Will AI replace Marketing Managers?

Not as a job, but it is already doing parts of the work. Across the 20 official task statements scored for Marketing Managers (United States, SOC 11-2021), 37% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 52 out of 100 (range 46–57, 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 “Marketing Managers” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Compile lists describing product or service offerings” (93/100, very high); “Evaluate the financial aspects of product development, such as budgets, expenditures, research and development appropriations, or return-on-investment and pr…” (75/100, high); “Use sales forecasting or strategic planning to ensure the sale and profitability of products, lines, or services, analyzing business developments and monitor…” (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 “Marketing Managers” stay human?

About 33% 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: “Direct the hiring, training, or performance evaluations of marketing or sales staff and oversee their daily activities” (18/100, minimal); “Coordinate or participate in promotional activities or trade shows, working with developers, advertisers, or production managers, to market products or services” (20/100, low); “Negotiate contracts with vendors or distributors to manage product distribution, establishing distribution networks or developing distribution strategies” (28/100, low). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Marketing Managers” do about AI?

Start from the ledger rather than the headline: 37% of this job's weighted core work is exposed, and roughly 33% 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 Marketing 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 20 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

Marketing Managers

identifying, developing or evaluating marketing strategy, consulting with product development personnel on product specifications and using sales forecasting or strategic planning to ensure the sale and profitability of products. If that's your week, this page is about your job.

The honest answer

This job is splitting in two: compiling lists describing product or service offerings is work AI now does quickly and cheaply, and coordinating or participating in promotional activities or trade shows 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: 37%
changing shape: 30%
staying human: 33%

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

Whole-job exposure score 52 out of 100 (46–57 allowing for uncertainty): partial exposure, across 20 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 marketing 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 2026-08-04. 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

Plan, direct, or coordinate marketing policies and programs, such as determining the demand for products and services offered by a firm and its competitors, and identify potential customers. Develop pricing strategies with the goal of maximizing the firm's profits or share of the market while ensuring the firm's customers are satisfied. Oversee product development or monitor trends that indicate the need for new products and services. The job title says “marketing managers”. The real job is the part underneath: coordinating or participating in promotional activities or trade shows. 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 marketing managers is not one task. It is 20 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is coordinating or participating in promotional activities or trade shows, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 37%
changing shape: 30%
staying human: 33%

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.

2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

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.

Evaluating the financial aspects of product development
This is reading one thing and writing another: the financial aspects of product development in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Evaluate the financial aspects of product development, such as budgets, expenditures, research and development appropriations, or return-on-investment and profit-loss projections.” (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, return-on-investment and profit projections are spreadsheet analysis built from company figures, which 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 0/4 · how much data exists 3/4.
Compiling lists describing product or service offerings
This is reading one thing and writing another: lists describing product in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Compile lists describing product or service offerings.” (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: Pulling product and service details into a clear list is routine writing from records the business 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.
Using sales forecasting or strategic planning to ensure the sale and profitability of products
This is reading one thing and writing another: sales in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Use sales forecasting or strategic planning to ensure the sale and profitability of products, lines, or services, analyzing business developments and monitoring market trends.” (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: Forecasting sales and tracking market trends is data analysis with plenty of documented method and available figures.
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.
Initiating market research studies or analyzing their findings
This is reading one thing and writing another: market research studies in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Initiate market research studies, or analyze their findings.” (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: Designing research and drawing conclusions from the results is analysis AI does well once the data is supplied.
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

5 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.

Identifying, developing or evaluating marketing strategy, based on knowledge of establishment objectives, market characteristics and cost and markup factors
The software now makes the first pass at strategy, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Identify, develop, or evaluate marketing strategy, based on knowledge of establishment objectives, market characteristics, and cost and markup factors.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: AI can draft a marketing plan from market and cost data, but deciding what the business should actually do stays human.
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.
Formulating, directing
The software now makes the first pass at activities, 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: “Formulate, direct, or coordinate marketing activities or policies to promote products or services, working with advertising or promotion managers.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Plans and briefs can be drafted quickly, but steering colleagues and agencies to deliver them is live coordination work.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Developing pricing strategies, balancing firm objectives and customer satisfaction
The software now makes the first pass at strategies, balancing firm objectives and customer satisfaction, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Develop pricing strategies, balancing firm objectives and customer satisfaction.” (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: Pricing methods are well known, but the decisive numbers and the appetite for risk are specific to the business.
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.

