Will AI replace Data analysts?

Not as a job, but it is already doing parts of the work. Across the 58 official task statements scored for Data analysts (United Kingdom, SOC 3544), 74% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 71 out of 100 (range 65–77, 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 “Data analysts” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Analyse large-scale data sets to identify patterns and insights” (93/100, very high); “Analyse and evaluate data by performing complex calculations using computers” (93/100, very high); “Analyse data using mathematical and statistical methods to identify trends” (93/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Data analysts” stay human?

About 8% 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: “Conduct interviews with staff and managers about their job responsibilities” (17/100, minimal); “Participate in training events to enhance professional skills” (17/100, minimal); “Review survey responses for errors like incorrect pen use” (34/100, 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 “Data analysts” do about AI?

Start from the ledger rather than the headline: 74% of this job's weighted core work is exposed, and roughly 8% 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 Data analysts 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 58 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.

UK dataswitch to US

Data analysts

analysing large-scale data sets to identify patterns and insights, generating key performance indicators and monitoring and evaluating data performance to improve analytical outcomes. 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: analysing large-scale data sets to identify patterns and insights. The tasks, though, are not you.

Your move: three real directions from here ↓

Where the work sits, by task weight

shifting to AI: 74%
changing shape: 18%
staying human: 8%

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

Whole-job exposure score 71 out of 100 (65–77 allowing for uncertainty): high exposure, across 58 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 data analysts 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; participating in training events to enhance professional skills is what this work rebuilds around. Your move starts there.

Your week, as this page understands it

Data analysts gather and organise a variety of data and analyse it to understand what it means for their organisation or society. The job title says “data analysts”. The real job is the part underneath: participating in training events to enhance professional skills. 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 data analysts is not one task. It is 58 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is participating in training events to enhance professional skills, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 74%
changing shape: 18%
staying human: 8%

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.

19 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

42 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.

Analysing large-scale data sets to identify patterns and insights
This is reading one thing and writing another: large-scale data sets in, a record out. That is the shape today's tools are built for.
importance 95 · 3544/00
Source: “Analyse large-scale data sets to identify patterns and insights.” (UK 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: Finding patterns in large datasets is one of the strongest things these tools do, working directly on workplace data.
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.
Analysing data to offer advanced insights and interpretations
This is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 95 · 3544/00
Source: “Analyse data to offer advanced insights and interpretations.” (UK 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: Software analyses data quickly and explains patterns, though an analyst checks the interpretation makes sense.
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.
Analysing and evaluating data by performing complex calculations using computers
This is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 90 · 3544/00
Source: “Analyse and evaluate data by performing complex calculations using computers.” (UK 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: Complex calculations on data are what computers do best.
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.
Analysing data using mathematical and statistical methods to identify trends
This is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 90 · 3544/00
Source: “Analyse data using mathematical and statistical methods to identify trends.” (UK 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: Spotting trends with statistical methods is core computing work.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

11 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.

Providing analytic support to colleagues by interpreting data and offering insights
The software now makes the first pass at analytic support, 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 80 · 3544/00
Source: “Provide analytic support to colleagues by interpreting data and offering insights.” (UK 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: Software can produce the interpretation, but colleagues usually want to talk it through with an analyst.
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.
Analysing ethical and legal implications of digital and technology solutions and making recommendations as a result of this analysis
The software now makes the first pass at ethical, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 80 · 3544/00
Source: “Analyse ethical and legal implications of digital and technology solutions and make recommendations as a result of this analysis.” (UK 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: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: The law and guidance are documented and software can draft the analysis, but the formal legal view is taken elsewhere.
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 0/4 · how much data exists 3/4.
Communicating with internal and external clients to clarify data content requirements
The software now makes the first pass at internal and external clients, 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 80 · 3544/00
Source: “Communicate with internal and external clients to clarify data content requirements.” (UK 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: Pinning down what a client actually wants from their data is a back-and-forth 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.

