Will AI replace Market Research Analysts and Marketing Specialists?

Not as a job, but it is already doing parts of the work. Across the 49 official task statements scored for Market Research Analysts and Marketing Specialists (United States, SOC 13-1161), 63% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 63 out of 100 (range 58–69, band: high). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

What should someone working in “Market Research Analysts and Marketing Specialists” do about AI?

Start from the ledger rather than the headline: 63% of this job's weighted core work is exposed, and roughly 6% 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 Market Research Analysts and Marketing Specialists calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 49 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

Market Research Analysts and Marketing Specialists

preparing reports of findings, seeking and providing information to help companies determine their position in the marketplace and gathering data on competitors and analyzing their prices. If that's your week, this page is about your job.

The honest answer

Most tasks in this job are the kind AI has learned to do: preparing reports of findings, illustrating data graphically and translating complex findings into written text. The tasks, though, are not you.

Your move: three real directions from here ↓

Where the work sits, by task weight

shifting to AI: 63%
changing shape: 31%
staying human: 6%

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

Whole-job exposure score 63 out of 100 (58–69 allowing for uncertainty): high exposure, across 49 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 market research analysts and marketing specialists is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

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

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

It would be a lie to soften that; directing trained survey interviewers is what this work rebuilds around. The plan below starts there.

Your week, as this page understands it

Research conditions in local, regional, national, or online markets. Gather information to determine potential sales of a product or service, or plan a marketing or advertising campaign. May gather information on competitors, prices, sales, and methods of marketing and distribution. May employ search marketing tactics, analyze web metrics, and develop recommendations to increase search engine ranking and visibility to target markets. The job title says “market research analysts” or “marketing specialists”: officially one job, two names. The real job is the part underneath: directing trained survey interviewers. 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 market research analysts and marketing specialists is not one task. It is 49 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is directing trained survey interviewers, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 63%
changing shape: 31%
staying human: 6%

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.

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

28 tasks

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

Preparing reports of findings, illustrating data graphically and translating complex findings into written text
This is reading one thing and writing another: reports of findings in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Prepare reports of findings, illustrating data graphically and translating complex findings into written text.” (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: Turning analysis into charts and clear written findings is one of the things AI does most reliably today.
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.
Collecting and analyzing Web metrics
This is reading one thing and writing another: web metrics in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Collect and analyze Web metrics, such as visits, time on site, page views per visit, transaction volume and revenue, traffic mix, click-through rates, conversion rates, cost per acquisition, or cost per click.” (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: Web analytics platforms already collect these numbers, and interpreting them follows widely documented methods.
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.
Optimizing digital assets, such as text, graphics or multimedia assets
This is reading one thing and writing another: digital assets in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Optimize digital assets, such as text, graphics, or multimedia assets, for search engine optimization (SEO) or for display and usability on internet-connected devices.” (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: Search optimisation guidance is published in detail and the work is text and file changes, 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 4/4.
Managing tracking and reporting of search-related activities and providing analyses to marketing executives
This is reading one thing and writing another: search-related activities in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Manage tracking and reporting of search-related activities and provide analyses to marketing executives.” (O*NET task statement)
How this row was scored
Exposure score: 70 out of 100 (66–74 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: Search data comes straight from analytics tools and the reporting repeats, so most of it can be automated.
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 4/4.

Changing shape

17 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.

Participating in the development or implementation of online marketing strategy
The software now makes the first pass at the development, 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 5 · Core
Source: “Participate in the development or implementation of online marketing strategy.” (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: AI contributes ideas and drafts, but taking part in strategy means working alongside colleagues who decide.
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.
Coordinating with developers to optimize Web site architecture
The software now makes the first pass at developers, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Coordinate with developers to optimize Web site architecture, server configuration, or page construction for search engine consumption and optimal visibility.” (O*NET task statement)
How this row was scored
Exposure score: 55 out of 100 (48–62 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: The technical recommendations are well documented, but coordinating changes with a development team involves working with people.
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 1/4 · how much data exists 4/4.
Conducting research on consumer opinions and marketing strategies
The software now makes the first pass at research, 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: “Conduct research on consumer opinions and marketing strategies, collaborating with marketing professionals, statisticians, pollsters, and other professionals.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: The research itself automates well, but this task is done jointly with specialists who shape the questions.
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 2/4 · how much data exists 3/4.

Staying human

4 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.

