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A closer look

Data analyst

AI can write the query. It can't tell you whether the answer is too convenient to be true.

The ILO files this under a catch-all group for database and network professionals, whose tasks are mostly about data security. AI could take a fair chunk of watching for threats and planning data protection; the rest is mostly out of its reach.

51 / 100

Changing significantly

AI could do a real share of the everyday tasks. The job is changing shape, not vanishing.

Not the odds you keep your job. It's how much of the everyday work still needs a person, out of 100.

← Robot's turf Human's turf →

The ILO's official verdict: Exposed: Gradient 2

13 of the 20 jobs up close

Why this score?

The ILO rated each task in this job for how much of it AI could do. They average 48.7 out of 100, so the job holds up at 100 − 48.7 = 51.3.

ILO group “Exposed: Gradient 2”: A good share of the tasks are affected.

AI helps JSA 2025
77
Usually good news: more of the work gets quicker without the job going away.
AI takes over JSA 2025
58
This is what most people mean by “exposed”. We keep it apart from helping on purpose.
Hands-on O*NET 31.0
4.1
Software can't do hands-on work, but robots might one day. How far they've got isn't on this site, because nobody measures it well enough yet.

from the source Working Paper 140, Generative AI and Jobs 2025 index O*NET 31.0 Anthropic Economic Index release 2026-06-26, period beginning 2026-05-01 Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use

Man v Bot · compare

Who holds up better?

Data analyst 51

v

Go on, try your partner's job. Or your sibling's. We won't tell.

Man v Bot

Where the bot helps, and where the human wins

Bot

What AI takes

The routine stuff. You may not miss it.

  • Writing queries and building routine reports.
  • Cleaning, joining and reshaping data.
  • Producing the standard chart pack.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Knowing which question is worth answering.
  • Recognising when a result is too convenient to be true.
  • Telling a decision-maker something they do not want to hear.

Both

What's arriving

New work that only exists because the AI does.

  • Checking machine-generated analysis that is fluent and occasionally wrong.
  • Governance over what data may be used, and how.
  • Far more analysis requested, because asking has become nearly free.

Written by a human, from the task data. Yes, we see the irony. Read it as one person's take.

Meet your two rivals

They're not the same machine. AI software can only reach the parts of a job done on a screen or in words. A chatbot can't climb a ladder. A robot might, one day, if it doesn't fall off.

The AI

Lives in a screen. Never had to fix a boiler.

Threat level: Medium

58 could do without you

It could take over some of the work, and help with more. It could help with 77 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Lower

4 how hands-on

Not much of this job needs hands, so the robot can sit this one out.

So could a robot do the physical part?

Less than half of this job is hands-on. Robots still matter, but for a smaller part of the work, so we don't sort it any further.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 9 out of 100. The higher it is, the longer the robot waits.

9 How fiddly
and cramped

Kind of hands-on work Less than half hands-on

The small print

It isn't a robot score, and nothing on this page is. Nobody measures how far robots have got well enough to put a number on it. This is the closest thing we have: how much fiddly handwork and cramped, awkward work the job takes, which is what Frey and Osborne found hardest for machines.

The numbers come straight from the US job database's entry for Data Scientists. Its wider group, Database and network professionals, lands in the same place.

finger skill 14.3 · hand skill 10.7 · cramped spaces 2 · hands-on 4.1 · caring 1.32 of 7 · rule: hands-on under 50

from the source O*NET 31.0 data 0.8-observed-use measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

Meanwhile, in real life…

Everything else here is about what AI could do. This is what people actually did. When they asked Claude for help with this kind of work, did they hand the whole task over, or roll their sleeves up and work through it together?

Out of every 100 conversations

53
47

worked through it together handed it over

The small print

Anthropic publishes this about its own product, and it doesn't feed into any score here. These are people who use Claude, not workers in general, so little use here doesn't mean AI can't reach the job. This job makes up 0.61% of the conversations.

These two add up to 100. The bars elsewhere on the page don't, because each is scored on its own, so we don't put them side by side.

observed use Anthropic Economic Index release 2026-06-26, period beginning 2026-05-01 data 0.8-observed-use 2026-05-01 · Anthropic, about its own product

So, should data analysts be worried?

Short answer: not worried, but paying attention. The job is changing.

The routine part is moving fast. Queries, cleaning data and the standard chart pack are exactly what AI tools do well. The work that grows is choosing the right question, checking fluent but wrong analysis, and deciding what data may be used. One caveat: this score comes from a broad ILO group mostly about data security.

Worth remembering: we score tasks, not jobs. When AI takes on a task, the job usually reshuffles around it rather than vanishing. Bank tellers survived the cash machine. The job just got more chatty.

Where this job could lead

Fancy a rematch?

Put data analysts up against another job, then send the result to someone who needs a little humbling.

Where this page's numbers come from