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Executive Secretaries and Executive Administrative Assistants

managing and maintaining executives' schedules, opening, sorting and distributing incoming correspondence and greeting visitors and determining whether they should be given access to specific individuals. 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: preparing invoices, reports, memos, letters, financial statements and other documents. The tasks, though, are not you.

Your move: three real directions from here ↓

It would be a lie to soften that; greeting visitors and determining whether they should be given access to specific individuals is what this work rebuilds around. The plan below starts there.

Your week, as this page understands it

Provide high-level administrative support by conducting research, preparing statistical reports, and handling information requests, as well as performing routine administrative functions such as preparing correspondence, receiving visitors, arranging conference calls, and scheduling meetings. May also train and supervise lower-level clerical staff. The job title says “executive secretaries” or “executive administrative assistants”: officially one job, two names. The real job is the part underneath: greeting visitors and determining whether they should be given access to specific individuals. 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 executive secretaries and executive administrative assistants is not one task. It is 22 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is greeting visitors and determining whether they should be given access to specific individuals, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI
68%
changing shape
10%
staying human
22%

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

Whole-job exposure score 65 out of 100 (59–70 allowing for uncertainty): high exposure, across 22 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 executive secretaries and executive administrative assistants 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.

Shifting to AI

15 tasks

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

  • Preparing invoices, reports, memos, letters, financial statements and other documents

    This is reading one thing and writing another: invoices, reports, memos, letters in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source: “Prepare invoices, reports, memos, letters, financial statements, and other documents, using word processing, spreadsheet, database, or presentation software.” (O*NET task statement)
    How this row was scored

    Exposure score: 100 out of 100 (96–100 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: Producing letters, memos, reports and spreadsheets from existing information is exactly what document software and AI do 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 0/4 · how much data exists 4/4.

  • Answering phone calls and directing calls to appropriate parties or taking messages

    This is reading one thing and writing another: phone calls in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source: “Answer phone calls and direct calls to appropriate parties or take messages.” (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: Automated phone systems already answer calls, route them to the right person and take messages reliably.

    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.

  • Performing general office duties, such as ordering supplies

    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 · Core
    Source: “Perform general office duties, such as ordering supplies, maintaining records management database systems, and performing basic bookkeeping work.” (O*NET task statement)
    How this row was scored

    Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Ordering, database upkeep and basic bookkeeping are all system tasks, though supplies still arrive physically in 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 4/4.

  • Preparing responses to correspondence containing routine inquiries

    This is reading one thing and writing another: responses in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source: “Prepare responses to correspondence containing routine inquiries.” (O*NET task statement)
    How this row was scored

    Exposure score: 100 out of 100 (96–100 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: Routine replies follow familiar patterns, and drafting them from the incoming message is a core strength of writing software.

    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 4/4.

Changing shape

2 tasks

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

  • Preparing agendas and making arrangements

    The software now makes the first pass at agendas, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 4 · Core
    Source: “Prepare agendas and make arrangements, such as coordinating catering for luncheons, for committee, board, and other meetings.” (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: Agendas and bookings are document and ordering tasks AI handles, though making sure everything arrives often 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.

  • Providing clerical support to other departments

    The software now makes the first pass at clerical support, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 4 · Core
    Source: “Provide clerical support to other departments.” (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 clerical support is document and data work software can take on, though some requests need someone physically 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.

Staying human

5 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

  • Opening, sorting and distributing incoming correspondence, including faxes and email

    This work happens in the physical world: correspondence, including faxes and email, in a real place. Software cannot follow it there.

    importance 4 · Core
    Source: “Open, sort, and distribute incoming correspondence, including faxes and email.” (O*NET task statement)
    How this row was scored

    Exposure score: 38 out of 100 (31–45 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: Email can be sorted and routed automatically, but opening and passing round physical post still needs someone in the office.

    The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Greeting visitors and determining whether they should be given access to specific individuals

    This work happens in the physical world: visitors, in a real place. Software cannot follow it there.

    importance 4 · Core
    Source: “Greet visitors and determine whether they should be given access to specific individuals.” (O*NET task statement)
    How this row was scored

    Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium 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: Check-in kiosks handle part of this, but meeting visitors at the door and judging who gets through means being there.

    The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.

  • Coordinating and directing office services

    The value here is that a specific person handles office services and stands behind it. That is earned, not computed.

    importance 4 · Core
    Source: “Coordinate and direct office services, such as records, departmental finances, budget preparation, personnel issues, and housekeeping, to aid executives.” (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 records are digital, but running office services means chasing people and handling things happening in the building.

    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.

  • Meeting with individuals, special interest groups and others on behalf of executives, committees and boards of directors

    This work happens in the physical world: individuals, in a real place. Software cannot follow it there.

    importance 4 · Supplemental
    Source: “Meet with individuals, special interest groups, and others on behalf of executives, committees, and boards of directors.” (O*NET task statement)
    How this row was scored

    Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium 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: Meeting people on an executive's behalf works because of who is in the room.