Directing trained survey interviewers
The value here is that a specific person handles trained survey interviewers and stands behind it. That is earned, not computed.
importance 3 · Core
Source: “Direct trained survey interviewers.” (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: Managing a team of interviewers means supervising people day to day, which software does not take over.
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 staff conferences to provide management with information and proposals concerning the promotion
The value here is that a specific person handles staff conferences and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Attend staff conferences to provide management with information and proposals concerning the promotion, distribution, design, and pricing of company products or services.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Proposals can be drafted by software, but presenting them and answering managers questions happens in the meeting.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Assisting in the evaluation or negotiation of contracts with vendors or online partners
The value here is that a specific person handles the evaluation and stands behind it. That is earned, not computed.
importance 3 · Supplemental
Source: “Assist in the evaluation or negotiation of contracts with vendors or online partners.” (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 same decision, made over and over; the value is that a specific person does it.
The rating behind it: Contract review drafts well, but negotiation is back-and-forth between people with legal sign-off at the end.
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 3/4.

Show the other 39 tasks

Monitoring industry statistics and following trends in trade literature
shifting to AI
This is reading one thing and writing another: industry statistics in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Monitor industry statistics and follow trends in trade literature.” (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: Trade publications and industry statistics are public text, so AI can monitor and summarise them continuously.
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.
Devising and evaluating methods and procedures for collecting data
shifting to AI
This is reading one thing and writing another: methods in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Devise and evaluate methods and procedures for collecting data, such as surveys, opinion polls, or questionnaires, or arrange to obtain existing data.” (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: Survey design is a well-documented craft with abundant examples, so AI drafts solid questionnaires for a specialist to refine.
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.
Developing transactional Web applications
shifting to AI
This is reading one thing and writing another: transactional web applications in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Develop transactional Web applications, using Web programming software and knowledge of programming languages, such as hypertext markup language (HTML) and extensible markup language (XML).” (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: Writing web code against documented standards is one of the strongest current uses of AI, with developers reviewing.
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.
Conducting market research analysis to identify search query trends
shifting to AI
This is reading one thing and writing another: market research analysis in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Conduct market research analysis to identify search query trends, real-time search and news media activity, popular social media topics, electronic commerce trends, market opportunities, or competitor performance.” (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: These trends come from public search and social data, which AI can gather and analyse continuously.
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.
Optimizing Web site exposure by analyzing search engine patterns to direct online placement of keywords or other content
shifting to AI
This is reading one thing and writing another: web site exposure in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Optimize Web site exposure by analyzing search engine patterns to direct online placement of keywords or other content.” (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: Keyword analysis and placement follow documented patterns in data tools, making this a natural fit for automation.
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.
Assisting in setting up or optimizing analytics tools for tracking visitors' behaviors
shifting to AI
This is reading one thing and writing another: setting up in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Assist in setting up or optimizing analytics tools for tracking visitors' behaviors.” (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: Analytics setup follows published documentation and standard code snippets, so AI can do most of the configuration.
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.
Combining secondary data sources with keyword research to more accurately profile and satisfy user intent
shifting to AI
This is reading one thing and writing another: secondary data sources in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Combine secondary data sources with keyword research to more accurately profile and satisfy user intent.” (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: Combining search data with other sources to understand what people want is quick, large-scale work for AI.
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.
Identifying appropriate Key Performance Indicators
shifting to AI
This is reading one thing and writing another: appropriate key performance indicators in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Identify appropriate Key Performance Indicators (KPIs) and report key metrics from digital campaigns.” (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: Choosing and reporting campaign measures draws on widely published standards and data already in the platforms.
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.
Improving search-related activities through ongoing analysis
shifting to AI
This is reading one thing and writing another: search-related activities in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Improve search-related activities through ongoing analysis, experimentation, or optimization tests, using A/B or multivariate methods.” (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: Designing and reading split tests is a well-documented statistical routine on data the tools already collect.
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.
Collecting and analyzing data on customer demographics
shifting to AI
This is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Collect and analyze data on customer demographics, preferences, needs, and buying habits to identify potential markets and factors affecting product demand.” (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: Customer data analysis follows well-known methods on data the company already holds digitally.
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.
Forecasting and tracking marketing and sales trends
shifting to AI
This is reading one thing and writing another: sales trends in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Forecast and track marketing and sales trends, analyzing collected data.” (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 from sales data uses documented statistical methods on numbers already in company systems.
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.
Measuring the effectiveness of marketing
shifting to AI
This is reading one thing and writing another: the effectiveness in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Measure the effectiveness of marketing, advertising, and communications programs and strategies.” (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: Campaign results sit in analytics platforms and the measurement methods are standard, so this analysis largely automates.
