositions, court orders or arrangements made in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Record case dispositions, court orders, or arrangements made for payment of court fees.” (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: Entering court outcomes into the record follows set formats and codes.
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.
Examining legal documents submitted to courts for adherence to laws or court procedures
This is reading one thing and writing another: legal documents in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Examine legal documents submitted to courts for adherence to laws or court 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 filings against court rules is a documented, repeatable comparison.
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.
Performing general office duties, such as taking or transcribing dictation
This is reading one thing and writing another: general office duties in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Perform general office duties, such as taking or transcribing dictation, typing or proofreading correspondence, distributing or filing official forms, or scheduling appointments.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 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: Typing, proofreading and scheduling are routine, though paper still has to be moved around the office.
The five ratings: output a model can produce 4/4 · 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.
Changing shape
9 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.
Performing administrative tasks, such as answering telephone calls, filing court documents or maintaining office supplies or equipment
The software now makes the first pass at administrative tasks, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Perform administrative tasks, such as answering telephone calls, filing court documents, or maintaining office supplies or equipment.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Most of this is routine office work, though filing paper and minding supplies needs someone present.
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 1/4 · how much data exists 3/4.
Answering questions or providing advice to the public regarding licensing policies
The software now makes the first pass at questions, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Answer questions or provide advice to the public regarding licensing policies, procedures, or regulations.” (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: Licensing rules are published, so most questions have a documented answer, with people for odd cases.
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.
Answering inquiries from the general public regarding judicial procedures
The software now makes the first pass at inquiries, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Answer inquiries from the general public regarding judicial procedures, court appearances, trial dates, adjournments, outstanding warrants, summonses, subpoenas, witness fees, or payment of fines.” (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: Court dates and procedures come straight from the case system, though clerks must stop short of legal advice.
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.
Staying human
4 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.
Verifying the authenticity of documents
This work happens in the physical world: the authenticity of documents, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Verify the authenticity of documents, such as foreign identification or immigration documents.” (O*NET task statement)
How this row was scored
Exposure score: 19 out of 100 (12–26 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Spotting a forged passport usually means handling the document and looking at its security features.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Training other workers or coordinating their work
The value here is that a specific person handles other workers and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Train other workers or coordinate their work, as necessary.” (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: Showing a colleague how the office works depends on being alongside them.
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.
Participating in the administration of municipal elections
This work happens in the physical world: the administration of municipal elections, in a real place. Software cannot follow it there.
importance 5 · SupplementalSource: “Participate in the administration of municipal elections, such as preparation or distribution of ballots, appointment or training of election officers, or tabulation or certification of results.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (1–15 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Ballots, poll workers and counts are physical, closely regulated work on the day.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Show the other 20 tasks
Preparing meeting agendas or packets of related information
shifting to AIThis is reading one thing and writing another: agendas in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Prepare meeting agendas or packets of related information.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Agendas and packets are assembled from documents that have already been submitted.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Coding information on license applications for entry into computers
shifting to AIThis is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Code information on license applications for entry into computers.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Coding form data for entry is routine and rules-driven.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Coordinating or maintaining office tracking systems for correspondence or follow-up actions
shifting to AIThis is reading one thing and writing another: office tracking systems in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Coordinate or maintain office tracking systems for correspondence or follow-up actions.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Tracking correspondence and follow-up actions is what case management software is for.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Preparing ordinances, resolutions or proclamations so that they
shifting to AIThis is reading one thing and writing another: ordinances, resolutions or proclamations in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Prepare ordinances, resolutions, or proclamations so that they can be executed, recorded, archived, or distributed.” (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: These are template documents, and drafting from a template is a strength of current software.
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.
Performing record checks on past or current licensees
shifting to AIThis is reading one thing and writing another: record checks in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Perform record checks on past or current licensees, as required by investigations.” (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: Searching records for an investigation is a documented lookup.
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 documents recording the outcomes of court proceedings
shifting to AIThis is reading one thing and writing another: documents recording the outcomes of court proceedings in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Prepare documents recording the outcomes of court proceedings.” (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: Recording outcomes uses fixed document formats and existing case 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 3/4.
Preparing dockets or calendars of cases
shifting to AIThis is reading one thing and writing another: dockets in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Prepare dockets or calendars of cases to be called.” (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: Building the day's case list from scheduling data is routine automation.
