AI Delegation Index

AI Delegation Index

What work could you hand off to an AI agent?

Statistical 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

    Enter data and maintain records

    Use an agent to enter source data into the right file or database, save the related notes, and compare the entered values against the originals.

  2. 2

    Code data and prepare analysis files

    Use an agent to apply your codebook to raw records, enter the coded values into the analysis file, and separate unclear cases into a review list.

  3. 3

    Compute statistics and choose tests

    Use an agent to calculate the statistics you assign, run the test formulas you choose, and keep the results together with the assumptions you used.

Search another job

O*NET-SOC 43-9111.00 · #27 of 923

Result context

How to read this result

Compile and compute data according to statistical formulas for use in statistical studies. May perform actuarial computations and compile charts and graphs for use by actuaries. Includes actuarial clerks.

National position
#27 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

Enter data and maintain records

How you could use an agent

Use an agent to enter source data into the right file or database, save the related notes, and compare the entered values against the originals. It can flag blanks, duplicates, and mismatched records so you have a cleaner set of data to hand off for analysis or reporting.

Where you stay involved

You review flagged records, decide how to handle uncertain entries, and confirm the final file is ready to use. You also handle any access or source-data issues that need a person’s attention.

Review level: Low

2

Code data and prepare analysis files

How you could use an agent

Use an agent to apply your codebook to raw records, enter the coded values into the analysis file, and separate unclear cases into a review list. It can check that code ranges, labels, and required fields line up before the file moves on to the next step.

Where you stay involved

You decide how to code any unclear record, review the flagged cases, and approve the final analysis file. You also update the code list when the project team changes the rules.

Review level: Low

3

Compute statistics and choose tests

How you could use an agent

Use an agent to calculate the statistics you assign, run the test formulas you choose, and keep the results together with the assumptions you used. It can cross-check key numbers, compare them with earlier runs, and prepare a clean summary for review.

Where you stay involved

You choose the statistical test, judge whether the data fit the method, and decide what the numbers mean. You also confirm the final figures are the right ones to share.

Review level: Medium

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 & frequency74
AI capability86
Digital actionability90
End-to-end leverage85
Safety & reversibility85
Meaningful-work coverage
78%
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.

Useful comparisons

Similar occupations

Compare side by side

Desktop Publishers

AI delegation potential: Strong

Top 3% of occupations · #20

Medical Secretaries and Administrative Assistants

AI delegation potential: Strong

Top 3% of occupations · #23

Order Clerks

AI delegation potential: Strong

Top 3% of occupations · #24

Receptionists and Information Clerks

AI delegation potential: Strong

Top 6% of occupations · #54