Share and intensity of work current AI systems can materially affect.
SOC 27-3091
Interpreters AI displacement risk
Machine translation handles written text far better than live speech. Real-time spoken and signed interpretation in courts, hospitals, and schools carries latency, accuracy, confidentiality, and liability demands that keep human interpreters preferred.
Likely potential for exposed tasks to move to software after workflow integration.
AI interpretation is improving for low-stakes conversations, but certified legal and medical settings require qualified humans by rule or by risk. Specialization and certification are the protective moves.
Distribution
Where Interpreters sits across 620 tracked roles
Displacement pressure 58 — higher than 87% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
17 O*NET task statements matched to SOC 27-3091. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $60,170 (May 2025, US national). The latest BLS row matched SOC 27-3091.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Interpreters
SOC 27-3091 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 58/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Interpreters
The current evidence import matched 17 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks17
SOC27-3091
- Core task / ID 9326
Follow ethical codes that protect the confidentiality of information.
- Core task / ID 9328
Translate messages simultaneously or consecutively into specified languages, orally or by using hand signs, maintaining message content, context, and style as much as possible.
- Core task / ID 9335
Listen to speakers' statements to determine meanings and to prepare translations, using electronic listening systems as necessary.
- Core task / ID 9333
Compile terminology and information to be used in translations, including technical terms such as those for legal or medical material.
- Core task / ID 9332
Refer to reference materials, such as dictionaries, lexicons, encyclopedias, and computerized terminology banks, as needed to ensure translation accuracy.
- Core task / ID 9330
Check translations of technical terms and terminology to ensure that they are accurate and remain consistent throughout translation revisions.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Interpret speech in real time
Exposure 72, automation 42%, augmentation 44%.
Render signed language interpretation
Exposure 48, automation 20%, augmentation 38%.
Prepare terminology for sessions
Exposure 68, automation 40%, augmentation 62%.
O*NET evidence: Compile terminology and information to be used in translations, including technical ter... (ID 9333)
Navigate cultural and ethical judgment
Exposure 24, automation 6%, augmentation 36%.
TaskExposureAutomationAugmentation
Interpret speech in real time7242%44%
Render signed language interpretation4820%38%
Prepare terminology for sessions6840%62%
Navigate cultural and ethical judgment246%36%
Transition pathways
Adjacent moves that preserve existing skills
credentialed transition
ModerateMedical Interpreter
Training horizon: 3-9 months. Skill overlap 78. Wage preservation signal 106.
- Earn healthcare interpreter certification
- Study clinical terminology
- Practice triadic encounter protocols
adjacent role
ModerateLocalization Specialist
Training horizon: 3-6 months. Skill overlap 62. Wage preservation signal 112.
- Learn localization tooling
- Review machine translation quality
- Build terminology glossaries
Comparison guides
Compare the next move before you commit
3-9 months
Interpreters to Medical Interpreter
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Interpreters into Medical Interpreter.
3-6 months
Interpreters to Localization Specialist
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Interpreters into Localization Specialist.
What the AI risk score means for Interpreters
The displacement pressure score for Interpreters is 58. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Interpret speech in real time carries 42% automation pressure, while Prepare terminology for sessions carries 62% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $60,170 (May 2025, US national). Employment context: Growing language-access role with real-time constraints. Typical education: Bachelor's degree common; certification often required.
Wage vulnerability is 56, while transition feasibility is 64. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Moderate displacement pressure
- Real-time constraints protect live work
- Certified medical and legal demand grows
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Interpreters, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Simultaneous interpretation
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Domain terminology
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Ethical confidentiality
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Cultural mediation
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Medical Interpreter, such as earn healthcare interpreter certification.
- By 90 days, compare internal openings and external postings for Medical Interpreter or Localization Specialist and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Interpreters
Will AI replace Interpreters?
Machine translation handles written text far better than live speech. Real-time spoken and signed interpretation in courts, hospitals, and schools carries latency, accuracy, confidentiality, and liability demands that keep human interpreters preferred. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Interpreters work are most exposed to AI?
Interpret speech in real time and Prepare terminology for sessions show the strongest automation pressure in this model. Prepare terminology for sessions and Interpret speech in real time are better treated as AI-augmented work.
What should Interpreters learn next?
Start with Simultaneous interpretation, Domain terminology, Ethical confidentiality. The most practical adjacent paths in this model are Medical Interpreter and Localization Specialist.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureOECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics