Will AI Replace Data Entry Keyers?
This is the clearest case in the data: the core of the job — moving text from documents into systems — is exactly what current AI and OCR do well, and BLS projects the steepest losses outside the very smallest occupations.
The exposure evidence — three sources, side by side
There is no official "AI risk score" for any job, and any site that gives you one number without a source is making it up. Here are the three serious research measures that exist for this occupation, each with what it actually counts:
| Research source | Exposure figure | What it actually measures |
|---|---|---|
| Anthropic Economic Index — observed exposure | 67% of tasks | Share of this occupation's tasks where AI use was actually observed in real Claude conversation data (Anthropic, "Labor market impacts of AI", March 2026 dataset). This is measured use, not a prediction. |
| GPTs are GPTs (OpenAI / University of Pennsylvania) — GPT-4 rating, β measure | 89% of tasks | Share of tasks rated exposed to large language models, counting direct exposure in full and assisted exposure at half weight (the paper's β measure). Human expert raters scored the same occupation at 50%. |
| Microsoft "Working with AI" — AI applicability score | 0.32 / 1.00 | A 0–1 score built from 200,000 real Bing Copilot conversations, weighting how often AI was used for this occupation's tasks and how well it completed them. It is a score, not a percentage; the median occupation scores about 0.14. |
The three sources measure different things — observed use, rated capability and Copilot-based applicability — so they rarely agree exactly. Where they disagree, that disagreement itself is information: it usually means a job is being changed by specialist tools, or in ways general chatbots do not show. All three agree on the framing: exposure is not replacement.
The work itself: what AI can help with, and what stays human
These are this occupation's real tasks, from the US Department of Labor's O*NET database (version 30.0). The split is our editorial analysis — the tasks on the left are the routine, screen-based ones current AI tools demonstrably assist with; the ones on the right involve physical presence, licensed responsibility, or judgement about people.
Where AI can already help
- Reading source documents (checks, sales reports, bills) and entering data into specific fields
- Comparing entered data against source documents to catch errors
- Compiling, sorting and verifying data before it is entered
- Storing completed documents and maintaining logs of completed work
What stays human
- Locating and correcting data entry errors, or escalating them to a supervisor
- Resolving garbled or indecipherable source material
- Choosing which materials and sources a work assignment actually needs
- Being accountable for the accuracy of records other people rely on
Pay, outlook and entry requirements (BLS)
| Median annual pay, 2024 | $39,850 |
|---|---|
| Employment change, 2024–34 | BLS projects employment to shrink by 25.9% from 2024 to 2034 (141,600 → 104,900 jobs, a change of -36,700). |
| Typical entry-level education | High school diploma or equivalent |
Basis: BLS Employment Projections, Table 1.2 (2024–34): occupation row, median annual wage 2024. The OOH does not profile this occupation separately; education is the Table 1.2 assignment. A median is the midpoint of what all workers in the occupation earn — it is not a starting wage, and it is not a forecast.
What to learn next
A roadmap only helps if it starts from the skills this job already builds. These three moves do — each is chosen because the destination work is growing, harder to automate, or carries a credential software cannot hold:
- Data quality and validation → Data Scientists. The checking half of data entry — spotting bad data and knowing why it is wrong — is the entry ramp into data work, and data scientists are projected to grow 34% by 2034. See: Data Scientists — projected +34% 2024–34 (BLS).
- Spreadsheet automation and OCR tools → a stronger position in the same field. Employers still need people who run the automated capture pipeline, handle its exceptions and audit its output — the job changes from typing to supervising the machine that types.
- Records and information management → a stronger position in the same field. Experience with records, verification and confidentiality transfers directly into medical and government records roles that involve judgement, not just keying.
AI tools in this job today
The tools reshaping this job are document-capture and AI extraction tools: software that reads forms, invoices and IDs and fills database fields automatically. If you work in data entry today, the practical move is to learn to operate and audit those tools — setting up templates, checking confidence flags and fixing exceptions — rather than competing with them on typing speed.
Frequently asked questions
Will AI replace data entry keyers?
Not on any schedule the research supports. The strongest exposure figure for this occupation is very high — about 89% of its tasks are within AI's reach — but exposure measures tasks, not whole jobs. BLS projects this occupation shrinking 25.9% between 2024 and 2034, and Anthropic's own labour-market research found no systematic rise in unemployment among workers in highly exposed occupations. The realistic outcome is a changed job, not an abolished one.
Which parts of a job are most exposed?
The tasks listed under “Where AI can already help” above — mostly the routine, text- and data-handling parts of the role. They come from this occupation's real O*NET task list, and they are the parts to stop competing on and start supervising.
How much do data entry keyers earn?
The BLS median annual pay was $39,850 in 2024 — half earned more, half earned less. It is a national median, not a starting salary or a promise; pay varies widely by state, employer and experience.
What should I learn next?
The roadmap above is built from this occupation's actual skills: Data quality and validation; Spreadsheet automation and OCR tools; Records and information management. Each points at work that is either growing, harder to automate, or both — with the BLS outlook for the target occupation stated next to it.
Where do these numbers come from?
Exposure figures come from three published research datasets — Anthropic's Economic Index, the OpenAI/University of Pennsylvania GPTs-are-GPTs study, and Microsoft's Working with AI paper. Pay, outlook and education come from the US Bureau of Labor Statistics (Employment Projections and Occupational Outlook Handbook, 2024–34 cycle). Every figure on this page is dated and sourced in the Sources section below, and our full method is on the Methodology page.
Sources
- Anthropic, "Labor market impacts of AI" (Massenkoff & McCrory, March 2026) and the Economic Index job-exposure dataset (observed exposure by occupation). Accessed 11 November 2026.
- Eloundou, Manning, Mishkin & Rock, "GPTs are GPTs" (OpenAI / University of Pennsylvania, 2023), occupation-level exposure data (β measure), official dataset. Accessed 11 November 2026.
- Microsoft Research, "Working with AI: Measuring the Applicability of Generative AI to Occupations" (2025), AI applicability scores (CC BY 4.0). Accessed 11 November 2026.
- US Bureau of Labor Statistics, Employment Projections Table 1.2 and Occupational Outlook Handbook (2024–34 cycle): pay, outlook, education. Public domain. Accessed 11 November 2026.
- US Department of Labor, O*NET Database 30.0 (CC BY 4.0): occupation tasks and skills. Accessed 11 November 2026.
Data checked November 2026. Figures are national US figures. Exposure scores describe tasks, not people, and none of the sources above predicts that this occupation will be eliminated.