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Will AI replace weighers, measurers, checkers, and samplers, recordkeeping?

A little.

Scales and software can hold the numbers, but someone still handles the goods, pulls the samples, and sorts out what the records miss. This job scores 79 out of 100 on (higher is safer). Today people do 23% of the work with AI’s help, and 77% still needs a person.

Updated 3 October 2026 43-5111 8144 2026-Q4
Office and Administrative SupportWeighers, Measurers, Checkers, and Samplers, Recordkeeping43-5111 · 2026-Q4
0% AI does it23% AI helps77% needs a human
Your job's name, lit by the work that still needs a human.Needs a human 77%AI helps 23%AI does it 0%

AI does it: AI can do the task largely by itself. AI helps: a person still does it, faster with AI. Needs a human: AI can do little of it yet.

Why the scale still has someone standing next to it

Weighing is easy to automate on paper. In practice, the job is weighing, checking, sampling, and then explaining the gap when the paperwork and the pallet disagree. Software can hold the record. Someone still has to handle the goods.

Most of a shift is physical and local. Material gets moved onto a scale or through a checkpoint. Samples get pulled from a drum, a bin, or a truckload and labeled for the lab. Loads get compared against a bill of lading, a spec sheet, or a customer order. When the count is short or the moisture looks wrong, the fix is a conversation with a driver, a supervisor, or a shipper, not a query.

That is also why the question “will ai replace weighers” has a different answer than it does for a desk-only clerk. The recordkeeping half of this job sits close to software. The handling half sits close to the dock. Our task split above separates the two, and the method behind it is set out in the scoring methodology.

What software does, what it assists, and what people keep

On the software side, the calculation and clerical work is the most exposed. Totaling quantities, converting units, filling in weight tickets, and pushing figures into an inventory or ERP system are steps that automated scales, barcode scans, and simple rules already handle in many plants. The share of task time that sits here is printed above: 0%. The idea behind that figure is explained in how we measure coverage.

A larger slice is assistance rather than substitution: 23%. Think of flagging a weight that falls outside tolerance, matching a received load to an open purchase order, or drafting the discrepancy note a checker then confirms. The tool narrows what to look at. The worker decides whether the reading is real, whether the sample was representative, and what happens to the load.

Then there is the part that stays with people: 77%. Collecting samples by hand, inspecting goods for damage, signaling and directing loading, weighing bulk material in awkward conditions, and sorting out a short shipment with the carrier all sit here. None of that is solved by a better spreadsheet.

What has actually been tested

No study on this page tests an AI system against a working weigher, checker, or sampler on the full job. Our evidence grade reflects that: D. A grade at the bottom of the scale means not measured, so we publish no parity number for this occupation. The grading scale is described in how we grade quality parity.

What would settle it is narrow and practical: a trial on real inbound and outbound loads, comparing automated weighing and vision checks against trained staff on count accuracy, damage detection, sample validity, and the rate of disputes that later need a human to unwind. Until something like that is published and dated, the honest position is that the clerical steps are demonstrably automatable and the handling steps are not yet shown to be.

The labor data is clearer. BLS counts about 53,300 people in this occupation in the United States, with median pay of $46,380, and projects employment to fall roughly 4.8% between 2025 and 2035 (BLS, 2025). That is erosion, not disappearance, and it matches the pattern on our list of shrinking occupations: fewer openings, especially at entry level, rather than an empty dock.

When the balance could shift

Most likely after 2037 (8 in 10 of our scenarios). What that window measures is explained in how we estimate the replacement year.

Two things could pull it earlier. First, in-line weighing and scanning built into conveyors and dock doors, which removes the manual ticket step entirely in new facilities. Second, the cost gap shown above: the software side of this work is cheap to run compared with a wage, so the clerical tasks are the first to go whenever a system is replaced.

Two things hold it back. The physical share of the work is large, and the robotics tier on this page is mobile robots, which means machines that can move around a yard or warehouse floor and handle varied material. That hardware is capital-heavy and slow to install, particularly in older plants, grain elevators, scrap yards, and agricultural sites. Second, accountability. A weight ticket can decide a payment, a tax, or a legal dispute, so someone has to sign for it and be able to explain it. Sectors differ here, which is why the picture in warehousing is not the same as in a food plant.

What to do: if your site is installing automated scales or scanning gates, ask to be the person who verifies, audits, and troubleshoots the new system rather than the person it replaces.

How to stay needed

Lean into the tasks that stay with people. Sampling done correctly, so the lab result means something. Inspection of incoming goods for damage, contamination, and wrong product. Discrepancy resolution with carriers and suppliers, where the record and the reality do not match.

Two skills raise your floor. One is instrumentation: calibrating scales, meters, and moisture testers, and knowing when a reading is drifting. The other is systems fluency, meaning comfort with the inventory or quality module your employer runs, including exports, audit trails, and correcting a bad entry properly. Both move you toward the supervisory and technical end of the material recording and distributing job family.

