AI Delegation Index

AI Delegation Index

What work could you hand off to an AI agent?

Survey Researchers

Strong

AI agents could handle several recurring digital workflows in this job, while people remain responsible for judgment and final decisions.

Where agents can help most

  1. 1

    Check survey data and flag issues

    Use an agent to review incoming survey records from spreadsheets, field notes, or export files, then code them against your disposition rules and flag odd or incomplete cases.

  2. 2

    Review survey results and findings

    Use an agent to run survey datasets through your chosen statistical software file or exported tables, then produce summaries, charts, and a draft write-up of the findings.

  3. 3

    Assemble survey documentation for review

    Use an agent to gather questionnaire drafts, sampling notes, collection methods, weighting decisions, and progress reports into one review folder.

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O*NET-SOC 19-3022.00 · #56 of 923

Result context

How to read this result

Plan, develop, or conduct surveys. May analyze and interpret the meaning of survey data, determine survey objectives, or suggest or test question wording. Includes social scientists who primarily design questionnaires or supervise survey teams.

National position
#56 of 923 occupations
Top 7% of occupations
Overall AI delegation potential
Strong
Potential of score-contributing workflows
Strong · 82/100
Meaningful work covered
76%

The overall rating combines how useful the best agent workflows are with how much of the occupation they address. It is not an estimate of job automation or replacement.

Recommended agent uses

3 workflows you could delegate to AI

1

Check survey data and flag issues

How you could use an agent

Use an agent to review incoming survey records from spreadsheets, field notes, or export files, then code them against your disposition rules and flag odd or incomplete cases. It can return a clean dataset and a short list of records that need human follow-up before analysis starts.

Where you stay involved

You decide how to handle doubtful cases, review anything that looks fabricated or mismatched, and approve the final cleaned file. You also decide whether a problem needs a supervisor’s attention.

Review level: Medium

2

Review survey results and findings

How you could use an agent

Use an agent to run survey datasets through your chosen statistical software file or exported tables, then produce summaries, charts, and a draft write-up of the findings. It can also check the numbers against the source data and point out unusual patterns, missing values, or outputs that do not line up.

Where you stay involved

You choose the analysis approach, read the results, and decide what the data really supports. You edit the write-up and make the final call on any interpretation that is unclear or important.

Review level: High

3

Assemble survey documentation for review

How you could use an agent

Use an agent to gather questionnaire drafts, sampling notes, collection methods, weighting decisions, and progress reports into one review folder. It can label each item, note missing pieces, and assemble a clean documentation packet for project review or audit use.

Where you stay involved

You check that the packet matches what was actually done, confirm the version history, and decide what documentation still needs to be added. You handle any unclear weighting or method questions yourself.

Review level: Medium

Documentation and coordination support

AI can assist without owning the physical outcome

These support steps are shown separately. They do not count as agentic workflows and do not increase this occupation's score.

Align client needs with survey scope

Use an agent to turn client notes, meeting minutes, and sample requests into a draft survey scope statement that spells out the population, timing, deliverables, and special sample needs. It can also compare the draft with the research plan so you can see where the client request and the survey design line up or conflict.

Supporting analysis

Why this occupation's AI delegation potential is Strong

Underlying methodology score

78 / 100

This technical score determines the qualitative rating; it is not an estimate of the share of the occupation that can be automated.

Importance & frequency75
AI capability84
Digital actionability88
End-to-end leverage83
Safety & reversibility79
Meaningful-work coverage
76%
Physical-work modifier
Limited
Safety modifier
Limited
Qualitative judgment
No material constraint
O*NET task evidence
16 tasks

O*NET 31.0 · methodology 3.3.0. Every workflow passes an action-level physical-execution and protected human-and-veterinary clinical-action gate. Documentation workflows must own a complete digital loop and use digital task evidence only; support-only workflows are disclosed separately and excluded from scoring. Artistic, editorial, normative, and policy-dependent work receives a transparent human-judgment constraint. National ranking within 923 scored O*NET occupations under methodology 3.3.0.

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