AI Delegation Index

AI Delegation Index

What work could you hand off to an AI agent?

Social Science Research Assistants

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

    Prepare analysis data and package results

    Use an agent to clean up the project dataset, run the standard analyses the team has already planned, and turn the outputs into labeled tables and charts.

  2. 2

    Code research data and verify entries

    Use an agent to code incoming survey or interview records from your code list, enter them into the project database, and flag anything that does not match cleanly.

  3. 3

    Prepare research documents for submission

    Use an agent to organize protocols, forms, and other project papers into a submission packet, check that required fields and attachments are present, and track the latest draft.

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O*NET-SOC 19-4061.00 · #25 of 923

Result context

How to read this result

Assist social scientists in laboratory, survey, and other social science research. May help prepare findings for publication and assist in laboratory analysis, quality control, or data management.

National position
#25 of 923 occupations
Top 3% of occupations
Overall AI delegation potential
Strong
Potential of score-contributing workflows
Strong · 84/100
Meaningful work covered
78%

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

Prepare analysis data and package results

How you could use an agent

Use an agent to clean up the project dataset, run the standard analyses the team has already planned, and turn the outputs into labeled tables and charts. It takes the files, codebook, and analysis notes you give it, then produces a results packet you can check before anything is shared or written up.

Where you stay involved

You decide which variables, models, and tables belong in the analysis, then review the output for fit with the research question and the team’s plan. You fix interpretation, approve final wording, and handle any unusual statistical results.

Review level: Medium

2

Code research data and verify entries

How you could use an agent

Use an agent to code incoming survey or interview records from your code list, enter them into the project database, and flag anything that does not match cleanly. It can compare source files against the database, catch missing or inconsistent entries, and leave you with a checked file and a list of records that need human review.

Where you stay involved

You decide how unclear responses should be treated, review the flagged records, and confirm any corrections before the data are used. You also decide whether a record is too ambiguous to code from the source alone.

Review level: Medium

3

Prepare research documents for submission

How you could use an agent

Use an agent to organize protocols, forms, and other project papers into a submission packet, check that required fields and attachments are present, and track the latest draft. It can compare your files to the submission checklist and produce a clean set of documents ready for your review.

Where you stay involved

You review the packet, confirm the content is correct, and approve any wording or attachment changes before submission. You also handle the researcher’s decisions about whether a protocol change is needed.

Review level: High

Supporting analysis

Why this occupation's AI delegation potential is Strong

Underlying methodology score

80 / 100

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

Importance & frequency69
AI capability89
Digital actionability94
End-to-end leverage88
Safety & reversibility80
Meaningful-work coverage
78%
Physical-work modifier
Limited
Safety modifier
Limited
Qualitative judgment
No material constraint
O*NET task evidence
22 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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