Nearby roles are worth a look if you want to shift toward work with more judgment or more scope. Inspectors, testers, sorters, samplers, and weighers use the same measurement instincts on quality problems. Shipping, receiving, and inventory clerks cover the same loads with wider responsibility for documentation and carriers. Production, planning, and expediting clerks move upstream into scheduling and materials flow.

To see how those options stack up, put two of them side by side on the job comparison tool, or search the full set in the job rankings.

Frequently asked questions

What does a weigher, measurer, checker, and sampler actually do?

They weigh, measure, and count materials coming in or going out, compare what arrives against orders and specifications, collect samples for testing, and keep the records that prove it. Much of the work happens on a dock, in a yard, or beside a production line. The task list above shows which of those steps software can take on and which still need a person.

Is the number of these jobs falling?

Yes, modestly. BLS projects employment in this occupation to decline about 4.8% between 2025 and 2035, from a base of roughly 53,300 US jobs with median pay of $46,380 (BLS, 2025). That is gradual thinning, mostly through fewer new openings, rather than sites clearing out their weighing and checking staff all at once.

Do automated scales make the role pointless?

No. An automated scale removes a reading and a keystroke. It does not pull a representative sample, spot a damaged pallet, direct a loader, or argue a short shipment with a carrier. Automation tends to change the mix of the shift rather than empty it, shifting time away from paperwork and toward handling, verification, and exceptions.

Which parts of the job are most exposed to AI?

The clerical core: calculating quantities, converting units, filling out tickets, and entering figures into inventory systems. Those steps are rule-based and already partly handled by connected scales and scanners. The task split near the top of this page groups every duty by whether AI can do it, assist with it, or leave it to a person.

What skills help most if I want to stay in this line of work?

Calibration and basic instrumentation, so you can tell a drifting scale from a bad load. Confident use of your employer’s inventory or quality software, including audit trails and corrections. Clear written communication for discrepancy reports. Forklift or equipment certifications also widen the roles you can cover when a site reorganizes its material flow.

How certain is the evidence behind this page?

Not very, and the page says so. No published study has tested an AI system against trained weighers and checkers on this job, so the evidence grade shown above sits at the bottom of the scale and no parity figure is given. The methodology pages explain what each grade means and what evidence would raise it.

Each ridge is a slice of the job's task time.Needs a human 77%AI helps 23%AI does it 0%
The job’s mark

No two jobs leave the same print

Every job gets its own fingerprint, drawn from its code. The amber ridges are the share of task time that still needs a person. Below them, the same ridges are written out in ones and zeros: slate for the work AI helps with, white for the work AI can do.

Weighers, Measurers, Checkers, and Samplers, Recordkeeping, O*NET-SOC 43-5111. 77% of the job’s task time still needs a human, so 77 of every 100 ridges are amber; slate is what AI helps with, white what AI can do.

What AI can and cannot do

The tasks that make up the job, from , and where AI stands on each today: , (a person does it, with AI speeding it up) or . 77% of the still needs a human.

Each block is one task; its height is its share of working time.Needs a human 77%AI helps 23%AI does it 0%
The job's task list: the parts AI can do are blacked out.Needs a human 77%AI helps 23%AI does it 0%
Document quantity, quality, type, weight, test result data, and value of materials or products to maintain shipping, receiving, and production records and files.AI helps
Weigh or measure materials, equipment, or products to maintain relevant records, using volume meters, scales, rules, or calipers.Needs a human
Collect or prepare measurement, weight, or identification labels and attach them to products.Needs a human
Examine products or materials, parts, subassemblies, and packaging for damage, defects, or shortages, using specification sheets, gauges, and standards charts.Needs a human
Signal or instruct other workers to weigh, move, or check products.Needs a human
Collect product samples and prepare them for laboratory analysis or testing.Needs a human
Maintain, monitor, and clean work areas, such as recycling collection sites, drop boxes, counters and windows, and areas around scale houses.Needs a human
Compare product labels, tags, or tickets, shipping manifests, purchase orders, and bills of lading to verify accuracy of shipment contents, quality specifications, or weights.AI helps
Remove from stock products or loads not meeting quality standards, and notify supervisors or appropriate departments of discrepancies or shortages.Needs a human
Inspect products and examination records to determine the number of defects per worker and the reasons for examiners' rejections.Needs a human
Store samples of finished products in labeled cartons and record their location.Needs a human
Count or estimate quantities of materials, parts, or products received or shipped.Needs a human
Communicate with customers and vendors to exchange information regarding products, materials, and services.AI helps
Fill orders for products and samples, following order tickets, and forward or mail items.Needs a human
Operate scalehouse computers to obtain weight information about incoming shipments such as those from waste haulers.AI helps
Sort products or materials into predetermined sequences or groupings for display, packing, shipping, or storage.Needs a human
Transport materials, products, or samples to processing, shipping, or storage areas, manually or using conveyors, pumps, or hand trucks.Needs a human
Unload or unpack incoming shipments.Needs a human

Is it better than a person? The evidence

No direct test against people in this job yet. Every study is , and vendor studies are labelled as such.

