Will AI replace Fundraisers?

Not as a job, but it is already doing parts of the work. Across the 28 official task statements scored for Fundraisers (United States, SOC 13-1131), 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 “Fundraisers” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Create or update donor databases” (93/100, very high); “Monitor budgets, expense reports, or other financial data for fundraising organizations” (93/100, very high); “Develop and maintain media contact lists” (93/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Fundraisers” stay human?

About 39% 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: “Prepare materials such as fundraising envelopes, bid sheets, or gift bags for charitable events” (0/100, minimal); “Attend community events, meetings, or conferences to promote organizational goals or solicit donations or sponsorships” (4/100, minimal); “Secure commitments of participation or donation from individuals or corporate donors” (13/100, 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 “Fundraisers” do about AI?

Start from the ledger rather than the headline: 50% of this job's weighted core work is exposed, and roughly 39% 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 Fundraisers 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 28 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

Fundraisers

identifying and building relationships with potential donors, developing fundraising activity plans that maximize participation or contributions and minimize costs and directing or supervising fundraising staff. If that's your week, this page is about your job.

The honest answer

This job is splitting in two: writing and sending letters of thanks to donors is work AI now does quickly and cheaply, and identifying and building relationships with potential donors 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: 11%
staying human: 39%

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 28 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 fundraisers 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.

Which half fills your week decides your exposure. That is more in your control than it sounds.

Your week, as this page understands it

Organize activities to raise funds or otherwise solicit and gather monetary donations or other gifts for an organization. May design and produce promotional materials. May also raise awareness of the organization's work, goals, and financial needs. The job title says “fundraisers”. The real job is the part underneath: identifying and building relationships with potential donors. 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 fundraisers is not one task. It is 28 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is identifying and building relationships with potential donors, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 50%
changing shape: 11%
staying human: 39%

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 2026-08-04. 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.

6 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

14 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.

Writing and sending letters of thanks to donors
This is reading one thing and writing another: letters of thanks in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Write and send letters of thanks to donors.” (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: Thank-you letters are short, templated writing that tools personalize well at scale.
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.
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.
Creating or updating donor databases
This is reading one thing and writing another: donor databases in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Create or update donor databases.” (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: Keeping donor records accurate and up to date is standard database work.
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.
Developing or implementing fundraising activities
This is reading one thing and writing another: activities in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Develop or implement fundraising activities, such as annual giving campaigns or direct mail programs.” (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: Annual appeals and mail campaigns are well-understood programs tools can plan and largely run.
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.

Changing shape

3 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.
Explaining the tax advantages of contributions to potential donors
The software now makes the first pass at the tax advantages of contributions, 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 · Core
Source: “Explain the tax advantages of contributions to potential donors.” (O*NET task statement)
How this row was scored
Exposure score: 46 out of 100 (39–53 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; the value is that a specific person does it.
The rating behind it: Tax rules on giving are well documented, but donors want a person to walk them through it.
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 2/4 · how much data exists 3/4.
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

11 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.

Identifying and building relationships with potential donors
The value here is that a specific person handles relationships and stands behind it. That is earned, not computed.
importance 5 · Core
Source: “Identify and build relationships with potential donors.” (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: Donor relationships are built person to person over time, though research can point you to the right people.
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.
Securing commitments of participation or donation from individuals or corporate donors
The value here is that a specific person handles commitments of participation and stands behind it. That is earned, not computed.
importance 5 · Core
Source: “Secure commitments of participation or donation from individuals or corporate donors.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (9–17 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Getting someone to actually commit money usually happens in a conversation with someone they trust.
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 3/4 · how much data exists 2/4.
Soliciting cash or in-kind donations or sponsorships from individual
The value here is that a specific person handles cash and stands behind it. That is earned, not computed.
importance 5 · Core
Source: “Solicit cash or in-kind donations or sponsorships from individual, business, or government donors.” (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: Asking for a donation face to face depends on the relationship behind the ask.
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.