Staying human

5 tasks

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

Presenting research findings to relevant stakeholders to support project development
The value here is that a specific person handles research findings and stands behind it. That is earned, not computed.
importance 85 · 3544/00
Source: “Present research findings to relevant stakeholders to support project development.” (UK task statement)
How this row was scored
Exposure score: 39 out of 100 (32–46 allowing for uncertainty): low 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 slides can be prepared automatically, but presenting to stakeholders and answering them is live.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Implementing quantitative structured solutions for new or expanded markets in collaboration with product development teams
The value here is that a specific person handles quantitative structured solutions and stands behind it. That is earned, not computed.
importance 70 · 3544/00
Source: “Implement quantitative structured solutions for new or expanded markets in collaboration with product development teams.” (UK 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: Building quantitative solutions with a product team depends on shared decisions about a new market.
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.
Reviewing survey responses for errors like incorrect pen
This work happens in the physical world: survey responses, in a real place. Software cannot follow it there.
importance 65 · 3544/00
Source: “Review survey responses for errors like incorrect pen use.” (UK task statement)
How this row was scored
Exposure score: 34 out of 100 (27–41 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Scanning can pick up most marking errors, but paper forms still have to be handled.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.

Show the other 48 tasks

Writing and editing reports
shifting to AI
This is reading one thing and writing another: reports in, a record out. That is the shape today's tools are built for.
importance 90 · 3544/00
Source: “Write and edit reports for internal and external use.” (UK 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: Writing and editing reports is one of the strongest uses of current software.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Analysing survey data to extract meaningful insights
shifting to AI
This is reading one thing and writing another: survey data in, a record out. That is the shape today's tools are built for.
importance 85 · 3544/00
Source: “Analyse survey data to extract meaningful insights.” (UK 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 insights out of survey data is standard analysis software does well.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Checking the quality of the data
shifting to AI
This is reading one thing and writing another: the quality of the data in, a record out. That is the shape today's tools are built for.
importance 85 · 3544/00
Source: “Check the quality of the data collected.” (UK 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: Checking collected data for quality problems is systematic, rule-based work these tools do accurately.
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.
Compiling reports, charts or graphs that describe and interpret findings of analyses
shifting to AI
This is reading one thing and writing another: reports, charts or graphs in, a record out. That is the shape today's tools are built for.
importance 85 · 3544/00
Source: “Compile reports, charts, or graphs that describe and interpret findings of analyses.” (UK task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Building charts and write-ups from analysis results is largely automated already.
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.
Generating reports about work
shifting to AI
This is reading one thing and writing another: reports in, a record out. That is the shape today's tools are built for.
importance 85 · 3544/00
Source: “Generate reports about work for colleagues, partners, and clients.” (UK 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: Writing up work for colleagues and clients is straightforward document work.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Monitoring and auditing data quality to ensure accuracy and reliability
shifting to AI
This is reading one thing and writing another: data quality in, a record out. That is the shape today's tools are built for.
importance 85 · 3544/00
Source: “Monitor and audit data quality to ensure accuracy and reliability.” (UK 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: Checking data for gaps and errors is a rule-based job software runs continuously.
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.
Generating key performance indicators
shifting to AI
This is reading one thing and writing another: key performance indicators in, a record out. That is the shape today's tools are built for.
importance 80 · 3544/00
Source: “Generate key performance indicators.” (UK 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: Producing performance measures from existing data is routine reporting work.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Monitoring and evaluating data performance to improve analytical outcomes
shifting to AI
This is reading one thing and writing another: data performance in, a record out. That is the shape today's tools are built for.
importance 80 · 3544/00
Source: “Monitor and evaluate data performance to improve analytical outcomes.” (UK 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: Tracking how data and reports are performing is monitoring software already does.
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.
Preparing analytical reports for data evaluation and decision-making
shifting to AI
This is reading one thing and writing another: analytical reports in, a record out. That is the shape today's tools are built for.
importance 80 · 3544/00
Source: “Prepare analytical reports for data evaluation and decision-making.” (UK 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: Turning data into an analytical report is one of software's strongest areas.
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.
Coding data prior to computer entry using lists of codes
shifting to AI
This is reading one thing and writing another: data prior in, a record out. That is the shape today's tools are built for.
importance 75 · 3544/00
Source: “Code data prior to computer entry using lists of codes.” (UK 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: Applying codes to data from a code list is exactly what software automates.