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.
Recording and editing the minutes of meetings and distributing to appropriate officials or staff members
shifting to AIThis is reading one thing and writing another: the minutes of meetings in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Record and edit the minutes of meetings and distribute to appropriate officials or staff members.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 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: Meeting notes can be transcribed and tidied automatically once the meeting is captured.
The five ratings: output a model can produce 4/4 · 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.
Preparing and issuing orders of the court
shifting to AIThis is reading one thing and writing another: orders of the court in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Prepare and issue orders of the court, such as probation orders, release documentation, sentencing information, or summonses.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 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; someone qualified has to answer for it.
The rating behind it: The wording is templated, but a court order only carries weight when issued under the court's own authority.
The five ratings: output a model can produce 4/4 · 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.
Issuing public notification of all official activities or meetings
shifting to AIThis is reading one thing and writing another: public notification of all official activities in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Issue public notification of all official activities or meetings.” (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: Public notices follow set wording and timetables, so drafting and issuing them can be largely automatic.
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.
Recording and maintaining all vital and fiscal records and accounts
shifting to AIThis is reading one thing and writing another: all vital and fiscal records and accounts in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Record and maintain all vital and fiscal records and accounts.” (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: Keeping records and accounts is desk work built around documented rules.
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.
Performing budgeting duties, such as assisting in budget preparation, expenditure review or budget administration
shifting to AIThis is reading one thing and writing another: duties in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Perform budgeting duties, such as assisting in budget preparation, expenditure review, or budget administration.” (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: Budget preparation and expenditure review are standard spreadsheet tasks.
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.
Responding to requests for information from the public
shifting to AIThis is reading one thing and writing another: requests in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Respond to requests for information from the public, other municipalities, state officials, or state and federal legislative offices.” (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: Answering records requests means finding documents and writing a standard reply.
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.
Searching files and contacting witnesses
changing shapeThe software now makes the first pass at files, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Search files and contact witnesses, attorneys, or litigants to obtain information for the court.” (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: Finding the file is easy for software; getting hold of witnesses and lawyers means chasing people.
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.
Instructing parties about timing of court appearances
changing shapeThe software now makes the first pass at parties, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Instruct parties about timing of court appearances.” (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: The dates come from the court calendar and the message is standard, though people often want to ask a person.
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.
Planning or directing the maintenance
changing shapeThe software now makes the first pass at the maintenance, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Plan or direct the maintenance, filing, safekeeping, or computerization of all municipal documents.” (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: Planning how records are stored and digitized is documented management work.
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.
Researching information in the municipal archives upon request of public officials or private citizens
changing shapeThe software now makes the first pass at information, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Research information in the municipal archives upon request of public officials or private citizens.” (O*NET task statement)
How this row was scored
Exposure score: 51 out of 100 (44–58 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: Digitized records can be searched instantly; older paper archives still need someone in the room.
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 2/4.
Questioning applicants to obtain required information
changing shapeThe software now makes the first pass at applicants, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Question applicants to obtain required information, such as name, address, or age, and record data on prescribed forms.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: The form-filling is automatic; someone still serves the person at the counter.
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 1/4 · how much data exists 3/4.
Performing contract administration duties, assisting with bid openings or the awarding of contracts
changing shapeThe software now makes the first pass at contract administration duties, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Perform contract administration duties, assisting with bid openings or the awarding of contracts.” (O*NET task statement)
How this row was scored
Exposure score: 42 out of 100 (35–49 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: Paperwork and checks can be automated, but bid openings are formal events held in public.
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 1/4 · how much data exists 3/4.
Issuing various permits and licenses
staying humanThis work happens in the physical world: various permits, in a real place. Software cannot follow it there.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Issue various permits and licenses, such as marriage, fishing, hunting, and dog licenses, and collect appropriate fees.” (O*NET task statement)
How this row was scored
Exposure score: 28 out of 100 (21–35 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: Online licensing handles much of it, but counter service and fee handling still involve a person.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
What this job pays, and how many people do it
- Median pay
- $48,700a 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
- 179,750in 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: case dispositions, court orders or arrangements made in, a record out. The rows above are exactly that shape: evaluating information on applications to verify completeness and accuracy and to determine whether applicants are qualified to obtain desired licenses and recording case dispositions, court orders or arrangements made for payment of court fees. What it cannot do is be answerable: the authenticity of documents needs 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
The exposed part of your job is the biggest part, and I am not going to dress that up: evaluating information on applications to verify completeness and accuracy and to determine whether applicants are qualified to obtain desired licenses is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 58% of this job's task weight sits in rows the software is already learning, 31% in rows that change shape rather than disappear, and 11% in rows it is nowhere near. That is the position, measured across 30 scored tasks. It is not a forecast about you.