When could it be replaced?

When AI could largely do this job: no sooner than 2037

Most likely after 2037 (8 in 10 of our scenarios). A range from our of how fast AI improves, how fast employers take it up and what holds it back, not a forecast that the job ends. “” has a strict meaning here. Today’s answer is at the top of the page; this is how it could change.

The sand is the human working years left, measured in the same 40-year glass for every job, so a safe trade starts nearly full and an exposed job with a thin layer.

The sand is the human working years left, in the same 40-year glass for every job.Years still needing a humanYears run out

How this job could shift, year by year

Where the job could sit on our scale each year to 2060, across the ten behind its .

Today
Will AI replace this job?
A little.
By 2045
60%
of our scenarios have AI largely doing this job by 2045 (Largely.)
10% still have it mostly needing a person (A little. or Nah.)
By 2060
90%
of our scenarios have AI largely doing this job by 2060 (Largely.)
10% still have it mostly needing a person (A little. or Nah.)

We run this job as ten scenarios spread across its replacement range. In each, the score moves towards the bottom band (Largely: AI could largely do the job) by the year that scenario reaches it, slowly at first and faster later, as adoption usually goes. Each bar splits the ten by the band they put the job in. The model stops at 2060. How the timeline works

Share of this job's scenarios in each verdict band, today to 20600%25%50%75%100%2026: 100.0% of scenarios: AI could do a little of this job (A little.)100%Today2030: 90.0% of scenarios: AI could do a little of this job (A little.)90%2030: 10.0% of scenarios: AI could partly do this job (Partly.)10%20302035: 40.0% of scenarios: AI could do a little of this job (A little.)40%2035: 30.0% of scenarios: AI could partly do this job (Partly.)30%2035: 20.0% of scenarios: AI could mostly do this job (Mostly.)20%2035: 10.0% of scenarios: AI could largely do this job (Largely.)10%20352040: 10.0% of scenarios: AI could do a little of this job (A little.)10%2040: 30.0% of scenarios: AI could partly do this job (Partly.)30%2040: 20.0% of scenarios: AI could mostly do this job (Mostly.)20%2040: 40.0% of scenarios: AI could largely do this job (Largely.)40%20402045: 10.0% of scenarios: AI could do a little of this job (A little.)10%2045: 10.0% of scenarios: AI could partly do this job (Partly.)10%2045: 20.0% of scenarios: AI could mostly do this job (Mostly.)20%2045: 60.0% of scenarios: AI could largely do this job (Largely.)60%20452050: 10.0% of scenarios: AI could do a little of this job (A little.)10%2050: 20.0% of scenarios: AI could mostly do this job (Mostly.)20%2050: 70.0% of scenarios: AI could largely do this job (Largely.)70%20502055: 10.0% of scenarios: AI could do a little of this job (A little.)10%2055: 90.0% of scenarios: AI could largely do this job (Largely.)90%20552060: 10.0% of scenarios: AI could do a little of this job (A little.)10%2060: 90.0% of scenarios: AI could largely do this job (Largely.)90%2060
Will AI replace the job?Largely.Mostly.Partly.A little.Nah.
Share of this job's scenarios in each band, year by year. Updated with every release.
Show the data
YearLargelyMostlyPartlyA littleNah
Today (2026)0.0%0.0%0.0%100.0%0.0%
20300.0%0.0%10.0%90.0%0.0%
203510.0%20.0%30.0%40.0%0.0%
204040.0%20.0%30.0%10.0%0.0%
204560.0%20.0%10.0%10.0%0.0%
205070.0%20.0%0.0%10.0%0.0%
205590.0%0.0%0.0%10.0%0.0%
206090.0%0.0%0.0%10.0%0.0%

What’s stopping AI taking over?

The things that keep this work with people, strongest first. Each is scored 0 to 100 from work context, licensing and the evidence we have.

Evidence gapNo study yet compares AI with people doing this job, so employers have no proof it is good enough.
Clients want a personFace-to-face contact is rated 4.6 and physical closeness 3.3 out of 5; caring for or serving people is 2.8 out of 5 in importance.
LiabilityMistakes are rated 3.0 out of 5 for consequence and decisions 3.0 out of 5 for impact; someone has to answer for them.
Physical work78% of the task time is physical; robots have been shown on 100% of that time.
RegulationWorkers rate responsibility for others' health and safety 3.5 out of 5.
LicensingUsual entry requirement (BLS): high school diploma or equivalent, then short-term on-the-job training.