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.
Entering data into computers for use in analyses or reports
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 75 · 3544/00
Source: “Enter data into computers for use in analyses or reports.” (UK 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: Getting data into a system is routine work already largely automated.
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.
Reviewing source data for completeness and accuracy
shifting to AI
This is reading one thing and writing another: source data in, a record out. That is the shape today's tools are built for.
importance 75 · 3544/00
Source: “Review source data for completeness and accuracy.” (UK 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: Checking source data for gaps and errors is a rule-based job software does thoroughly.
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.
Compiling operational records, including time and production records, stock data and testing results
shifting to AI
This is reading one thing and writing another: operational records, including time and production records in, a record out. That is the shape today's tools are built for.
importance 70 · 3544/00
Source: “Compile operational records, including time and production records, stock data, and test results.” (UK 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 together time, production and stock records is routine compilation work.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Filing data and related information
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 65 · 3544/00
Source: “File data and related information.” (UK 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: Filing and organising data is routine work software does.
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.
Selecting statistical tests for analysing data
shifting to AI
This is reading one thing and writing another: statistical tests in, a record out. That is the shape today's tools are built for.
importance 80 · 3544/00
Source: “Select statistical tests for analysing data.” (UK task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Choosing the right statistical test is textbook knowledge software knows well, though study design matters.
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.
Creating computer models to test possible business solutions
shifting to AI
This is reading one thing and writing another: computer models in, a record out. That is the shape today's tools are built for.
importance 70 · 3544/00
Source: “Create computer models to test possible business solutions.” (UK 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: Building computer models to test options is well-documented technical work software does strongly.
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.
Applying engineering principles to the software development process
shifting to AI
This is reading one thing and writing another: principles in, a record out. That is the shape today's tools are built for.
importance 65 · 3544/00
Source: “Apply engineering principles to the software development process, including requirements analysis and design.” (UK 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: Requirements analysis and design are heavily documented disciplines, so tools produce strong work engineers 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.
Designing high-quality software solutions
shifting to AI
This is reading one thing and writing another: high-quality software solutions in, a record out. That is the shape today's tools are built for.
importance 65 · 3544/00
Source: “Design high-quality software solutions.” (UK 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: Software design is abundantly documented, so these tools produce solid solutions that developers 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.
Publishing data and information
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 70 · 3544/00
Source: “Publish data and information.” (UK task statement)
How this row was scored
Exposure score: 81 out of 100 (74–88 allowing for uncertainty): very high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Publishing prepared data and information is a routine process, with a sign-off before release.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Manipulating, analysing and interpreting complex data sets relating to the employer's business
shifting to AI
This is reading one thing and writing another: complex data sets in, a record out. That is the shape today's tools are built for.
importance 90 · 3544/00
Source: “Manipulate, analyse and interpret complex data sets relating to the employer's business.” (UK 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 through complex business data sets is analysis software and AI handle 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.
Analysing business and technical requirements to select and specify appropriate technology solutions
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 85 · 3544/00
Source: “Analyse business and technical requirements to select and specify appropriate technology solutions.” (UK 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: Matching requirements to technology options is well supported by these tools, with a specialist confirming the fit.
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 reporting processes to ensure accurate data collection
shifting to AI
This is reading one thing and writing another: processes in, a record out. That is the shape today's tools are built for.
importance 85 · 3544/00
Source: “Develop reporting processes to ensure accurate data collection.” (UK 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: Designing how data gets collected and reported is well-documented work software drafts 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.
Providing sector and competitor benchmarking to inform data analysis
shifting to AI
This is reading one thing and writing another: sector in, a record out. That is the shape today's tools are built for.
importance 85 · 3544/00
Source: “Provide sector and competitor benchmarking to inform data analysis.” (UK 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: Benchmarking against competitors is research and comparison software does well, with figures needing checking.
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.
Setting up and maintaining automated data processes
shifting to AI
This is reading one thing and writing another: and maintaining automated data processes in, a record out. That is the shape today's tools are built for.
importance 85 · 3544/00