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.
Gathering data on competitors and analyzing their prices
shifting to AI
This is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Gather data on competitors and analyze their prices, sales, and method of marketing and distribution.” (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: Competitor pricing and marketing is mostly public, so collecting and comparing it suits automation 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.
Preparing electronic commerce designs or prototypes
shifting to AI
This is reading one thing and writing another: electronic commerce designs in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Prepare electronic commerce designs or prototypes, such as storyboards, mock-ups, or other content, using graphics design software.” (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: Mock-ups and storyboards can be generated quickly by design and AI tools for a designer to refine.
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 financial modeling for online marketing programs or Web site revenue forecasting
shifting to AI
This is reading one thing and writing another: this work in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Conduct financial modeling for online marketing programs or Web site revenue forecasting.” (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: Spreadsheet forecasting uses standard methods and the companys own figures, so models can be built largely automatically.
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.
Optimizing shopping cart experience or Web site conversion rates against Key Performance Indicators
shifting to AI
This is reading one thing and writing another: cart experience in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Optimize shopping cart experience or Web site conversion rates against Key Performance Indicators (KPIs).” (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: Conversion analysis and test design follow documented methods on the sites own data, so software does much of it.
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 online marketing initiatives, such as paid ad placement, affiliate programs, sponsorship programs, email promotions or viral marketing campaigns on social media Web sites
shifting to AI
This is reading one thing and writing another: online marketing initiatives in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Conduct online marketing initiatives, such as paid ad placement, affiliate programs, sponsorship programs, email promotions, or viral marketing campaigns on social media Web sites.” (O*NET task statement)
How this row was scored
Exposure score: 70 out of 100 (63–77 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: Ad platforms are increasingly automated and AI writes the creative, though spending decisions stay with the marketer.
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 4/4.
Creating content strategies for digital media
shifting to AI
This is reading one thing and writing another: content strategies in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Create content strategies for digital media.” (O*NET task statement)
How this row was scored
Exposure score: 70 out of 100 (63–77 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: Content planning is well documented and AI drafts strong plans, though brand judgment and sign-off stay with the team.
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 4/4.
Executing or managing social media campaigns to inform search marketing tactics
shifting to AI
This is reading one thing and writing another: social media campaigns in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Execute or manage social media campaigns to inform search marketing tactics.” (O*NET task statement)
How this row was scored
Exposure score: 70 out of 100 (63–77 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: Scheduling posts, writing copy and reading the results are all things AI and social tools already do well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.
Measuring and assessing customer and employee satisfaction
shifting to AI
This is reading one thing and writing another: customer in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Measure and assess customer and employee satisfaction.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (60–68 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Surveys and written feedback can be gathered and analysed automatically, with people deciding what to do next.
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.
Seeking and providing information to help companies determine their position in the marketplace
shifting to AI
This is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Seek and provide information to help companies determine their position in the marketplace.” (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: Gathering and summarising market information is strong AI work; deciding what it means for the company involves discussion.
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.
Developing and implementing procedures for identifying advertising needs
shifting to AI
This is reading one thing and writing another: procedures in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Develop and implement procedures for identifying advertising needs.” (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: Written procedures and checklists draft easily from documented practice; putting them into use involves the team.
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.
Evaluating new emerging media or technologies and making recommendations for their application within Internet marketing or searching marketing campaigns
shifting to AI
This is reading one thing and writing another: new emerging media in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Evaluate new emerging media or technologies and make recommendations for their application within Internet marketing or search marketing campaigns.” (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: Scanning and summarising new tools suits AI well; the recommendation still reflects the companys own priorities.
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.
Executing or managing banner, video or other non-text link ad campaigns
shifting to AI
This is reading one thing and writing another: banner, video or other non-text link ad campaigns in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Execute or manage banner, video, or other non-text link ad campaigns.” (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: Ad platforms automate much of the buying and AI can produce the creative, with the marketer approving what runs.
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.
Identifying methods for interfacing Web application technologies with enterprise resource planning or other system software
changing shape
The software now makes the first pass at methods, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 2 · Supplemental
Source: “Identify methods for interfacing Web application technologies with enterprise resource planning or other system software.” (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: Integration options are documented by the software vendors, so AI can map out approaches for engineers to validate.
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.
Proposing online or multiple-sales-channel campaigns to marketing executives
changing shape
The software now makes the first pass at online, 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: “Propose online or multiple-sales-channel campaigns to marketing executives.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: A proposal document is easy to draft, but persuading executives to back a campaign happens in the room.
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 2/4 · how much data exists 3/4.
Implementing online customer service processes to ensure positive and consistent user experiences
changing shape
The software now makes the first pass at online customer service processes, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · Supplemental
Source: “Implement online customer service processes to ensure positive and consistent user experiences.” (O*NET task statement)
How this row was scored
Exposure score: 49 out of 100 (42–56 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: Process documents and automated replies are easy to produce; embedding them in how a team works takes people.
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 1/4 · how much data exists 3/4.
Identifying, evaluating or procure hardware or software for implementing online marketing campaigns
changing shape
The software now makes the first pass at procure hardware, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · Core
Source: “Identify, evaluate, or procure hardware or software for implementing online marketing campaigns.” (O*NET task statement)
How this row was scored
Exposure score: 49 out of 100 (42–56 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: Comparing tools against documented features is easy for AI; buying decisions and vendor dealings involve people.
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 1/4 · how much data exists 3/4.
Identifying and developing commercial or technical specifications
changing shape
The software now makes the first pass at commercial, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · Supplemental
Source: “Identify and develop commercial or technical specifications, such as usability, pricing, checkout, or data security, to promote transactional internet-enabled commerce functionality.” (O*NET task statement)
How this row was scored
Exposure score: 49 out of 100 (42–56 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: Specifications draft well from documented practice, but the choices behind them are business decisions made with colleagues.
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 1/4 · how much data exists 3/4.
Assisting in the development of online transaction or security policies
changing shape
The software now makes the first pass at the development of online transaction, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · Supplemental
Source: “Assist in the development of online transaction or security policies.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 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: Policy templates are widely published, though security decisions need review by the people accountable for them.
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 1/4 · how much data exists 3/4.
Keeping abreast of government regulations and emerging Web technology to ensure regulatory compliance by reviewing current literature
changing shape
The software now makes the first pass at abreast of government regulations, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · Core
Source: “Keep abreast of government regulations and emerging Web technology to ensure regulatory compliance by reviewing current literature, talking with colleagues, participating in educational programs, attending meetings or workshops, or participating in professional organizations or conferences.” (O*NET task statement)
How this row was scored
Exposure score: 41 out of 100 (34–48 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 track published rules and technology news, but this task also includes conferences and talking with peers.
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 1/4 · how much data exists 4/4.
Defining product requirements, based on market research analysis, in collaboration with user interface design and engineering staff
changing shape
The software now makes the first pass at product requirements, 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 3 · Supplemental
Source: “Define product requirements, based on market research analysis, in collaboration with user interface design and engineering staff.” (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: Requirements documents draft well, but agreeing them with designers and engineers is joint decision-making.
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.
Coordinating sales or other promotional strategies with merchandising
changing shape
The software now makes the first pass at sales, 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 3 · Core
Source: “Coordinate sales or other promotional strategies with merchandising, operations, or inventory control staff to ensure product catalogs are current, accurate, and organized for best findability against user intent.” (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: Catalogue data can be checked and updated automatically, though coordinating across departments relies on working with colleagues.
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.
Communicating and collaborating with merchants
changing shape
The software now makes the first pass at merchants, 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 3 · Core
Source: “Communicate and collaborate with merchants, Webmasters, bloggers, or online editors to strategically place hyperlinks.” (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: Outreach messages draft easily, but link placements are won through relationships with real publishers and editors.
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.
Collaborating with other marketing staff to integrate and complement marketing strategies across multiple sales channels
changing shape
The software now makes the first pass at other marketing staff, 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: “Collaborate with other marketing staff to integrate and complement marketing strategies across multiple sales channels.” (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: The work is joining up plans with colleagues, so it depends on ongoing conversation across the marketing team.
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.
Collaborating with Web
changing shape
The software now makes the first pass at web, 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 3 · Core
Source: “Collaborate with Web, multimedia, or art design staffs to create multimedia Web sites or other internet content that conforms to brand and company visual format.” (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: AI generates draft visuals and copy, but this task is producing them together with the design team.
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.
Executing and managing communications with digital journalists or bloggers
changing shape
The software now makes the first pass at communications, 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 3 · Supplemental
Source: “Execute and manage communications with digital journalists or bloggers.” (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: Pitch emails write themselves easily, but press coverage depends on journalists trusting the person contacting them.
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.
Purchasing or negotiating placement of listings in local search engines
changing shape
The software now makes the first pass at placement of listings, 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 3 · Core
Source: “Purchase or negotiate placement of listings in local search engines, directories, or digital mapping technologies.” (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: Submissions can be automated, but negotiating placement and price is a commercial conversation between people.
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.
Resolving product availability problems in collaboration with customer service staff
staying human
The value here is that a specific person handles product availability problems and stands behind it. That is earned, not computed.
importance 3 · Supplemental
Source: “Resolve product availability problems in collaboration with customer service staff.” (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: Stock data is visible in systems, but sorting out a shortage means working it through with the service team.
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.

