US dataswitch to UK
Librarians and Media Collections Specialists
checking books in and out of the library, responding to customer complaints, taking action and explaining use of library facilities. If that's your week, this page is about your job.
The honest answer
AI is already taking a real slice of the routine work here: searching standard reference materials, including online sources and the Internet, to answer patrons' reference questions. That is a slice of tasks, not of you.
That slice is not coming back; the core of the job, checking books in and out of the library, stays yours. The tools change hands, the accountability doesn't.
Your week, as this page understands it
Administer and maintain libraries or collections of information, for public or private access through reference or borrowing. Work in a variety of settings, such as educational institutions, museums, and corporations, and with various types of informational materials, such as books, periodicals, recordings, films, and databases. Tasks may include acquiring, cataloging, and circulating library materials, and user services such as locating and organizing information, providing instruction on how to access information, and setting up and operating a library's media equipment. The job title says “librarians” or “media collections specialists”: officially one job, two names. The real job is the part underneath: checking books in and out of the library. 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 librarians and media collections specialists 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 checking books in and out of the library, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 31%
- changing shape
- 18%
- staying human
- 52%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 40 out of 100 (35–45 allowing for uncertainty): partial 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 librarians and media collections specialists is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.
How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.
Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.
Your job, task by task
These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.
- 3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
9 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.
Reviewing and evaluating materials, using book reviews, catalogs, faculty recommendations and current holdings to select and order print, audio-visual and electronic resources
This is reading one thing and writing another: materials in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Review and evaluate materials, using book reviews, catalogs, faculty recommendations, and current holdings to select and order print, audio-visual, and electronic resources.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (60–68 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: Weighing reviews, catalogs and what the library already owns is document work a computer handles well from existing records.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Keeping up-to-date records of circulation and materials
This is reading one thing and writing another: up-to-date records of circulation in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Keep up-to-date records of circulation and materials, maintain inventory, and correct cataloging errors.” (O*NET task statement)
How this row was scored
Exposure score: 62 out of 100 (58–66 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: Circulation records and catalog corrections live in software already, with only occasional shelf checking needed in person.
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 4/4.
Searching standard reference materials, including online sources and the Internet, to answer patrons' reference questions
This is reading one thing and writing another: standard reference materials in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Search standard reference materials, including online sources and the Internet, to answer patrons' reference questions.” (O*NET task statement)
How this row was scored
Exposure score: 85 out of 100 (81–89 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: Looking up answers in reference sources and online is exactly what search and language tools now do very well.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.
Evaluating materials to determine outdated or unused items
This is reading one thing and writing another: materials in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Evaluate materials to determine outdated or unused items to be discarded.” (O*NET task statement)
How this row was scored
Exposure score: 62 out of 100 (58–66 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: Deciding what to discard runs mostly on borrowing figures and dates the library system already holds.
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 4/4.
Changing shape
5 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.
Analyzing patrons' requests to determine needed information and assist in furnishing or locating that information
The software now makes the first pass at patrons' requests, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Analyze patrons' requests to determine needed information and assist in furnishing or locating that information.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Working out what someone actually needs and finding it is mostly information work, though fetching items still needs a person.
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 4/4.
Explaining use of library facilities
The software now makes the first pass at use of library facilities, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Explain use of library facilities, resources, equipment, and services, and provide information about library policies.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (44–52 allowing for uncertainty): partial exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Explaining opening hours, services and rules is standard information already published, so software can answer most of it.
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.
Locating unusual or unique information in response to specific requests
The software now makes the first pass at unusual, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Locate unusual or unique information in response to specific requests.” (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: Hunting for obscure information suits automated search, though some answers only exist in physical archives someone must visit.
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.
Staying human
16 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.
Teaching library patrons basic computer skills
The value here is that a specific person handles library patrons basic computer skills and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Teach library patrons basic computer skills, such as searching computerized databases.” (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: Software can explain how databases work, but sitting beside a nervous beginner at a library computer is the real job.
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.