What would it cost to hand the work to AI?

The share of the year AI could handle (329 of 2,080 hours a year), priced two ways. Both are ranges, not quotes.

AI model usage, a year
$30–$3,290
A person’s wage for the same hours
$5,580–$9,620

AI cost covers model usage only: no integration, licences, oversight or the human time still needed to review the work. Human cost is the wage for the same hours, without benefits or overheads. As of 2026-10.

Robots and humanoids

AI software can only take the work at a screen. The rest needs a robot that can do it.

78%
of the task time is physical work
Mobile robots
the kind of robot the physical work would need
Commercial in warehouses, hospitals and some outdoor sites; hands are still limited.

Source: Anthropic Economic Index, 'What work can robots do?' (30 September 2026); O*NET 31.0 task weights.

Which AI skills does this job lean on?

The job’s task time split by what an AI model would need to be good at, and where models stand today.

Each star is a task, grouped by the AI skill it leans on.Needs a human 77%AI helps 23%AI does it 0%
Writing · 11.6% of time
Strong
Drafts, edits and translates most routine documents at professional quality.
Analysis · 12.7% of time
Strong
Reliable on structured data and rules; uneven on judgement calls with thin information.
Coding · 0% of time
Strong
Agents complete many routine software tasks end to end; larger systems still need people.
Vision and design · 11.8% of time
Good
Reads documents, images and layouts well; specialist imaging needs dedicated, approved tools.
Speech · 0% of time
Good
Voice agents handle routine calls and live interpreting; complex or sensitive calls still go to people.
Planning and agents · 5.1% of time
Emerging
Multi-step agents work in narrow, well-tooled workflows; open-ended coordination is unreliable.
Physical manipulation · 58.7% of time
Early
Robots handle structured, repetitive handling; general dexterity outside fixed settings is not commercial.
Care and persuasion · 0% of time
Limited
Can script, coach and advise; trust, presence and accountability still need a person.
Amber matter holds its orbit, slate circles the inner disc, white falls in.Needs a human 77%AI helps 23%AI does it 0%
How exposed is it?

Still needs a human: 79/100↑ safer

The amber matter on the outside holds its orbit: that is the task time that still needs a person. The slate matter circling the inner disc is the work AI helps with. The white matter is the share AI can do; it spirals in and is gone over the edge.

The matter follows the job’s task split: 77% needs a human, 23% AI helps, 0% AI does it. Still needs a human: 79/100 ↑ safer. Will AI replace them? A little.

What the AIs say

We asked four AI assistants the same question: will AI replace this job in the next 10 years? One word (Yes, Partly or No), then one sentence. Our own answer, from the data: Still needs a human: 79/100 ↑ safer. Will AI replace them? A little.

ChatGPTPartly

AI and automation will take over many routine weighing tasks, but human weighers will still be needed for oversight, exceptions, maintenance, compliance, and customer-facing situations.

gpt-5.5-2026-04-23 · asked 2026-10-03
ClaudeNo

While AI and automated sensors will increasingly handle routine weighing and data logging, the role of weighers—especially those involving oversight, calibration, judgment calls, and handling exceptions in industrial or regulatory settings—will likely persist in some form, though it may evolve or shrink in scope.

claude-sonnet-5 · asked 2026-10-03
GeminiPartly

While automated systems and AI will handle the majority of routine measurement, sorting, and data-logging tasks, human oversight will still be needed for equipment calibration, handling irregular goods, and resolving system anomalies.

gemini-3.8-flash · asked 2026-10-03
PerplexityPartly

AI will likely automate routine weighing and recordkeeping, while humans remain for oversight, exceptions, and quality judgment.

sonar · asked 2026-10-03

Assistants answer from what they learned in training (Perplexity also searches the web), so they can be confidently wrong, and the same question can get a different answer tomorrow. Our score is built from task data and graded evidence. Answers collected through DataForSEO.

Cite this page

NeedsAHuman.com (2026). Will AI replace Weighers, Measurers, Checkers, and Samplers, Recordkeeping? A little. Still needs a human: 79/100, higher is safer; release 2026-Q4. https://needsahuman.com/jobs/weighers-measurers-checkers-and-samplers-recordkeeping/ (accessed 4 October 2026).

Scores change with each , so cite the release. The data is open under : credit NeedsAHuman.com with a link. Open data · Press

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The badge updates itself with each release and links back to this page.

Sources

  • Tasks and work context: 31.0, ().
  • Jobs, pay and projections: US , and 2025–35.
  • How AI is used today: ; Microsoft Research, .
  • What AI can do: our task ratings ( r1) and the quality evidence register.
  • UK names and employment: coding index and .

How each score is built: methodology. Every figure on this page: open data. Release 2026-Q4.