Source: “Set up and maintain automated data processes.” (UK 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: Software can write most of the pipeline code, though setting it up for a business takes judgement.
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.
Building and maintaining databases to support organisational operations and data management
shifting to AI
This is reading one thing and writing another: databases in, a record out. That is the shape today's tools are built for.
importance 80 · 3544/00
Source: “Build and maintain databases to support organisational operations and data management.” (UK 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: Software writes most database code, but how a business's data should be structured is a design decision.
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 records management processes and policies
shifting to AI
This is reading one thing and writing another: records management processes in, a record out. That is the shape today's tools are built for.
importance 80 · 3544/00
Source: “Develop records management processes and policies.” (UK 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: Records policies follow well-established patterns, so software can draft them around the organisation's own needs.
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.
Analysing organisational changes and advising management on strategic adjustments
shifting to AI
This is reading one thing and writing another: organisational changes in, a record out. That is the shape today's tools are built for.
importance 75 · 3544/00
Source: “Analyse organisational changes and advise management on strategic adjustments.” (UK 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: Analysing what a change means and writing up the advice is analysis AI does well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Producing a range of standard and non-standard statistical and data analysis reports in the Model Building phase
shifting to AI
This is reading one thing and writing another: a range of standard in, a record out. That is the shape today's tools are built for.
importance 75 · 3544/00
Source: “Produce a range of standard and non-standard statistical and data analysis reports in the Model Building phase.” (UK 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: Standard reports are easy to automate; the non-standard ones still need an analyst's shaping.
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.
Evaluating the effectiveness of implemented changes
shifting to AI
This is reading one thing and writing another: the effectiveness of implemented changes in, a record out. That is the shape today's tools are built for.
importance 70 · 3544/00
Source: “Evaluate the effectiveness of implemented changes.” (UK task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Comparing before-and-after data to see whether a change worked is straightforward analysis.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Conducting and presenting effective research
shifting to AI
This is reading one thing and writing another: effective research in, a record out. That is the shape today's tools are built for.
importance 75 · 3544/00
Source: “Conduct and present effective research using engaging, well-structured approaches.” (UK 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: Research and well-structured write-ups are strongly supported by these tools, though presenting it stays human.
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.
Organising and distributing survey forms for data collection
shifting to AI
This is reading one thing and writing another: survey forms in, a record out. That is the shape today's tools are built for.
importance 70 · 3544/00
Source: “Organise and distribute survey forms for data collection.” (UK task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 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: Survey distribution is mostly online now and easily automated, with only paper forms needing handling.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Drawing conclusions and recommending an appropriate response
shifting to AI
This is reading one thing and writing another: conclusions in, a record out. That is the shape today's tools are built for.
importance 90 · 3544/00
Source: “Draw conclusions and recommend an appropriate response, offer guidance or interpretation to aid understanding of the data.” (UK 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: Software can draft conclusions and recommendations, but someone has to stand behind the advice.
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.
Designing dashboard and data visualisations for customer reports
shifting to AI
This is reading one thing and writing another: dashboard in, a record out. That is the shape today's tools are built for.
importance 85 · 3544/00
Source: “Design dashboard and data visualisations for customer reports.” (UK 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: Software can build the dashboard, though what a customer wants to see is agreed with them.
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.
Making recommendations for improvements to optimise data processes and insights
shifting to AI
This is reading one thing and writing another: recommendations in, a record out. That is the shape today's tools are built for.
importance 85 · 3544/00
Source: “Make recommendations for improvements to optimise data processes and insights.” (UK 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: Software can suggest improvements, but choosing which to make is the analyst's call.
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.
Analysing business processes to identify opportunities for improvement
shifting to AI
This is reading one thing and writing another: business processes in, a record out. That is the shape today's tools are built for.
importance 80 · 3544/00
Source: “Analyse business processes to identify opportunities for improvement.” (UK 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: Process data is captured in systems and the improvement methods are well documented, so software can produce solid analysis.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Identifying, evaluating and implementing external services and tools to support data validation and cleansing
shifting to AI
This is reading one thing and writing another: external services in, a record out. That is the shape today's tools are built for.
importance 80 · 3544/00
Source: “Identify, evaluate and implement external services and tools to support data validation and cleansing.” (UK 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: Comparing and setting up cleansing tools is well-documented work, with the choice sitting with the analyst.