Checking books in and out of the library
This work happens in the physical world: books, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Check books in and out of the library.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (5–13 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Issuing and returning physical books means handling them at the desk or a kiosk.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Conferring with colleagues, faculty and community members and organizations to conduct informational programs
The value here is that a specific person handles colleagues, faculty and community members and organizations and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Confer with colleagues, faculty, and community members and organizations to conduct informational programs, make collection decisions, and determine library services to offer.” (O*NET task statement)
How this row was scored
Exposure score: 17 out of 100 (13–21 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Reaching agreement with faculty and community groups depends on live discussion and local relationships software cannot join.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Show the other 20 tasks
Developing, maintaining and troubleshooting information access aids, such as databases, annotated bibliographies, Web pages, electronic pathfinders, software programs and online tutorials
shifting to AIThis is reading one thing and writing another: information access aids in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Develop, maintain, and troubleshoot information access aids, such as databases, annotated bibliographies, Web pages, electronic pathfinders, software programs, and online tutorials.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Building guides, web pages, bibliographies and tutorials is written and coded output that software produces quickly and well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Compiling lists of books, periodicals, articles and audio-visual materials on particular subjects
shifting to AIThis is reading one thing and writing another: lists of books, periodicals, articles and audio-visual materials in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Compile lists of books, periodicals, articles, and audio-visual materials on particular subjects.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Pulling together subject reading lists is fast automated work, though references still need checking because some come out wrong.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Coding, classifying and catalog books, publications, films, audio-visual aids and other library materials
shifting to AIThis is reading one thing and writing another: catalog books, publications, films in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Code, classify, and catalog books, publications, films, audio-visual aids, and other library materials, based on subject matter or standard library classification systems.” (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: Classification systems are published rules computers apply very reliably, though the item itself is usually examined by hand first.
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 4/4.
Developing library policies and procedures
shifting to AIThis is reading one thing and writing another: library policies in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Develop library policies and procedures.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (60–68 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: Library policies and procedures follow well-known models and are straightforward documents to draft.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Maintaining inventory of audio-visual equipment
shifting to AIThis is reading one thing and writing another: inventory of audio-visual equipment in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Maintain inventory of audio-visual equipment.” (O*NET task statement)
How this row was scored
Exposure score: 62 out of 100 (58–66 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: Equipment inventories are database records, with only occasional physical counting to confirm what is actually there.
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 4/4.
Arranging for interlibrary loans of materials not available in a particular library
changing shapeThe software now makes the first pass at interlibrary loans of materials not available, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Arrange for interlibrary loans of materials not available in a particular library.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (49–57 allowing for uncertainty): partial exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Interlibrary loan runs through shared request systems that software navigates well, with staff only handling the physical items.
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 4/4.
Evaluating vendor products and performance
changing shapeThe software now makes the first pass at vendor products, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Evaluate vendor products and performance, negotiate contracts, and place orders.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Comparing vendor products is straightforward desk work, but negotiating terms is a live conversation between organizations.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Supervising daily library operations, budgeting, planning and personnel activities, such as hiring, training, scheduling and performance evaluations
staying humanThe value here is that a specific person handles library operations, budgeting, planning and personnel activities and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Supervise daily library operations, budgeting, planning, and personnel activities, such as hiring, training, scheduling, and performance evaluations.” (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: Budgets and rotas can be drafted by software, but hiring, appraising and leading a team rests on people knowing each other.
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.
Conferring with teachers to select course materials and to determine which training aids are best suited to particular grade levels
staying humanThe value here is that a specific person handles teachers and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Confer with teachers to select course materials and to determine which training aids are best suited to particular grade levels.” (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: Suggesting materials is easy automatically, but agreeing what suits a particular class comes from talking with the teacher.
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.
Responding to customer complaints, taking action
staying humanThe value here is that a specific person handles customer complaints, taking action and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Respond to customer complaints, taking action as necessary.” (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 value is that a specific person does it.
The rating behind it: A reply can be drafted automatically, but calming an upset visitor and choosing a fair remedy still needs a person.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Planning and teaching classes on topics
staying humanThis work happens in the physical world: classes, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Plan and teach classes on topics such as information literacy, library instruction, and technology use.” (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; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Lesson plans and slides are easy to draft, but standing in front of a class and reading the room is not.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 4/4.
Planning and delivering client-centered programs and services
staying humanThis work happens in the physical world: client-centered programs, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Plan and deliver client-centered programs and services, such as special services for corporate clients, storytelling for children, newsletters, or programs for special groups.” (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 same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Programs can be planned on paper, but storytelling to children and running sessions means being in the room.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Directing and training library staff in duties
staying humanThis work happens in the physical world: library staff, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Direct and train library staff in duties, such as receiving, shelving, researching, cataloging, and equipment use.” (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 same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Training materials can be written automatically, but showing staff how to shelve and handle equipment happens face to face.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Training faculty and media staff on the use of software and audio-visual equipment
staying humanThis work happens in the physical world: faculty, 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: “Train faculty and media staff on the use of software and audio-visual equipment.” (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 same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Guides and walkthroughs can be produced automatically, but hands-on training sessions with staff happen in person.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Engaging in professional development activities
staying humanThis work happens in the physical world: professional development activities, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Engage in professional development activities, such as taking continuing education classes and attending or participating in conferences, workshops, professional meetings, and associations.” (O*NET task statement)
How this row was scored
Exposure score: 17 out of 100 (10–24 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Software can summarize the reading, but the point is the librarian personally gaining and keeping current skills.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Troubleshooting problems with audio-visual equipment
staying humanThis work happens in the physical world: problems, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Troubleshoot problems with audio-visual equipment.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (6–14 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Fixing a projector or microphone means having hands on the equipment, not just knowing what is wrong.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Maintaining hardware and software, including computers, media equipment, scanners, color copiers and color laser printers
staying humanThis work happens in the physical world: hardware, 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: “Maintain hardware and software, including computers, media equipment, scanners, color copiers, and color laser printers.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (6–14 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Keeping computers, copiers and media gear working means physically servicing machines, not just diagnosing them.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Representing library or institution on internal and external committees
staying humanThis work happens in the physical world: library, in a real place. Software cannot follow it there.
