Interpreters and translators: AI and the work ahead
Machine translation can accelerate text work, while accuracy still depends on context, terminology and purpose. Interpreting a live exchange adds interaction and timing that a written translation workflow does not capture.
The official occupation combines interpreters and translators. Legal, medical, literary and commercial work have distinct requirements, so this combined score is especially sensitive to which tasks fill your day.
Compare the duties below with a normal week in your own role. Time
spent, responsibility, employer tools and access to reliable data can
change the picture substantially. Use the personal task audit to
identify those differences.
Task-by-task exposure
We selected 8 of 8 O*NET core
tasks by importance. Importance is an O*NET rating on a 1–5 scale; it
is not the percentage of time spent. The category and explanation are
our interpretation, not an O*NET assessment of AI.
0 routine automation 4 ai-assisted 4 human-led
Reading uncertainty: changing one task by one
category step would put this index at approximately 19–31/100. This is a
sensitivity example, not a statistical confidence interval. Small
score differences are not a sound reason to change careers.
Selected core tasks and our interpretation
Task and source detail
Assessment and reason
Follow ethical codes that protect the confidentiality of information.
O*NET task 9326 · Importance 4.86/5
Task source: 08/2021 · Rating: 08/2021
Human-led
Confidentiality depends on how information is handled and shared. A translation capability does not establish permission to upload sensitive material.
Translate messages simultaneously or consecutively into specified languages, orally or by using hand signs, maintaining message content, context, and style as much as possible.
O*NET task 9328 · Importance 4.73/5
Task source: 08/2021 · Rating: 08/2021
AI-assisted
Language tools may assist some oral or written exchanges, but this duty also includes signed and live interpretation. Context, modality and consequences limit broad automation claims.
Listen to speakers' statements to determine meanings and to prepare translations, using electronic listening systems as necessary.
O*NET task 9335 · Importance 4.48/5
Task source: 08/2021 · Rating: 08/2021
Human-led
Listening for intended meaning includes ambiguity, speaker context and real-time clarification. A transcript or translated phrase may miss what requires a question.
Compile terminology and information to be used in translations, including technical terms such as those for legal or medical material.
O*NET task 9333 · Importance 4.45/5
Task source: 08/2021 · Rating: 08/2021
AI-assisted
Terminology suggestions can help build a working glossary. A specialist must verify equivalents against the domain and intended audience.
Check translations of technical terms and terminology to ensure that they are accurate and remain consistent throughout translation revisions.
O*NET task 9330 · Importance 4.35/5
Task source: 08/2021 · Rating: 08/2021
AI-assisted
Glossaries support consistency across revisions. Consistent wording is not necessarily correct wording, so specialized terminology must be evaluated in context.
Identify and resolve conflicts related to the meanings of words, concepts, practices, or behaviors.
O*NET task 9327 · Importance 4.30/5
Task source: 08/2021 · Rating: 08/2021
Human-led
Resolving conflicting meanings may require consulting the parties or explaining cultural context. A single generated equivalent cannot settle every ambiguity.
Compile information on content and context of information to be translated and on intended audience.
O*NET task 9337 · Importance 4.24/5
Task source: 08/2021 · Rating: 08/2021
Human-led
The translator must establish purpose, audience and background. Gathering these requirements determines whether a fluent translation is actually suitable.
Routine automation includes conventional configured software; it does
not imply autonomous AI or adoption by every employer. Human-led
allows supporting tools. Vendor links document a capability, not a
validated rating of this complete task. Older task dates are shown
even though the database release is August 2026.
Task wording and importance: O*NET occupation 27-3091.00,
O*NET 31.0 Database
, U.S. Department of Labor, Employment and Training Administration, CC BY 4.0.
Selected and adapted by Career Risk Score; USDOL/ETA has not approved,
endorsed or tested these changes.
What assistance looks like in practice
Hypothetical workflow to illustrate the boundary; not a reported case
study.
A translator reviews a machine-produced product guide. A glossary supports consistent terminology, but the reviewer checks whether a technically correct word has the wrong meaning in the particular instruction.
