Will AI replace Fundraising Managers?

Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Fundraising Managers (United States, SOC 11-2033), 50% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 53 out of 100 (range 48–59, 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 “Fundraising Managers” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Write interesting and effective press releases, prepare information for media kits, and develop and maintain company internet or intranet Web pages” (93/100, very high); “Design and edit promotional publications, such as brochures” (83/100, very high); “Conduct research to identify the goals, net worth, charitable donation history, or other data related to potential donors, potential investors, or general do…” (75/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Fundraising Managers” stay human?

About 35% 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: “Establish and maintain effective working relationships with clients, government officials, and media representatives and use these relationships to develop n…” (5/100, minimal); “Contact corporate representatives, government officials, or community leaders to increase awareness of organizational causes, activities, or needs” (21/100, low); “Assign, supervise, and review the activities of fundraising staff” (21/100, 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 “Fundraising Managers” do about AI?

Start from the ledger rather than the headline: 50% of this job's weighted core work is exposed, and roughly 35% 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 Fundraising Managers 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 16 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Fundraising Managers

developing strategies to encourage new or increased contributions, establishing goals for soliciting funds and establishing and maintaining effective working relationships with clients. If that's your week, this page is about your job.

The honest answer

This job is splitting in two: developing strategies to encourage new or increased contributions is work AI now does quickly and cheaply, and establishing and maintaining effective working relationships with clients is work it can't touch.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 50%
changing shape: 15%
staying human: 35%

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

Whole-job exposure score 53 out of 100 (48–59 allowing for uncertainty): partial exposure, across 16 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 fundraising managers 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 2026-08-05. Read the full method.

Which half fills your week decides your exposure. Moving toward the second half is a real, doable plan.

Your week, as this page understands it

Plan, direct, or coordinate activities to solicit and maintain funds for special projects or nonprofit organizations. The job title says “fundraising managers”. The real job is the part underneath: establishing and maintaining effective working relationships with clients. 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 fundraising managers is not one task. It is 16 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is establishing and maintaining effective working relationships with clients, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 50%
changing shape: 15%
staying human: 35%

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

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. 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.

9 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

8 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.

Developing strategies to encourage new or increased contributions
This is reading one thing and writing another: strategies in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Develop strategies to encourage new or increased contributions.” (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: Giving strategies follow well-documented patterns that tools draft strongly from your own donor data.
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.
Managing fundraising budgets
This is reading one thing and writing another: budgets in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Manage fundraising budgets.” (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: Budget tracking and reporting is spreadsheet work that software handles well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Establishing goals for soliciting funds
This is reading one thing and writing another: goals in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Establish goals for soliciting funds, develop policies for collection and safeguarding of contributions, and coordinate disbursement of funds.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Targets, collection policies and disbursement rules follow standard financial practice that software can draft.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Conducting research to identify the goals
This is reading one thing and writing another: research in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Conduct research to identify the goals, net worth, charitable donation history, or other data related to potential donors, potential investors, or general donor markets.” (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: Researching a prospect's wealth and giving history draws on public records and databases software searches 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.

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.

Developing fundraising activity plans that maximize participation or contributions and minimize costs
The software now makes the first pass at activity plans, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Develop fundraising activity plans that maximize participation or contributions and minimize costs.” (O*NET task statement)
How this row was scored
Exposure score: 50 out of 100 (43–57 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Campaign plans can be drafted quickly, but the useful detail comes from knowing this charity's donors.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Compiling or developing materials to submit to granting or other funding organizations
The software now makes the first pass at materials, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Compile or develop materials to submit to granting or other funding organizations.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (54–62 allowing for uncertainty): partial exposure, high confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Funding applications can be drafted automatically but need heavy editing to match a specific funder.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Staying human

6 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.

Planning and directing special events
This work happens in the physical world: special events, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Plan and direct special events for fundraising, such as silent auctions, dances, golf events, or walks.” (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; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Event planning can be largely drafted, but running an auction or a walk on the day needs people there.
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 1/4 · how much data exists 3/4.
Establishing and maintaining effective working relationships with clients
This work happens in the physical world: effective working relationships, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Establish and maintain effective working relationships with clients, government officials, and media representatives and use these relationships to develop new fundraising opportunities.” (O*NET task statement)
How this row was scored
Exposure score: 5 out of 100 (1–9 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: These relationships are the job itself; officials and donors deal with a person they know.
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 4/4 · how much data exists 1/4.
Contacting corporate representatives, government officials or community leaders to increase awareness of organizational causes, activities or needs
The value here is that a specific person handles corporate representatives, government officials or community leaders and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Contact corporate representatives, government officials, or community leaders to increase awareness of organizational causes, activities, or needs.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (17–25 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Getting a corporate or community leader interested depends on a personal approach.
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 2/4.
Directing activities of external agencies
The value here is that a specific person handles activities of external agencies and stands behind it. That is earned, not computed.
importance 4 · Supplemental
Source: “Direct activities of external agencies, establishments, or departments that develop and implement fundraising strategies and programs.” (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: Directing outside agencies depends on holding them to account through working relationships.
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.