Staying human

7 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.

Coordinating or participating in promotional activities or trade shows
This work happens in the physical world: promotional activities, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Coordinate or participate in promotional activities or trade shows, working with developers, advertisers, or production managers, to market products or services.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (16–24 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Materials and schedules can be prepared in advance, though trade shows and promotions are attended in person.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Directing the hiring, training or performance evaluations of marketing or sales staff and overseeing their daily activities
The value here is that a specific person handles the hiring and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Direct the hiring, training, or performance evaluations of marketing or sales staff and oversee their daily activities.” (O*NET task statement)
How this row was scored
Exposure score: 18 out of 100 (14–22 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: Hiring, developing and appraising a team depends on trust and judgement built with real people over time.
The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Negotiating contracts with vendors or distributors to manage product distribution
The value here is that a specific person handles contracts and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Negotiate contracts with vendors or distributors to manage product distribution, establishing distribution networks or developing distribution strategies.” (O*NET task statement)
How this row was scored
Exposure score: 28 out of 100 (24–32 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: Terms can be drafted in advance, but the deal itself turns on a live relationship with the other side.
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 3/4 · how much data exists 2/4.

Show the other 10 tasks

Conducting economic or commercial surveys to identify potential markets for products or services
shifting to AI
This is reading one thing and writing another: economic in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Conduct economic or commercial surveys to identify potential markets for products or services.” (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: Designing surveys and analysing market data is well documented work that AI does quickly with the responses provided.
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 business cases for environmental marketing strategies
shifting to AI
This is reading one thing and writing another: business cases in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Develop business cases for environmental marketing strategies.” (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: Business cases draft well from documented costs, benefits and evidence.
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.
Integrating environmental information into product or company marketing strategies
shifting to AI
This is reading one thing and writing another: environmental information in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Integrate environmental information into product or company marketing strategies, policies, or activities.” (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: Working environmental facts into marketing plans and materials is largely writing and editing against published information.
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.
Advising business or other groups on local
shifting to AI
This is reading one thing and writing another: business in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Advise business or other groups on local, national, or international factors affecting the buying or selling of products or services.” (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: Briefings on trading conditions rely on published economic and market information that AI can gather and explain clearly.
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.
Recommending modifications to products, packaging, production processes or other characteristics to improve the environmental soundness or sustainability of products
changing shape
The software now makes the first pass at modifications, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · Supplemental
Source: “Recommend modifications to products, packaging, production processes, or other characteristics to improve the environmental soundness or sustainability of products.” (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: General sustainability options are documented, but recommending changes depends on how this particular product is made.
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.
Consulting with product development personnel on product specifications
changing shape
The software now makes the first pass at product development personnel, 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: “Consult with product development personnel on product specifications, such as design, color, or packaging.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Design and packaging ideas can be drafted, but agreeing specifications with the product team is a working conversation.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Selecting products or accessories to be displayed at trade or special production shows
staying human
The ratings behind this row put products well outside what today's tools can do on their own.
importance 3 · Supplemental
Source: “Select products or accessories to be displayed at trade or special production shows.” (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: mistakes that are cheap to catch.
The rating behind it: Data helps shortlist what to show, but choosing and setting up a stand is done with the products in front of you.
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 0/4 · how much data exists 2/4.
Consulting with buying personnel to gain advice regarding the types of products or services expected to be in demand
staying human
The value here is that a specific person handles buying personnel and stands behind it. That is earned, not computed.
importance 3 · Supplemental
Source: “Consult with buying personnel to gain advice regarding the types of products or services expected to be in demand.” (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: The insight sought lives in colleagues heads, so the value comes from the conversation rather than the write-up.
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.
Consulting with buying personnel to gain advice regarding environmentally sound or sustainable products
staying human
The value here is that a specific person handles buying personnel and stands behind it. That is earned, not computed.
importance 3 · Supplemental
Source: “Consult with buying personnel to gain advice regarding environmentally sound or sustainable products.” (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: Sustainability facts are published, but what buyers expect to sell comes from talking to them directly.
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.
Conferring with legal staff to resolve problems
staying human
The value here is that a specific person handles legal staff and stands behind it. That is earned, not computed.
importance 3 · Core
Source: “Confer with legal staff to resolve problems, such as copyright infringement or royalty sharing with outside producers or distributors.” (O*NET task statement)
How this row was scored
Exposure score: 31 out of 100 (24–38 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: The issues can be summarised, but copyright and royalty problems need a qualified lawyer working on the specifics.
The five ratings: output a model can produce 2/4 · 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 2/4.