What you have that the software does not is verifying the authenticity of documents, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.
This week: one thing
Sit on the machine's side of the desk. Pick one real piece of case dispositions, court orders or arrangements made you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.
- What you end up holding
- a written list of the machine’s mistakes, in your handwriting
- How long it takes
- an evening, or an hour if you pick one job rather than one client
If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of case dispositions, court orders or arrangements made, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.
Over the next 90 days
Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is evaluating information on applications to verify completeness and accuracy and to determine whether applicants are qualified to obtain desired licenses” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.
Over the next 12 months
Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of verifying the authenticity of documents you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to court, municipal, and license clerks (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was court reporters and simultaneous captioners: only about 7% of its durable work is work you already do and there are far fewer of those jobs than of yours. I am not going to pretend that is comfortable news: 58% of your own task list is already in the top exposure band. But the answer on this evidence is not a sideways jump into a job with the same problem. It is to walk toward the end of this one that answers for things. “perform administrative tasks” is the part that stays with a person, and spending a year getting formally recognised for it beats a standing start somewhere else.
How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.
3 moves I checked and rejected
These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.
Court Reporters and Simultaneous Captioners
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already prepare documents recording the outcomes of court proceedings, and their equivalent is to log and store exhibits from court proceedings. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 12,870 of those jobs against 179,750 of yours (OEWS May 2025), 7% as many seats.
Bookkeeping, Accounting, and Auditing Clerks
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already perform general office duties, and their equivalent is to perform general office duties. 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. I will not move you off one melting floe onto another: that job is projected to shrink 5.8% between 2024 and 2034 (BLS Employment Projections), and 71% of its own task list already scores in the top exposure band (75/100 in this release), so the same software is eating it.
Judges, Magistrate Judges, and Magistrates
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already examine legal documents submitted to courts for adherence to laws or court procedures, and their equivalent is to supervise other judges, court officers, and the court's administrative staff. 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. The pay gap is the market pricing a barrier: $153,990 against your $48,700 is 3.16× (OEWS May 2025 (both)), and you would be crossing it holding about 2% of their durable work. A gap that size with an overlap that small is a wish, not a route. And it is a narrow door: about 24,030 of those jobs against 179,750 of yours (OEWS May 2025), 13% 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: 58% of its task weight, across 30 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: verifying the authenticity of documents is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Inspectors of standards and regulations 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 Inspectors of standards and regulations. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
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
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for court / municipal / license clerks, and we are not going to point you at the nearest one and call it a fit.
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 58% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for court / municipal / license clerks. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for court / municipal / license clerks 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 Court, Municipal, and License Clerks?
- Not as a job, but it is already doing parts of the work. Across the 30 official task statements scored for Court, Municipal, and License Clerks (United States, SOC 43-4031), 58% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 63 out of 100 (range 57–68, band: high). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Court, Municipal, and License Clerks” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Prepare meeting agendas or packets of related information” (93/100, very high); “Code information on license applications for entry into computers” (93/100, very high); “Coordinate or maintain office tracking systems for correspondence or follow-up actions” (93/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Court, Municipal, and License Clerks” stay human?
- About 11% 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: “Participate in the administration of municipal elections, such as preparation or distribution of ballots, appointment or training of election officers, or ta…” (8/100, minimal); “Verify the authenticity of documents, such as foreign identification or immigration documents” (19/100, minimal); “Issue various permits and licenses, such as marriage, fishing, hunting, and dog licenses, and collect appropriate fees” (28/100, 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 “Court, Municipal, and License Clerks” do about AI?
- Start from the ledger rather than the headline: 58% of this job's weighted core work is exposed, and roughly 11% 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 Court, Municipal, and License Clerks 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 30 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.
- 2 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.