    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.

Show the other 12 tasks

  • Compiling, transcribing and distributing minutes of meetings

    shifting to AI

    This is reading one thing and writing another: minutes of meetings in, a record out. That is the shape today's tools are built for.

    importance 4 · Supplemental
    Source: “Compile, transcribe, and distribute minutes of meetings.” (O*NET task statement)
    How this row was scored

    Exposure score: 100 out of 100 (96–100 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: Transcribing recordings and circulating written minutes is already handled end to end by widely available note-taking tools.

    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 4/4.

  • Processing payroll information

    shifting to AI

    This is reading one thing and writing another: payroll information in, a record out. That is the shape today's tools are built for.

    importance 4 · Supplemental
    Source: “Process payroll information.” (O*NET task statement)
    How this row was scored

    Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Payroll runs on set rules applied to timesheet and salary data, and payroll software already does the processing.

    The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.

  • Making travel arrangements for executives

    shifting to AI

    This is reading one thing and writing another: travel arrangements in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source: “Make travel arrangements for executives.” (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: Flights, hotels and itineraries are all booked through online systems, which software can search and arrange directly.

    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.

  • Reading and analyzing incoming memos

    shifting to AI

    This is reading one thing and writing another: memos in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source: “Read and analyze incoming memos, submissions, and reports to determine their significance and plan their distribution.” (O*NET task statement)
    How this row was scored

    Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Reading incoming documents, summarising them and deciding who needs to see them is text work software does quickly.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Attending meetings to record minutes

    shifting to AI

    This is reading one thing and writing another: meetings in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source: “Attend meetings to record minutes.” (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: Meeting transcription and minute-writing tools are already widely used and produce accurate notes automatically.

    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.

  • Reviewing operating practices and procedures to determine whether improvements can be made in areas

    shifting to AI

    This is reading one thing and writing another: practices in, a record out. That is the shape today's tools are built for.

    importance 3 · Supplemental
    Source: “Review operating practices and procedures to determine whether improvements can be made in areas such as workflow, reporting procedures, or expenditures.” (O*NET task statement)
    How this row was scored

    Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Spotting inefficiencies in documented workflows and spending is analysis AI can do well from the organisation's own records.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Conducting research, compile data and preparing papers for consideration and presentation by executives, committees and boards of directors

    shifting to AI

    This is reading one thing and writing another: research, compile data and preparing papers in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source: “Conduct research, compile data, and prepare papers for consideration and presentation by executives, committees, and boards of directors.” (O*NET task statement)
    How this row was scored

    Exposure score: 72 out of 100 (65–79 allowing for uncertainty): high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Gathering information and turning it into a clear briefing paper is research and writing work AI does strongly.

    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 4/4.

  • Filing and retrieving corporate documents

    shifting to AI

    This is reading one thing and writing another: corporate documents in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source: “File and retrieve corporate documents, records, and reports.” (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: Documents are mostly stored and searched electronically, so filing and finding them is already largely automated.

    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.

  • Managing and maintaining executives' schedules

    shifting to AI

    This is reading one thing and writing another: executives' schedules in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source: “Manage and maintain executives' schedules.” (O*NET task statement)
    How this row was scored

    Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Calendar tools and AI assistants already handle booking and rescheduling, though judging which meetings matter takes knowing the executive.

    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.

  • Setting up and overseeing administrative policies and procedures for offices or organizations

    shifting to AI

    This is reading one thing and writing another: and overseeing administrative policies in, a record out. That is the shape today's tools are built for.

    importance 4 · Supplemental
    Source: “Set up and oversee administrative policies and procedures for offices or organizations.” (O*NET task statement)
    How this row was scored

    Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Policy documents are easy to draft from common templates, but making sure people follow them needs someone in the organisation.

    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.

  • Interpreting administrative and operating policies and procedures for employees

    shifting to AI

    This is reading one thing and writing another: administrative and operating policies and procedures in, a record out. That is the shape today's tools are built for.

    importance 4 · Supplemental
    Source: “Interpret administrative and operating policies and procedures for employees.” (O*NET task statement)
    How this row was scored

    Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Explaining policy to staff is documented information work AI handles well.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

  • Supervising and training other clerical staff and arranging for employee training by scheduling training or organizing training material

    staying human

    The value here is that a specific person handles other clerical staff and stands behind it. That is earned, not computed.

    importance 3 · Supplemental
    Source: “Supervise and train other clerical staff and arrange for employee training by scheduling training or organizing training material.” (O*NET task statement)
    How this row was scored

    Exposure score: 24 out of 100 (17–31 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.

    The rating behind it: Training material and schedules can be prepared automatically, but supervising and coaching colleagues depends on working with them directly.

    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 3/4 · how much data exists 3/4.

