AI Automation Index

Will AI Replace Computer User Support Specialists?

Task-level automation exposure for Computer User Support Specialists: 16 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

61% weighted task index

16 of 16 tasks scored

Median Salary

$61,860 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Very High

717,190 people hold this job — size, not growth

Projected 2025–35

-3.5% employment change

39,700 openings a year · BLS line item

Pay by state

BLS OEWS publishes 54 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$36,250 – $80,950

Highest medians

District of Columbia $80,950

California $79,570

Washington $75,220

Massachusetts $75,070

New York $72,560

Lowest medians

Puerto Rico $36,250

Guam $37,460

Arkansas $46,360

South Dakota $48,260

Alabama $49,630

State figures come from the 15-1232 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.

What the score is made of

Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.

Automation potential 61%

Importance-weighted mean of the scored tasks. This is the headline index.

Physical work 7%

Share of weighted tasks in hands-on or machine-operating work activities.

Judgment & influence 32%

Share in analysis, decisions, teaching, directing, advising, or caring for others.

Routine administration 7%

Share in documenting, recording, and administrative activities.

Rewritten by O*NET 0%

Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.

Consequence of error 1/100 Low

O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.

Constituent Task Mix

16 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.

High exposure (≥ 60% of the task is automatable) 65%

10 tasks

Medium exposure (≥ 35%) 16%

3 tasks

Low exposure (below 35%) 19%

3 tasks

How would you survive?

Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.

Simulate Upskilling →

Constituent Tasks

#1285 · Core · 85% automatable

Oversee the daily performance of computer systems.

data analysis — AI monitors system metrics and detects anomalies

Monitor computer system performance to ensure proper operation.

#1286 · Core · 15% automatable

Set up equipment for employee use, performing or ensuring proper installation of cables, operating systems, or appropriate software.

physical manipulation — Hardware installation is physical hands-on work

Install computer hardware.

#1288 · Core · 69% automatable

Read technical manuals, confer with users, or conduct computer diagnostics to investigate and resolve problems or to provide technical assistance and support.

information retrieval — Gather technical info from documents is core

Read documents to gather technical information. Resolve computer software problems. Collaborate with others to resolve information technology issues.

#1282 · Core · 70% automatable

Answer user inquiries regarding computer software or hardware operation to resolve problems.

decision support — Software support is largely diagnosable with human review

Provide technical support for software maintenance or use.

#1284 · Core · 31% automatable

Install and perform minor repairs to hardware, software, or peripheral equipment, following design or installation specifications.

physical manipulation — Software install often needs hands-on machine access

Install computer software. Install computer hardware. Maintain computer hardware.

#1289 · Core · 55% automatable

Confer with staff, users, and management to establish requirements for new systems or modifications.

interpersonal — Collaborative spec-setting requires human negotiation

Collaborate with others to determine design specifications or details.

#1283 · Core · 72% automatable

Enter commands and observe system functioning to verify correct operations and detect errors.

data analysis — AI can run and analyze software performance tests

Test software performance.

#1287 · Core · 80% automatable

Maintain records of daily data communication transactions, problems and remedial actions taken, or installation activities.

text generation — Document routine operational activities from inputs

Document operational activities.

#1291 · Core · 62% automatable

Refer major hardware or software problems or defective products to vendors or technicians for service.

decision support — IT troubleshooting collaboration well-supported by AI

Collaborate with others to resolve information technology issues.

#1292 · Core · 71% automatable

Prepare evaluations of software or hardware, and recommend improvements or upgrades.

decision support — Drafts sound recommendations; human validates against real systems

Evaluate utility of software or hardware technologies. Provide recommendations to others about computer hardware. Recommend changes to improve computer or information systems.

#1290 · Core · 53% automatable

Develop training materials and procedures, or train users in the proper use of hardware or software.

interpersonal — Software training content AI-generatable, live coaching human

Teach others to use computer equipment or hardware. Train others in computer interface or software use.

#1295 · Core · 70% automatable

Inspect equipment and read order sheets to prepare for delivery to users.

information retrieval — Gather technical info from documents is core

Read documents to gather technical information. Test computer hardware performance.

#1293 · Core · 80% automatable

Read trade magazines and technical manuals, or attend conferences and seminars to maintain knowledge of hardware and software.

information retrieval — AI scans trend sources; human weighs relevance

Update knowledge about emerging industry or technology trends.

#1297 · Supplemental · 42% automatable

Conduct office automation feasibility studies, including workflow analysis, space design, or cost comparison analysis.

data analysis — Product research aided; physical testing human

Conduct research to gain information about products or processes.

#15204 · Supplemental · 34% automatable

Hire, supervise, and direct workers engaged in special project work, problem-solving, monitoring, and installation of data communication equipment and software.

decision support — Human owns staffing judgment and accountability

Supervise information technology personnel. Participate in staffing decisions.

#1296 · Supplemental · 70% automatable

Modify and customize commercial programs for internal needs.

text generation — Performance refactoring largely AI; human tests and approves

Modify software programs to improve performance.

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Community Sentiment

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Method

How this was scored

1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.

Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.

Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast

See the full method →

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