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.
Prioritising and documenting these requirements using relevant techniques
shifting to AI
This is reading one thing and writing another: these requirements in, a record out. That is the shape today's tools are built for.
importance 70 · 3544/00
Source: “Prioritise and document these requirements using relevant techniques.” (UK 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: Writing requirements up is document work, though prioritising them means agreeing with the people who asked.
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.
Conducting customer satisfaction surveys via telephone or online to gather feedback on products or services
shifting to AI
This is reading one thing and writing another: customer satisfaction surveys via telephone in, a record out. That is the shape today's tools are built for.
importance 65 · 3544/00
Source: “Conduct customer satisfaction surveys via telephone or online to gather feedback on products or services.” (UK 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: Online surveys are automated already, and telephone surveys follow a script.
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.
Designing and conducting surveys to collect data
changing shape
The software now makes the first pass at surveys, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 75 · 3544/00
Source: “Design and conduct surveys to collect data.” (UK 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; mistakes that are cheap to catch.
The rating behind it: Survey design is well documented and easily drafted, though fieldwork means reaching real respondents.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.
Analysing client needs using a range of Operational Research methods
changing shape
The software now makes the first pass at client needs, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 75 · 3544/00
Source: “Analyse client needs using a range of Operational Research methods, adapting and developing them whilst understanding their limitations.” (UK 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: Adapting operational research methods to a client's problem, and knowing their limits, is expert judgement.
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.
Applying strategic practice to technology solutions development
changing shape
The software now makes the first pass at strategic practice, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 70 · 3544/00
Source: “Apply strategic practice to technology solutions development.” (UK 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: Strategy can be drafted, but applying it to a particular organisation needs inside knowledge.
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.
Implementing specific business theories to solve organisational problems
changing shape
The software now makes the first pass at specific business theories, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 65 · 3544/00
Source: “Implement specific business theories to solve organisational problems.” (UK 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: Applying a business theory to a real organisation's problem needs judgement about the particular situation.
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 areas for operational or processing improvement
changing shape
The software now makes the first pass at areas, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 75 · 3544/00
Source: “Identify areas for operational or process improvement.” (UK task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Software can spot inefficiencies in process data, though confirming them usually means talking to the team.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Managing operational research projects
changing shape
The software now makes the first pass at operational research projects, 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 75 · 3544/00
Source: “Manage operational research projects.” (UK 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: Running a project means coordinating people and decisions, not producing a single document.
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.
Practising continuous self-learning to keep up to date with industry trends and developments to enhance relevant skills and take responsibility for own professional development
changing shape
The software now makes the first pass at continuous, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 75 · 3544/00
Source: “Practise continuous self-learning to keep up to date with industry trends and developments to enhance relevant skills and take responsibility for own professional development.” (UK 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 rating behind it: Keeping your own skills current is something only the individual can do.
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 0/4 · how much data exists 3/4.
Applying change management to technology solutions development
changing shape
The software now makes the first pass at change management, 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 70 · 3544/00
Source: “Apply change management to technology solutions development.” (UK 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: Change management is about carrying people with you, which tools support with materials rather than perform.
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.
Participating in training events to enhance professional skills
staying human
The value here is that a specific person handles training events and stands behind it. That is earned, not computed.
importance 65 · 3544/00
Source: “Participate in training events to enhance professional skills.” (UK task statement)
How this row was scored
Exposure score: 17 out of 100 (10–24 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Taking part in training events is about being present and building your own skills.
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.
Conducting interviews with staff and managers about their job responsibilities
staying human
The value here is that a specific person handles interviews and stands behind it. That is earned, not computed.
importance 60 · 3544/00
Source: “Conduct interviews with staff and managers about their job responsibilities.” (UK task statement)
How this row was scored
Exposure score: 17 out of 100 (10–24 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Interviewing staff about their jobs depends on a live conversation and on people being willing to talk.
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.

