Will AI replace Life, Physical, and Social Science Technicians, All Other?

Not as a job, but it is already doing parts of the work. Across the 47 official task statements scored for Life, Physical, and Social Science Technicians, All Other (United States, SOC 19-4099), 50% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 50 out of 100 (range 45–55, band: partial). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

What should someone working in “Life, Physical, and Social Science Technicians, All Other” do about AI?

Start from the ledger rather than the headline: 50% of this job's weighted core work is exposed, and roughly 38% 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 Life, Physical, and Social Science Technicians, All Other 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 47 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

Life, Physical, and Social Science Technicians, All Other

collecting geospatial data, using technologies, aerial photography, investigating or reporting questionable test results, calibrating data collection equipment and training other analysts to perform laboratory procedures and assays. If that's your week, this page is about your job.

The honest answer

This job is splitting in two: compiling laboratory test data and performing appropriate analyses is work AI now does quickly and cheaply, and conducting routine and non-routine analyses of in-process materials is work it can't touch.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 50%
changing shape: 12%
staying human: 38%

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

Whole-job exposure score 50 out of 100 (45–55 allowing for uncertainty): partial exposure, across 47 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 life, physical, and social science technicians, all other is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

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

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

Which half fills your week decides your exposure. The ledger below shows which rows you can move toward.

Your week, as this page understands it

All life, physical, and social science technicians not listed separately. The job title says “life”, “physical”, “social science technicians” or “all other”: officially one job, several names. The real job is the part underneath: conducting routine and non-routine analyses of in-process materials. 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 life, physical, and social science technicians, all other is not one task. It is 47 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is conducting routine and non-routine analyses of in-process materials, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 50%
changing shape: 12%
staying human: 38%

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.

5 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

24 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.

Interpreting test results
This is reading one thing and writing another: test results in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Interpret test results, compare them to established specifications and control limits, and make recommendations on appropriateness of data for release.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 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: Comparing results against specification limits and flagging what passes is rule-based checking software does reliably.
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.
Integrating remotely sensed data with other geospatial data
This is reading one thing and writing another: sensed data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Integrate remotely sensed data with other geospatial 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: Combining remote sensing with other geospatial layers is well-documented software work with abundant published method.
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.
Completing documentation needed to support testing procedures
This is reading one thing and writing another: documentation in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Complete documentation needed to support testing procedures, including data capture forms, equipment logbooks, or inventory forms.” (O*NET task statement)
How this row was scored
Exposure score: 61 out of 100 (54–68 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: Test documentation and logbooks follow fixed formats, though entries are made at the bench as work happens.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Compiling laboratory test data and performing appropriate analyses
This is reading one thing and writing another: laboratory test data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Compile laboratory test data and perform appropriate analyses.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Compiling and analyzing laboratory test data is standard number work that software handles 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.

Changing shape

6 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 out-of-specification and failure investigations and recommending corrective actions
The software now makes the first pass at out-of-specification, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Participate in out-of-specification and failure investigations and recommend corrective actions.” (O*NET task statement)
How this row was scored
Exposure score: 42 out of 100 (35–49 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: Failure investigations follow a documented structure, though evidence often has to be gathered in the laboratory.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Manipulating raw data to enhance interpretation
The software now makes the first pass at raw data, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Manipulate raw data to enhance interpretation, either on the ground or during remote sensing flights.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 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: Enhancing raw data is software work, though some of it happens during flights or in the field.
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 0/4 · how much data exists 3/4.
Consulting with remote sensing scientists
The software now makes the first pass at remote sensing scientists, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Consult with remote sensing scientists, surveyors, cartographers, or engineers to determine project needs.” (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: Working out what a project actually needs comes from conversation with the scientists and engineers involved.
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

17 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.

Conducting routine and non-routine analyses of in-process materials
This work happens in the physical world: routine and non-routine analyses, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Conduct routine and non-routine analyses of in-process materials, raw materials, environmental samples, finished goods, or stability samples.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Running analyses on materials and samples is hands-on laboratory work with the physical sample.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Calibrating, validating or maintaining laboratory equipment
This work happens in the physical world: laboratory equipment, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Calibrate, validate, or maintain laboratory equipment.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Calibrating and maintaining laboratory equipment is hands-on instrument work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Ensuring that lab cleanliness and safety standards
This work happens in the physical world: lab cleanliness, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Ensure that lab cleanliness and safety standards are maintained.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (5–13 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Keeping the laboratory clean and safe is physical work done in the laboratory.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

