Finance & Banking
Will AI replace data entry clerks?
BLS places data entry keyers in its “Very high” AI-exposure category (of four, relative to other occupations). BLS projects employment to decline 25.5% from 2025 to 2035, with about 7,700 openings a year. OEWS May 2025 median pay is $41,340 (10th–90th percentile $31,200–$58,790). Typical entry education: high school diploma or equivalent.
Sources: BLS AI exposure categories and Employment Projections 2025–35 (released 27 August 2026); BLS OEWS May 2025; tasks from O*NET 31.0 (CC BY 4.0).
Figures are for the BLS occupation Data entry keyers (SOC 43-9021).
Data entry clerks enter, check and maintain records in an organization’s databases and systems.
The figures, with their sources
| AI-exposure category | Very high (of Low, Moderate, High, Very high)BLS AI exposure categories, 2025–35 |
|---|---|
| Employment, 2025 → 2035 | 131,800 → 98,200BLS Employment Projections 2025–35 |
| Projected change, 2025–35 | −25.5%BLS Employment Projections 2025–35 |
| Openings per year (2025–35 average) | 7,700BLS Employment Projections 2025–35 |
| Median annual pay | $41,340BLS OEWS, May 2025 |
| 10th–90th percentile pay | $31,200 – $58,790BLS OEWS, May 2025 |
| Typical entry education | High school diploma or equivalentBLS Employment Projections 2025–35 |
National figures, all industries and all experience levels. Employment and openings are published by BLS in thousands; pay is annual, in US dollars.
What BLS says, in its own words
“Very high” relative AI exposure generally indicates that, compared to other occupations, a larger fraction of an occupation’s tasks can be completed or assisted by AI technology, and that LLMs have been observed performing some of the occupation’s tasks.
“An exposure category is not a forecast of employment growth or decline.” “Exposure does not imply job loss, productivity gains, automation probability, or wage effects.”
“Capital/labor substitution - share decreases as organizations increasingly manage, store, and analyze large quantities of data, requiring traditional data entry methods to be replaced by automated processes through use of expanded technologies, including artificial intelligence (AI), machine learning (ML), mobile data capture, and optical character recognition (OCR).”
Our count from the BLS table: 141 of the 206 occupations in the “Very high” category are projected to grow from 2025 to 2035. That is our arithmetic on BLS data, not a BLS statement.
What the work is: O*NET® tasks
The occupation’s core tasks, most important first, as O*NET 31.0 words them.
- 1.Locate and correct data entry errors, or report them to supervisors.Accountability
- 2.Compile, sort, and verify the accuracy of data before it is entered.
- 3.Compare data with source documents, or re-enter data in verification format to detect errors.
- 4.Store completed documents in appropriate locations.
- 5.Select materials needed to complete work assignments.
How we tag: a task is tagged Judgment, Accountability or Relationships when O*NET’s wording uses one of these verbs. Judgment: evaluate, assess, determine, decide, interpret, diagnose, recommend, investigate, approve, prioritize, resolve. Accountability: direct, supervise, manage, oversee, ensure, plan, establish, authorize, comply. Relationships: confer, consult, negotiate, advise, collaborate, liaise, mentor, train, counsel, persuade, interview, present, meet with. This is our reading of the task text, not an O*NET or BLS classification, and it is not a measure of AI exposure.
For new graduates
- Typical entry education
- High school diploma or equivalent
- BLS Employment Projections 2025–35
- Openings per year
- 7,700
- All levels, not entry-level only
The tasks to show proof for: 1 of 5 are about judgment, accountability or relationships
We point you at these because our method treats them as the work that is hardest to hand to AI. That is our judgment, not a BLS or O*NET finding. A capstone, an internship or a part-time job where you made the call, owned the outcome or worked with the people involved is proof. Write what you did, not what you were assigned.
Show the 1 tasks
- Locate and correct data entry errors, or report them to supervisors.
What this data does not tell you
- How many openings are entry-level, or how many go to new graduates. BLS counts openings at every level.
- How AI will change hiring for junior roles in this occupation. The exposure category is about the occupation’s tasks, and BLS says it is not a forecast.
- Pay or openings in your city or industry. These are national figures.
- Whether your own tasks match these. O*NET describes the occupation, not your job.
BLS also publishes the work experience and on-the-job training usually needed; see the BLS projections table.
Proof worth finding for a Data Entry Clerk application
Our editorial starting list, not a survey of postings. The posting you apply to decides what counts.
- Quantified accuracy rates and volume metrics (records processed, error rates) from current role
- Experience with the specific database or ERP systems used (SAP, Oracle, Salesforce, etc.)
- Evidence of quality control or verification responsibilities beyond data input
- Any process improvement or automation-adjacent contributions (flagging errors, building checks)
- Demonstrated domain knowledge in your industry (financial records, medical coding, legal documents)
Show your proof for one real posting
- Step 1Job Match · 5 tokensPaste a real posting. For each requirement, we quote the line in your resume that proves it, or say nothing was found.
- Step 2Resume · 15 tokensRewrite toward that posting from your own words. Any number we could not find in them is listed for you to check.
- Step 3Know your tasks · FreeYour own tasks against the data entry keyers data above, labeled task by task.
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Questions people ask
Will AI replace data entry clerks?
The data does not say so. BLS places data entry keyers in its “Very high” relative AI-exposure category and states that “An exposure category is not a forecast of employment growth or decline.” In BLS’s words: “Very high” relative AI exposure generally indicates that, compared to other occupations, a larger fraction of an occupation’s tasks can be completed or assisted by AI technology, and that LLMs have been observed performing some of the occupation’s tasks. BLS projects employment to decline 25.5% from 2025 to 2035 (BLS Employment Projections 2025–35). By our count from the BLS table, 141 of the 206 occupations in the “Very high” category are projected to grow.
How many data entry clerks jobs open each year?
BLS projects about 7,700 openings a year for data entry keyers, averaged over 2025–35. BLS defines openings as net employment change plus workers permanently leaving the occupation, so a declining occupation can still have openings. Employment was 131,800 in 2025.
How much do data entry clerks earn?
BLS OEWS (May 2025) puts the national median annual wage for data entry keyers at $41,340. The 10th percentile is $31,200, the 25th $35,760, the 75th $48,410 and the 90th $58,790. These are all industries and all experience levels in the United States; pay in your city and at your level will differ.
What education do data entry clerks need?
BLS lists “High school diploma or equivalent” as the typical education needed to enter data entry keyers. It is the typical path, not a rule: postings set their own requirements.
How does AISkillScore check a Data Entry Clerk application?
We count, we do not guess. Job Match reads the posting’s requirements and, for each one, quotes the line in your resume that proves it, or says nothing was found. The resume rewrite keeps your claims and lists any number it could not find in your own words so you can check it before you send.
Sources
U.S. Bureau of Labor Statistics: Artificial Intelligence (AI) exposure categories; Occupational projections, 2025–35, and worker characteristics; Employment Projections definitions; Occupational Employment and Wage Statistics, May 2025: Data entry keyers. BLS data is in the public domain.
This page includes information from the O*NET 31.0 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA. O*NET 31.0 Database · CC BY 4.0 · Data entry keyers on O*NET OnLine. Changes: we show up to eight of the occupation’s core tasks, most important first, merged across the O*NET codes BLS groups into one occupation, and we add our own task tags.
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