Will AI replace Computer, Automated Teller, and Office Machine Repairers?
Work in Computer, Automated Teller, and Office Machine Repairers sits in the in-between: AI reaches some of it (~37% in theory) but is only measured doing about 11% today — part human, part machine.
O*NET-SOC 49-2011
How your 24 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 moderate share of this job's tasks (~37%). By late 2025, real-world AI use had reached about 11% 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.
The signals here line up
Theoretical reach (~37%), real-world use (~11%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 24 tasks, ratedBased on real task-level AI scores — click to collapse
- Advise customers concerning equipment operation, maintenance, or programming.
- Reinstall software programs or adjust settings on existing software to fix machine malfunctions.
- Maintain records of equipment maintenance work or repairs.
- Test new systems to ensure that they are in working order.
- Complete repair bills, shop records, time cards, or expense reports.
- Install and configure new equipment, including operating software or peripheral equipment.
- Enter information into computers to copy programs from one electronic component to another or to draw, modify, or store schematics.
- Converse with customers to determine details of equipment problems.
- Maintain parts inventories and order any additional parts needed for repairs.
- Analyze equipment performance records to assess equipment functioning.
- Read specifications, such as blueprints, charts, or schematics, to determine machine settings or adjustments.
- Reassemble machines after making repairs or replacing parts.
- Disassemble machines to examine parts, such as wires, gears, or bearings for wear or defects, using hand or power tools and measuring devices.
- Align, adjust, or calibrate equipment according to specifications.
- Repair, adjust, or replace electrical or mechanical components or parts, using hand tools, power tools, or soldering or welding equipment.
- Travel to customers' stores or offices to service machines or to provide emergency repair service.
- Operate machines to test functioning of parts or mechanisms.
- Clean, oil, or adjust mechanical parts to maintain machines' operating efficiency and to prevent breakdowns.
- Update existing equipment, performing tasks such as installing updated circuit boards or additional memory.
- Test components or circuits of faulty equipment to locate defects, using oscilloscopes, signal generators, ammeters, voltmeters, or special diagnostic software programs.
- Assemble machines according to specifications, using hand or power tools and measuring devices.
- Lay cable and hook up electrical connections between machines, power sources, and phone lines.
- Fill machines with toners, inks, or other duplicating fluids.
- Train new repairers.
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.