What this job pays, and how many people do it

Median pay: $78,760a 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: 899,580in 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: reports of findings in, a record out. The rows above are exactly that shape: preparing reports of findings and collecting and analyzing Web metrics. What it cannot do is be trusted in person, which is what trained survey interviewers 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: preparing reports of findings, illustrating data graphically and translating complex findings into written text is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 63% of this job's task weight sits in rows the software is already learning, 31% in rows that change shape rather than disappear, and 6% in rows it is nowhere near. That is the position, measured across 49 scored tasks. It is not a forecast about you.

What you have that the software does not is directing trained survey interviewers, 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 reports of findings 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 reports of findings, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.

Over the next 90 days

Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is preparing reports of findings, illustrating data graphically and translating complex findings into written text” 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 directing trained survey interviewers 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 market research analysts and marketing specialists (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was survey researchers: only about 4% of its durable work is work you already do, it pays 11.8% less and there are far fewer of those jobs than of yours. I am not going to pretend that is comfortable news: 63% of your own task list is already in the top exposure band. But the answer on this evidence is not a sideways jump into a job with the same problem. It is to walk toward the end of this one that answers for things. “participate in the development or implementation of online marketing strategy” is the part that stays with a person, and spending a year getting formally recognised for it beats a standing start somewhere else.

How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

Survey Researchers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already direct trained survey interviewers, and their equivalent is to direct and review the work of staff members. 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: $69,460 against your $78,760, 11.8% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 8,290 of those jobs against 899,580 of yours (OEWS May 2025), 1% as many seats.
Look at that job’s page anyway →
Web Developers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already keep abreast of government regulations and emerging Web technology to ensure regulatory compliance…, and their equivalent is to maintain understanding of current Web technologies or programming practices through continuing education, reading…. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 89% of its own task list already scores in the top exposure band (74/100 in this release), so the same software is eating it. And it is a narrow door: about 70,190 of those jobs against 899,580 of yours (OEWS May 2025), 8% as many seats.
Look at that job’s page anyway →
Advertising and Promotions Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already measure the effectiveness of marketing, advertising, and communications programs and strategies, and their equivalent is to plan and execute advertising policies and strategies for organizations. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 21,470 of those jobs against 899,580 of yours (OEWS May 2025), 2% as many seats.
Look at that job’s page anyway →

What I’d stop worrying about

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

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 63% of its task weight, across 49 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: directing trained survey interviewers 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 Advertising and marketing associate professionals 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 Advertising and marketing associate professionals, Advertising accounts managers and creative directors and Marketing and commercial 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.

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 market research analysts 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 Microsoft 365 Report Builders, a community for people who build business reports in Excel, Power Query and Power BI without a data team behind them. It overlaps with the part of your job that is growing: turning survey, campaign and market data into reports people trust. It does not cover research design, brand or campaign work. If that overlap isn't you, the free route below covers the same ground.

Try Microsoft 365 Report Builders 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 market research analysts 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 Market Research Analysts and Marketing Specialists?: Not as a job, but it is already doing parts of the work. Across the 49 official task statements scored for Market Research Analysts and Marketing Specialists (United States, SOC 13-1161), 63% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 63 out of 100 (range 58–69, band: high). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Market Research Analysts and Marketing Specialists” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Prepare reports of findings, illustrating data graphically and translating complex findings into written text” (83/100, very high); “Monitor industry statistics and follow trends in trade literature” (83/100, very high); “Devise and evaluate methods and procedures for collecting data, such as surveys, opinion polls, or questionnaires, or arrange to obtain existing data” (83/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Market Research Analysts and Marketing Specialists” stay human?: About 6% 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 trained survey interviewers” (17/100, minimal); “Attend staff conferences to provide management with information and proposals concerning the promotion, distribution, design, and pricing of company products…” (30/100, low); “Assist in the evaluation or negotiation of contracts with vendors or online partners” (35/100, 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 “Market Research Analysts and Marketing Specialists” do about AI?: Start from the ledger rather than the headline: 63% of this job's weighted core work is exposed, and roughly 6% 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 Market Research Analysts and Marketing Specialists calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 49 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.
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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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.