AI Automation Index

Will AI Replace Database Administrators?

Task-level automation exposure for Database Administrators: 18 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

69% weighted task index

18 of 18 tasks scored

Median Salary

$104,620 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Medium

69,990 people hold this job — size, not growth

Projected 2025–35

-0.1% employment change

3,300 openings a year · BLS line item

Pay by state

BLS OEWS publishes 52 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$51,880 – $135,750

Highest medians

Utah $135,750

Massachusetts $129,300

New Jersey $125,860

Maryland $124,300

District of Columbia $118,540

Lowest medians

Puerto Rico $51,880

Maine $70,500

West Virginia $73,490

North Dakota $78,730

Wyoming $82,480

State figures come from the 15-1242 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 69%

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

Physical work 0%

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

Judgment & influence 63%

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

Routine administration 0%

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 8/100 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

18 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) 88%

16 tasks

Medium exposure (≥ 35%) 12%

2 tasks

Low exposure (below 35%) 0%

0 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

#1299 · Core · 80% automatable

Modify existing databases and database management systems or direct programmers and analysts to make changes.

data analysis — Database updates routine and automatable

Create databases to store electronic data. Update computer database information.

#1301 · Core · 65% automatable

Plan, coordinate, and implement security measures to safeguard information in computer files against accidental or unauthorized damage, modification or disclosure.

text generation — AI drafts security policies; human approves

Implement security measures for computer or information systems. Develop computer or information security policies or procedures.

#21649 · Core · 65% automatable

Plan and install upgrades of database management system software to enhance database performance.

text generation — Database creation largely automatable; human reviews

Create databases to store electronic data. Install computer software.

#1305 · Core · 60% automatable

Specify users and user access levels for each segment of database.

decision support — Security implementation needs human judgment and deployment

Implement security measures for computer or information systems.

#21651 · Core · 68% automatable

Test changes to database applications or systems.

data analysis — Database performance metrics analyzable from logs

Test computer system operations to ensure proper functioning. Assess database performance.

#1300 · Core · 75% automatable

Test programs or databases, correct errors, and make necessary modifications.

data analysis — Database performance metrics analyzable from logs

Assess database performance. Modify software programs to improve performance.

#1303 · Core · 55% automatable

Train users and answer questions.

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

Train others in computer interface or software use.

#21650 · Core · 70% automatable

Provide technical support to junior staff or clients.

decision support — Software support is largely diagnosable with human review

Provide technical support for software maintenance or use.

#1302 · Core · 49% automatable

Approve, schedule, plan, and supervise the installation and testing of new products and improvements to computer systems, such as the installation of new databases.

text generation — Project plans drafted; scope and risk owned by humans

Develop detailed project plans. Coordinate software or hardware installation.

#1298 · Core · 70% automatable

Develop standards and guidelines for the use and acquisition of software and to protect vulnerable information.

data analysis — IT performance metrics well-suited to AI

Develop performance metrics or standards related to information technology.

#1313 · Core · 77% automatable

Write and code logical and physical database descriptions and specify identifiers of database to management system, or direct others in coding descriptions.

text generation — Code generation strong; human reviews and integrates

Develop database parameters or specifications. Write computer programming code.

#1306 · Core · 60% automatable

Develop data models describing data elements and how they are used, following procedures and using pen, template, or computer software.

data analysis — AI models comms systems; engineer verifies performance

Develop models of information or communications systems.

#1311 · Core · 85% automatable

Select and enter codes to monitor database performance and to create production databases.

data analysis — Database updates routine and automatable

Update computer database information.

#21648 · Core · 70% automatable

Identify, evaluate and recommend hardware or software technologies to achieve desired database performance.

decision support — Software utility evaluation aided by research and testing

Evaluate utility of software or hardware technologies. Provide recommendations to others about computer hardware.

#1309 · Core · 84% automatable

Review procedures in database management system manuals to make changes to database.

information retrieval — Gather technical info from documents is core

Read documents to gather technical information.

#1312 · Supplemental · 79% automatable

Identify and evaluate industry trends in database systems to serve as a source of information and advice for upper management.

data analysis — Trend and relationship detection in data

Analyze market or customer related data. Analyze data to identify trends or relationships among variables.

#1314 · Supplemental · 84% automatable

Review workflow charts developed by programmer analyst to understand tasks computer will perform, such as updating records.

information retrieval — Gather technical info from documents is core

Read documents to gather technical information.

#1315 · Supplemental · 72% automatable

Revise company definition of data as defined in data dictionary.

text generation — Schema specs generated; human reviews requirements

Develop database parameters or specifications.

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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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