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AI Automation Index
Will AI Replace Database Architects?
Task-level automation exposure for Database Architects: 25 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
69%
weighted task index
25 of 25 tasks scored
Median Salary
$139,500 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Medium
67,140 people hold this job — size, not growth
Projected 2025–35
+9.4%
employment change
3,900 openings a year · BLS line item
Pay by state
BLS OEWS publishes 47 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$95,620 – $170,160
Highest medians
California $170,160
Massachusetts $161,650
Virginia $160,360
Arizona $156,100
Colorado $154,560
Lowest medians
Puerto Rico $95,620
Mississippi $98,420
Arkansas $104,070
Alabama $105,450
North Dakota $109,430
State figures come from the 15-1243 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 59%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 9%
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 10/100
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
25 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) 89%
22 tasks
Medium exposure (≥ 35%) 11%
3 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
#16115 · Core · 79% automatable
Develop and document database architectures.
text generation — Draft technical specs from given requirements
Create databases to store electronic data. Document technical specifications or requirements.
#16113 · Core · 55% automatable
Collaborate with system architects, software architects, design analysts, and others to understand business or industry requirements.
interpersonal — Collaborative spec-setting requires human negotiation
Collaborate with others to determine design specifications or details.
#16114 · Core · 72% automatable
Develop database architectural strategies at the modeling, design and implementation stages to address business or industry requirements.
text generation — AI drafts data management procedures; human approves
Develop procedures for data management.
#16109 · Core · 75% automatable
Design databases to support business applications, ensuring system scalability, security, performance, and reliability.
text generation — Database creation largely automatable; human reviews
Create databases to store electronic data.
#16108 · Core · 60% automatable
Develop data models for applications, metadata tables, views or related database structures.
text generation — Modeling code drafted; validation and domain framing human
Design computer modeling or simulation programs. Develop models of information or communications systems.
#16110 · Core · 74% automatable
Design database applications, such as interfaces, data transfer mechanisms, global temporary tables, data partitions, and function-based indexes to enable efficient access of the generic database structure.
text generation — Database creation largely automatable; human reviews
Develop database parameters or specifications. Create databases to store electronic data.
#21653 · Core · 70% automatable
Develop methods for integrating different products so they work properly together, such as customizing commercial databases to fit specific needs.
text generation — AI drafts implementation guidelines; human tailors
Develop guidelines for system implementation.
#16112 · Core · 71% automatable
Create and enforce database development standards.
text generation — AI drafts data management procedures; human approves
Develop performance metrics or standards related to information technology. Develop procedures for data management.
#16105 · Core · 74% automatable
Document and communicate database schemas, using accepted notations.
text generation — Draft procedure docs from described process
Document design or development procedures. Communicate project information to others.
#21652 · Core · 60% automatable
Develop data model describing data elements and their use, 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.
#21659 · Core · 50% automatable
Work as part of a project team to coordinate database development and determine project scope and limitations.
decision support — AI coordinates cross-team plans; human resolves conflicts
Coordinate project activities with other personnel or departments.
#21655 · Core · 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.
#16100 · Core · 72% automatable
Set up database clusters, backup, or recovery processes.
text generation — Schema specs generated; human reviews requirements
Develop database parameters or specifications.
#16111 · Core · 80% automatable
Demonstrate database technical functionality, such as performance, security and reliability.
data analysis — Database performance metrics analyzable from logs
Assess database performance.
#16107 · Core · 75% automatable
Develop load-balancing processes to eliminate down time for backup processes.
data analysis — Backup configuration largely automatable with oversight
Create electronic data backup to prevent loss of information.
#16102 · 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.
#16101 · 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.
#21657 · 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.
#16104 · Core · 60% automatable
Identify and correct deviations from database development standards.
decision support — Software troubleshooting aided but human verifies
Resolve computer software problems.
#21656 · Core · 68% automatable
Review project requests describing database user needs to estimate time and cost required to accomplish project.
data analysis — Project time/cost estimation from parameters
Estimate time or monetary resources needed to complete projects.
#21660 · 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.
#16106 · Core · 72% automatable
Develop or maintain archived procedures, procedural codes, or queries for applications.
text generation — AI drafts data management procedures; human approves
Develop procedures for data management.
#16099 · 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.
#21654 · Core · 72% automatable
Establish and calculate optimum values for database parameters, using manuals and calculators.
text generation — Schema specs generated; human reviews requirements
Develop database parameters or specifications.
#21658 · Supplemental · 55% automatable
Train users and answer questions.
interpersonal — Software training content AI-generatable, live coaching human
Train others in computer interface or software use.
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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
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