importance 3 · CoreSource: “Represent library or institution on internal and external committees.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (5–13 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Sitting on a committee means a named person in the room whose standing and judgment carry the library voice.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Setting up, adjust and operating audio-visual equipment, such as cameras
staying humanThis work happens in the physical world: up, adjust and operating audio-visual equipment, in a real place. Software cannot follow it there.
importance 3 · CoreSource: “Set up, adjust, and operate audio-visual equipment, such as cameras, film and slide projectors, and recording equipment, for meetings, events, classes, seminars, and video conferences.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Setting up cameras, projectors and recording gear is hands-on equipment work that has to happen in the room.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Assembling and arranging display materials
staying humanThis work happens in the physical world: display materials, in a real place. Software cannot follow it there.
importance 3 · CoreSource: “Assemble and arrange display materials.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: A display can be designed on screen, but assembling and arranging the physical materials takes hands on the shelves.
The five ratings: output a model can produce 1/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
What this job pays, and how many people do it
- Median pay
- $68,270a 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
- 133,790in 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: standard reference materials in, a record out. The rows above are exactly that shape: searching standard reference materials and reviewing and evaluating materials, using book reviews. What it cannot do is be there in the room, and that is still where books get done. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
Your week is splitting in two, and which half fills it is the whole question. Searching standard reference materials, including online sources and the Internet, to answer patrons' reference questions is going; checking books in and out of the library is not.
So, given all that: 31% of this job's task weight sits in rows the software is already learning, 18% in rows that change shape rather than disappear, and 52% in rows it is nowhere near. That is the position, measured across 30 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 (checking books in and out of the library) 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 checking books in and out of the library, 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 librarians and media collections specialists (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was library technicians: only about 17% of its durable work is work you already do and it pays 34.7% less. Your own job splits about 31/69: 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 “teach library patrons basic computer skills”, 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.
Library Technicians
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already check books in and out of the library, and their equivalent is to check for damaged library materials. Across both published task lists that is about 17% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 17% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. It is a pay cut, in those words: $44,580 against your $68,270, 34.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Library Assistants, Clerical
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already maintain inventory of audio-visual equipment, and their equivalent is to operate and maintain audio-visual equipment. Across both published task lists that is about 12% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 12% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. It is a pay cut, in those words: $36,910 against your $68,270, 45.9% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
First-Line Supervisors of Housekeeping and Janitorial Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already check books in and out of the library, and their equivalent is to check and maintain equipment to ensure that it is in working order. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $49,100 against your $68,270, 28.1% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
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: 31% of its task weight, across 30 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 Librarians 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 Librarians. 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 librarians / media collections specialists, 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 31% 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 librarians / media collections specialists. 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 librarians / media collections specialists launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.
No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace Librarians and Media Collections Specialists?
- Not as a job, but it is already doing parts of the work. Across the 30 official task statements scored for Librarians and Media Collections Specialists (United States, SOC 25-4022), 31% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 40 out of 100 (range 35–45, 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 “Librarians and Media Collections Specialists” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Search standard reference materials, including online sources and the Internet, to answer patrons' reference questions” (85/100, very high); “Develop, maintain, and troubleshoot information access aids, such as databases, annotated bibliographies, Web pages, electronic pathfinders, software program…” (83/100, very high); “Compile lists of books, periodicals, articles, and audio-visual materials on particular subjects” (83/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Librarians and Media Collections Specialists” stay human?
- About 52% 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: “Assemble and arrange display materials” (0/100, minimal); “Set up, adjust, and operate audio-visual equipment, such as cameras, film and slide projectors, and recording equipment, for meetings, events, classes, semin…” (0/100, minimal); “Represent library or institution on internal and external committees” (9/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
- What should someone working in “Librarians and Media Collections Specialists” do about AI?
- Start from the ledger rather than the headline: 31% of this job's weighted core work is exposed, and roughly 52% 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 Librarians and Media Collections Specialists calculated?
- Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 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.
- 3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt-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.