Will AI replace File Clerks?
Work in File Clerks sits in the in-between: AI reaches some of it (~79% in theory) but is only measured doing about 16% today — part human, part machine.
O*NET-SOC 43-4071
How your 15 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a high share of this job's tasks (~79%). By late 2025, real-world AI use had reached about 16% of its task activity (growing but still limited). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
Read this as a range, not a verdict
The signals here partly disagree — AI's theoretical reach (~79%) and its real-world use (~16%) tell different stories. AI-risk scores also shift a lot by which model does the rating (2.7%–51.5% in one 2026 study), so this is a direction of travel, not a fixed answer.
See all 15 tasks, ratedBased on real task-level AI scores — click to collapse
- Input data, such as file numbers, new or updated information, or document information codes into computer systems to support document and information retrieval.
- Perform general office activities, such as typing, answering telephones, operating office machines, processing mail, or securing confidential materials.
- Sort or classify information according to guidelines, such as content, purpose, user criteria, or chronological, alphabetical, or numerical order.
- Answer questions about records or files.
- Keep records of materials filed or removed, using logbooks or computers and generate computerized reports.
- Add new material to file records or create new records as necessary.
- Gather materials to be filed from departments or employees.
- Track materials removed from files to ensure that borrowed files are returned.
- Eliminate outdated or unnecessary materials, destroying them or transferring them to inactive storage, according to file maintenance guidelines or legal requirements.
- Modify or improve filing systems or implement new filing systems.
- Design forms related to filing systems.
- Scan or read incoming materials to determine how and where they should be classified or filed.
- Find, retrieve, and make copies of information from files in response to requests and deliver information to authorized users.
- Perform periodic inspections of materials or files to ensure correct placement, legibility, or proper condition.
- Place materials into storage receptacles, such as file cabinets, boxes, bins, or drawers, according to classification and identification information.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.