Show the other 18 tasks

Monitoring budgets, expense reports or other financial data for fundraising organizations
shifting to AI
This is reading one thing and writing another: budgets, expense reports or other financial data in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Monitor budgets, expense reports, or other financial data for fundraising organizations.” (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: Budget and expense tracking is standard finance work that software handles.
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.
Developing and maintaining media contact lists
shifting to AI
This is reading one thing and writing another: media contact lists in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Develop and maintain media contact lists.” (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: Building and maintaining a media contact list is routine data work.
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.
Writing reports or preparing presentations to communicate fundraising program data
shifting to AI
This is reading one thing and writing another: reports in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Write reports or prepare presentations to communicate fundraising program data.” (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: Fundraising reports and presentations are built directly from data the organization already holds.
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 or producing materials, posters, Web sites or newsletters to promote, market or advertise fundraising events
shifting to AI
This is reading one thing and writing another: materials, posters, web sites or newsletters in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Design or produce materials such as posters, Web sites, or newsletters to promote, market, or advertise fundraising events.” (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: Posters, newsletters and web pages are quick for design and writing tools to produce.
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.
Directing or coordinating Web-based fundraising activities
shifting to AI
This is reading one thing and writing another: web-based fundraising activities in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Direct or coordinate Web-based fundraising activities, such as online auctions or donation Web sites.” (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: Online auctions and donation pages run on standard platforms that tools set up and manage 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.
Writing speeches, press releases or other promotional materials to increase awareness of the causes, missions or goals of organizations seeking funds
shifting to AI
This is reading one thing and writing another: speeches in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Write speeches, press releases, or other promotional materials to increase awareness of the causes, missions, or goals of organizations seeking funds.” (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: Speeches and press releases draft well, though the voice and the final call stay with a person.
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.
Establishing fundraising or participation goals for special events or specified time periods
shifting to AI
This is reading one thing and writing another: participation goals in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Establish fundraising or participation goals for special events or specified time periods.” (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: Setting targets from past performance is straightforward analysis.
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.
Monitoring progress of fundraising drives
shifting to AI
This is reading one thing and writing another: progress of fundraising drives in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Monitor progress of fundraising drives.” (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: Tracking how a campaign is going against target is reporting work software does 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.
Conducting research to identify the goals
shifting to AI
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.
Developing corporate fundraising programs, such as employer gift-matching
shifting to AI
This is reading one thing and writing another: corporate fundraising programs in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Develop corporate fundraising programs, such as employer gift-matching.” (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: Gift-matching schemes follow documented designs that tools draft, with employers still to be won over.
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.
Securing speakers for charitable events
staying human
The value here is that a specific person handles speakers and stands behind it. That is earned, not computed.
importance 3 · Core
Source: “Secure speakers for charitable events, community meetings, or conferences to increase awareness of charitable, nonprofit, or political causes.” (O*NET task statement)
How this row was scored
Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Booking a speaker depends on who you know and can persuade to say yes.
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 2/4.
Recruiting sponsors, participants or volunteers for fundraising events
staying human
The value here is that a specific person handles sponsors, participants or volunteers and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Recruit sponsors, participants, or volunteers for fundraising events.” (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: Signing up sponsors and volunteers depends on personal asks, though the admin is easy to automate.
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 directing special events
staying human
This work happens in the physical world: special events, in a real place. Software cannot follow it there.
importance 3 · 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.
Coordinating transportation or delivery of materials
staying human
This work happens in the physical world: transportation, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Coordinate transportation or delivery of materials, supplies, or donations for fundraising events.” (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: Getting supplies and donations to the right place involves physical logistics on the day.
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.
Contacting corporate representatives, government officials or community leaders to increase awareness of organizational causes, activities or needs
staying human
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 or supervising fundraising staff
staying human
The value here is that a specific person handles staff and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Direct or supervise fundraising staff, including volunteer staff members.” (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: Supervising staff and volunteers depends on being someone they will follow.
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.
Attending community events, meetings or conferences to promote organizational goals or solicit donations or sponsorships
staying human
This work happens in the physical world: community events, meetings or conferences, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Attend community events, meetings, or conferences to promote organizational goals or solicit donations or sponsorships.” (O*NET task statement)
How this row was scored
Exposure score: 4 out of 100 (0–8 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: Turning up at community events and talking to people is the whole point of this task.
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 3/4 · how much data exists 2/4.
Preparing materials, fundraising envelopes, bid sheets or gift bags for charitable events
staying human
This work happens in the physical world: materials, fundraising envelopes, bid sheets or gift bags, in a real place. Software cannot follow it there.
importance 2 · Supplemental
Source: “Prepare materials such as fundraising envelopes, bid sheets, or gift bags for charitable events.” (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: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Stuffing envelopes and packing gift bags is physical preparation by hand.
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 3/4.

What this job pays, and how many people do it

Median pay: $72,550a 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: 111,040in 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: letters of thanks in, a record out. The rows above are exactly that shape: writing and sending letters of thanks to donors and developing strategies to encourage new or increased contributions. What it cannot do is be trusted in person, which is what relationships run on: someone specific doing it and standing behind it. 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: writing and sending letters of thanks to donors 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, 11% in rows that change shape rather than disappear, and 39% in rows it is nowhere near. That is the position, measured across 28 scored tasks. It is not a forecast about you.

What you have that the software does not is identifying and building relationships with potential donors, 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 letters of thanks 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 letters of thanks, 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 writing and sending letters of thanks to donors” 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 identifying and building relationships with potential donors 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 fundraisers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was fundraising managers: only about 24% of its durable work is work you already do. 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 “identify and build relationships with potential donors”, 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.

Fundraising Managers
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 24% of the work in that job the software is not taking.
Why I am not recommending it: You would be starting most of it from nothing: about 24% 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 →
Social and Community Service Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already direct or supervise fundraising staff, and their equivalent is to direct activities of professional and technical staff members and volunteers. Across both published task lists that is about 11% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 11% 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 →
Meeting, Convention, and Event Planners
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already direct or supervise fundraising staff, and their equivalent is to hire, train, and supervise volunteers and support staff required for events. 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: $61,160 against your $72,550, 15.7% 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 28 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: identifying and building relationships with potential donors is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Debt, rent and other cash collectors 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 Debt, rent and other cash collectors 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:
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:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

A nearby route

There's no Space built for fundraisers 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: owning AI-drafted text (verifying the claims in it, keeping it in your own voice, and setting a review gate before anything is sent). 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 fundraisers 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 Fundraisers?: Not as a job, but it is already doing parts of the work. Across the 28 official task statements scored for Fundraisers (United States, SOC 13-1131), 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 “Fundraisers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Create or update donor databases” (93/100, very high); “Monitor budgets, expense reports, or other financial data for fundraising organizations” (93/100, very high); “Develop and maintain media contact lists” (93/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Fundraisers” stay human?: About 39% 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: “Prepare materials such as fundraising envelopes, bid sheets, or gift bags for charitable events” (0/100, minimal); “Attend community events, meetings, or conferences to promote organizational goals or solicit donations or sponsorships” (4/100, minimal); “Secure commitments of participation or donation from individuals or corporate donors” (13/100, 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 “Fundraisers” do about AI?: Start from the ledger rather than the headline: 50% of this job's weighted core work is exposed, and roughly 39% 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 Fundraisers 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 28 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.
6 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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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 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.