Will AI replace Phlebotomists?
Most of the work in Phlebotomists still leans on things AI struggles with — research rates its theoretical AI reach at only ~18%, and real-world use lower still.
O*NET-SOC 31-9097
How your 17 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 relatively low share of this job's tasks (~18%). By late 2025, real-world AI use had reached about 0% of its task activity (still rare). 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 (~18%), real-world use (~0%) 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 17 tasks, ratedBased on real task-level AI scores — click to collapse
- Enter patient, specimen, insurance, or billing information into computer.
- Document route of specimens from collection to laboratory analysis and diagnosis.
- Explain fluid or tissue collection procedures to patients.
- Provide sample analysis results to physicians to assist diagnosis.
- Dispose of contaminated sharps, in accordance with applicable laws, standards, and policies.
- Organize or clean blood-drawing trays, ensuring that all instruments are sterile and all needles, syringes, or related items are of first-time use.
- Draw blood from veins by vacuum tube, syringe, or butterfly venipuncture methods.
- Match laboratory requisition forms to specimen tubes.
- Dispose of blood or other biohazard fluids or tissue, in accordance with applicable laws, standards, or policies.
- Conduct standards tests, such as blood alcohol, blood culture, oral glucose tolerance, glucose screening, blood smears, or peak and trough drug levels tests.
- Collect specimens at specific time intervals for tests, such as those assessing therapeutic drug levels.
- Process blood or other fluid samples for further analysis by other medical professionals.
- Draw blood from capillaries by dermal puncture, such as heel or finger stick methods.
- Conduct hemoglobin tests to ensure donor iron levels are normal.
- Transport specimens or fluid samples from collection sites to laboratories.
- Collect fluid or tissue samples, using appropriate collection procedures.
- Train other medical personnel in phlebotomy or laboratory techniques.
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.