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AI Automation Index
Will AI Replace Water Resource Specialists?
Task-level automation exposure for Water Resource Specialists: 21 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
54%
weighted task index
21 of 21 tasks scored
Median Salary
$167,220 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
High
108,690 people hold this job — size, not growth
Projected 2025–35
+8.2%
employment change
9,200 openings a year · BLS line item
Pay by state
BLS OEWS publishes 49 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$107,810 – $267,290
Highest medians
Massachusetts $267,290
Connecticut $210,670
California $210,230
New Jersey $207,030
Washington $182,480
Lowest medians
Hawaii $107,810
Indiana $108,250
Wyoming $109,300
Iowa $109,760
Florida $112,220
State figures come from the 11-9121 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 54%
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 58%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 14%
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
21 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) 33%
7 tasks
Medium exposure (≥ 35%) 52%
11 tasks
Low exposure (below 35%) 16%
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
#15618 · Core · 30% automatable
Perform hydrologic, hydraulic, or water quality modeling.
physical manipulation — Testing green tech installations requires physical setup
Test green technologies or processes.
#15633 · Core · 72% automatable
Analyze storm water systems to identify opportunities for water resource improvements.
decision support — AI surfaces green initiative ideas; human validates
Identify opportunities for green initiatives.
#15631 · Core · 45% automatable
Conduct, or oversee the conduct of, investigations on matters such as water storage, wastewater discharge, pollutants, permits, or other compliance and regulatory issues.
decision support — Checks program against standards; human verifies compliance
Test green technologies or processes. Evaluate green operations or programs for compliance with standards or regulations.
#15634 · Core · 48% automatable
Develop strategies for watershed operations to meet water supply and conservation goals or to ensure regulatory compliance with clean water laws or regulations.
decision support — Remediation plans drafted; regulatory and site judgment human
Develop environmental remediation or protection plans.
#15630 · Core · 30% automatable
Conduct technical studies for water resources on topics such as pollutants and water treatment options.
physical manipulation — Testing green tech installations requires physical setup
Test green technologies or processes.
#15616 · Core · 62% automatable
Review or evaluate designs for water detention facilities, storm drains, flood control facilities, or other hydraulic structures.
data analysis — Evaluates sustainability projects; human owns criteria
Evaluate environmental or sustainability projects.
#15624 · Core · 55% automatable
Present water resource proposals to government, public interest groups, or community groups.
text generation — Draft applications; human owns funding strategy
Prepare proposals or grant applications to obtain project funding.
#15628 · Core · 48% automatable
Develop plans to protect watershed health or rehabilitate watersheds.
decision support — Remediation plans drafted; regulatory and site judgment human
Develop environmental remediation or protection plans.
#15621 · Core · 57% automatable
Write proposals, project reports, informational brochures, or other documents on wastewater purification, water supply and demand, or other water resource subjects.
text generation — Sustainability messaging drafted by AI, delivered by human
Prepare proposals or grant applications to obtain project funding. Present sustainable products or services information to the public.
#15632 · Core · 68% automatable
Conduct cost-benefit studies for watershed improvement projects or water management alternatives.
data analysis — Feasibility analysis from data, human validates assumptions
Analyze data to determine project feasibility.
#15623 · Core · 65% automatable
Provide technical expertise to assist communities in the development or implementation of storm water monitoring or other water programs.
information retrieval — Green energy advice is knowledge-based with tailoring
Advise others on green energy or related technologies.
#15620 · Core · 83% automatable
Compile and maintain documentation on the health of a body of water.
data analysis — Compiling operational data from sources is automatable
Compile operational data. Maintain operational records.
#15627 · Core · 55% automatable
Identify and characterize specific causes or sources of water pollution.
decision support — Concern identification aided; site judgment human
Identify environmental concerns.
#15635 · Core · 55% automatable
Conduct, or oversee the conduct of, chemical, physical, and biological water quality monitoring or sampling to ensure compliance with water quality standards.
decision support — AI tracks regulatory compliance across documents
Monitor organizational compliance with regulations. Evaluate quality of materials or products.
#15619 · Core · 85% automatable
Compile water resource data, using geographic information systems (GIS) or global position systems (GPS) software.
data analysis — Compiling operational data from sources is automatable
Compile operational data.
#15622 · Core · 58% automatable
Recommend new or revised policies, procedures, or regulations to support water resource or conservation goals.
text generation — Policy drafting aided; governance decisions human
Develop sustainable organizational policies or practices.
#15629 · Core · 50% automatable
Develop or implement standardized water monitoring and assessment methods.
text generation — AI drafts evaluation procedures; human adapts to org
Implement organizational process or policy changes. Develop procedures to evaluate organizational activities.
#15615 · Core · 28% automatable
Supervise teams of workers who capture water from wells and rivers.
interpersonal — Directing workers in the field is human supervision
Supervise workers performing environmentally sustainable activities.
#15617 · Core · 35% automatable
Negotiate for water rights with communities or water facilities to meet water supply demands.
interpersonal — Contract negotiation needs authority and accountability
Negotiate contracts for environmental remediation, green energy, or renewable resources.
#15626 · Core · 50% automatable
Monitor water use, demand, or quality in a particular geographic area.
decision support — Broad resource monitoring needs human judgment and physical checks
Monitor resources.
#15625 · Supplemental · 72% automatable
Identify methods for distributing purified wastewater into rivers, streams, or oceans.
decision support — AI surfaces green initiative ideas; human validates
Identify opportunities for green initiatives.
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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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