Nurse anesthetists, nurse midwives, and nurse practitioners: AI exposure and career outlook
Nurse anesthetists, nurse midwives, and nurse practitioners (SOC 29-1171) sit at the 55th percentile for measured AI exposure among the 342 occupations tracked here, measured from a composite of Microsoft Research and Anthropic Economic Index telemetry. An estimated 29% of tasks are already automated and 55% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 38% follows an automation pattern and 62% an augmentation pattern, which places it in the Orchestrator opportunity group: AI reaches deep into this work and amplifies it. The leverage goes to whoever directs it, and that can be you. In Canada the role maps to NOC 31302 (Nurse practitioners), and ESDC's COPS 2024–2033 outlook for that unit group is strong risk of shortage.
Figures last updated 2026-06. Every number on this page is labelled measured or modelled; where a source has no coverage for this occupation, it says so rather than showing a zero.
Key facts
- SOC code
- 29-1171
- Canada NOC 2021
- 31302
- TEER level
- 1 — University Degree
- COPS outlook
- Strong risk of Shortage
What the measurements actually say
Three independent studies measure AI exposure for this occupation directly. The two percentages below them are ours, modelled from those measurements.
| Metric | Value | Provenance |
|---|---|---|
| AI applicability | 14% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. |
| Observed AI usage | 9% | Measured — Anthropic Economic Index, share of tasks observed being performed with Claude (CC-BY 4.0). |
| Academic AI exposure | 56th percentile | Measured — Felten-Raj-Seamans AIOE index across 774 occupations; the index underlying Statistics Canada's Canadian estimates. |
| Automation-pattern usage | 38% | Measured — the share of observed Claude usage in this occupation where the task is handed over rather than iterated on (Anthropic Economic Index). |
| Augmentation-pattern usage | 62% | Measured — the complement of the row above; the two sum to 100% by construction. This is the pattern where the person stays in the loop. |
| Estimated task automation (modelled) | 29% | Modelled from this occupation's measured telemetry composite, mapped through anchors calibrated to Anthropic's published aggregate findings. Our estimate, not per-occupation telemetry. |
| Estimated task reshaping (modelled) | 55% | Share of tasks where AI acts as co-pilot rather than replacement. Modelled from the measured telemetry composite, calibrated to BCG's published aggregate reshaping rates. Our estimate. |
AI is transforming how this work is done. Professionals who adapt their workflows will thrive; those who don't face increasing competition.
In Canada: NOC 31302
Nurse anesthetists, nurse midwives, and nurse practitioners map to NOC 31302 — Nurse practitioners at TEER 1 (University Degree). ESDC's COPS 2024–2033 projection for this unit group is Strong risk of Shortage.
Mapped via Statistics Canada's official SOC 2018 → NOC 2016 → NOC 2021 correspondence tables (closest match of several correspondence candidates). Several candidates were available, and this one matched the occupation title on every word — no other did — so the TEER level read off its second digit is reported.
What this job actually involves
These are the O*NET task statements for Nurse anesthetists, nurse midwives, and nurse practitioners— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 27, core tasks first.
The exposure figures above are for the occupation as a whole. We do not have a per-task measurement: no published dataset tells us which of these specific tasks AI is doing. Read this as what the job is made of, not as a ranking of what is at risk.
4 of these 12statements do carry one measured signal: Anthropic publishes, per O*NET task, how much of the observed Claude usage on it looked like automation rather than iteration. Those are marked below. It still says how people use AI on that task, not how much of it AI can do — and the unmarked statements carry nothing rather than inheriting the occupation's figure.
Microsoft's applicability score was itself produced by classifying Copilot conversations against the O*NET work-activity catalogue — the same catalogue these groups come from. The published file gives one number per occupation, not one per activity, so we can tell you that 14% of this occupation's work activities are covered by observed AI usage, but not which ones.
Making Decisions and Solving Problems · 3 tasks
Diagnose or treat complex, unstable, comorbid, episodic, or emergency conditions in collaboration with other health care providers as necessary.
Core task · Importance 4.8/5 (O*NET)
Diagnose or treat chronic health care problems, such as high blood pressure and diabetes.
Core task · Importance 4.7/5 (O*NET)
Diagnose or treat acute health care problems, such as illnesses, infections, or injuries.
Core task · Importance 4.7/5 (O*NET) · 67% of observed AI use on this task is automation-pattern
Monitoring and Controlling Resources · 3 tasks
Prescribe medication dosages, routes, and frequencies, based on such patient characteristics as age and gender.