What this job pays, and how many people do it

Median pay
$76,590a 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
459,910in 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: invoices, reports, memos, letters in, a record out. The rows above are exactly that shape: preparing invoices, reports, memos, letters, financial statements and other documents and answering phone calls and directing calls to appropriate parties or taking messages. What it cannot do is be there in the room, and that is still where visitors 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

The exposed part of your job is the biggest part, and I am not going to dress that up: preparing invoices, reports, memos, letters, financial statements and other documents is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 68% of this job's task weight sits in rows the software is already learning, 10% in rows that change shape rather than disappear, and 22% in rows it is nowhere near. That is the position, measured across 22 scored tasks. It is not a forecast about you.

What you have that the software does not is greeting visitors and determining whether they should be given access to specific individuals, 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 invoices, reports, memos, letters 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 invoices, reports, memos, letters, 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 preparing invoices, reports, memos, letters, financial statements and other documents” 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 greeting visitors and determining whether they should be given access to specific individuals 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 executive secretaries and executive administrative assistants (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was medical secretaries and administrative assistants: only about 9% of its durable work is work you already do and it pays 40.0% less. I am not going to pretend that is comfortable news: 68% 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. “open, sort, and distribute incoming correspondence” 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.

  • Medical Secretaries and Administrative Assistants

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already greet visitors and determine whether they should be given access to specific individuals, and their equivalent is to greet visitors, ascertain purpose of visit, and direct them to appropriate staff. Across both published task lists that is about 9% of the durable work in that job.

    Why I am not recommending it: Almost none of it is work you already do: about 9% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $45,930 against your $76,590, 40.0% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.

    Look at that job’s page anyway →

  • Secretaries and Administrative Assistants, Except Legal, Medical, and Executive

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already greet visitors and determine whether they should be given access to specific individuals, and their equivalent is to greet visitors or callers and handle their inquiries or direct them to the…. 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. I will not move you off one melting floe onto another: 58% of its own task list already scores in the top exposure band (61/100 in this release), so the same software is eating it. It is a pay cut, in those words: $47,540 against your $76,590, 37.9% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.

    Look at that job’s page anyway →

  • Health Information Technologists and Medical Registrars

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already provide clerical support to other departments, and their equivalent is to manage the department or supervise clerical workers, directing or controlling activities of personnel…. 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. It is a pay cut, in those words: $68,020 against your $76,590, 11.2% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 38,100 of those jobs against 459,910 of yours (OEWS May 2025), 8% as many seats.

    Look at that job’s page anyway →

What I’d stop worrying about

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

  • The headline number you read somewhere

    The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 68% of its task weight, across 22 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: greeting visitors and determining whether they should be given access to specific individuals 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.

If you run a team doing this job

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

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Company secretaries and administrators is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

The other groups this work is counted across:

In UK official statistics this job is counted as Company secretaries and administrators and Personal assistants and other secretaries. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

Your route through this

Two honest options, and no deadline on either

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

A guided route for this

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Microsoft 365 Productivity Workers is built for coordinators, administrators, team leads and project owners who live in Outlook, Teams, meetings, files and task lists (the people this page is about). It works on the part of your job that is growing rather than shrinking: running an executive's diary, papers and follow-ups as one system, so the drafting a machine can do is the small part and the judgement about what matters stays yours.

Try Microsoft 365 Productivity Workers free →

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

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

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

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No Space for executive assistants yet. Should there be one?

Collab365 launches new communities where the need is real. If one for executive assistants existed, with researched problems, courses and people in the same boat, would you want in?

We use your email address for one thing: to tell you if a Space for executive assistants launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

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 Executive Secretaries and Executive Administrative Assistants?
Not as a job, but it is already doing parts of the work. Across the 22 official task statements scored for Executive Secretaries and Executive Administrative Assistants (United States, SOC 43-6011), 68% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 65 out of 100 (range 59–70, 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 “Executive Secretaries and Executive Administrative Assistants” can AI already do?
The highest-scoring tasks in release 2026-q4.1 are: “Prepare invoices, reports, memos, letters, financial statements, and other documents, using word processing, spreadsheet, database, or presentation software” (100/100, very high); “Prepare responses to correspondence containing routine inquiries” (100/100, very high); “Compile, transcribe, and distribute minutes of meetings” (100/100, 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 “Executive Secretaries and Executive Administrative Assistants” stay human?
About 22% 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: “Meet with individuals, special interest groups, and others on behalf of executives, committees, and boards of directors” (9/100, minimal); “Greet visitors and determine whether they should be given access to specific individuals” (9/100, minimal); “Supervise and train other clerical staff and arrange for employee training by scheduling training or organizing training material” (24/100, 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 “Executive Secretaries and Executive Administrative Assistants” do about AI?
Start from the ledger rather than the headline: 68% of this job's weighted core work is exposed, and roughly 22% 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 Executive Secretaries and Executive Administrative Assistants 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 22 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.
  • 4 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
  • Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Task statements
onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights
onet-db (im-rt)
Scores
Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
Pay and employment
bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

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

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

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

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Using these figures?

Cite this

Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published . 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.