What must be checked
Compare with the source, confirm ambiguous terms with the client and preserve units, warnings and intended register. For live interpreting, review the assignment's separate competence and confidentiality requirements.
Practical skills to strengthen
Develop subject expertise and a documented terminology process.
Show a revision sample explaining substantive translation errors.
Learn to negotiate a brief that specifies audience, purpose and review responsibilities.
Changes worth watching
Watch whether text assignments shift toward post-editing and quality assurance.
Track demand for specialist domains and live interactions in your language pair.
There is no supported date when this occupation becomes “safe” or
disappears. Revisit these observations as your tasks and tools change.
Tools behind these capability examples
These are relevant documented capabilities, not endorsements,
adoption statistics or hands-on product reviews. Features depend on
the product, plan and employer configuration. References checked
September 2026.
DeepL glossariesTerminology control in machine translation; context and specialized meaning still need evaluation.
Pay, demand and entry context
BLS reports median annual pay of $60,170
in 2025, with 6,000 projected
openings per year on average during 2025–35. Openings include
replacement needs as well as growth; they are not a count of currently
advertised vacancies.
The projected employment change is + 2%
over ten years. Employment growth can coexist with automation of particular tasks. It does not make every worker or location equally likely to benefit.
Typical entry education
Bachelor's degree
Related work experience
None
Typical on-the-job training
None
Technical writing requires original content development and product knowledge. Training requires facilitation and assessment in addition to strong language skills.
BLS categories describe typical entry, not a complete qualification
checklist. “None” does not mean no learning is needed. National
medians are not starting salaries; location, sector, experience, hours
and benefits matter. Wage data exclude self-employed earnings. Consult
local job descriptions and relevant credential authorities before
paying for training.
These links identify transferable work and a concrete gap to explore.
They are not guaranteed pathways, “AI-proof” jobs or recommendations
based on a small score difference.
Create and verify original instructions instead of only translating an existing source.
BLS typical entry: Bachelor's degree; related experience: Less than 5 years.
10 shared skills among each role's top
12
Speaking, Active Listening, Reading Comprehension, Writing, Critical Thinking, Monitoring, Active Learning, Social Perceptiveness, Judgment and Decision Making, Coordination.
These broad O*NET skills are selected by importance, with
ties broken by skill ID. Overlap does not measure
proficiency or hiring readiness.
Develop a short bilingual lesson with practice and an assessment of understanding.
BLS typical entry: Bachelor's degree; related experience: Less than 5 years.
11 shared skills among each role's top
12
Speaking, Active Listening, Reading Comprehension, Writing, Critical Thinking, Monitoring, Active Learning, Social Perceptiveness, Judgment and Decision Making, Learning Strategies, Coordination.
These broad O*NET skills are selected by importance, with
ties broken by skill ID. Overlap does not measure
proficiency or hiring readiness.
Use your own plausible pay figures to explore a move. Both pay fields start
at this occupation's national median so the calculator does not imply a
salary increase. A destination median describes existing workers, not your
likely starting offer.
A fixed-pay illustration in nominal dollars. Unpaid months occur at the
start of preparation; costs are charged at the start. Transition must fit
within the comparison period. Excludes taxes, benefits, raises, inflation,
investment returns and the chance of getting a job. A negative result is
possible. No inputs are saved or sent by this calculator.
Calculation and a worked example
Stay = current pay × years. Move = preparation pay × (preparation years −
unpaid months ÷ 12) + new pay × (years − preparation years) − one-time
cost.
For $60,000 current pay, $70,000 new pay, $50,000 preparation pay, two
years preparing, three unpaid months, $8,000 costs and a ten-year
comparison: stay = $600,000; move = $639,500. The $39,500 difference is a
scenario result, not a forecast. Break-even checks cumulative totals each
month under these same assumptions.
Check the evidence and limits
The index weights these sampled tasks equally. It does not measure
time, adoption, cost savings, unemployment or individual ability. The
examples and transition exercises are editorial inferences. Product
documentation supports the narrower capability described, not the
claim that a whole job can be replaced.