Show the other 37 tasks

Maintaining records of survey data
shifting to AI
This is reading one thing and writing another: records of survey data in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Maintain records of survey data.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (86–100 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: Keeping survey data organised and retrievable is routine record-keeping that software handles reliably.
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.
Documenting methods used and writing technical reports containing information
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 3 · Supplemental
Source: “Document methods used and write technical reports containing information collected.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing up methods and technical reports from collected information is straightforward drafting.
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.
Verifying integrity and accuracy of data contained in remote sensing image analysis systems
shifting to AI
This is reading one thing and writing another: integrity in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Verify integrity and accuracy of data contained in remote sensing image analysis systems.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Verifying that data held in an image analysis system is complete and accurate is systematic checking.
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 documentation or presentations, including charts, photos or graphs
shifting to AI
This is reading one thing and writing another: documentation in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Prepare documentation or presentations, including charts, photos, or graphs.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Turning results into charts, graphs and presentations is one of the strongest uses of current tools.
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.
Merging scanned images or building photo mosaics of large areas
shifting to AI
This is reading one thing and writing another: scanned images in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Merge scanned images or build photo mosaics of large areas, using image processing software.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Merging scans and building photo mosaics is largely automated in image processing software 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.
Correcting raw data for errors due
shifting to AI
This is reading one thing and writing another: raw data in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Correct raw data for errors due to factors such as skew or atmospheric variation.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Correcting raw imagery for skew and atmospheric effects uses standard published algorithms.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Developing specialized computer software routines to customize and integrating image analysis
shifting to AI
This is reading one thing and writing another: specialized computer software routines in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Develop specialized computer software routines to customize and integrate image analysis.” (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 software routines for image analysis is coding, which AI does well with review by the technician.
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 or maintaining geospatial information databases
shifting to AI
This is reading one thing and writing another: geospatial information databases in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Develop or maintain geospatial information databases.” (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: Building and maintaining geospatial databases is well-documented software work AI supports 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.
Adjusting remotely sensed images for optimum presentation by using software to select image displays
shifting to AI
This is reading one thing and writing another: sensed images in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Adjust remotely sensed images for optimum presentation by using software to select image displays, define image set categories, or choose processing routines.” (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 displays and processing routines to present imagery well is documented software work with plenty of published guidance.
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.
Providing remote sensing data for use in addressing environmental issues
shifting to AI
This is reading one thing and writing another: remote sensing data in, a record out. That is the shape today's tools are built for.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Provide remote sensing data for use in addressing environmental issues, such as surface water modeling or dust cloud detection.” (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: Supplying processed remote sensing data for environmental modeling is documented data preparation work.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Writing technical reports or documentation
shifting to AI
This is reading one thing and writing another: technical reports in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Write technical reports or documentation, such as deviation reports, testing protocols, and trend analyses.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 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: Deviation reports, protocols and trend analyses are highly structured documents that draft well from the underlying data.
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.
Writing or revising standard quality control operating procedures
shifting to AI
This is reading one thing and writing another: standard quality control operating procedures in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Write or revise standard quality control operating procedures.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 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: Standard operating procedures follow strict formats with abundant examples, so drafts and revisions come easily.
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.
Preparing or reviewing required method transfer documentation
shifting to AI
This is reading one thing and writing another: required method transfer documentation in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Prepare or review required method transfer documentation including technical transfer protocols or reports.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 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: Method transfer protocols and reports are structured documents that draft and review well.
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.
Evaluating new technologies and methods to make recommendations regarding
shifting to AI
This is reading one thing and writing another: new technologies in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Evaluate new technologies and methods to make recommendations regarding their use.” (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: Reviewing new technologies and recommending whether to adopt them is research and write-up work.
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.
Identifying quality problems and recommending solutions
shifting to AI
This is reading one thing and writing another: quality problems in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Identify quality problems and recommend solutions.” (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: Spotting quality problems in data and proposing fixes is analysis that AI supports 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 3/4.
Evaluating remote sensing project requirements to determine the types of equipment or computer software necessary to meet project requirements
shifting to AI
This is reading one thing and writing another: remote sensing project requirements in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Evaluate remote sensing project requirements to determine the types of equipment or computer software necessary to meet project requirements, such as specific image types or output resolutions.” (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: Matching project requirements to the right equipment and software is a documented comparison job.
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 analytical methods and procedures to determine how they
shifting to AI
This is reading one thing and writing another: analytical methods in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Evaluate analytical methods and procedures to determine how they might be improved.” (O*NET task statement)
How this row was scored
Exposure score: 68 out of 100 (61–75 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Reviewing methods for possible improvement is analysis, though the useful ideas depend on that laboratory setup.
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 2/4.
Supplying quality control data necessary for regulatory submissions
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 4 · Core
Source: “Supply quality control data necessary for regulatory submissions.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 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: Pulling together the quality data a submission needs is compiling and formatting existing records.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Coordinating testing with contract laboratories and vendors
shifting to AI
This is reading one thing and writing another: contract laboratories in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Coordinate testing with contract laboratories and vendors.” (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: Coordinating testing with outside laboratories is scheduling and correspondence, with some relationship management.
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.
Participating in the planning or development of mapping projects
shifting to AI
This is reading one thing and writing another: the planning or development of mapping projects in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Participate in the planning or development of mapping projects.” (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: Mapping project plans follow documented method and draft well from the project requirements.
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.
Reviewing data from contract laboratories to ensure accuracy and regulatory compliance
changing shape
The software now makes the first pass at data, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Review data from contract laboratories to ensure accuracy and regulatory compliance.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 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; someone qualified has to answer for it.
The rating behind it: Checking incoming data against specifications is the kind of review software does well, with a qualified sign-off.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Collecting remote sensing data for forest or carbon tracking activities involved in assessing the impact of environmental change
changing shape
The software now makes the first pass at remote sensing data, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Collect remote sensing data for forest or carbon tracking activities involved in assessing the impact of environmental change.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 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: Forest and carbon data work is largely processing and analysis, with some fieldwork attached.
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 0/4 · how much data exists 3/4.
Serving as a technical liaison between quality control and other departments
changing shape
The software now makes the first pass at a technical liaison, 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: “Serve as a technical liaison between quality control and other departments, vendors, or contractors.” (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: Acting as the bridge between quality control and other teams runs on working relationships and local 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 2/4 · how much data exists 3/4.
Investigating or reporting questionable test results
staying human
The rules require a named, qualified person to answer for questionable test results, and that person cannot be a piece of software.
importance 4 · Core
Source: “Investigate or report questionable test results.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Investigating a doubtful result means going back to the sample and the equipment, with a qualified person owning the conclusion.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Collaborating with agricultural workers to apply remote sensing information to efforts to reduce negative environmental impacts of farming practices
staying human
The value here is that a specific person handles agricultural workers and stands behind it. That is earned, not computed.
importance not published
O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Collaborate with agricultural workers to apply remote sensing information to efforts to reduce negative environmental impacts of farming practices.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Getting remote sensing insight used on farms depends on working alongside the growers who must act on it.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Monitoring testing procedures to ensure that all tests are performed according to established item specifications
staying human
This work happens in the physical world: procedures, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Monitor testing procedures to ensure that all tests are performed according to established item specifications, standard test methods, or protocols.” (O*NET task statement)
How this row was scored
Exposure score: 25 out of 100 (18–32 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Making sure tests are actually run to method requires watching the work as it is done.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Participating in internal assessments and audits
staying human
This work happens in the physical world: internal assessments, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Participate in internal assessments and audits as required.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (14–28 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Audits involve walking the site and questioning people, not only reading documents.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Collecting geospatial data, using technologies, aerial photography
staying human
This work happens in the physical world: geospatial data, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Collect geospatial data, using technologies such as aerial photography, light and radio wave detection systems, digital satellites, or thermal energy systems.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (13–27 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Some geospatial data arrives from satellites automatically, but airborne and field collection needs people and equipment.
The five ratings: output a model can produce 1/4 · 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 3/4.
Performing validations or transfers of analytical methods in accordance with applicable policies or guidelines
staying human
This work happens in the physical world: validations, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Perform validations or transfers of analytical methods in accordance with applicable policies or guidelines.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (5–13 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Transferring or validating an analytical method means running it on real equipment with real samples.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Identifying and troubleshooting equipment problems
staying human
This work happens in the physical world: equipment problems, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Identify and troubleshoot equipment problems.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Tracking down and fixing equipment faults means working on the machine.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Monitoring raw data quality during collection
staying human
This work happens in the physical world: raw data quality during collection, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Monitor raw data quality during collection, and make equipment corrections as necessary.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Watching data quality during collection and correcting equipment happens where the equipment is.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Training other analysts to perform laboratory procedures and assays
staying human
This work happens in the physical world: other analysts, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Train other analysts to perform laboratory procedures and assays.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (3–11 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Teaching another analyst to run an assay happens at the bench, hands on the method.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Developing and qualifying new testing methods
staying human
This work happens in the physical world: new testing methods, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Develop and qualify new testing methods.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (3–11 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Developing and proving a new test method is experimental bench work.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Performing visual inspections of finished products
staying human
This work happens in the physical world: visual inspections of finished products, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Perform visual inspections of finished products.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Visually inspecting finished product means having the product in front of you.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Receiving and inspecting raw materials
staying human
This work happens in the physical world: raw materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Receive and inspect raw materials.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Receiving and inspecting raw materials means handling the delivered goods.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Collecting verification data on the ground
staying human
This work happens in the physical world: verification data, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Collect verification data on the ground, using equipment such as global positioning receivers, digital cameras, or notebook computers.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Collecting verification data on the ground means physically going to the site.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Calibrating data collection equipment
staying human
This work happens in the physical world: data collection equipment, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Calibrate data collection equipment.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Calibrating data collection equipment means handling the instruments themselves.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