Core task · Importance 4.8/5 (O*NET)
Prescribe medications based on efficacy, safety, and cost as legally authorized.
Core task · Importance 4.7/5 (O*NET)
Recommend diagnostic or therapeutic interventions with attention to safety, cost, invasiveness, simplicity, acceptability, adherence, and efficacy.
Core task · Importance 4.7/5 (O*NET)
Monitoring Processes, Materials, or Surroundings · 1 task
Detect and respond to adverse drug reactions, with special attention to vulnerable populations such as infants, children, pregnant and lactating women, or older adults.
Core task · Importance 4.7/5 (O*NET)
Analyzing Data or Information · 1 task
Analyze and interpret patients' histories, symptoms, physical findings, or diagnostic information to develop appropriate diagnoses.
Core task · Importance 4.8/5 (O*NET) · 58% of observed AI use on this task is automation-pattern
Thinking Creatively · 1 task
Develop treatment plans, based on scientific rationale, standards of care, and professional practice guidelines.
Core task · Importance 4.9/5 (O*NET)
Documenting/Recording Information · 1 task
Maintain complete and detailed records of patients' health care plans and prognoses.
Core task · Importance 4.9/5 (O*NET)
Interpreting the Meaning of Information for Others · 1 task
Provide patients with information needed to promote health, reduce risk factors, or prevent disease or disability.
Core task · Importance 4.8/5 (O*NET) · 42% of observed AI use on this task is automation-pattern
Providing Consultation and Advice to Others · 1 task
Counsel patients about drug regimens and possible side effects or interactions with other substances, such as food supplements, over-the-counter (OTC) medications, or herbal remedies.
Core task · Importance 4.6/5 (O*NET) · 30% of observed AI use on this task is automation-pattern
Task statements and work activities from the O*NET Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under CC BY 4.0. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not reviewed or approved this material.
That's the average. Is this you?
Everything above describes Nurse anesthetists, nurse midwives, and nurse practitioners as a whole. Three questions, and nothing is stored or sent.
Where are you in your career?
What does your week mostly look like?
Is your employer deploying AI?
Where the leverage is
Upskill in AI-driven automation (e.g., Make, Zapier) and generative tools in your domain. The opportunity is to pivot from execution to directing AI-augmented workflows.
AI is transforming tasks rather than replacing entire jobs. Routine scheduling and communication automate, but human decision-making remains critical.
Master workflow automation platforms (e.g., Make, Zapier, CRM integrations) to double your output and position for fractional leadership roles.
This guidance is written per exposure band — three texts across all 342 occupations, framed on the ILO's transformation-versus-displacement distinction. It is editorial guidance for the reshaping band, not an occupation-specific research finding about nurse anesthetists, nurse midwives, and nurse practitioners.
Sources for this occupation
Only the sources that hold data for nurse anesthetists, nurse midwives, and nurse practitioners are listed. A study that does not cover this occupation is not cited here.
- Microsoft Research — Working with AI (Tomlinson et al., arXiv:2507.07935): Measured AI applicability, from 200,000 anonymised Copilot conversations classified against O*NET work activities.
- Anthropic Economic Index: Measured observed AI usage per occupation, from Anthropic's open per-SOC dataset (CC-BY 4.0).
- Felten, Raj & Seamans — AI Occupational Exposure index: Measured academic exposure percentile; the index underlying Statistics Canada's Canadian AI-exposure estimates (Mehdi & Morissette, 2024).
- O*NET Database 31.0 (USDOL/ETA), CC BY 4.0: The occupation's task statements and work activities, verbatim. Joined on SOC 2018 with no crosswalk — O*NET-SOC is built on SOC — and carrying no claim about which tasks AI touches.
- Anthropic Economic Index — collaboration split (release 2026-06-26, CC-BY 4.0): Measured share of observed Claude usage following an automation rather than an augmentation pattern. This is the second axis of the grouping above.
- Statistics Canada NOC 2021 concordance: Official SOC 2018 → NOC 2016 V1.3 → NOC 2021 V1.0 correspondence tables. TEER is the second digit of the resolved code, so we report it only where the concordance gave a single candidate.
- ESDC COPS 2024–2033: Projected labour-market assessment per NOC 2021 unit group, from the Canadian Occupational Projection System open dataset.
- BCG — AI Will Reshape More Jobs Than It Replaces (April 3, 2026): Published aggregate reshaping rates, used to calibrate our modelled percentages. The per-occupation figures here are our estimates, not BCG's data.
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