What this job pays, and how many people do it

Median pay: $166,790a 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: 395,240in 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: lists describing product in, a record out. The rows above are exactly that shape: compiling lists describing product or service offerings and evaluating the financial aspects of product development. What it cannot do is be there in the room, and that is still where promotional activities get done. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Your week is splitting in two, and which half fills it is the whole question. Compiling lists describing product or service offerings is going; coordinating or participating in promotional activities or trade shows is not.

So, given all that: 37% of this job's task weight sits in rows the software is already learning, 30% in rows that change shape rather than disappear, and 33% in rows it is nowhere near. That is the position, measured across 20 scored tasks. It is not a forecast about you.

The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.

This week: one thing

Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.

What you end up holding: your own week, on one page, sorted into what is shifting and what is not
How long it takes: about twenty minutes

If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.

Over the next 90 days

Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (coordinating or participating in promotional activities or trade shows) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.

Over the next 12 months

Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles coordinating or participating in promotional activities or trade shows, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest US occupations to marketing managers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was financial and investment analysts: it pays 38.4% less. Your own job splits about 37/63: 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, develop, or evaluate marketing strategy, based on knowledge of establishment objectives…”, 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.

Financial and Investment Analysts
Why it looked obvious: Real overlap in the financial-evaluation part of marketing management: 'Evaluate the financial aspects of product development, such as budgets, expenditures... return-on-investment and profit-loss projections' and 'Use sales forecasting or strategic planning...'
Why I am not recommending it: It is a pay cut: 102,740 against 166,790 is -64,050, a 38.4% cut - and the origin occupation is growing (+6.6%, BLS EP) with no forced exit. Per block 5 the honest output is 'I looked at the obvious moves and none of them beats deepening what you have', not a route that costs the reader nearly two-fifths of their pay for no reason.
Look at that job’s page anyway →
Sales Engineers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already select products or accessories to be displayed at trade or special production shows, and their equivalent is to attend trade shows and seminars to promote products or to learn about industry…. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $124,900 against your $166,790, 25.1% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 51,790 of those jobs against 395,240 of yours (OEWS May 2025), 13% as many seats.
Look at that job’s page anyway →
General and Operations Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already formulate, direct, or coordinate marketing activities or policies to promote products or services…, and their equivalent is to plan or direct activities. 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: $105,770 against your $166,790, 36.6% 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: 37% of its task weight, across 20 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

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 compiling lists describing product or service offerings, 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 Marketing, sales and advertising 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:

In UK official statistics this job is counted as Marketing, sales and advertising directors, Marketing and commercial managers and Sales accounts and business development managers. 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.

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A nearby route

There's no Space built for marketing 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: the judgement half: briefing AI properly, checking what it produces, and turning one-off prompts into a workflow a team can rely on. It is not a marketing Space - no channels, no brand, no campaigns. 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 marketing 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 Marketing Managers?: Not as a job, but it is already doing parts of the work. Across the 20 official task statements scored for Marketing Managers (United States, SOC 11-2021), 37% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 52 out of 100 (range 46–57, 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 “Marketing Managers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Compile lists describing product or service offerings” (93/100, very high); “Evaluate the financial aspects of product development, such as budgets, expenditures, research and development appropriations, or return-on-investment and pr…” (75/100, high); “Use sales forecasting or strategic planning to ensure the sale and profitability of products, lines, or services, analyzing business developments and monitor…” (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 “Marketing Managers” stay human?: About 33% 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: “Direct the hiring, training, or performance evaluations of marketing or sales staff and oversee their daily activities” (18/100, minimal); “Coordinate or participate in promotional activities or trade shows, working with developers, advertisers, or production managers, to market products or services” (20/100, low); “Negotiate contracts with vendors or distributors to manage product distribution, establishing distribution networks or developing distribution strategies” (28/100, low). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Marketing Managers” do about AI?: Start from the ledger rather than the headline: 37% of this job's weighted core work is exposed, and roughly 33% 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 Marketing 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 20 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
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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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.