What this job pays, and how many people do it

Median pay: $62,280a 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: 73,910in 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: laboratory test data in, a record out. The rows above are exactly that shape: compiling laboratory test data and performing appropriate analyses and interpreting test results. What it cannot do is be there in the room, and that is still where routine and non-routine analyses get done. Which is why this page talks about your tasks changing, not your job ending.

Your move

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

The exposed part of your job is the biggest part, and I am not going to dress that up: compiling laboratory test data and performing appropriate analyses is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 50% of this job's task weight sits in rows the software is already learning, 12% in rows that change shape rather than disappear, and 38% in rows it is nowhere near. That is the position, measured across 47 scored tasks. It is not a forecast about you.

What you have that the software does not is conducting routine and non-routine analyses of in-process materials, 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 laboratory test data 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 laboratory test data, 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 compiling laboratory test data and performing appropriate analyses” 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 conducting routine and non-routine analyses of in-process materials 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 life, physical, and social science technicians, all other (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was physical scientists, all other: only about 15% of its durable work is work you already do. Your own job splits about 50/50: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “conduct routine and non-routine analyses of in-process materials, raw materials, environmental samples…”, and let the exposed end go.

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

3 moves I checked and rejected

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

Physical Scientists, All Other
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already integrate remotely sensed data with other geospatial data, and their equivalent is to set up or maintain remote sensing data collection systems. Across both published task lists that is about 15% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 15% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Look at that job’s page anyway →
Chemists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already identify and troubleshoot equipment problems, and their equivalent is to maintain laboratory instruments to ensure proper working order and troubleshoot malfunctions when needed. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Industrial Engineers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already identify and troubleshoot equipment problems, and their equivalent is to design, install, or troubleshoot manufacturing equipment. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →

What I’d stop worrying about

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

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 50% of its task weight, across 47 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: conducting routine and non-routine analyses of in-process materials 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 Quality assurance technicians 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 Quality assurance technicians, Science, engineering and production technicians n.e.c. and Laboratory technicians. 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

Where to go next, and what it costs

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

No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for life / physical / social science technicians / all other, and we are not going to point you at the nearest one and call it a fit.

There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 50% of the work on this page is already inside what they can do.

Try The AI Authority free →

7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.

The AI Authority is a general community about working with AI, not a course for life / physical / social science technicians / all other. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.

Noted, and thank you. We’ll email you if a Space for life / physical / social science technicians / all other 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 Space for life / physical / social science technicians / all other yet. Should there be one?

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

Collab365 launches new communities where the need is real. If one for life / physical / social science technicians / all other existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for life / physical / social science technicians / all other launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

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 Life, Physical, and Social Science Technicians, All Other?: Not as a job, but it is already doing parts of the work. Across the 47 official task statements scored for Life, Physical, and Social Science Technicians, All Other (United States, SOC 19-4099), 50% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 50 out of 100 (range 45–55, band: partial). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Life, Physical, and Social Science Technicians, All Other” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Compile laboratory test data and perform appropriate analyses” (93/100, very high); “Maintain records of survey data” (93/100, very high); “Document methods used and write technical reports containing information collected” (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 “Life, Physical, and Social Science Technicians, All Other” stay human?: About 38% 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: “Calibrate data collection equipment” (0/100, minimal); “Collect verification data on the ground, using equipment such as global positioning receivers, digital cameras, or notebook computers” (0/100, minimal); “Conduct routine and non-routine analyses of in-process materials, raw materials, environmental samples, finished goods, or stability samples” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Life, Physical, and Social Science Technicians, All Other” do about AI?: Start from the ledger rather than the headline: 50% of this job's weighted core work is exposed, and roughly 38% 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 Life, Physical, and Social Science Technicians, All Other 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 47 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.
5 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

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

How we score a job Download this release Look up another job

Using these figures?

Cite this

Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

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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.