Show the other 6 tasks

Writing interesting and effective press releases
shifting to AI
This is reading one thing and writing another: effective press releases in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Write interesting and effective press releases, prepare information for media kits, and develop and maintain company internet or intranet Web pages.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Press releases, media kits and web page updates follow set formats software produces 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 0/4 · how much data exists 3/4.
Designing and editing promotional publications
shifting to AI
This is reading one thing and writing another: promotional publications in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Design and edit promotional publications, such as brochures.” (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: Brochure copy and layout can be produced to a good standard, with someone approving the final look.
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.
Evaluating advertising and promotion programs for compatibility with fundraising efforts
shifting to AI
This is reading one thing and writing another: promotion programs in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Evaluate advertising and promotion programs for compatibility with fundraising efforts.” (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: Comparing advertising and promotion against fundraising goals is a data and reporting task.
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.
Formulating policies and procedures related to fundraising programs
shifting to AI
This is reading one thing and writing another: policies in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Formulate policies and procedures related to fundraising programs.” (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: Fundraising policies and procedures follow well-established practice that software can draft accurately.
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.
Producing films and other video products
staying human
This work happens in the physical world: films, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Produce films and other video products, regulate their distribution, and operate film library.” (O*NET task statement)
How this row was scored
Exposure score: 25 out of 100 (18–32 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Scripts and edits can be automated, but filming and running a video library needs people and equipment.
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 0/4 · how much data exists 2/4.
Assigning, supervising and reviewing the activities of fundraising staff
staying human
The value here is that a specific person handles the activities of fundraising staff and stands behind it. That is earned, not computed.
importance 3 · Core
Source: “Assign, supervise, and review the activities of fundraising staff.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (17–25 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Supervising and reviewing staff works through an ongoing relationship with each 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 3/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $125,470a 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: 38,810in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: activity plans in, a record out. The rows above are exactly that shape: developing strategies to encourage new or increased contributions and managing fundraising budgets. What it cannot do is be there in the room, and that is still where effective working relationships 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: developing strategies to encourage new or increased contributions is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 50% of this job's task weight sits in rows the software is already learning, 15% in rows that change shape rather than disappear, and 35% in rows it is nowhere near. That is the position, measured across 16 scored tasks. It is not a forecast about you.

What you have that the software does not is establishing and maintaining effective working relationships with clients, 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 activity plans 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 activity plans, 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 developing strategies to encourage new or increased contributions” 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 establishing and maintaining effective working relationships with clients 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 fundraising managers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was fundraisers: only about 26% of its durable work is work you already do and it pays 42.2% less. Your own job splits about 50/50: 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 “plan and direct special events for fundraising”, 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.

Fundraisers
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “contact corporate representatives, government officials, or community leaders to increase awareness of organizational causes, activities, or…”. Across the whole of both lists that adds up to about 26% of the work in that job the software is not taking.
Why I am not recommending it: It is closer than most, and still not close enough: about 26% of that job's durable work is already yours, against the 35% I want to see before I will call something a route. It is a pay cut, in those words: $72,550 against your $125,470, 42.2% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Public Relations Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already establish and maintain effective working relationships with clients, government officials, and media representatives…, and their equivalent is to establish and maintain effective working relationships with clients, government officials, and media representatives…. Across both published task lists that is about 23% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 23% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Look at that job’s page anyway →
Health Education Specialists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already compile or develop materials to submit to granting or other funding organizations, and their equivalent is to develop, prepare, and coordinate grant applications and grant-related activities to obtain funding for…. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $64,070 against your $125,470, 48.9% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
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: 50% of its task weight, across 16 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: establishing and maintaining effective working relationships with clients 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 developing strategies to encourage new or increased contributions, 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 Public relations and communications directors 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:

Advertising and marketing associate professionals

In UK official statistics this job is counted as Public relations and communications directors and Advertising and marketing associate professionals. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

Your route through this

Two honest options, and no deadline on either

Free, and complete

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

Anywhere in the US:
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Check what your state actually requires before you pay for anything.
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Anywhere in the US:
CareerOneStop
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A nearby route

There's no Space built for fundraising managers yet.

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

What a Space actually is, in full

The closest match is The AI Authority, a community for non-technical managers and domain experts turning one-off AI prompts into workflows a team can trust. It overlaps with the part of your job that is growing: briefing, checking and delegating AI-assisted work so it is repeatable rather than one person’s trick, and setting the rules that keep a team’s AI output consistent. It covers no fundraising strategy, no donor relationships and nothing about charity regulation. If that overlap isn't you, the free route below covers the same ground.

Try The AI Authority free →

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

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

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

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No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Fundraising Managers?: Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Fundraising Managers (United States, SOC 11-2033), 50% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 53 out of 100 (range 48–59, 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 “Fundraising Managers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Write interesting and effective press releases, prepare information for media kits, and develop and maintain company internet or intranet Web pages” (93/100, very high); “Design and edit promotional publications, such as brochures” (83/100, very high); “Conduct research to identify the goals, net worth, charitable donation history, or other data related to potential donors, potential investors, or general do…” (75/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Fundraising Managers” stay human?: About 35% 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: “Establish and maintain effective working relationships with clients, government officials, and media representatives and use these relationships to develop n…” (5/100, minimal); “Contact corporate representatives, government officials, or community leaders to increase awareness of organizational causes, activities, or needs” (21/100, low); “Assign, supervise, and review the activities of fundraising staff” (21/100, 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 “Fundraising Managers” do about AI?: Start from the ledger rather than the headline: 50% of this job's weighted core work is exposed, and roughly 35% 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 Fundraising Managers 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 16 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.
9 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 2026-08-05.
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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Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

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

BibTeX

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

Permalink

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

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.