What this job pays, and how many people do it

Median pay: £38,600a year, before tax, the middle of the range, so half earn more and half earn less.ashe-t14, 2025 · ASHE 2025 provisional (reference April 2025)Provisional, because the ONS revises this figure in the autumn.
How we know this
Source: ashe-t14
Reference period: ASHE 2025 provisional (reference April 2025)
Rounding: Shown to the nearest £100. The exact published figure is in the downloadable dataset. We do not render pounds the survey cannot support.
People doing this job: 140,900in the UK, 2026.nomis-aps · Apr 2025-Mar 2026 (latest APS 12-month period)This headcount comes from a survey, not a census, so treat it as a good estimate rather than an exact count.

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: large-scale data sets in, a record out. The rows above are exactly that shape: analysing large-scale data sets to identify patterns and insights and analysing data to offer advanced insights and interpretations. What it cannot do is be trusted in person, which is what training events 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: analysing large-scale data sets to identify patterns and insights is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 74% of this job's task weight sits in rows the software is already learning, 18% in rows that change shape rather than disappear, and 8% in rows it is nowhere near. That is the position, measured across 58 scored tasks. It is not a forecast about you.

What you have that the software does not is participating in training events to enhance professional skills, 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 large-scale data sets 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 large-scale data sets, 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 analysing large-scale data sets to identify patterns and insights” 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 participating in training events to enhance professional skills 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, spend an hour with National Careers Service. It is free and government-funded, 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 UK occupations to data analysts (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was other researchers, unspecified discipline: only about 7% of its durable work is work you already do. I am not going to pretend that is comfortable news: 74% 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. “present research findings to relevant stakeholders to support project development” 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 412 UK 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.

Other researchers, unspecified discipline
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already communicate with internal and external clients to clarify data content requirements, and their equivalent is to maintain working relationships and communicate effectively with internal and external stakeholders. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Information technology professionals n.e.c.
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already apply change management to technology solutions development, and their equivalent is to demonstrate leadership and change management skills to manage technology-driven change. Across both published task lists that is about 5% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 5% 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: 62% of its own task list already scores in the top exposure band (63/100 in this release), so the same software is eating it.
Look at that job’s page anyway →
Actuaries, economists and statisticians
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “present research findings to relevant stakeholders to support project development”. Across the whole of both lists that adds up to about 4% of the work in that job the software is not taking.
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. I will not move you off one melting floe onto another: 62% of its own task list already scores in the top exposure band (64/100 in this release), so the same software is eating it.
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: 74% of its task weight, across 58 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: participating in training events to enhance professional skills 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 Kingdom figures

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

Switch to the United States page →partial match

The other groups this work is counted across:

In US official statistics this job is counted as Data Scientists, Mathematical Science Occupations, All Other, Social Science Research Assistants and Statistical Assistants. 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.

In England:
National Careers Service
A free hour with a government-funded careers adviser is worth more than another evening of reading. In England that's the National Careers Service.
Free, government-funded
- Discover your skills and careers (the skills assessment)
In England:
National Careers Service - Find a course
Search what's actually running near you before you spend anything.
Free to search; individual courses vary
In England:
Free courses for jobs (Level 3 qualifications)
A free Level 3 qualification you already qualify for beats a paid course you don't need.
Free for eligible adults
In England:
Skills Bootcamps
Free, up to 16 weeks, and you get a job interview at the end. In England these are Skills Bootcamps - search what's running near you.
Free for eligible adults in England
- Skills Bootcamps - what they are and who can do them (DfE, Skills for Careers)
- Skills Bootcamps - JobHelp (DWP)
In Scotland:
My World of Work (Skills Development Scotland)
In Scotland it's My World of Work, from Skills Development Scotland.
Free, publicly funded
- Skills Development Scotland
In Wales:
Careers Wales
In Wales it's Careers Wales.
Free, Welsh Government-funded
In Northern Ireland:
Careers Service Northern Ireland
In Northern Ireland it's the Careers Service on nidirect.
Free, Department for the Economy-funded

A guided route for this

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

What a Space actually is, in full

Microsoft 365 Report Builders is built for business analysts, reporting leads and Excel power users who are responsible for business reports without a dedicated data team (the people this page is about). It works on the part of your job that is growing rather than shrinking: building reporting that survives real use - source data you can trust, measures that are defined, refreshes that do not break.

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 data analysts 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 Data analysts?: Not as a job, but it is already doing parts of the work. Across the 58 official task statements scored for Data analysts (United Kingdom, SOC 3544), 74% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 71 out of 100 (range 65–77, 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 “Data analysts” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Analyse large-scale data sets to identify patterns and insights” (93/100, very high); “Analyse and evaluate data by performing complex calculations using computers” (93/100, very high); “Analyse data using mathematical and statistical methods to identify trends” (93/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Data analysts” stay human?: About 8% 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: “Conduct interviews with staff and managers about their job responsibilities” (17/100, minimal); “Participate in training events to enhance professional skills” (17/100, minimal); “Review survey responses for errors like incorrect pen use” (34/100, 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 “Data analysts” do about AI?: Start from the ledger rather than the headline: 74% of this job's weighted core work is exposed, and roughly 8% 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 Data analysts 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 58 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

About the data on this page

Provisional, because the ONS revises this figure in the autumn.
This headcount comes from a survey, not a census, so treat it as a good estimate rather than an exact count.
The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
19 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: gaisi-indexProcessing: catalogue-bridge → ssc-relatedness-weighting → task-scoring → score-aggregation
Task weights: gaisi-index (relatedness)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
Pay and employment: ashe-t14 (ASHE 2025 provisional (reference April 2025))nomis-aps (Apr 2025-Mar 2026 (latest APS 12-month period))

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.